Index (io.github.cmu-phil:tetrad-lib 8.0.0 API)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

a() - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Returns the value of the a record component.
a() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair: Returns the value of the a record component.
aa - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Arrow-to-arrow
abs(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the absolute value of the given double using the specified implementation.
abs(float) - Static method in class edu.cmu.tetrad.util.TMath: Computes the absolute value of the given float using the specified implementation.
abs(int) - Static method in class edu.cmu.tetrad.util.TMath: Computes the absolute value of the given integer using the specified implementation.
abs(long) - Static method in class edu.cmu.tetrad.util.TMath: Computes the absolute value of the given long integer using the specified implementation.
ABSOLUTE - Enum constant in enum class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue.DistanceType: Calculate the absolute distance between the true edge strength and the range of estimated coefficients.
absoluteValue - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The absolute value.
AbstractAnnotations<T> - Class in edu.cmu.tetrad.annotation: Abstract class for annotations.
AbstractAnnotations(String, Class) - Constructor for class edu.cmu.tetrad.annotation.AbstractAnnotations: Constructor.
AbstractBootstrapAlgorithm - Class in edu.cmu.tetrad.algcomparison.algorithm: This is a base class for bootstrap algorithms.
AbstractMultiBootstrapAlgorithm - Class in edu.cmu.tetrad.algcomparison.algorithm: A base class for bootstrapping multi-dataset algorithms (such as IMaGES), parallel to AbstractBootstrapAlgorithm for single-dataset algorithms.
AbstractNbComponent - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: Abstract NB component.
AbstractNbComponent(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Constructs a new component with the given factor, power, parents, and
AbstractVariable - Class in edu.cmu.tetrad.data: Base class for variable specifications for DataSet.
ac - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Arrow-to-circle
accepts(Graph, SepsetMap, String) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Whether a candidate that has passed the caller's legality check also passes the gates that are on.
AcceptsKnowledge - Interface in edu.cmu.tetrad.algcomparison.utils: Stores a knowledge object.
acos(double) - Static method in class edu.cmu.tetrad.util.TMath: Calculates the arc cosine (inverse cosine) of a given value.
active() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: isActive.
active() - Method in class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: isActive.
active() - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig: States whether the config is active or not.
ActiveLagGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.graph: Adds Javabean property change events so that it can be used in a MVC type architecture.
ActiveLagGraph() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Creates new ActiveLagGraph
AD_TREE - Enum constant in enum class edu.cmu.tetrad.search.test.ChiSquareTest.CellTableType: The AD tree cell table.
ADAPTIVE_BASIS_SELECTION - Static variable in class edu.cmu.tetrad.util.Params: Constant ADAPTIVE_BASIS_SELECTION="adaptiveBasisSelection"
adaptivePrunedEmbedding(DataSet, int) - Static method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Computes the adaptive basis-column decision for a single data set: the embedding that new BasisFunctionBicScore(dataSet, truncationLimit, lambda, true) would score against.
add(int, DataModel) - Method in class edu.cmu.tetrad.data.DataModelList: Adds the given DataModel to the list at the given index.
add(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.Algorithms: Adds an algorithm.
add(Simulation) - Method in class edu.cmu.tetrad.algcomparison.simulation.Simulations: Adds an simulation.
add(Statistic) - Method in class edu.cmu.tetrad.algcomparison.statistic.Statistics: Adds a statistic.
add(DataSet, MissingnessIndicatorAdder.Spec) - Static method in class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder: Adds missingness indicators under the given spec.
add(IndependenceFact) - Method in class edu.cmu.tetrad.data.IndependenceFacts: add.
add(OutputStream, Level) - Method in class edu.cmu.tetrad.util.LogUtils: Adds the given stream to logging.
ADD - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Add an edge.
ADD_COLLIDER - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Add a collider.
ADD_ORIGINAL_DATASET - Static variable in class edu.cmu.tetrad.util.Params: Constant ADD_ORIGINAL_DATASET="addOriginalDataset"
addAll(SepsetMap) - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Adds all entries in the given sepset map to the current one.
addAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds an ambiguous triple to the list of ambiguous triples.
addAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: addAmbiguousTriple.
addAmbiguousTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: addAmbiguousTriple.
addAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: addAmbiguousTriple.
addAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: addAmbiguousTriple.
addAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds an ambiguous triple to the list of ambiguous triples.
addAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: addAmbiguousTriple.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.data.ContinuousVariable: addAttribute.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.data.DiscreteVariable: addAttribute.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.graph.Dag: Adds an attribute to the graph.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: addAttribute.
addAttribute(String, Object) - Method in interface edu.cmu.tetrad.graph.Graph: addAttribute.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.graph.GraphNode: addAttribute.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.graph.LagGraph: addAttribute.
addAttribute(String, Object) - Method in interface edu.cmu.tetrad.graph.Node: addAttribute.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.graph.SemGraph: addAttribute.
addAttribute(String, Object) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a key-value pair to the attributes map.
addBidirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Bidirected edges are not allowed in DAGs.
addBidirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds a bidirected edges <-> to the graph.
addBidirectedEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Adds a bidirected edges <-> to the graph.
addBidirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds a bidirected edges <-> to the graph.
addBidirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds a bidirected edges <-> to the graph.
addBidirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a bidirected edge between two nodes.
addCategory(int, int) - Method in class edu.cmu.tetrad.bayes.Proposition: addCategory.
addCluster(Set, List, int, List) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: For a discovered cluster Vs of rank k, creates a new cover over it, or extends an existing one (Python addCluster).
addCollider(Triple) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Add another collider operation to the GraphChange.
addConditioningVariable(String, double, double) - Method in class edu.cmu.tetrad.data.Histogram: Adds a continuous conditioning variable with a specified range.
addConditioningVariable(String, int) - Method in class edu.cmu.tetrad.data.Histogram: Adds a discrete conditioning variable with a specified value.
addContemporaneousEdge_mirrorsToAllLags() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Adding X:0 -- Y:0 (shift 0) should mirror to X:1 -- Y:1 and X:2 -- Y:2.
addContemporaneousEdgeAtLag1_mirrorsToOtherLags() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Adding X:1 -- Y:1 (shift 0 at lag 1) should also mirror to lag 0 and lag 2.
addCounts(int, int, int) - Method in class edu.cmu.tetrad.bayes.CptMapCounts: Adds the specified count to the cell count at the given row and column.
addCrossLagEdge_mirrorsWhereNodesExist() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Adding X:1 --> Y:0 (shift -1, i.e.
addDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds a directed edge between two nodes.
addDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds a directed edge --> to the graph.
addDirectedEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Adds a directed edge --> to the graph.
addDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds a directed edge --> to the graph.
addDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds a directed edge --> to the graph.
addDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a directed edge between two nodes to the graph.
addDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds a dotted underline triple to the graph.
addDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: addDottedUnderlineTriple.
addDottedUnderlineTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: addDottedUnderlineTriple.
addDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: addDottedUnderlineTriple.
addDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: addDottedUnderlineTriple.
addDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a triple with dotted underline to the list of triples.
addDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: addDottedUnderlineTriple.
addEdge(Edge) - Method in class edu.cmu.tetrad.graph.Dag: Adds a directed edge to the Directed Acyclic Graph (DAG).
addEdge(Edge) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds the specified edge to the graph, provided it is not already in the graph.
addEdge(Edge) - Method in interface edu.cmu.tetrad.graph.Graph: Adds the specified edge to the graph, provided it is not already in the graph.
addEdge(Edge) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds the specified edge to the graph, provided it is not already in the graph.
addEdge(Edge) - Method in class edu.cmu.tetrad.graph.ReplicatingGraph: Adds an edge to the graph and performs additional processing to mirror edges based on the defined edge replication policy.
addEdge(Edge) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds the specified edge to the graph, provided it is not already in the graph.
addEdge(Edge) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a directed edge to the graph.
addEdge(Edge) - Static method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Creates a CandidateEdit operation to add the specified edge to a graph.
addEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Adds an edge to the given factor at lag 0 from the specified lagged factor.
addEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Adds an edge to the given factor at lag 0 from the specified lagged factor.
addEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Adds an edge to the given factor at lag 0 from the specified lagged factor.
addEdge(String, LaggedFactor) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Adds an edge to the given factor at lag 0 from the specified lagged factor.
addEdge_doesNotCreateSpuriousSameVariableEdges() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Adding X:0 -- Y:0 must not accidentally create X:0 -- X:1 or Y:0 -- Y:1, i.e.
addEdge_noInfiniteRecursion() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Adding many edges should not cause a StackOverflowError.
addEdgeSpecializationMarkup(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Adds markups for edge specializations for the edges in the given graph.
addEdgeTypeProbability(EdgeTypeProbability) - Method in class edu.cmu.tetrad.graph.Edge: addEdgeTypeProbability.
adDep() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the adDep record component.
adDep() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the adDep record component.
addFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Adds a factor to the graph.
addFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Adds a factor to the graph.
addFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Adds a factor to the graph.
addFactor(String) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Adds a factor to the graph.
addFactors(String, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
addFactors(String, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
addFactors(String, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
addFactors(String, int) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
addForbiddenReverseEdgesForDirectedEdges(Graph, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Adds forbidden reverse edges for directed edges in the given graph based on the knowledge.
addIndex(DataSet) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: Creates new time series dataset from the given one with index variable (e.g., time)
ADDITIVE_NOISE_SEM - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant CAUSAL_PERCEPTRON_NETWORK="Causal Peceptron Network (CPN)"
additiveHingeTest(double[], double[][], double) - Static method in class edu.cmu.tetrad.search.utils.NonlinearityTests: Performs an additive-component test using hinge basis functions to assess nonlinearity in the data.
AdditiveLocalScorer - Interface in edu.cmu.tetrad.search.score: Interface for scoring a target node in a graphical model based on its local structure, specifically under an additive scoring framework.
AdditiveNoiseSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a Causal Perceptron Network.
AdditiveNoiseSimulation - Class in edu.cmu.tetrad.sem: AdditiveNoiseSimulation
AdditiveNoiseSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Constructs a SemSimulation object with the given RandomGraph object.
AdditiveNoiseSimulation(Graph, int, Sampler, int[], double, Function) - Constructor for class edu.cmu.tetrad.sem.AdditiveNoiseSimulation: Constructs a new AdditiveNoiseSimulation instance with the specified parameters.
addKnowledgeGroup(KnowledgeGroup) - Method in class edu.cmu.tetrad.data.Knowledge: Adds a knowledge group.
addKnownSeparator(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Declares a test-confirmed separator for a pair, trusted by the I-map check whenever it m-separates the pair in a candidate MAG.
addMissingData(DataSet, double[]) - Static method in class edu.cmu.tetrad.data.DataTransforms: Adds missing data values to cases in accordance with probabilities specified in a double array which has as many elements as there are columns in the input dataset.
addMissingnessIndicators(DataSet) - Static method in class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder: Adds missingness indicators using the default spec.
addNode(Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds a Node to the graph.
addNode(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds a node to the graph.
addNode(Node) - Method in interface edu.cmu.tetrad.graph.Graph: Adds a node to the graph.
addNode(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds a node to the graph.
addNode(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds a node to the graph.
addNode(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a node to the graph.
addNonCollider(Triple) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Add another non-collider operation to the GraphChange.
addNondirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds a nondirected edge between two nodes in the graph.
addNondirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds a nondirected edges o-o to the graph.
addNondirectedEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Adds a nondirected edges o-o to the graph.
addNondirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds a nondirected edges o-o to the graph.
addNondirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds a nondirected edges o-o to the graph.
addNondirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a nondirected edge between two nodes.
addObserver(ModelObserver) - Method in class edu.cmu.tetrad.search.MarkovCheck: Adds a ModelObserver to the list of observers.
addOrient(Node, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Add another orient operation to the GraphChange.
addOrUpdateCover(Cover, Set) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Convenience overload with no fake children.
addOrUpdateCover(Cover, Set, Set) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Adds a cover with the given children, or extends an existing one (Python addOrUpdateCover).
addOutputStream(OutputStream) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets the OutputStream that is used to log matters out to.
addParent(NbComponent, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Returns the parents of this component.
addParent(NbComponent, int) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbComponent: Returns the parents of this component.
addPartiallyOrientedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds a partially oriented edge between two nodes.
addPartiallyOrientedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds a partially oriented edge o-> to the graph.
addPartiallyOrientedEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Adds a partially oriented edge o-> to the graph.
addPartiallyOrientedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds a partially oriented edge o-> to the graph.
addPartiallyOrientedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds a partially oriented edge o-> to the graph.
addPartiallyOrientedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a partially oriented edge between two given nodes.
addProperty(Edge.Property) - Method in class edu.cmu.tetrad.graph.Edge: addProperty.
addProperty(Edge.Property) - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: addProperty.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Adds a property change listener.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Adds a property change listener.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.graph.Dag: Adds a PropertyChangeListener to the underlying graph object.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds a PropertyChangeListener to the graph.
addPropertyChangeListener(PropertyChangeListener) - Method in interface edu.cmu.tetrad.graph.Graph: Adds a PropertyChangeListener to the graph.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.graph.GraphNode: Adds a property change listener.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds a PropertyChangeListener to the graph.
addPropertyChangeListener(PropertyChangeListener) - Method in interface edu.cmu.tetrad.graph.Node: Adds a property change listener.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds a PropertyChangeListener to the graph.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds a PropertyChangeListener to the list of listeners that are notified when a bound property is changed.
addPropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Registers a listener to events concerning the lag graph.
addRankDefSet(Set, int, Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Saves a rank-deficient set of covers for merging into clusters later (Python addRankDefSet).
addRecord(Node, SortedSet, SortedSet, SortedSet) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: addRecord.
addRefutedSeed(Node, Node) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Declares that the seeded separator for a pair was refuted by the test, so that the trial map's entry for the pair is not trusted by the I-map check.
addRemove(Edge) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Add another remove operation to the GraphChange.
addRepairListener(VertexRepairSearch.RepairListener) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Registers a listener for repair-progress events.
addRow(List) - Method in interface edu.pitt.isp.sverchkov.data.DataTable: addRow.
addRow(List) - Method in class edu.pitt.isp.sverchkov.data.DataTableImpl: addRow.
addTetradLoggerConfig(Class, TetradLoggerConfig) - Method in class edu.cmu.tetrad.util.TetradLogger: Adds the given TetradLoggerConfig to the logger, so that it can be used throughout the life of the application.
addTetradLoggerListener(TetradLoggerListener) - Method in class edu.cmu.tetrad.util.TetradLogger: Adds a TetradLoggerListener to the TetradLogger.
addTierStructure(KnowledgeTierStructure) - Method in class edu.cmu.tetrad.data.Knowledge: Adds an additional tier structure.
addToTier(int, String) - Method in class edu.cmu.tetrad.data.Knowledge: Adds the given variable or wildcard cpdag to the given tier.
addToTier(int, String) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Adds the given variable to the given tier of this structure, removing it from any other tier of this structure first.
addToTiersByVarNames(List) - Method in class edu.cmu.tetrad.data.Knowledge: Puts a variable into tier i if its name is xxx:ti for some xxx and some i.
addTransitiveInputEdges(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Adds all transitive input-to-latent edges implied by the current directed latent structure.
addTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Adds a triple consisting of three given nodes to the data structure.
addTwoCycles(Graph, int) - Static method in class edu.cmu.tetrad.graph.RandomGraph: addTwoCycles.
addUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds an underline triple to the current object.
addUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: addUnderlineTriple.
addUnderlineTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: addUnderlineTriple.
addUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: addUnderlineTriple.
addUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: addUnderlineTriple.
addUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds an underline triple consisting of three nodes to the graph.
addUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: addUnderlineTriple.
addUndirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Adds an undirected edge between two nodes.
addUndirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Adds an undirected edge --- to the graph.
addUndirectedEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Adds an undirected edge --- to the graph.
addUndirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Adds an undirected edge --- to the graph.
addUndirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Adds an undirected edge --- to the graph.
addUndirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Adds an undirected edge between two nodes.
addUnorientedLink(Cover, Cover) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Links two covers as LatentGroups.addOrUpdateCover(Cover, Set) does, with the first as parent of the second, and records that the choice of parent was not made by the data, so that LatentGroups.toAdjacency() writes the link as an undirected edge.
addVariable(int, Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: Adds the given variable at the given index.
addVariable(int, Node) - Method in interface edu.cmu.tetrad.data.DataSet: Adds the given variable at the given index.
addVariable(int, Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Adds the given variable at the given index.
addVariable(Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: Adds the given variable to the data set.
addVariable(Node) - Method in interface edu.cmu.tetrad.data.DataSet: Adds the given variable to the data set.
addVariable(Node) - Method in class edu.cmu.tetrad.data.MixedDataBox: addVariable.
addVariable(Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Adds the given variable to the data set.
addVariable(String) - Method in class edu.cmu.tetrad.data.Knowledge: addVariable.
addVariable(String) - Method in class edu.cmu.tetrad.graph.LagGraph: addVariable.
AdequacyParams - Class in edu.cmu.tetrad.sem: Parameters for model adequacy assessment.
AdequacyParams() - Constructor for class edu.cmu.tetrad.sem.AdequacyParams: Default constructor for AdequacyParams.
AdequacyReport - Class in edu.cmu.tetrad.sem: Represents a report evaluating the adequacy of a model's performance relative to a baseline.
adInd() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the adInd record component.
adInd() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the adInd record component.
adjacency() - Method in record class edu.cmu.tetrad.search.rlcd.LatentGroups.AdjacencyResult: Returns the value of the adjacency record component.
AdjacencyConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for adjacencies--i.e.
AdjacencyConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.AdjacencyConfusion: Constructs a new AdjacencyConfusion object from the given graphs.
adjacencyF1 - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.GraphDiff: Represents the adjacency F1 score, which measures the similarity between the adjacency structures of two graphs in terms of precision and recall.
AdjacencyFn - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AdjacencyFn() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFn: Constructs the statistic.
AdjacencyFp - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AdjacencyFp() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFp: Constructs the statistic.
AdjacencyFpr - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency true positive rate.
AdjacencyFpr() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFpr: Constructs the statistic.
adjacencyFrequency(String, String) - Method in class edu.cmu.tetrad.search.rlcd.RlcdStabilitySummary: The fraction of runs in which two observed variables are adjacent, orientation ignored.
adjacencyInstability(List, List) - Static method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Computes the StARS-style adjacency instability of a set of graphs over the given variables: the mean over unordered variable pairs of 2 * theta * (1 - theta), where theta is the fraction of graphs in which the pair is adjacent.
Adjacency metrics - Section in class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer
AdjacencyPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AdjacencyPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyPrecision: Constructs the statistic.
AdjacencyRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency recall.
AdjacencyRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyRecall: Constructs the statistic.
AdjacencyResult(int[][], List) - Constructor for record class edu.cmu.tetrad.search.rlcd.LatentGroups.AdjacencyResult: Creates an instance of a AdjacencyResult record class.
AdjacencyTn - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AdjacencyTn() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTn: Constructs the statistic.
AdjacencyTp - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AdjacencyTp() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTp: Constructs the statistic.
AdjacencyTpr - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency true positive rate.
AdjacencyTpr() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTpr: Constructs the statistic.
adjacent(Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns True iff a is adjacent to b in the current graph.
adjacent(FciOrientDijkstra.Graph, Node, Node) - Static method in class edu.cmu.tetrad.search.FciOrientDijkstra: Checks whether a node is adjacent to another node in the graph.
AdjCor - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The number of adjacency correct edges.
AdjCor - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AdjCor
adjF1() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: F1 score for adjacency recovery.
AdjFn - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The number of adjacency false negative edges.
AdjFn - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AdjFn
AdjFp - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The number of adjacency false positive edges.
AdjFp - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AdjFp
AdjPrec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The adjacency precision.
AdjPrec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AdjPrec
adjPrecision() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the adjPrecision record component.
AdjRec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The adjacency recall.
AdjRec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AdjRec
adjRecall() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the adjRecall record component.
adjust(double[][]) - Method in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: Computes the adjustment matrix for an annotation under this mode.
adjustedEvaluate(double) - Method in class edu.cmu.tetrad.search.utils.GaussianProcessRBF: Evaluates the adjusted value of the simulated function at a given point.
adjustedRandIndex(int[], int[]) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Computes the Adjusted Rand Index (ARI) to measure the similarity between two cluster labelings.
AdjustmentEffectEstimatorV1 - Class in edu.cmu.tetrad.estimate.v1: v1: Propensity model = logistic regression via IRLS on mixed-feature basis.
AdjustmentEffectEstimatorV1.ConfigV1 - Class in edu.cmu.tetrad.estimate.v1: v1: Configuration knobs with conservative defaults.
AdjustmentEffectEstimatorV1.EffectEstimateResultV1 - Class in edu.cmu.tetrad.estimate.v1: v1: Result object returned to UI/table.
AdjustmentEffectEstimatorV1SmokeTest - Class in edu.cmu.tetrad.estimate.v1: Smoke test suite for the AdjustmentEffectEstimatorV1 class, which validates the functionality and stability of the causal effect estimation methods.
AdjustmentEffectEstimatorV1SmokeTest() - Constructor for class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1SmokeTest: Private constructor for the AdjustmentEffectEstimatorV1SmokeTest class.
AdjustmentHarness - Class in edu.cmu.tetrad.search: PDAG harness for benchmarking adjustment-set generation and total-effect estimation.
AdjustmentHarness() - Constructor for class edu.cmu.tetrad.search.AdjustmentHarness: Constructs an instance of the AdjustmentHarness class.
adjustmentSet - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: A list of variable names that are included in the adjustment set.
adjustmentSet(Node, Node) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns a valid adjustment set for (x, y) in the PAG, or null if none is found.
adjustmentSets(Node, Node, int) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns up to maxNumSets valid adjustment sets for (x, y) in the PAG.
adjustmentSets(Node, Node, String, int, int, int, int) - Method in class edu.cmu.tetrad.graph.Paths: Computes and returns a list of adjustment sets for given nodes and parameters.
adjustmentSets(Node, Node, String, int, int, int, int, boolean, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Computes and returns a list of adjustment sets for given nodes and parameters.
adjustmentSets(Node, Node, String, int, int, int, int, RecursiveAdjustment.ColliderPolicy, boolean, Set, Set, Set) - Method in class edu.cmu.tetrad.search.RecursiveAdjustment: Computes a list of adjustment sets for estimating the causal effect of X on Y within a given graph structure.
adjustmentSets(Node, Node, String, int, int, int, int, String) - Method in class edu.cmu.tetrad.graph.Paths: Computes the adjustment sets needed to estimate the causal effect of node X on node Y in a given graph structure under specified parameters.
adjustmentSets(Node, Node, String, int, int, int, int, String, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Computes the adjustment sets needed to estimate the causal effect of node X on node Y in a given graph structure under specified parameters.
adjustmentSets(Set, Set, String, int, int, int, int, boolean, Set, Set) - Method in class edu.cmu.tetrad.search.RecursiveAdjustmentMultiple: Entry point analogous to Adjustment.adjustmentSetsRB, but for set-valued X and Y.
adjustmentSpread(Node, Node, int) - Method in class edu.cmu.tetrad.search.GRegressionPag: Diagnostic: the spread (maximum minus minimum) of the estimate over up to maxNumSets valid adjustment sets, all evaluated at Sigma-hat.
adPValue() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Uniformity: Returns the value of the adPValue record component.
AdTree - Class in edu.cmu.tetrad.search.utils: A count-only variant of AdTree.
AdTree<A,V> - Class in edu.pitt.isp.sverchkov.data: An implementation of a static AD tree based on Moore and Lee 1998 (mostly)
AdTree(int[][], Map, List, int) - Constructor for class edu.cmu.tetrad.search.utils.AdTree: Constructs an AdTreeCount using pre-initialized discrete data, avoiding re-scanning the dataset.
AdTree(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.AdTree: Constructs an AdTreeCount for the given dataset using all rows.
AdTree(DataSet, List) - Constructor for class edu.cmu.tetrad.search.utils.AdTree: Constructs an AdTreeCount for the given dataset and row subset.
AdTree(DataTable) - Constructor for class edu.pitt.isp.sverchkov.data.AdTree: Constructs an AD tree for the given data set.
AdTreeCount - Class in edu.cmu.tetrad.search.utils: A count-only variant of AdTree.
AdTreeCount(int[][], Map, List, int) - Constructor for class edu.cmu.tetrad.search.utils.AdTreeCount: Constructs an AdTreeCount using pre-initialized discrete data, avoiding re-scanning the dataset.
AdTreeCount(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.AdTreeCount: Constructs an AdTreeCount for the given dataset using all rows.
AdTreeCount(DataSet, List) - Constructor for class edu.cmu.tetrad.search.utils.AdTreeCount: Constructs an AdTreeCount for the given dataset and row subset.
AdTreeStoreRows - Class in edu.cmu.tetrad.search.utils: An AD tree is a data structure used to store the data for a given dataset in a way that makes it easy to calculate cell counts for a multidimensional contingency table for a given set of variables.
AdTreeStoreRows(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Constructs an AD Leaf Tree for the given dataset, without subsampling.
AdTreeStoreRows(DataSet, List) - Constructor for class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Constructs an AD Leaf Tree for the given dataset.
AdTreeTest - Class in edu.pitt.isp.sverchkov.data: A test of the AD tree implementation.
AdTreeTest() - Constructor for class edu.pitt.isp.sverchkov.data.AdTreeTest: Creates a new AdTreeTest object.
advice() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: Rule-based advice for handling the missingness in this dataset, as a list of short paragraphs, in decreasing order of importance.
aggregation - Variable in class edu.cmu.tetrad.search.CordEngine5: Defines the aggregation method to be used across rotations.
AhdCor - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The number of arrowhead false positive edges.
AhdCor - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AhdCor
ahdF1() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: F1 score for arrowhead recovery.
AhdFn - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The number of arrowhead false negative edges.
AhdFn - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AhdFn
AhdFp - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The number of arrowhead false positive edges.
AhdFp - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AhdFp
AhdPrec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The arrowhead precision.
AhdPrec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AhdPrec
ahdPrecision() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the ahdPrecision record component.
AhdRec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The arrowhead recall.
AhdRec - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant AhdRec
ahdRecall() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the ahdRecall record component.
Algorithm - Interface in edu.cmu.tetrad.algcomparison.algorithm: Interface that algorithm must implement.
Algorithm - Annotation Interface in edu.cmu.tetrad.annotation: Sep 5, 2017 10:47:30 AM
AlgorithmAnnotations - Class in edu.cmu.tetrad.annotation: Annotations for algorithms.
AlgorithmDescriptions - Class in edu.cmu.tetrad.util: Algorithm descriptions.
AlgorithmFactory - Class in edu.cmu.tetrad.algcomparison.algorithm: Aug 30, 2017 3:14:40 PM
Algorithm overview - Section in class edu.cmu.tetrad.search.Tsc
AlgorithmRunner - Interface in edu.cmu.tetrad.search.harness.tsc: Common interface for the four cluster-recovery algorithms used in the simulation study: TSC, BPC, FOFC, and FTFC.
Algorithms - Class in edu.cmu.tetrad.algcomparison.algorithm: A list of algorithm to be compared.
Algorithms() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.Algorithms: Constructs an empty list of algorithms.
algorithmSummaries() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.ConditionResult: Returns the value of the algorithmSummaries record component.
AlgorithmSummary(String, double, double, double, int, int) - Constructor for record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Creates an instance of a AlgorithmSummary record class.
algoType() - Element in annotation interface edu.cmu.tetrad.annotation.Algorithm: Description of the algorithm.
AlgType - Enum Class in edu.cmu.tetrad.annotation: Author : Jeremy Espino MD Created 6/30/17 10:36 AM
alignNodes(Graph, Graph) - Static method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns the estimated graph with its nodes replaced, by name, with the true graph's node objects.
alignVariableOrder(List, List) - Static method in class edu.cmu.tetrad.data.DataTransforms: Puts the variables of each of the given data sets in the given order of names.
All - Enum constant in enum class edu.cmu.tetrad.data.DataType: All.
allDirectedPaths(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds all directed paths from node1 to node2 with a maximum length.
allEigenvaluesAreSmallerThanOneInModulus(Matrix) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: allEigenvaluesAreSmallerThanOneInModulus.
allGreaterThanAlpha(List, double) - Method in class edu.cmu.tetrad.search.ntad_test.NtadTest: Checks if all ntads in the provided list have a value greater than the specified alpha.
allNodePairs(List) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Generates NodePairs of all possible pairs of nodes from given list of nodes.
ALLOW - Enum constant in enum class edu.cmu.tetrad.search.Pc.AllowBidirected: Represents the option to allow bidirected edges in a graph.
ALLOW - Enum constant in enum class edu.cmu.tetrad.search.PcAR.AllowBidirected: Represents the option to allow bidirected edges in a graph.
ALLOW_BIDIRECTED - Static variable in class edu.cmu.tetrad.util.Params: Constant ALLOW_BIDIRECTED="allowBidirected"
ALLOW_INTERNAL_RANDOMNESS - Static variable in class edu.cmu.tetrad.util.Params: Constant ALLOW_INTERNAL_RANDOMNESS="allowInternalRandomness"
allow_latent_common_causes - Enum constant in enum class edu.cmu.tetrad.annotation.AlgType: If an algorithm allows latent common causes.
allPaths(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds all paths from node1 to node2 within a specified maximum length.
allPaths(Node, Node, int, int, Set, Map, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Finds all paths between two nodes satisfying certain conditions.
allPaths(Node, Node, int, Set, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Finds all paths between two nodes within a given maximum length, considering optional condition set and selection bias.
allPathsOutOf(Node, int, Set, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Generates all paths out of a given node within a specified maximum length and conditional set.
AllSubsetsIndependenceFacts(Set, Set) - Constructor for class edu.cmu.tetrad.search.MarkovCheck.AllSubsetsIndependenceFacts: Constructor.
allToString() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: allToString.
AlmostCycleRemover - Class in edu.cmu.tetrad.search.utils: A class for heuristically removing almost cycles from a PAG to avoid unfaithfulness in an estimated PAG.
AlmostCycleRemover() - Constructor for class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Constructs a new instance of the AlmostCycleRemover class with the specified Graph.
alongPathIn(Graph) - Method in class edu.cmu.tetrad.graph.Triple: alongPathIn.
alpha - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents the significance level used for statistical decision-making.
alpha - Variable in class edu.cmu.tetrad.search.CordEngine2: Significance level used for statistical hypothesis testing in decision-making processes.
alpha - Variable in class edu.cmu.tetrad.search.CordEngine3: The significance level used for hypothesis testing in the CordEngine3 class.
alpha - Variable in class edu.cmu.tetrad.search.CordEngine4: Represents the significance level used for hypothesis testing or decision-making.
alpha - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the significance level for statistical decision-making processes.
alpha() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the alpha record component.
alpha() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the alpha record component.
alpha() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns the value of the alpha record component.
alpha() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the alpha record component.
alpha(double) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the significance level recorded for this search.
alpha(double) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the significance level recorded for this search.
alpha(double) - Method in class edu.cmu.tetrad.search.Pcmci.Builder: Sets the significance level (alpha) to be used in the Pcmci algorithm.
alpha(double, int, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The per-pair false-positive probability P(chi-square(df) > c df ln N), computed from the upper regularized incomplete gamma function so that it stays accurate far into the tail (1 - CDF would round to zero).
alpha(double, int, PenaltyDiscountCalibration.NullFit, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: P(kappa * chi-square(nu) > c df ln N): the per-pair false-positive probability under a fitted null for a class whose tests have df degrees of freedom in the penalty.
ALPHA - Static variable in interface edu.cmu.tetrad.graph.Node: Constant ALPHA
ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant ALPHA="alpha"
ALPHA_DEFAULT_0_05 - Static variable in class edu.cmu.tetrad.util.Params: Constant ALPHA_DEFAULT_0_05="alphaDefault0.05"
ALPHA_NUM - Static variable in interface edu.cmu.tetrad.graph.Node: Constant ALPHA_NUM
alphaAt(double, int) - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The per-pair false-positive level for one df class at a given penalty discount.
AlphaCalibration - Class in edu.cmu.tetrad.search.test: Chooses a significance level for a constraint-based search the way PenaltyDiscountCalibration chooses a penalty discount for a score: from a budget on the expected number of spurious edges over the whole search, with an effect-size criterion alongside it.
alphaForExpectedFalseEdges(int, double, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The level at which the expected number of spurious adjacencies is at most expectedFalseEdges.
alphaForFalseDiscoveryRate(int, double, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The level at which the expected number of spurious adjacencies is fdr times the expected number of true edges, p * expectedDegree / 2.
alphaForMinPartialCorrelation(double, int, int, int, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The level at which a df-degree-of-freedom test attains the requested power against a partial correlation of minPartialCorrelation.
alphaLargestDf() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns the value of the alphaLargestDf record component.
AlphaReport - Class in edu.cmu.tetrad.search.test: A calibrated significance level together with what it costs on the recall side, per degrees-of-freedom class: the level the false-edge budget allows, the smallest partial correlation each class of pair can then detect, the level each class would need to detect a requested effect, whether the budget's level suffices for the hardest class, the number of conditioning sets tried per pair, and the equivalent penalty discount.
AlphaReport(int[], int, double, double, int, double, double, int) - Constructor for class edu.cmu.tetrad.search.test.AlphaReport: Builds a report.
AlphaReport.Row - Record Class in edu.cmu.tetrad.search.test: One row of the sweep: a level and what it implies.
alphas() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the alphas record component.
alphaSmallestDf() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns the value of the alphaSmallestDf record component.
AM_BETA_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_BETA_ALPHA="amBetaAlpha"
AM_BETA_BETA - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_BETA_BETA="amBetaBeta"
AM_COEF_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_COEF_HIGH="amCoefHigh"
AM_COEF_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_COEF_LOW="amCoefLow"
AM_COEF_SYMMETRIC - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_COEF_SYMMETRIC="amCoefSymmetric"
AM_DERIVATIVE_MAX - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_DERIVATIVE_MAX="amDerivativeMax"
AM_DERIVATIVE_MIN - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_DERIVATIVE_MIN="amDerivativeMin"
AM_DISTORTION_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_DISTORTION_TYPE="amDistortionType"
AM_ENSURE_INVERTIBILITY - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_ENSURE_INVERTIBILITY="amEnsureInvertibility"
AM_FIRST_DERIVATIVE_MAX - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_FIRST_DERIVATIVE_MAX="amFirstDerivMax"
AM_FIRST_DERIVATIVE_MIN - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_FIRST_DERIVATIVE_MIN="amFirstDerivMin"
AM_RESCALE_MAX - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_RESCALE_MAX="amRescaleMax"
AM_RESCALE_MIN - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_RESCALE_MIN="amRescaleMin"
AM_TAYLOR_SERIES_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant AM_TAYLOR_SERIES_DEGREE="amTaylorSeriesDegree"
AMBIGUOUS - Enum constant in enum class edu.cmu.tetrad.search.utils.GraphSearchUtils.CpcTripleType: An ambiguous triple.
AMBIGUOUS - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.VcPc.CpcTripleType: The triple is ambiguous.
AMBIGUOUS - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.VcPcFast.CpcTripleType: Constant AMBIGUOUS
amenable() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility: Determines whether the current eligibility instance is classified as amenable.
AncestorF1 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for adjacencies.
AncestorF1() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AncestorF1: Constructs the statistic.
AncestorPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: Ancestor precision.
AncestorPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AncestorPrecision: Constructs the statistic.
AncestorRecall - Class in edu.cmu.tetrad.algcomparison.statistic: Ancestor recall.
AncestorRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AncestorRecall: Constructs the statistic.
ancestorsAvoiding(Graph, Node, Set) - Static method in class edu.cmu.tetrad.search.GRegression: Returns the ancestors of y in the subgraph of the graph induced by V \ excluded, i.e., the set of nodes from which y is reachable by a directed path all of whose vertices lie outside the excluded set.
AncestralPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
AncestralPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AncestralPrecision: Constructs the statistic.
AncestralRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
AncestralRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AncestralRecall: Constructs the statistic.
ANCHORED - Enum constant in enum class edu.cmu.tetrad.search.utils.EdgePriors.Origin: Produced by EdgePriors.toWeightsAnchoredAtAlpha(double).
andersonDarling - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The Anderson-Darling score.
AndersonDarlingTest - Class in edu.cmu.tetrad.data: Implements the Anderson-Darling test for normality, with P values calculated as in R's ad.test method (in package nortest).
AndersonDarlingTest(double[]) - Constructor for class edu.cmu.tetrad.data.AndersonDarlingTest: Constructs an Anderson-Darling test for the given column of data.
ANM_NOISE_KIND - Static variable in class edu.cmu.tetrad.util.Params: Constant ANM_NOISE_KIND="anmNoiseKind"
ANM_NOISE_STRENGTH - Static variable in class edu.cmu.tetrad.util.Params: Constant ANM_NOISE_STRENGTH="anmNoiseStrength"
ANM_NONLINEARITY - Static variable in class edu.cmu.tetrad.util.Params: Constant ANM_NONLINEARITY="anmNonlinearity"
ANM_PRESET - Static variable in class edu.cmu.tetrad.util.Params: Constant ANM_PRESET="anmPreset"
annealStartT - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Starting temperature for the simulated annealing process.
annealStartT - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the starting temperature for the simulated annealing algorithm.
annealStartT - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: The starting temperature used for the annealing process.
annealSteps - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Number of steps in the simulated annealing process.
annealSteps - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the random seed for initializing the random number generator.
annealSteps - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: The number of annealing steps to be performed during the specified optimization process.
AnnotatedClass<T> - Class in edu.cmu.tetrad.annotation: Represents a class that encapsulates a class along with its associated annotation.
AnnotatedClass(Class, T) - Constructor for class edu.cmu.tetrad.annotation.AnnotatedClass: Creates an annotated class.
AnnotatedClassUtils - Class in edu.cmu.tetrad.annotation: Sep 6, 2017 11:11:38 AM
annotation() - Method in class edu.cmu.tetrad.annotation.AnnotatedClass: Gets the annotation.
AnnotationNormalization - Enum Class in edu.cmu.tetrad.search.harness: How a raw per-(SNP, trait) annotation matrix is turned into the adjustment scores that EdgePriorFacade and EdgePriorScoreFacade scale by tau.
answer() - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Returns the value of the answer record component.
anteriority(Graph, Node...) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Computes the anteriority of the given nodes in a graph.
anteriority(Node...) - Method in class edu.cmu.tetrad.graph.Paths: Returns the set of nodes that are in the anteriority of the given nodes in the graph.
ANY_DAG - Static variable in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Any DAG uniformly selected
anyGateOn() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Reports whether either gate is on; when neither is, the saturating step runs.
anyMissing() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: True just in case the dataset contains at least one missing entry.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous
append(int[], int) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous
append(int[], int) - Method in class edu.cmu.tetrad.search.score.FfMl: Appends an integer value to the end of an integer array.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous
append(int[], int) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Appends an integer to the end of an array, creating a new array with the additional element.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore
append(int[], int) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Appends an integer to the end of an array of integers.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Appends an integer to an int array, returning a new array.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Appends an integer to an int array, returning a new array.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Appends a given integer to the end of an array, creating a new array.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Appends a new element to the end of a given array and returns the resulting array.
append(int[], int) - Method in interface edu.cmu.tetrad.search.score.Score: Appends an extra int to a list of ints.
append(int[], int) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Appends an integer value to the end of an array.
appendBlockIdColumn(DataSet, List, String) - Static method in class edu.cmu.tetrad.data.DataTransforms: Appends a new block ID column to the given dataset.
appendChangeIndexAsLastColumn(DataSet, double[], String) - Static method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Backwards-compatible helper: append a single continuous change-index column as the last column.
appendWithinBlockCenteredColumns(DataSet, String, String) - Static method in class edu.cmu.tetrad.data.DataTransforms: Appends within-block centered ("fixed effects" / within-transformed) copies of the data set's continuous columns, in place: for each continuous column that varies within blocks, a new column named name + suffix is appended holding the values minus their within-block observed means.
applied - Variable in class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText.ApplyResult: The names of parameters whose values were applied, in order.
apply(double) - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Applies this spec to a single value.
apply(DataSet) - Method in class edu.cmu.tetrad.data.LongToWide: Applies the transform.
apply(DataSet, DataSet) - Method in class edu.cmu.tetrad.data.JoinDatasets: Applies the join.
APPLY_R1 - Static variable in class edu.cmu.tetrad.util.Params: Constant APPLY_R1="applyR1"
applyDmSearch(Graph, Set, double) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: applyDmSearch.
applyEdit(VertexRepairSearch.CandidateEdit) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Applies the given candidate edit to the working graph and returns the resulting graph (or null if the edit could not be applied).
applyForbiddenCircleResolution(Graph, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Resolves forbidden circles in the given partially oriented graph (PAG) based on the provided knowledge.
applyOption(int, Score, double) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Applies one of the three correctness options and reports whether the caller's saturating step should run.
ApplyResult() - Constructor for class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText.ApplyResult: Constructs an empty result.
applySettingsText(String, Parameters) - Static method in class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText: Parses settings text in the format produced by the render methods (" name = value" lines under title lines) and applies the parameter values to the given Parameters object.
applySingleClusterPolicy(BlockSpec, SingletonClusterPolicy, double) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Applies a single-cluster policy to the provided BlockSpec.
applyTo(Graph) - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Applies the specified CandidateEdit operation to the given graph.
applyTo(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Outputs a new PAG, a copy of the input excepting the applied changes of this object.
ApproximateUpdater - Class in edu.cmu.tetrad.bayes: Calculates updated marginals for a Bayes net by simulating data and calculating likelihood ratios.
ApproximateUpdater(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.ApproximateUpdater: Constructs a new updater for the given Bayes net.
ApproximateUpdater(BayesIm, Evidence) - Constructor for class edu.cmu.tetrad.bayes.ApproximateUpdater: Constructs a new updater for the given Bayes net.
ar(DataSet, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: ar.
ar2(DataSet, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: ar2.
archiveCurrentDirectory() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Creates a zip archive of the currently serialized files in getCurrentDirectory(), placing the archive in getArchiveDirectory().
arctanh(double) - Static method in class edu.cmu.tetrad.util.MathUtils: Computes the inverse hyperbolic tangent (arctanh) of the given input.
Ark - Class in edu.cmu.tetrad.search.ntad_test: The Ark class extends the NtadTest class and provides a mechanism to perform statistical operations based on tetrads and their probabilities.
Ark(SimpleMatrix, double, int) - Constructor for class edu.cmu.tetrad.search.ntad_test.Ark: Constructs an Ark object using the provided data matrix, split proportion, and effective sample size.
arrangeByLayout(Graph, HashMap) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: arrangeByLayout.
arrangeBySourceGraph(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes in the result graph according to their positions in the source graph.
arrayPermute(int[]) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: arrayPermute.
ARROW - Enum constant in enum class edu.cmu.tetrad.graph.Endpoint: Arrow endpoint.
ArrowConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for arrows--i.e.
ArrowConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: Constructs a new ArrowConfusion object.
ArrowConfusion(Graph, Graph, boolean) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: Constructs a new ArrowConfusion object.
arrowF1 - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.GraphDiff: Represents the arrow F1 score, which measures the similarity of directed edges (arrows) between two graphs.
ArrowheadFn - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow precision.
ArrowheadFn() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFn: Constructs the statistic.
ArrowheadFp - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow precision.
ArrowheadFp() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFp: Constructs the statistic.
ArrowheadFpr - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency true positive rate.
ArrowheadFpr() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFpr: Constructs the statistic.
Arrowhead metrics - Section in class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer
ArrowheadPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow precision.
ArrowheadPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecision: Constructs the statistic.
ArrowheadPrecisionCommonEdges - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow precision.
ArrowheadPrecisionCommonEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecisionCommonEdges: Constructs the statistic.
ArrowheadRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow recall.
ArrowheadRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecall: Constructs the statistic.
ArrowheadRecallCommonEdges - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow recall.
ArrowheadRecallCommonEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecallCommonEdges: Constructs the statistic.
ArrowheadTn - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow precision.
ArrowheadTn() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTn: Constructs the statistic.
ArrowheadTp - Class in edu.cmu.tetrad.algcomparison.statistic: The arrow precision.
ArrowheadTp() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTp: Constructs the statistic.
artifactEdges - Variable in class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: Absent from the seed but separable in truth — points at dagToPag/G*, not the seed.
ASCENDING - Static variable in class edu.cmu.tetrad.util.RocCalculator: Constant ASCENDING=0
asin(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the arc sine (inverse sine) of the given value.
asList(int[]) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Creates a new list containing the elements of the given array.
asList(int[], List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Constructs a list of nodes from the given nodes list at the given indices in that list.
asRow(double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: asRow.
asSet(int[], List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts an array of indices into a set of corresponding nodes from a given list of nodes.
asSet(Node...) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the given array of nodes into a Set of nodes.
assign(double) - Method in class edu.cmu.tetrad.util.Vector: assign.
assign(Matrix) - Method in class edu.cmu.tetrad.util.Matrix: assign.
assign(Vector) - Method in class edu.cmu.tetrad.util.Vector: assign.
assignColumn(int, Vector) - Method in class edu.cmu.tetrad.util.Matrix: assignColumn.
assignPart(int[], int[], Matrix) - Method in class edu.cmu.tetrad.util.Matrix: Assigns a part of the given matrix to a specified submatrix while adding the values to the existing data.
assignPart(int[], Vector) - Method in class edu.cmu.tetrad.util.Vector: Updates the specified elements of a data structure with values from the given vector.
assignRow(int, Vector) - Method in class edu.cmu.tetrad.bayes.CptMapProbs: Assigns the values in the provided vector to a specific row in the probability map.
assignRow(int, Vector) - Method in class edu.cmu.tetrad.util.Matrix: assignRow.
at - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Arrow-to-tail
at() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.Mark: Returns the value of the at record component.
atan(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the arc tangent of a value.
atan2(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the angle θ (in radians) between the positive x-axis of a plane and the point (x, y) on it.
ateDr - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the average treatment effect on the outcome (ATE) estimated using the doubly robust (DR) method.
ateOr - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the average treatment effect on the outcome (ATE) estimated using the outcome regression (OR) method.
attachKnowledge() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the value of the attachKnowledge record component.
attachSingletonChildren(SimpleMatrix, int, double, int, int, List, List, List, List) - Static method in class edu.cmu.tetrad.search.SingletonAttacher: Returns latent->singleton edges to add.
attritionOrder() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Returns a copy of the attrition order, or null.
audit(Graph, IndependenceTest) - Method in interface edu.cmu.tetrad.search.PcAR.MarkovAuditor: Audits a finished graph: enumerates (some or all of) the conditional independencies the graph implies, tests each afresh against the data, and returns those the data reject.
auditFailures(Graph, IndependenceTest, ConditioningSetType, double, boolean) - Static method in class edu.cmu.tetrad.search.MarkovAuditUtils: Runs a Markov audit of graph against test and returns the implied independence facts rejected under Benjamini-Hochberg at level q -- NOT the raw per-test rejections.
auditFailures(Graph, IndependenceTest, ConditioningSetType, double, boolean, Fas.DeterminismGuard) - Static method in class edu.cmu.tetrad.search.MarkovAuditUtils: Audits a given graph for independence test failures and applies false discovery rate (FDR) correction at a specified level to return only significant failures.
AuditFinding - Class in edu.cmu.tetrad.data.audit: A single immutable finding produced by a DataAudit: a machine-readable code, a severity, the names of the variables involved, a map of named numeric values supporting the finding, and a human-readable message.
AuditFinding(FindingCode, AuditFinding.Severity, List, Map, String) - Constructor for class edu.cmu.tetrad.data.audit.AuditFinding: Constructs a finding.
AuditFinding.Severity - Enum Class in edu.cmu.tetrad.data.audit: The severity of a finding.
AUTO - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.CovarianceMode: Represents the automatic mode for covariance calculations.
autoCutpointsForDiscreteChild(Node, DataSet, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Populate cutpoints for each continuous parent of a DISCRETE child using equal-frequency binning.
autoMargin(IndTestFisherZ, double, double, int) - Static method in class edu.cmu.tetrad.search.test.IndTestEquivalence: An equivalence test whose margin is scaled to the sample size, as a multiple of the smallest margin that sample size could certify at all.
average - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Wilkinson's method
average - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: `The average method.
AverageDegreeEst - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AverageDegreeEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeEst: Constructs the statistic.
AverageDegreeTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
AverageDegreeTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeTrue: Constructs the statistic.
averageDeviation(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: averageDeviation.
averageDeviation(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: averageDeviation.
averageDeviation(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: averageDeviation.
averageDeviation(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: averageDeviation.
averageLogLikelihood(double[][], GaussianMixtureEM.Model) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Computes the average log-likelihood of the data under a given Gaussian Mixture Model (GMM).
averagetest - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Average method
averagetest - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The average test method.
AVG_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant AVG_DEGREE="avgDegree"
avgMarginalEffect - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Signed average marginal effect of the parent on the child: the average, over all held-out rows across the k folds, of the central finite difference (f(x + h) − f(x − h)) / (2h) of the fold's fitted mechanism with respect to the parent, with the other parents held at their observed values and h = 0.05 · SD(X).
avgMarginalEffectStd - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Standardized average marginal effect: PartialEdgeStrengthResult.avgMarginalEffect · SD(X) / SD(Y), with SDs taken from the observed data.
avgRowCorr - Variable in class edu.cmu.tetrad.util.EssLikePython.Result: Represents the average off-diagonal value of the row-row correlation matrix computed during an estimation process.
avgRowCorrelation - Variable in class edu.cmu.tetrad.util.RowCorrelationEffN.Result: The adjusted average pairwise row correlation value, computed as rho_hat and clamped to the range [0, 1).

B

b() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair: Returns the value of the b record component.
b() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.CovAndCoefs: Returns the value of the b record component.
B - Variable in class edu.cmu.tetrad.search.TwoStep.Result: Deprecated.

A coefficient matrix representing the relationships between variables in the output of the TwoStep algorithm.
backwardPrune(HybridCgModel.HybridCgPm, DataSet, double, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: Per-child backward elimination of edges by the per-edge LRT: for each child, while any parent's test on the child's current family is testable with p > alpha, the parent with the largest such p is removed, the family is refit without it, and the survivors are re-tested — so drop-one p-values are recomputed after every removal and a redundant pair loses at most one member.
bagFraction - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: Specifies the fraction of rows to use in each subsample during the bagging process.
bags - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: Specifies the number of subsamples to use during the bagging process.
bandwidth - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: The bandwidth used in the density estimation.
bandwidth1 - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The bandwidths used in the density estimation in the first and second dimensions.
bandwidth2 - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The bandwidths used in the density estimation in the first and second dimensions.
BasalInitializer - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Initializes a history array by setting the value of each variable to basal if it is unregulated (has no parents other than itself one time step back) and to a random value chosen from a N(basal, initStDev) distribution otherwise.
BasalInitializer(UpdateFunction, double, double) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasalInitializer: Constructs a new history that will initialize genes using the given basal expression and initial standard deviation.
base() - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Returns the value of the base record component.
baselineLoss - Variable in class edu.cmu.tetrad.sem.NodeAdequacySummary: The loss value
baselineMse - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Baseline MSE: variance of the child variable estimated from the full dataset.
baselineXent - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Baseline cross-entropy: entropy of the empirical marginal distribution of the child variable.
basicCpdag(Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Get a graph and direct only the unshielded colliders.
basicCpdagRestricted2(Graph, Node) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: basicCpdagRestricted2.
BasicGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Basic functionality that all graph-derived classes should provide.
BasicGraph(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Creates a Graph reading it from file fname.
BasicGraph(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Creates a graph with gName name, and n nodes.
BasicLagGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Stores a time series in the "update" (rather than, say, the "repeated") form--that is, for a given set of factors (the word "factor" is being used here to avoid ambiguity), only lags behind the getModel time step are recorded temporally, with causal edges extending from lagged factors with lags >= 1 to factors in the getModel time step (lag = 0) only.
BasicLagGraph() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Constructs an empty update graph--that is, a graph with no factors (and therefore no edges).
BasicLagGraph(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Constructs a copy of the given lag graph.
BasicLTMatrix - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a space-efficient Lower Triangular Matrix of elements of type short
BasicLTMatrix(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicLTMatrix: Creates a lower triangular matrix reading it from file fname.
BasicLTMatrix(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicLTMatrix: Creates a lower triangular matrix with nrows rows.
BasicMatrix - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Basic functionality of a Matrix
BasicMatrix(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Creates a matrix reading it from a file fname.
BasicMatrix(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Creates a matrix with nrows rows, and with name mname.
BASIS_RANK_TRANSFORM - Static variable in class edu.cmu.tetrad.util.Params: Constant BASIS_RANK_TRANSFORM="basisRankTransform"
BASIS_SCALE - Static variable in class edu.cmu.tetrad.util.Params: Constant BASIS_SCALE="basisScale"
BASIS_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant BASIS_TYPE="basisType"
basisDegree - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Represents the basis degree for continuous covariates in the configuration.
BasisExpandedGcm - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for BE-GCM (Basis-Expanded Generalised Covariance Measure Test).
BasisExpandedGcm() - Constructor for class edu.cmu.tetrad.algcomparison.independence.BasisExpandedGcm: Initializes a new instance of the BasisExpandedGcm wrapper.
BasisFunctionBgeScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for BF-BGe, the BGe (Normal-Wishart marginal likelihood) score over the basis-function embedding that BF-BIC uses.
BasisFunctionBgeScore - Class in edu.cmu.tetrad.search.score: BF-BGe: the BGe score applied to the basis-function embedding used by BasisFunctionBicScore.
BasisFunctionBgeScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.BasisFunctionBgeScore: Constructs a new instance of the wrapper.
BasisFunctionBgeScore(DataSet, int) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Constructs a BF-BGe score with the Legendre embedding of BasisFunctionBicScore.
BasisFunctionBgeScore(DataSet, int, boolean) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Constructs a BF-BGe score with the Legendre embedding of BasisFunctionBicScore, optionally applying the same adaptive basis-column screen that BF-BIC uses.
BasisFunctionBgeScore(DataSet, int, boolean, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: As BasisFunctionBgeScore(DataSet, int, boolean), with an explicit missing-data specification.
BasisFunctionBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Basis Function BIC Score (Basis-BIC) version.
BasisFunctionBicScore - Class in edu.cmu.tetrad.search.score: Calculates the basis function BIC score for a given dataset.
BasisFunctionBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Initializes a new instance of the BasisFunctionBicScore class.
BasisFunctionBicScore(DataSet, int, double) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Constructs a BasisFunctionBicScore object with the specified parameters.
BasisFunctionBicScore(DataSet, int, double, boolean) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Constructs a BasisFunctionBicScore object with the specified parameters.
BasisFunctionBicScore(DataSet, int, double, boolean, boolean) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBicScore: As BasisFunctionBicScore(DataSet, int, double, boolean), with the option to rank-transform continuous variables to [-1, 1] before the Legendre embedding.
BasisFunctionBicScore(DataSet, int, double, boolean, boolean, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBicScore: As BasisFunctionBicScore(DataSet, int, double, boolean, boolean), with an explicit missing-data specification.
BasisFunctionBicScore(DataSet, int, double, Map, boolean) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Constructs a BasisFunctionBicScore that scores against an externally supplied embedding - a map from variable index to the embedded columns to use for that variable.
BasisFunctionBicScoreFullSample - Class in edu.cmu.tetrad.search.score: Calculates the basis function BIC score for a given dataset.
BasisFunctionBicScoreFullSample(DataSet, int, double) - Constructor for class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Constructs a BasisFunctionBicScore object with the specified parameters.
BasisFunctionBicScoreTabular - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for the full-sample (tabular) Basis Function BIC Score, delegating to BasisFunctionBicScoreFullSample.
BasisFunctionBicScoreTabular() - Constructor for class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScoreTabular: Initializes a new instance of the BasisFunctionBicScoreTabular wrapper.
BasisFunctionBlocksIndTest - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for BF-Blocks-Test.
BasisFunctionBlocksIndTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.BasisFunctionBlocksIndTest: Initializes a new instance of the DegenerateGaussianLrt class.
BasisFunctionLrt - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for BF-LRT (Basis Function Likelihood Ratio Test).
BasisFunctionLrt() - Constructor for class edu.cmu.tetrad.algcomparison.independence.BasisFunctionLrt: Initializes a new instance of the BasisFunctionLrt wrapper.
basisFunctionValue(int, int, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Performs a calculation that involves repeatedly multiplying an initial value of `1.0` by the product of `0.95` and a given parameter `x`, iterating `index` times.
batchBivariate(double[][], List, double[], int) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Evaluates p(Yi, Yj | X = targets) for every (i,j) pair in the supplied list, using a single shared weight vector.
batchBivariateAllPairs(double[][], double[], int) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Convenience form: builds all upper-triangle pairs from data automatically.
batchSize - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: The number of samples to process in a single batch during training or computation.
batchSize - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Represents the number of samples to be processed together in a single batch during training.
batchSize - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Batch size for the simulator.
batchSize - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Batch size for the simulator.
BayesBifParser - Class in edu.cmu.tetrad.bayes: The BayesBifParser class provides a set of static methods for parsing Bayesian Network Interchange Format (BIF) files.
BayesBifRenderer - Class in edu.cmu.tetrad.bayes: Renders Bayes nets and related models in XML.
BayesianConstraintInference - Class in edu.pitt.dbmi.algo.bayesian.constraint.inference: Feb 22, 2014 3:26:17 PM
BayesianConstraintInference() - Constructor for class edu.pitt.dbmi.algo.bayesian.constraint.inference.BayesianConstraintInference: Constructor.
BayesianConstraintInferenceTest - Class in edu.pitt.dbmi.algo.bayesian.constraint.inference: Feb 22, 2014 3:35:38 PM
BayesianConstraintInferenceTest() - Constructor for class edu.pitt.dbmi.algo.bayesian.constraint.inference.BayesianConstraintInferenceTest: Constructor.
BayesIm - Interface in edu.cmu.tetrad.bayes: Interface implemented by Bayes instantiated models.
BayesImParser - Class in edu.cmu.tetrad.search.utils: Takes an xml element representing a bayes semIm and converts it to a BayesIM
BayesImParser() - Constructor for class edu.cmu.tetrad.search.utils.BayesImParser: Creates a new BayesImParser.
BayesImProbs - Class in edu.cmu.tetrad.bayes: Calculates cell probabilities from conditional BayesIm probabilities on the fly without constructing the entire table.
BayesImProbs(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.BayesImProbs: Constructs a BayesImProbs object from the given BayesIm.
BayesNetSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: Bayes net simulation.
BayesNetSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Constructs a new BayesNetSimulation.
BayesNetSimulation(BayesIm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Constructs a new BayesNetSimulation.
BayesNetSimulation(BayesPm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Constructs a new BayesNetSimulation.
BayesPm - Class in edu.cmu.tetrad.bayes: Implements a discrete Bayes parametric model--that is, a DAG together with a map from the nodes in the graph to a set of discrete variables, specifying the number of categories for each variable and the name of each category for each variable.
BayesPm(BayesPm) - Constructor for class edu.cmu.tetrad.bayes.BayesPm: Copy constructor.
BayesPm(Graph) - Constructor for class edu.cmu.tetrad.bayes.BayesPm: Construct a new BayesPm using the given DAG, assigning each variable two values named "value1" and "value2" unless nodes are discrete variables with categories already defined.
BayesPm(Graph, int, int) - Constructor for class edu.cmu.tetrad.bayes.BayesPm: Constructs a new BayesPm from the given DAG, assigning each variable a random number of values between lowerBound and upperBound.
BayesPm(Graph, BayesPm) - Constructor for class edu.cmu.tetrad.bayes.BayesPm: Constructs a new BayesPm using a given DAG, using as much information from the old BayesPm as possible.
BayesPm(Graph, BayesPm, int, int) - Constructor for class edu.cmu.tetrad.bayes.BayesPm: Constructs a new BayesPm from the given DAG, using as much information from the old BayesPm as possible.
BayesPm(Graph, DataSet) - Constructor for class edu.cmu.tetrad.bayes.BayesPm: Constructs a new BayesPm using the provided DAG and dataset.
BayesProperties - Class in edu.cmu.tetrad.bayes: Calculates likelihood-based properties for discrete Bayes nets against data.
BayesProperties(DataSet) - Constructor for class edu.cmu.tetrad.bayes.BayesProperties: Constructs a new BayesProperties object for the given data set.
BayesProperties.LikelihoodRet - Class in edu.cmu.tetrad.bayes: Result container for LR test.
BayesUpdater - Interface in edu.cmu.tetrad.bayes: Interface for a discrete Bayes updating algorithm.
BayesXmlParser - Class in edu.cmu.tetrad.bayes: Parses Bayes elements back to objects.
BayesXmlParser() - Constructor for class edu.cmu.tetrad.bayes.BayesXmlParser: A parser for Bayes XML files.
BayesXmlRenderer - Class in edu.cmu.tetrad.bayes: Renders Bayes nets and related models in XML.
BAYS_NET - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant BAYS_NET="Bayes Net (Multinomial)"
BCCausalInference - Class in edu.pitt.dbmi.algo.bayesian.constraint.inference: This is a thread-safe version of BCInference.
BCCausalInference(int[], int[][]) - Constructor for class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCCausalInference: Constructor
BCCausalInference.OP - Enum Class in edu.pitt.dbmi.algo.bayesian.constraint.inference: An enum for the type of operation.
BCInference - Class in edu.pitt.dbmi.algo.bayesian.constraint.inference: Feb 26, 2014 8:07:20 PM
BCInference(int[][], int[]) - Constructor for class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference: Cases is a two-dimensional array dataset.
BCInference.OP - Enum Class in edu.pitt.dbmi.algo.bayesian.constraint.inference: Enum for the type of constraint.
BdeMetricCache - Class in edu.cmu.tetrad.bayes: Provides a method for computing the score of a model, called the BDe metric (Bayesian Dirchlet likelihood equivalence), given a dataset (assumes no missing values) and a Bayes parameterized network (assumes no latent variables).> 0
BdeMetricCache(DataSet, BayesPm) - Constructor for class edu.cmu.tetrad.bayes.BdeMetricCache: Constructs a BdeMetricCache object for a given dataset and BayesPm.
BdeScore - Class in edu.cmu.tetrad.search.score: Calculates the BDe score (Bayes Dirichlet Equivalent) score for analyzing discrete multinomial data.
BdeScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.BdeScore: Constructs a BDe score for the given dataset.
BDeu - Enum constant in enum class edu.cmu.tetrad.sem.ScoreType: the BDeu score
BdeuScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Fisher Z test.
BdeuScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.BdeuScore: Constructs a new instance of the test.
BDeuScore - Class in edu.cmu.tetrad.search.score: Calculates the BDeu score, which the BDe (Bayes Dirichlet Equivalent) score with uniform priors.
BDeuScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.BDeuScore: Constructs a BDe score for the given dataset.
BDeuScore(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.BDeuScore: Constructs the score from a dataset with an explicit missing-data specification.
BDeuScoreWOprior - Class in edu.cmu.tetrad.search.is: BDeu score (Dirichlet–multinomial, decomposable) with tunable equivalent sample size ("samplePrior") and no structure prior.
BDeuScoreWOprior(DataSet) - Constructor for class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Constructs a BDeuScoreWOprior object for scoring discrete data based on the BDeu scoring metric.
BDeuScoreWOprior.CountObjects - Class in edu.cmu.tetrad.search.is: The CountObjects class encapsulates the counts of objects at different levels of a hierarchy, represented with one-dimensional and two-dimensional arrays.
beginConflictCollection() - Static method in class edu.cmu.tetrad.search.RecursiveBlocking: Begins collecting load-bearing blocked-collider marks on this thread.
beginRun(Graph, SepsetMap) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Begins a run: builds the RICF scorer and scores the start graph when the score step is on, and computes the start graph's uncertified pairs when the I-map gate is on.
bernoulliRand(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: bernoulliRand.
bes(Graph, List) - Method in class edu.cmu.tetrad.search.utils.Bes: Runs BES for a graph over the given list of variables
bes(Graph, List, List) - Method in class edu.cmu.tetrad.search.utils.BesPermutation: Runs BES.
Bes - Class in edu.cmu.tetrad.search.utils: Extracts the backward step of GES for use GES but also in other algorithms.
Bes(Score) - Constructor for class edu.cmu.tetrad.search.utils.Bes: Constructs the search.
Bes.Arrow - Class in edu.cmu.tetrad.search.utils: An arrow in the search.
BesPermutation - Class in edu.cmu.tetrad.search.utils: Implements a version of the BES (Best Equivalent Search) algorithm that takes a permutation as input and yields a permtuation as output, where the related DAG or CPDAG models are implied by the ordering or variables in these permutations.
BesPermutation(Score) - Constructor for class edu.cmu.tetrad.search.utils.BesPermutation: Constructor.
BestJaccardScorer - Class in edu.cmu.tetrad.search.harness.tsc: Computes best-Jaccard precision and recall between a set of true clusters and a set of recovered clusters.
BestJaccardScorer.ClusterScore - Record Class in edu.cmu.tetrad.search.harness.tsc: Immutable result of a best-Jaccard evaluation.
bestOrder(List) - Method in class edu.cmu.tetrad.search.Grasp: Given an initial permutation, 'order,' of the variables, searches for a best permutation of the variables by rearranging the variables in 'order.'
bestOrder(List) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: bestOrder.
bestScore - Variable in class edu.cmu.tetrad.search.ScoredClusterFinder.ClusterHit: score at chosenRank
beta - Variable in class edu.cmu.tetrad.regression.v1.RegressionUtilV1.FitOlsV1: v1: Estimated coefficients for the linear regression model.
beta(double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the value of beta for doubles
Beta - Class in edu.cmu.tetrad.util.dist: Implements a Beta distribution for purposes of drawing random numbers.
BETA_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant BETA_HIGH="betaHigh"
BETA_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant BETA_LOW="betaLow"
betaCdf(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Incomplete Beta function.
betaPdf(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: betaPdf.
betaQuantile(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: betaQuantile.
betaRand(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Beta distribution random generator.
better() - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Returns the value of the better record component.
betterMutation(TeyssierScorer) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: betterMutation.
betweenVariance() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the betweenVariance record component.
BfBlocksBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Blocks BIC Score (Blocks-BIC) version.
BfBlocksBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.BfBlocksBicScore: Initializes a new instance of the BasisFunctionBicScore class.
Bfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Adjusts FGES-FCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.
Bfci - Class in edu.cmu.tetrad.search: Uses BOSS in the *-FCI algorithm.
Bfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: No-arg constructor.
Bfci(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Constructs a new BOSS-FCI algorithm using the given test and score.
Bfci(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.Bfci: Constructor.
BfciBryan - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: BFCI-Bryan: BOSS followed by the from-scratch, sepset-map-as-state formulation of the gated StarFCI extra-edge-removal step, with sepset RE-SELECTION (a gate-rejected separating set does not retire the pair; the next separating set is tried) and NO saturating step.
BfciBryan - Class in edu.cmu.tetrad.search: FROZEN REFERENCE (2026-10-21): this formulation became the shipping StarFci/Bfci; the class is kept unchanged, off the interface, so that saved sessions that used "BFCI-Bryan" still load.
BfciBryan() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: No-arg constructor.
BfciBryan(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Constructs a new BFCI-Bryan algorithm using the given test and score.
BfciBryan(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.BfciBryan: Constructor.
BfciBryanScore - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: BFCI-Bryan-Score: BFCI-Bryan (sepset re-selection under the legality gate) with a scoring step: a legal candidate is committed only if the RICF Gaussian BIC of its MAG strictly improves on the committed model's, following Bryan Andrews's "BFCI-Bryan with scoring" pseudocode.
BfciBryanScore - Class in edu.cmu.tetrad.search: BFCI-Bryan-Score: BfciBryan with a SCORING STEP added to every gated move, following Bryan Andrews's pseudocode "BFCI-Bryan with scoring".
BfciBryanScore() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: No-arg constructor.
BfciBryanScore(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Constructs a new BFCI-Bryan algorithm using the given test and score.
BfciBryanScore(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.BfciBryanScore: Constructor.
BfciC - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Adjusts FGES-FCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.
BfciC - Class in edu.cmu.tetrad.search: Uses BOSS in the sepset-projection *-FCI variant; see StarFciC.
BfciC() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: No-arg constructor.
BfciC(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Constructs a new BOSS-FCI algorithm using the given test and score.
BfciC(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.BfciC: Constructor.
BfciD - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Adjusts FGES-FCI to use a permutation algorithm (such as BOSS-Tuck) to do the initial steps of finding adjacencies and unshielded colliders.
BfciD - Class in edu.cmu.tetrad.search: Uses BOSS in the sepset-projection-with-R4-fallback *-FCI variant; see StarFciD.
BfciD() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: No-arg constructor.
BfciD(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Constructs a new BOSS-FCI algorithm using the given test and score.
BfciD(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.BfciD: Constructor.
BfciScoreCheck - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: / Hidden from the interface on 2026-10-21 (no @Algorithm annotation): kept only so that saved sessions
BfciScoreCheck - Class in edu.cmu.tetrad.search: EXPERIMENTAL: BFCI with a per-removal SCORE CHECK ("tests propose, scores dispose").
BfciScoreCheck() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: No-arg constructor.
BfciScoreCheck(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Constructs a new BFCI-Score-Check algorithm using the given test and score.
BfciScoreCheck(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.BfciScoreCheck: Constructor.
BGE_ALPHA_MU - Static variable in class edu.cmu.tetrad.util.Params: Constant BGE_ALPHA_MU="bgeAlphaMu"
BGE_ALPHA_W_OFFSET - Static variable in class edu.cmu.tetrad.util.Params: Constant BGE_ALPHA_W_OFFSET="bgeAlphaWOffset"
BgeScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for the BGe (Bayesian Gaussian equivalent) score, the Gaussian analog of BDeu: the local score is the exact log marginal likelihood of a family under a Normal-Wishart prior rather than a BIC-penalized maximum likelihood.
BgeScore - Class in edu.cmu.tetrad.search.score: Implements the BGe (Bayesian Gaussian equivalent) score of Geiger and Heckerman (1994, 2002), in the corrected form given by Kuipers, Moffa, and Heckerman (2014).
BgeScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.BgeScore: Constructs a new instance of the BGe score wrapper.
BgeScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.BgeScore: Constructs a BGe score from a continuous data set.
BgeScore(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.BgeScore: Constructs a BGe score from a continuous data set with an explicit missing-data specification.
BgeScore(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.BgeScore: Constructs a BGe score from a covariance matrix.
bic - Variable in class edu.cmu.tetrad.bayes.BayesProperties.LikelihoodRet: Represents the Bayesian Information Criterion (BIC), a model selection criterion used in statistical analysis.
bic() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Returns the value of the bic record component.
bic() - Method in record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Returns the value of the bic record component.
bic() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns the value of the bic record component.
bic(double, int) - Static method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Computes the Fisher-Z BIC contribution for a partial correlation and effective sample size.
bic(int) - Method in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: Computes the Bayesian Information Criterion (BIC) for the Gaussian Mixture Model (GMM) given the number of rows used to fit the model.
bic(GaussianMixtureEM.Model, int) - Static method in class edu.cmu.tetrad.search.unmix.EmUtils: Computes the Bayesian Information Criterion (BIC) for the given Gaussian Mixture Model and the number of data points.
BIC - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The Bayesian Information Criterion.
BIC_BRIDGE - Enum constant in enum class edu.cmu.tetrad.search.utils.EdgePriors.Origin: Produced by EdgePriors.toWeightsViaBicBridge(double, int, double).
BicDelta(double, double, double) - Constructor for class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.BicDelta: Constructs a BicDelta instance that represents the Bayesian Information Criterion (BIC) values for mixture models with K=1 and K=2 clusters, along with the difference (delta) between these values.
BicDiff - Class in edu.cmu.tetrad.algcomparison.statistic: Difference between the true and estimated BIC scores.
BicDiff() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BicDiff: Constructs a new instance of the statistic.
BicDiffPerRecord - Class in edu.cmu.tetrad.algcomparison.statistic: Difference between the true and estiamted BIC scores.
BicDiffPerRecord() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord: Constructs a new instance of the statistic.
bicDiscrete(DataSet, Graph) - Static method in class edu.cmu.tetrad.sem.DagScores: Computes the Bayesian Information Criterion (BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of discrete variables.
BicEst - Class in edu.cmu.tetrad.algcomparison.statistic: Estimated BIC score.
BicEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BicEst: No-arg constructor.
bicK1 - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.BicDelta: Stores the Bayesian Information Criterion (BIC) value for a mixture model with a single cluster (K=1).
bicK2 - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.BicDelta: Represents the Bayesian Information Criterion (BIC) value for a mixture model with two clusters (K=2).
bicMixedConditionalGaussian(DataSet, Graph) - Static method in class edu.cmu.tetrad.sem.DagScores: Computes the Bayesian Information Criterion (BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of mixed or conditional Gaussian relationships.
BicTrue - Class in edu.cmu.tetrad.algcomparison.statistic: True BIC score.
BicTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BicTrue: Constructs a new instance of the statistic.
BidirectedConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for bidireced edges--i.e.
BidirectedConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.BidirectedConfusion: Constructs a new confusion matrix for bidirected edges.
bidirectedEdge(Node, Node) - Static method in class edu.cmu.tetrad.graph.Edges: Constructs a new bidirected edge from nodeA to nodeB (<->).
BidirectedEst - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
BidirectedEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedEst: Constructs a new instance of the statistic.
bidirectedExistsLatentCommonCause(Edge, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines whether a bidirected edge exists due to a latent common cause in the given graph.
bidirectedExistsLatentCommonCauseUncovered(Edge, Graph, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines whether the given bidirected edge has a latent common cause in the true graph via a trek x <~~ L ~~> y whose source L is latent, but where at least one consecutive pair of nodes (w, r) along the trek is NOT adjacent in the estimated graph.
bidirectedExistsLatentConfounder(Edge, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines if the given bidirected edge has a latent confounder in the true graph--that is, whether for X <-> Y there is a latent node Z such that X <- (Z) -> Y.
BidirectedFP - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected false negatives.
BidirectedFP() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedFP: Constructs a new instance of the statistic.
BidirectedLatentExistsLatentCommonCause - Class in edu.cmu.tetrad.algcomparison.statistic: The BidirectedLatentPrecision class implements the Statistic interface and represents a statistic that calculates the percentage of bidirected edges in an estimated graph for which a latent common cause exists in the true graph.
BidirectedLatentExistsLatentCommonCause() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentExistsLatentCommonCause: The BidirectedLatentPrecision class implements the Statistic interface and represents a statistic that calculates the percentage of bidirected edges in an estimated graph for which a latent common cause exists in the true graph.
BidirectedLatentPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The BidirectedLatentPrecision class implements the Statistic interface and represents a statistic that calculates the percentage of bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
BidirectedLatentPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentPrecision: The BidirectedLatentPrecision class implements the Statistic interface and represents a statistic that calculates the percentage of bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
BidirectedPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected edge precision.
BidirectedPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedPrecision: Constructs a new instance of the statistic.
BidirectedRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected edge precision.
BidirectedRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedRecall: Constructs a new instance of the statistic.
bidirectedToUndirected(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts a bidirected graph to an undirected graph.
BidirectedTP - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
BidirectedTP() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedTP: Constructs a new instance of the statistic.
BidirectedTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
BidirectedTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.BidirectedTrue: Constructs a new instance of the statistic.
binDep() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the binDep record component.
binInd() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the binInd record component.
binomialCdf(int, int, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Binomial cumulative distribution function.
binomialDep() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the binomialDep record component.
binomialInd() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the binomialInd record component.
binomialPmf(int, int, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: binomialPmf.
binomialQuantile(double, int, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: binomialQuantile.
binomialRand(int, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Binomial random generator from Numerical Recipes
biNormalCdf(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Bivariate normal CDF.
BINS_PER_CONT_XY - Static variable in class edu.cmu.tetrad.util.Params: Constant BINS_PER_CONT_XY="binsPerContXY"
BINS_PER_CONT_Z - Static variable in class edu.cmu.tetrad.util.Params: Constant BINS_PER_CONT_Z="binsPerContZ"
binsPerDim() - Method in record class edu.cmu.tetrad.search.utils.MinimaxBinningConfig: Returns the value of the binsPerDim record component.
Biolingua - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua: Implements an algorithm for revising regulatory models with expression data.
BiolinguaAlgorithm(SymMatrixF, BiolinguaDigraph) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Biolingua: Runs the biolingua algorithm using the given correlation matrix (all values are assumed significant) and the initial graph, and uses some default values for the coefficients in the evaluation metric for annotations, errors, links, and predictions.
BiolinguaAlgorithm(SymMatrixF, BiolinguaDigraph, float, float, float, float) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Biolingua: Runs the biolingua algorithm using the given correlation matrix (all values are assumed significant), an initial graph, and the coefficients in the evaluation metric for annotations, errors, links, and predictions.
BiolinguaAlgorithm(SymMatrixF, SymMatrixF, BiolinguaDigraph, float, float, float, float) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Biolingua: Runs the biolingua algorithm using the given correlation matrix, significance matrix, the initial graph, and the coefficients in the evaluation metric for annotations, errors, links, and predictions.
BiolinguaDigraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua: Implements a digraph to be used by the Biolingua algorithm.
BiolinguaDigraph(BiolinguaDigraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Copy constructor.
BiolinguaDigraph(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Creates a BiolinguaDigraph reading it from file fname.
BiolinguaDigraph(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Creates a BiolinguaDigraph with name gName and n nodes
BiolinguaRunner - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua: Client of the Biolingua class, can be used to easily run the algorithm with different inputs.
BiolinguaRunner() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaRunner: Initializes a new instance of the BiolinguaRunner class.
blankDirichletIm(BayesPm) - Static method in class edu.cmu.tetrad.bayes.DirichletBayesIm: blankDirichletIm.
BlockDiscoverer - Interface in edu.cmu.tetrad.search.blocks: An interface for discovering clusters or "blocks" of indices based on data and algorithmic techniques.
BlockDiscoverers - Class in edu.cmu.tetrad.search.blocks: A utility class providing static factory methods for creating instances of various BlockDiscoverer implementations.
BLOCKED - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking.Blockable: All paths through this branch are blocked by Z.
BLOCKED - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking1.Blockable: Indicates that the entity is currently blocked.
BLOCKED - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Blockable: Represents the state where an entity is explicitly unable to perform certain actions or be accessed due to restrictions applied.
BlockedDeletion(Node, Node, Set) - Constructor for record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Creates an instance of a BlockedDeletion record class.
blockIds(DataSet, List) - Static method in class edu.cmu.tetrad.search.utils.PerFactEss: Computes block ids (0..K-1, one per row) from the distinct joint values of the given columns.
BlockIndependenceWrapper - Interface in edu.cmu.tetrad.algcomparison.independence: An interface that extends functionality of the IndependenceWrapper and HasParameters interfaces by adding support for block-specific configuration in independence testing.
BlockingResult(Set, boolean) - Constructor for record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: Creates an instance of a BlockingResult record class.
blockingSet() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: Returns the value of the blockingSet record component.
blockPathsLocalMarkov(Graph, Node) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Returns a set of nodes that are the parents of the given node in the graph.
blockPathsRecursively(Graph, Node, Node, Set, int) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingChokePointA: Identifies and blocks paths between two nodes in a graph recursively, ensuring that all possible open paths between the nodes are restricted by adding blocking nodes to a set.
blockPathsRecursively(Graph, Node, Node, Set, int) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingChokePointB: Identifies and blocks paths in the given graph by iteratively finding and addressing chokepoints and eligible non-collider nodes.
blockPathsRecursively(Graph, Node, Node, Set, Set, int) - Static method in class edu.cmu.tetrad.search.RecursiveBlocking1: Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.
blockPathsRecursively(Graph, Node, Node, Set, Set, int) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose: Blocks paths recursively between two nodes in a graph, based on specified constraints and conditions.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.search.RecursiveBlocking: Recursively blocks paths in a graph between two nodes based on specified parameters and constraints.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.search.ShallowRecursiveBlocking: Shallow ("near-x") separating-set search between x and y, with no deadline.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.search.ShallowRecursiveBlockingMemo: Shallow ("near-x") separating-set search, no deadline.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.search.ShallowRecursiveBlockingStack: Shallow ("near-x") separating-set search, no deadline.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.search.RecursiveBlocking: Single canonical entry point for separating-set search.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.search.ShallowRecursiveBlocking: Shallow ("near-x") separating-set search between x and y.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.search.ShallowRecursiveBlockingMemo: Shallow ("near-x") separating-set search.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.search.ShallowRecursiveBlockingStack: Shallow ("near-x") separating-set search.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean, long, RecursiveBlocking.Strategy) - Static method in class edu.cmu.tetrad.search.RecursiveBlocking: Blocks paths in a graph recursively based on the given parameters and strategy.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, Knowledge) - Static method in class edu.cmu.tetrad.search.RecursiveBlocking1: Variant of RecursiveBlocking1.blockPathsRecursively(Graph, Node, Node, Set, Set, int) that additionally accepts an optional Knowledge object.
blockPathsRecursively(Graph, Node, Node, Set, Set, int, Knowledge) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose: Recursively blocks paths in a graph between two nodes under specified constraints.
blockPathsRecursivelyVerbose(Graph, Node, Node, Set, Set, int, Knowledge, PrintStream) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose: Blocks paths recursively between two nodes in a graph with detailed verbose tracing output.
blockPathsRecursivelyVerbose(Graph, Node, Node, Set, Set, int, PrintStream) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose: Blocks paths recursively between two nodes in a graph while tracing the process.
blockPathsWithMarkovBlanket(Node, Graph) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Identifies the set of nodes that form the Markov Blanket for a given node in a graph.
blocks() - Method in class edu.cmu.tetrad.search.blocks.BlockSpec: Returns the blocks associated with this BlockSpec instance.
Blocks - Enum constant in enum class edu.cmu.tetrad.data.DataType: Defined over blocks in the data.
BlocksBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Blocks BIC Score (Blocks-BIC) version.
BlocksBicScore - Class in edu.cmu.tetrad.search.score: BlocksBicScoreTrekSoft
BlocksBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Initializes a new instance of the BasisFunctionBicScore class.
BlocksBicScore(BlockSpec) - Constructor for class edu.cmu.tetrad.search.score.BlocksBicScore: Constructs a BlocksBicScoreTrekSoft object with the specified block specification.
BlockScore - Interface in edu.cmu.tetrad.search.score: Tagging interface for scores over blocks of variables.
BlockScoreWrapper - Interface in edu.cmu.tetrad.algcomparison.score: Represents an interface for scoring data models using predefined block structures.
BlocksIndTest - Class in edu.cmu.tetrad.algcomparison.independence: Deprecated.
BlocksIndTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.BlocksIndTest: Deprecated.

Initializes a new instance of the DegenerateGaussianLrt class.
BlocksIndTestLemma10 - Class in edu.cmu.tetrad.algcomparison.independence: Deprecated.
BlocksIndTestLemma10() - Constructor for class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestLemma10: Deprecated.

Initializes a new instance of the DegenerateGaussianLrt class.
BlocksIndTestTs - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for BF-Blocks-Test.
BlocksIndTestTs() - Constructor for class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestTs: Initializes a new instance of the DegenerateGaussianLrt class.
BlockSpec - Class in edu.cmu.tetrad.search.blocks: Represents a block specification that organizes data into blocks, each associated with a variable and a rank.
BlockSpec(DataSet, List, List) - Constructor for class edu.cmu.tetrad.search.blocks.BlockSpec: Constructs an instance of BlockSpec with the specified data set, blocks, and block variables.
BlockSpec(DataSet, List, List, List) - Constructor for class edu.cmu.tetrad.search.blocks.BlockSpec: Constructs an instance of BlockSpec with the specified data set, blocks, block variables, and their corresponding ranks.
BlockSpecTextCodec - Class in edu.cmu.tetrad.search.blocks: Text codec for BlockSpec: - Supports LHS names with optional rank: L1, L1(2) - Validates duplicate block names (ERROR) - Warns if #index refers to a named variable (prefer name) - Preserves user-supplied names and ranks; does not reorder blocks - Sorts members within each block (stable) and removes within-line duplicates
BlockSpecTextCodec.Issue - Record Class in edu.cmu.tetrad.search.blocks: Represents an issue encountered during the parsing or processing of block specifications.
BlockSpecTextCodec.ParseResult - Record Class in edu.cmu.tetrad.search.blocks: Represents the result of parsing a textual representation of a block specification.
BlockSpecTextCodec.Severity - Enum Class in edu.cmu.tetrad.search.blocks: Represents the severity level of a condition or situation.
BlocksTrekBicScore - Class in edu.cmu.tetrad.search.score: BlocksTrekBicScore: A trek-aware blocks-based BIC-style local score that mirrors the split construction used in block trek-separation tests.
BlocksTrekBicScore(BlockSpec) - Constructor for class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Constructs an instance of the BlocksTrekBicScore class using the provided block specification.
BlocksUtil - Class in edu.cmu.tetrad.search.blocks: Utility class for handling operations related to blocks, such as creating block variables, canonicalizing blocks, ensuring valid indices, and applying various cluster policies.
BlocksUtil.NamingMode - Enum Class in edu.cmu.tetrad.search.blocks: An enumeration representing different naming modes for assigning names to latent variables in the context of block specifications.
BlockTest - Interface in edu.cmu.tetrad.search.test: Tags an independence test that operates over blocks.
blockVariables() - Method in class edu.cmu.tetrad.search.blocks.BlockSpec: Returns the list of block variables associated with this BlockSpec instance.
BollenTing - Class in edu.cmu.tetrad.search.ntad_test: The BollenTing class extends the NtadTest class and provides statistical methods for computing tetrad and tetrads statistics.
BollenTing(SimpleMatrix, boolean, int) - Constructor for class edu.cmu.tetrad.search.ntad_test.BollenTing: Constructs a BollenTing object for performing statistical operations.
BollenTing(SimpleMatrix, int) - Constructor for class edu.cmu.tetrad.search.ntad_test.BollenTing: Constructs a BollenTing object for performing statistical operations.
bookmark() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Bookmarks the current pi with index Integer.MIN_VALUE.
bookmark(int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Bookmarks the current pi as the index key.
bool2(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.BoolSearch: Implements the BOOL-2 algorithm of Akutsu, et al, found in section 2.2 of their paper "Algorithms for Inferring Qualitative Models of Biological Networks".
bool2(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.BoolSearch: Implements the BOOL-2 algorithm of Akutsu, et al, found in section 2.2 of their paper "Algorithms for Inferring Qualitative Models of Biological Networks".
BOOLEAN_GLASS_SIMULATION - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant BOOLEAN_GLASS_SIMULATION="Boolean Glass Simulation"
BooleanFunction - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Stores a boolean function from a set of boolean-valued parents to a single boolean-valued column.
BooleanFunction(IndexedParent[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Constructs a new boolean function for the given array of parents.
BooleanGlassFunction - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Updates a gene given a history using the formula Gi.0 = max(Gi.1 - decayRate * -Gi.1 + booleanInfluenceRate * F(Parents(Gi) in the graph \ Gi.1), lowerBound), as described in Edwards and Glass, (2000), "Combinatorial explosion in model gene networks", American Institute of Physics.
BooleanGlassFunction(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Constructs a new random boolean Glass function using the given lag graph, with a basalExpression of 0.0, a true value of +1.0 and a false value of -1.0.
BooleanGlassFunction(LagGraph, double, double) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Constructs a new random boolean Glass function using the given lag graph, lower bound, and basalExpression.
booleanRepresentation(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.BoolSearch: Computes a byte vector which corresponds to the argument ind.
booleanRepresentation(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: Computes a byte vector which corresponds to the argument ind.
booleanRepresentation(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: Computes a byte vector which corresponds to the argument ind.
booleanRepresentation(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.BoolSearch: Computes a byte vector which corresponds to the argument ind.
booleanRepresentation(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: Computes a byte vector which corresponds to the argument ind.
booleanRepresentation(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: Computes a byte vector which corresponds to the argument ind.
BoolSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu: This class contains as a member variable (cases) the time series data stored in an int array of microarray measurements.
BoolSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: This class contains as a member variable (cases) the time series data stored in an int array of microarray measurements.
BoolSearch(int[][], String[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.BoolSearch: Constructor
BoolSearch(int[][], String[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.BoolSearch: Constructor for BoolSearch.
bootstrap(Graph, DataSet, Node, Node, int) - Static method in class edu.cmu.tetrad.search.GRegressionPag: Estimates a total effect on the full data set and its standard error by the nonparametric bootstrap.
bootstrap(Graph, DataSet, List, Node, int) - Static method in class edu.cmu.tetrad.search.GRegression: Estimates a total effect by G-regression on the full data set and estimates its sampling covariance by the nonparametric bootstrap (resampling rows with replacement), as in the eff2 R package.
BOOTSTRAP - Enum constant in enum class edu.cmu.tetrad.search.Cstar.SampleStyle: Use bootstrap.
bootstrapB - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Number of bootstrap
Bootstrapping - Annotation Interface in edu.cmu.tetrad.annotation: Indicate algorithm can do bootstrapping.
BOOTSTRAPPING_NUM_THREADS - Static variable in class edu.cmu.tetrad.util.Params: Constant BOOTSTRAPPING_NUM_THREADS="bootstrappingNumThreads"
BootstrapResult(Node, Node, double, double, int) - Constructor for record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Creates an instance of a BootstrapResult record class.
BootstrapResult(List, Node, double[], Matrix) - Constructor for record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Creates an instance of a BootstrapResult record class.
bootstrapRoots - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: A boolean variable that indicates whether the bootstrap process should include root nodes.
bootstrapRoots - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Indicates whether bootstrap sampling should be used when initializing roots during the execution of certain algorithms.
bootstrapRoots - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Whether to preserve marginal distributions at root nodes during simulation.
bootstrapRoots - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Whether to preserve marginal distributions at root nodes during simulation.
BootstrapSampler - Class in edu.cmu.tetrad.data: Provides a static method for sampling with replacement from a dataset to create a new dataset with a sample size supplied by the user.
BootstrapSampler() - Constructor for class edu.cmu.tetrad.data.BootstrapSampler: Constructs a sample that does not do any logging.
bootStrapSampling(DataSet, int) - Method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: bootStrapSampling.
Boss - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: BOSS (Best Order Score Search)
Boss - Class in edu.cmu.tetrad.search: Implements Best Order Score Search (BOSS).
Boss() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Constructs a new BOSS algorithm.
Boss(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Constructs a new BOSS algorithm with the given score.
Boss(Score) - Constructor for class edu.cmu.tetrad.search.Boss: This algorithm will work with an arbitrary BIC score.
BOSS - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.GraphLearner: Represents the BOSS graph learning algorithm.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.Cstar.CpdagAlgorithm: The BOSS algorihtm.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

A permutation search with the BOSS algorithm.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Start with BOSS.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.harness.VertexRepairSimulation.StartingGraph: Run BOSS and use its output as the starting graph.
BOSS - Enum constant in enum class edu.cmu.tetrad.search.Rlcd.Stage1Method: BOSS with the SEM BIC score.
BOSS_ALG - Static variable in class edu.cmu.tetrad.util.Params: Constant BOSS_ALG="bossAlg"
BossCcd - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: BOSS-CCD (Cyclic Causal Discovery with a BOSS adjacency superstructure).
BossCcd - Class in edu.cmu.tetrad.search: BOSS-CCD: Cyclic Causal Discovery (Richardson's CCD) with the adjacency phase delegated to BOSS.
BossCcd() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Constructs a new BOSS-CCD algorithm.
BossCcd(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Constructs a new BOSS-CCD algorithm with the given independence test and score.
BossCcd(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.BossCcd: Constructs a BossCcd search with the given test (for the CCD phase) and score (for the BOSS phase).
BossCdnodScore - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: BOSS + score-based CD-NOD context augmentation.
BossCdnodScore() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore: Default constructor for the BossCdnodScore class.
BossCdnodScore(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore: Constructs a BossCdnodScore instance with a specified ScoreWrapper.
bossCollider() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the bossCollider record component.
BossFci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: BOSS-FCI, the former name of BFCI.
BossFci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: No-arg constructor.
BossFci(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Constructs a new BOSS-FCI algorithm using the given test and score.
BossIlsFlopStudy - Class in edu.cmu.tetrad.search.harness: Six-arm study on linear Gaussian data.
BossIlsFlopStudy() - Constructor for class edu.cmu.tetrad.search.harness.BossIlsFlopStudy: Constructs a new instance of the BossIlsFlopStudy class.
BossMissingDataStudy - Class in edu.cmu.tetrad.study.missingness: Compares the ways Tetrad currently has of running BOSS on data with missing values, on simulated data whose missingness is injected by MissingnessInjector with a chem-shaped rate profile.
BossMissingDataStudy() - Constructor for class edu.cmu.tetrad.study.missingness.BossMissingDataStudy: Default constructor for the BossMissingDataStudy class.
BossMultipleImputationStudy - Class in edu.cmu.tetrad.study.missingness: Compares BOSS accuracy under the ways Tetrad has of handling missing values, with the two multiple-imputation engines as separate arms: chained equations (MiceLiteImputer) and conditional draws from an EM-fit multivariate normal (MvnImputer).
BossMultipleImputationStudy() - Constructor for class edu.cmu.tetrad.study.missingness.BossMultipleImputationStudy: Default constructor for the BossMultipleImputationStudy class.
BossPod - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: BOSS-POD, removed in Tetrad 8.0.0.
BossPod() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: No-arg constructor.
BossTopK - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: BOSS-TopK (Best Order Score Search, Top K).
BossTopK - Class in edu.cmu.tetrad.search: Implements Best Order Score Search (BOSS), extended to return the top k models by score.
BossTopK() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Constructs a new BOSS-TopK algorithm.
BossTopK(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Constructs a new BOSS-TopK algorithm with the given score.
BossTopK(Score) - Constructor for class edu.cmu.tetrad.search.BossTopK: This algorithm will work with an arbitrary BIC score.
BossTrekMimic - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Deprecated.
BossTrekMimic - Class in edu.cmu.tetrad.search.mimic: Deprecated.
BossTrekMimic() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Constructs the wrapper.
BossTrekMimic(DataSet, Parameters, Score) - Constructor for class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Constructs a BossTrekMimic search with data, parameters, and score.
BossTrekMimic(Score) - Constructor for class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Constructs an uninitialized BossTrekMimic search.
BossTrekMimicRunner - Class in edu.cmu.tetrad.search.mimic: Deprecated.
BossTrekMimicRunner() - Constructor for class edu.cmu.tetrad.search.mimic.BossTrekMimicRunner: Deprecated.

Constructor for the BossTrekMimicRunner class.
BOTH - Enum constant in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: The expression can occur in both the prefix and infix position.
BOUNDARY_ATOM - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable has a large share of its observed values at the top or bottom of its observed range, with no gap separating that value from the rest of the distribution: a grade of 100, a detector at its maximum, a scale that ran out.
BoxDataSet - Class in edu.cmu.tetrad.data: Wraps a DataBox in such a way that mixed data sets can be stored.
BoxDataSet(BoxDataSet) - Constructor for class edu.cmu.tetrad.data.BoxDataSet: Makes of copy of the given data set.
BoxDataSet(DataBox, List) - Constructor for class edu.cmu.tetrad.data.BoxDataSet: Constructs a new data set with the given number of rows and columns, with all values set to missing.
bpc(DataSet, double, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: Constructs a BpcBlockDiscoverer instance, which implements the BPC algorithm for discovering clusters or "blocks" of indices in a dataset.
bpc(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: BPC over a pooled covariance matrix; see BpcBlockDiscoverer.
Bpc - Class in edu.cmu.tetrad.search: BuildPureClusters (BPC) inspired by Silva, Scheines, Glymour, Spirtes (JMLR 2006).
Bpc(CovarianceMatrix, double, int) - Constructor for class edu.cmu.tetrad.search.Bpc: Constructor for the Bpc class.
BpcAlgorithmType - Enum Class in edu.cmu.tetrad.search.utils: Enumerates the algorithm types for BuildPureClusters, and Purify.
BpcBlockDiscoverer - Class in edu.cmu.tetrad.search.blocks: The BpcBlockDiscoverer class is an implementation of the BlockDiscoverer interface, designed to discover clusters or "blocks" of indices within a dataset using the BPC (Bayesian Partitioning for Causal Discovery) algorithm.
BpcBlockDiscoverer(DataSet, double, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.BpcBlockDiscoverer: Constructor for the BpcBlockDiscoverer class, responsible for initiating the discovery of clusters or blocks of indices within a dataset using the BPC (Bayesian Partitioning for Causal Discovery) algorithm.
BpcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.BpcBlockDiscoverer: Constructs a BpcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
BpcRunner - Class in edu.cmu.tetrad.search.harness.tsc: AlgorithmRunner implementation for the BPC (Build Pure Clusters) algorithm.
BpcRunner(double) - Constructor for class edu.cmu.tetrad.search.harness.tsc.BpcRunner: Creates a BPC runner with the given significance level.
BpcTestType - Enum Class in edu.cmu.tetrad.search.utils: Enumerates the test types for BuildPureClusters, and Purify.
BpcTetradPurifyWashdown - Class in edu.cmu.tetrad.search.work_in_progress: Implements a really simple idea for building pure clusters, just using the Purify algorithm.
BpcTetradPurifyWashdown(DataSet, BpcTestType, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.BpcTetradPurifyWashdown: Construct the algorithm using a data set.
briefSummary(DataSet) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: A one-line missingness summary cheap enough to compute in a constructor: one O(n * p) pass giving the overall missing rate, the number of complete rows, and the worst variable.
BryanSensitivityStudy - Class in edu.cmu.tetrad.study.examples.conditions: An example script to simulate data and run a comparison analysis on it.
bucketDecomposition(Graph) - Static method in class edu.cmu.tetrad.search.GRegression: Returns the bucket decomposition of the graph: the maximal connected components of its undirected subgraph (Section 3.4.1 of Guo and Perković, 2022).
build() - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Builds the configuration with the specified settings.
build() - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Builds the Cdnod object with the configured parameters.
build() - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Builds and returns an instance of CdnodBoss using the parameters specified in the Builder.
build() - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss: Builds the measurement model and all derived objects needed by downstream parent-recovery stages.
build() - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Builds the measurement model and associated derived objects.
build() - Method in class edu.cmu.tetrad.search.Pcmci.Builder: Builds and returns a Pcmci object configured with the parameters set in the Builder instance.
build(DataSet, ParentSupersetBuilder.Config) - Static method in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder: Builds a map representing a superset of parent nodes for each variable node in the given data set.
build(IndTestFisherZ, int, double, double, double) - Static method in class edu.cmu.tetrad.algcomparison.independence.EquivalenceTest: Builds the equivalence test from an already-constructed Fisher Z test, resolving the margin the same way EquivalenceTest.getTest(DataModel, Parameters) does.
BUILD_PURE_CLUSTERS - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: This one will work and does a good job for medium-sized models.
builder() - Static method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Creates and returns a new instance of the Builder class initialized with default configuration values.
Builder() - Constructor for class edu.cmu.tetrad.search.Cdnod.Builder: Constructs a new builder instance.
Builder() - Constructor for class edu.cmu.tetrad.search.CdnodBoss.Builder: Constructs a new instance of the Builder class.
Builder(DataSet, IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Pcmci.Builder: Constructs a Builder instance for configuring and creating a Pcmci object.
buildIndexing(List) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: buildIndexing.
buildParameters() - Method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Builds a Parameters object reflecting the current configuration.
buildTable(List) - Method in class edu.cmu.tetrad.search.utils.AdTree: Builds the count table for the given variables.
buildTable(List) - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Builds the count table for the given variables.
buildTable(List) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Builds the contingency table based on the given list of discrete variables.
bumpDataVersion() - Method in class edu.cmu.tetrad.search.test.FfCi: Call this after any operation that changes the contents of `data` (resimulate, edit, reload, etc.).
bumpInitialization(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Bumps the given expression value in the manner prescribed for the getModel dish.
BW_MAX_ROWS - Static variable in class edu.cmu.tetrad.util.Params: Constant BW_MAX_ROWS="bwMaxRows"
BY_CACHE_KEY - Enum constant in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Dedup: Strategy indicating deduplication will occur based on a cache key.
BY_KEY - Static variable in class edu.cmu.tetrad.search.rlcd.CoverSets: Orders covers by their sorted variable names.
ByteDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of byte data.
ByteDataBox(byte[][]) - Constructor for class edu.cmu.tetrad.data.ByteDataBox: Constructs a new data box using the given 2D byte data array as data.
ByteDataBox(int, int) - Constructor for class edu.cmu.tetrad.data.ByteDataBox: Constructs an 2D byte array consisting entirely of missing values (-99).

C

ca - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Circle-to-arrow
CACHE_SCORES - Static variable in class edu.cmu.tetrad.util.Params: Constant CACHE_SCORES="cacheScores"
CachedIndependenceQueries - Class in edu.cmu.tetrad.search.test: Shared cache + utilities for independence testing keyed by (X,Y|Z) using variable NAMES for mapping into the test, but using a structured int-ID cache key for speed/GC reduction.
CachedIndependenceQueries() - Constructor for class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Default constructor for the CachedIndependenceQueries class.
CachedIndependenceQueries(CachedIndependenceQueries.ErrorPolicy) - Constructor for class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Constructs a new instance of CachedIndependenceQueries with the specified error-handling policy.
CachedIndependenceQueries(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Constructs a new instance of CachedIndependenceQueries with the specified IndependenceTest.
CachedIndependenceQueries.Dedup - Enum Class in edu.cmu.tetrad.search.test: Enum representing deduplication strategies.
CachedIndependenceQueries.ErrorPolicy - Enum Class in edu.cmu.tetrad.search.test: ErrorPolicy represents the policies that can be applied to handle errors encountered during the execution of a process or task.
CachedIndependenceQueries.Eval - Record Class in edu.cmu.tetrad.search.test: This record represents an evaluation with a binary indicator of independence and an associated p-value.
CachedIndependenceQueries.ImpliedFactProvider - Interface in edu.cmu.tetrad.search.test: Functional interface representing a provider responsible for generating implied facts associated with a given vertex in a graph-like data structure.
cacheReport() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Human-readable cache statistics.
cacheSize() - Method in class edu.cmu.tetrad.data.missing.TestwiseRows: The number of column sets currently cached.
Caching - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
Caching - Section in class edu.cmu.tetrad.search.test.FfCi
Caching - Section in class edu.cmu.tetrad.search.test.Rcit
Caching - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
CachingIndependenceTest - Class in edu.cmu.tetrad.search.test: A caching wrapper for IndependenceTest.
CachingIndependenceTest(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.test.CachingIndependenceTest: Constructs a caching wrapper for an IndependenceTest.
calcAdjacencyGraph(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Calculates the adjacency graph for the given Directed Acyclic Graph (DAG).
calcChiSquare(int[], double) - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: Calculates chi square for a conditional cross-tabulation table for independence question 0 _||_ 1 | 2, 3, ...max by summing up chi square and degrees of freedom for each conditional table in turn.
calcChiSquare(Sextad[]) - Method in class edu.cmu.tetrad.search.utils.DeltaSextadTest: Takes a list of tetrads for the given data set and returns the chi square value for the test.
calcChiSquare(Set) - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest2: Calculates the chi-square statistic for a given set of tetrads.
calcMargin(int[]) - Method in interface edu.cmu.tetrad.data.CellTable: Calculates the marginal sum for the cell table based on the given coordinates.
calcMargin(int[]) - Method in class edu.cmu.tetrad.data.CellTableAdTree: Calculates a marginal sum for the cell table.
calcMargin(int[]) - Method in class edu.cmu.tetrad.data.CellTableCountSample: Calculates a marginal sum for the cell table.
calcMargin(int[], int[]) - Method in interface edu.cmu.tetrad.data.CellTable: Calculates the marginal sum for the cell table based on the given coordinates and margin variables.
calcMargin(int[], int[]) - Method in class edu.cmu.tetrad.data.CellTableAdTree: An alternative way to specify a marginal calculation.
calcMargin(int[], int[]) - Method in class edu.cmu.tetrad.data.CellTableCountSample: An alternative way to specify a marginal calculation.
CALCULATE_FROM_SEM - Enum constant in enum class edu.cmu.tetrad.sem.StandardizedSemIm.Initialization: Calculate from SEM.
calculateCentralMoment(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the central moment of a dataset for a given order.
calculateCumulant(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the specified cumulant of a dataset based on the given order.
calculateMinimumAbsoluteTotalEffectsOnY(Node) - Method in class edu.cmu.tetrad.search.Ida: Returns a map from nodes in V \ {Y} to their minimum absolute effects on Y.
calculateMinimumTotalEffectsOnY(Node) - Method in class edu.cmu.tetrad.search.Ida: Returns a map from nodes in V \ {Y} to their minimum effects.
calculateMinimumTotalEffectsOnY(Node) - Method in class edu.cmu.tetrad.search.PdagPagIda: Calculates the minimum total effects of all potential cause nodes on the specified target node y.
calculateMoment(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the i-th moment of the given data array.
calculatePriorMarginals(int) - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Calculates the prior marginal probabilities of the given node.
calculatePriorMarginals(int) - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Calculates the prior marginal probabilities of the given node.
calculatePriorMarginals(int) - Method in class edu.cmu.tetrad.bayes.Identifiability: Calculates the prior marginal probabilities of the given node.
calculatePriorMarginals(int) - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Calculates the prior marginals for a specified node in the Bayesian network.
calculatePriorMarginals(int) - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater
calculatePriorMarginals(int) - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Calculates the prior marginals for a specified node in the Bayesian network.
calculateUpdatedMarginals(int) - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Calculates the updated marginal probabilities of the given node, given the evidence.
calculateUpdatedMarginals(int) - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Calculates the updated marginal probabilities of the given node, given the evidence.
calculateUpdatedMarginals(int) - Method in class edu.cmu.tetrad.bayes.Identifiability: Calculates the updated marginal probabilities of the given node, given the evidence.
calculateUpdatedMarginals(int) - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Computes the updated marginal probabilities for a given node in a Bayesian network.
calculateUpdatedMarginals(int) - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater
calculateUpdatedMarginals(int) - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Computes the updated marginal probabilities for a given node in a Bayesian network.
calibratedPValue(int, int, int[]) - Method in interface edu.cmu.tetrad.search.score.ProvidesCalibratedPValue: Returns a p-value for the hypothesis that x and y are conditionally independent given z, asymptotically Uniform(0, 1) under that null.
calibratedPValue(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the p-value of the likelihood-ratio test for x _||_ y | z, recovered from the local score difference.
Calibration - Section in class edu.cmu.tetrad.data.MissingnessInjector
calibrationActive() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns true just in case calibrated p-value reporting has been requested AND the wrapped score can supply it in its current configuration.
Cam - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Represents the CAM algorithm, which is a causal additive model search algorithm.
Cam - Class in edu.cmu.tetrad.search: Implements the CAM (Causal Additive Model) algorithm for causal discovery from continuous observational data under the assumption that each variable is a nonlinear additive function of its parents plus independent noise.
Cam() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam: Constructor for the Cam class.
Cam(DataSet) - Constructor for class edu.cmu.tetrad.search.Cam: Constructs a Cam instance with the given data set.
CamAdditivePsplineBic - Class in edu.cmu.tetrad.search.score: CAM scorer using penalized cubic B-splines (P-splines; Eilers & Marx).
CamAdditivePsplineBic(DataSet) - Constructor for class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Constructs an instance of CamAdditivePsplineBic.
canBeSubsampled() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Determines whether the current independence test supports subsampling.
canBeSubsampled() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Indicates whether the test supports subsampling.
cancel() - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Requests early termination of an in-progress VertexRepairSearch.search() call.
CanCor(double[][], double[][]) - Static method in class edu.cmu.tetrad.search.utils.EstimateRank: Compute canonical correlations from data.
CanCor(int[], int[], double[][]) - Static method in class edu.cmu.tetrad.search.utils.EstimateRank: Compute canonical correlations from covariance matrix.
Candidate(String, double, double, String, int) - Constructor for record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Creates an instance of a Candidate record class.
candidateThresholds(MissingDataAudit, int) - Static method in class edu.cmu.tetrad.data.missing.MissingnessThreshold: The distinct missingness rates present in the data, ascending, as the thresholds worth sweeping: the dropped set only changes as the threshold crosses one of these, so nothing between them is informative.
CANONICAL_TABLE_ORDER - Static variable in class edu.cmu.tetrad.search.VertexRepairSearch: Canonical ranking: best candidate sorts first.
canonicalCorrelations(int[], int[]) - Method in class edu.cmu.tetrad.search.rlcd.Chi2RankTest: Canonical correlations between the two column sets, in descending order, min(|P|, |Q|) of them, clamped to [0, 1].
canonicalizeBlocks(List) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Canonicalizes a list of blocks by removing null or empty blocks, sorting the contents of each block, and ensuring the resulting blocks are unique.
CARTESIAN_PRODUCT - Enum constant in enum class edu.cmu.tetrad.graph.RandomMim.LatentLinkMode: All possible links between the two groups' latents are formed.
CaseExpander - Class in edu.cmu.tetrad.data: Makes a new data set in which cases in the given data set that have been assigned multiplicies other than n = 1 are copied out n times.
CaseExpander() - Constructor for class edu.cmu.tetrad.data.CaseExpander: Initializes a new instance of the CaseExpander class.
CAT_RHO - Static variable in class edu.cmu.tetrad.util.Params: Constant CAT_RHO="catRho"
cauchyCdf(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Cauchy CDF
cauchyPdf(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: cauchyPdf.
cauchyQuantile(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: cauchyQuantile.
cauchyRand() - Static method in class edu.cmu.tetrad.util.ProbUtils: Cauchy random generator.
CAUSAL_ORDER - Enum constant in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: Rows by causal order; see LayoutUtil.layoutByCausalOrder(Graph, NodeSize).
CausalUnmixer - Class in edu.cmu.tetrad.search.unmix: The CausalUnmixer class provides functionality for extracting unmixed results from datasets using a graph-less approach.
CausalUnmixer() - Constructor for class edu.cmu.tetrad.search.unmix.CausalUnmixer: Default constructor for the CausalUnmixer class.
CausalUnmixer.Config - Class in edu.cmu.tetrad.search.unmix: The Config class encapsulates the configuration settings used for the unmixing or clustering process within the CausalUnmixer framework.
Caveats - Section in class edu.cmu.tetrad.search.MarkovCheckNullReference
cbrt(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the cube root of a given double value using the specified implementation strategy.
cc - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Circle-to-circle
Cca - Class in edu.cmu.tetrad.search.ntad_test: The Cca class extends the NtadTest class and provides a mechanism to perform Canonical Correlation Analysis (CCA) as a rank-based way of getting a p-value for a tetrad.
Cca(SimpleMatrix, boolean, int) - Constructor for class edu.cmu.tetrad.search.ntad_test.Cca: Constructs a Cca object with the provided data matrix, covariance flag, and sample size.
Ccd - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: CCD (Cyclic Causal Discovery)
Ccd - Class in edu.cmu.tetrad.search: Cyclic Causal Discovery (CCD) after Richardson.
Ccd() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Constructs a new CCD algorithm.
Ccd(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Constructs a new CCD algorithm with the given independence test.
Ccd(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Ccd: Constructs a Ccd object with the given independence test.
Ccd.ColliderRule - Enum Class in edu.cmu.tetrad.search: How Step B decides collider versus non-collider for an unshielded triple <a, b, c>.
CCI_SCORE_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant CCI_SCORE_ALPHA="cciScoreAlpha"
CciTest - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Daudin Conditional Independence test.
CciTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.CciTest: Initializes a new instance of the CciTest class.
cdfClip - Variable in class edu.cmu.tetrad.search.CordEngine1: A small positive constant used to clip values within the cumulative distribution function (CDF) for numerical stability and to prevent extreme values from causing computational issues.
cdfClip - Variable in class edu.cmu.tetrad.search.CordEngine2: The minimum allowable value for the cumulative distribution function (CDF) to prevent numerical issues during calculations.
cdfClip - Variable in class edu.cmu.tetrad.search.CordEngine3: A small constant used to clip or constrain cumulative distribution function (CDF) values in various computations to avoid numerical instability or division by zero.
cdfClip - Variable in class edu.cmu.tetrad.search.CordEngine4: Specifies the minimum cumulative distribution function (CDF) value to be used when computations involving the CDF are clipped or thresholded.
cdfClip - Variable in class edu.cmu.tetrad.search.CordEngine5: A small threshold value used to clip the cumulative distribution function (CDF) to avoid numerical issues such as underflow or overflow during computations.
Cdnod - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: CD-NOD wrapper for algcomparison.
Cdnod - Class in edu.cmu.tetrad.search: Implements the CD-NOD (Causal Discovery from Non-stationary/heterogeneous Data) algorithm, which learns a causal graph from data in the presence of context variables that index changing causal mechanisms across data segments or environments.
Cdnod() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Default constructor for the Cdnod class.
Cdnod(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Constructs a new instance of the Cdnod class using the provided IndependenceWrapper instance.
CDNOD_COLLIDER_STYLE - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Parameter key selecting the collider orientation style for triples BOSS leaves unresolved: 1 = SEPSETS, 2 = CONSERVATIVE, 3 = MAX_P.
CDNOD_COLLIDER_STYLE - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_COLLIDER_STYLE="cdnodColliderStyle"
CDNOD_CONTEXT_MARGIN - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_CONTEXT_MARGIN="cdnodContextMargin"
CDNOD_EXCLUDE_CONTEXTS_FROM_S - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Parameter key: if true, context variables are excluded from conditioning sets in the collider-orientation step, uniformly across styles (including RecursiveBlocking hints).
CDNOD_EXCLUDE_CONTEXTS_FROM_S - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_EXCLUDE_CONTEXTS_FROM_S="cdnodExcludeContextsFromS"
CDNOD_GREEDY_CONTEXTS - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_GREEDY_CONTEXTS="cdnodGreedyContexts"
CDNOD_MAX_CONTEXTS_PER_NODE - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_MAX_CONTEXTS_PER_NODE="cdnodMaxContextsPerNode"
CDNOD_MAX_P_MARGIN - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Parameter key for the MAX_P tie-guard margin.
CDNOD_MAX_P_MARGIN - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_MAX_P_MARGIN="cdnodMaxPMargin"
CDNOD_SCORE_CONTEXTS - Static variable in class edu.cmu.tetrad.util.Params: Constant CDNOD_SCORE_CONTEXTS="cdnodScoreContexts"
Cdnod.Builder - Class in edu.cmu.tetrad.search: Builder class for creating instances of the Cdnod class with customized parameters.
Cdnod.ColliderOrientationStyle - Enum Class in edu.cmu.tetrad.search: Enumeration representing different strategies for orienting colliders in causal discovery.
CdnodBoss - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Algcomparison wrapper for the CD-NOD-BOSS search (CdnodBoss): a BOSS backbone with constraint-based collider orientation applied only to unshielded triples that BOSS leaves fully undirected, followed by Meek closure.
CdnodBoss - Class in edu.cmu.tetrad.search: Implements a variant of the CD-NOD (Causal Discovery from Non-stationary/ heterogeneous Data) algorithm that uses BOSS (Best Order Score Search) for the backbone rather than FAS.
CdnodBoss() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Default constructor.
CdnodBoss(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Constructs the wrapper with a score wrapper for the BOSS backbone.
CdnodBoss(ScoreWrapper, IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Constructs the wrapper with both a score wrapper (for the BOSS backbone) and an independence wrapper (for the collider-orientation step).
CdnodBoss.Builder - Class in edu.cmu.tetrad.search: Builder class for creating instances of the CdnodBoss class with customized parameters.
CdnodBoss.ColliderOrientationStyle - Enum Class in edu.cmu.tetrad.search: Strategies for the CI test's collider verdict in the audit.
CdnodBoss.Disagreement - Record Class in edu.cmu.tetrad.search: One disagreement between the BOSS CPDAG and the CI test on an unshielded triple x *-* z *-* y.
CdnodPag - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: CD-NOD wrapper for algcomparison.
CdnodPag - Class in edu.cmu.tetrad.search.cdnod_pag: CD-NOD-PAG runner where ALL Tier-0 variables are treated as contexts.
CdnodPag() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Default constructor for the Cdnod class.
CdnodPag(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Constructs a new instance of Cdnod with the specified IndependenceWrapper.
CdnodPag(DataSet, double, ChangeTest, CdnodPag.PagBuilder, Function, Function, Knowledge) - Constructor for class edu.cmu.tetrad.search.cdnod_pag.CdnodPag: Constructs an instance of the CdnodPag class with the specified parameters.
CdnodPag.PagBuilder - Interface in edu.cmu.tetrad.search.cdnod_pag: Functional interface representing a builder for constructing the baseline PAG.
CdnodPagOrienter - Class in edu.cmu.tetrad.search.cdnod_pag: Orients arrowheads in a PAG using context-based change/stability evidence, maintaining strong PAG legality throughout.
CdnodPagOrienter(Graph, ChangeOracle, Function, Function) - Constructor for class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Constructs a CdnodPagOrienter.
ceil(double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the smallest (closest to negative infinity) double value that is greater than or equal to the argument and is equal to a mathematical integer.
Ceiling - Section in class edu.cmu.tetrad.search.harness.ChemMaskHarness
CELL_TABLE_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant CELL_TABLE_TYPE="cellTableType"
CellTable - Interface in edu.cmu.tetrad.data: Represents a cell table that stores the values of the cells in a table of arbitrary dimension, for use, e.g., in the context of a contingency table--e.g.
CellTableAdTree - Class in edu.cmu.tetrad.data: Stores a cell count table of arbitrary dimension.
CellTableAdTree(int[][], List, int[], List) - Constructor for class edu.cmu.tetrad.data.CellTableAdTree: Constructs a new CellTableAdTree instance using the provided data, variables, test indices, and rows.
CellTableAdTree(DataSet, int[]) - Constructor for class edu.cmu.tetrad.data.CellTableAdTree: Constructs a new CellTableAdTree using the provided data set and test indices.
CellTableAdTree(DataSet, int[], List) - Constructor for class edu.cmu.tetrad.data.CellTableAdTree: Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
CellTableCountSample - Class in edu.cmu.tetrad.data: Stores a cell count table of arbitrary dimension.
CellTableCountSample(int[][], List, int[], List) - Constructor for class edu.cmu.tetrad.data.CellTableCountSample: Constructs a CellTableCountSample object with the specified data, variables, test indices, and rows.
CellTableProbs - Class in edu.cmu.tetrad.bayes: Estimates probabilities from data by constructing the entire cell count table for the data.
CellTableProbs(DataSet) - Constructor for class edu.cmu.tetrad.bayes.CellTableProbs: Creates a cell count table for the given data set.
CensoredMvnCovarianceEstimator - Class in edu.cmu.tetrad.data.missing: Estimates the covariance matrix of data in which some values are censored rather than missing: a value at a ceiling (a grade of 100, a detector at its maximum, a score at the top of its scale) is known only to be at least that bound, and a value at a floor to be at most it.
CensoredMvnCovarianceEstimator(DataSet, double[], double[]) - Constructor for class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Constructs the estimator.
center(double[]) - Static method in class edu.cmu.tetrad.data.DataTransforms: Centers the values in the given array by subtracting the mean of the array from each element.
center(double[]) - Static method in class edu.cmu.tetrad.search.Fask: Adjusts the values in the given array by subtracting the mean of the array from each element, effectively centering the dataset around zero.
center(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: Subtracts the mean of each column from each datum that column.
center(Matrix) - Static method in class edu.cmu.tetrad.search.FastIca: Centers the rows of the given matrix by subtracting the mean of each row from its elements.
center(List) - Static method in class edu.cmu.tetrad.data.DataTransforms: center.
centerData(Matrix) - Static method in class edu.cmu.tetrad.data.DataTransforms: centerData.
CENTERED_LOG_MIN_MAX - Enum constant in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: Centred log min-max, per trait column: AnnotationNormalization.CENTERED_MIN_MAX applied to log(w) over the column's entries that are finite and strictly positive; non-positive weights are treated as having no information.
CENTERED_MIN_MAX - Enum constant in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: Centred min-max, per trait column: a = (w - mean(w)) / (max(w) - min(w)) over the column's finite entries.
centeringComp() - Static method in class edu.cmu.tetrad.util.JOptionUtils: centeringComp.
centroids - Variable in class edu.cmu.tetrad.search.unmix.KMeans.Result: A 2-dimensional array representing the centroids of clusters computed by the KMeans algorithm.
Cfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: CFCI (Conservative FCI).
Cfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Constructor.
Cfci(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Constructor.
cfg - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: Represents the configuration used for generating and evaluating facts.
cfi() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Comparative Fit Index (CFI) for a given dataset and DAG.
CG_EXACT - Static variable in class edu.cmu.tetrad.util.Params: Constant CG_EXACT="cgExact"
CgLrtChangeTest - Class in edu.cmu.tetrad.search.cdnod_pag: Residual-vs-environment change test.
CgLrtChangeTest() - Constructor for class edu.cmu.tetrad.search.cdnod_pag.CgLrtChangeTest: Default constructor.
chainRuleLrt(double[][], int[], int[], int[]) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The chain-rule LRT for adding x's block to y's block with no other parents, as Basis Function BIC computes it: sum over the components y_i of y's block of N * ln( var(y_i | y_<i) / var(y_i | y_<i, x-block) ), with x's rows read through perm.
changeName(String, String) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: changeName.
ChangeOracle - Class in edu.cmu.tetrad.search.cdnod_pag: Thin wrapper around ChangeTest providing multi-context checks for the orienter.
ChangeOracle(DataSet, Node, double, ChangeTest) - Constructor for class edu.cmu.tetrad.search.cdnod_pag.ChangeOracle: Constructor for the ChangeOracle class using a single context.
ChangeOracle(DataSet, Collection, double, ChangeTest) - Constructor for class edu.cmu.tetrad.search.cdnod_pag.ChangeOracle: Constructs a new instance of the ChangeOracle class with multiple contexts.
changes(DataSet, Node, Set, Node, double) - Method in class edu.cmu.tetrad.search.cdnod_pag.CgLrtChangeTest: Determines whether the conditional distribution of y given Z changes with the environment variable, by testing the OLS residuals of y on Z against env.
changes(DataSet, Node, Set, Node, double) - Method in interface edu.cmu.tetrad.search.cdnod_pag.ChangeTest: Evaluates whether there is a change or instability with respect to the given context variable.
changes(DataSet, Node, Set, Node, double) - Method in class edu.cmu.tetrad.search.cdnod_pag.MsepOracleChangeTest: Returns true iff y and env are m-connected given Z in the true graph.
changes(Node, Set) - Method in class edu.cmu.tetrad.search.cdnod_pag.ChangeOracle: Determines if any changes occur with respect to the specified node and set of nodes given the current data, contexts, and alpha significance level.
changesAny(DataSet, Node, Set, Collection, double) - Method in interface edu.cmu.tetrad.search.cdnod_pag.ChangeTest: Determines whether there is any change or instability with respect to any of the specified context variables.
ChangeTest - Interface in edu.cmu.tetrad.search.cdnod_pag: Change/instability test used by CD-NOD-PAG.
changeVariable(Node, Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: Changes the variable for the given column from from to to.
changeVariable(Node, Node) - Method in interface edu.cmu.tetrad.data.DataSet: Changes the variable for the given column from from to to.
changeVariable(Node, Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Changes the variable for the given column from from to to.
chebyshev(int, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the value of the Chebyshev polynomial of a given degree at a specified point x.
check(Graph, Graph, Knowledge, int, int, boolean) - Static method in class edu.cmu.tetrad.search.utils.SeedMarkovCheck: Does the check.
CHECK_ADJACENCY_SEPSETS - Static variable in class edu.cmu.tetrad.util.Params: Constant CHECK_ADJACENCY_SEPSETS="checkAdjacencySepsets"
CHECK_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant CHECK_TYPE="checkType"
checkAgainstAndersonDarlingTest(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Tests a list of p-values against the Anderson-Darling Test.
checkFacts(List) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Run the joint wild-bootstrap check against an explicit list of independence facts.
checkIndependence(Node, Node, Node...) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Checks the independence of two variables, x and y, given a set of conditioning variables z.
checkIndependence(Node, Node, Node...) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Evaluates the independence between two nodes, x and y, given a set of conditioning nodes, z.
checkIndependence(Node, Node, Node...) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Checks the independence fact in question and returns and independence result.
checkIndependence(Node, Node, Node...) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Checks the independence fact in question and returns and independence result.
checkIndependence(Node, Node, Node...) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Checks the independence fact in question and returns and independence result.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.IndTestIod: Checks the independence between two nodes given a set of nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Checks the independence between two variables, x and y, given a set of conditioning variables z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Checks the probabilistic independence between two nodes, x and y, given a set of conditioning nodes, z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: IndependenceTest API: returns an IndependenceResult.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Evaluates the conditional independence between two nodes given a condition set of nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Checks the independence of x and y given z, judging the weighted p-value against the base alpha.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.FfCi: Checks the independence between two nodes, x and y, given a set of conditioning nodes, z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.FfCi1: Checks the independence between two nodes, x and y, given a set of conditioning nodes, z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Evaluates the independence between two nodes given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.Gcm: Checks the independence between two nodes given a conditioning set of nodes.
checkIndependence(Node, Node, Set) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Checks the independence between two variables x and y given a conditioning set z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Checks the independence of x and y given z using the basis-expanded GCM statistic.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Checks for statistical independence between two given variables (nodes), conditioned on a set of other variables.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Tests for the conditional independence of two nodes given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Tests for the conditional independence of two nodes, x and y, given a set of conditioning nodes z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Determines whether variable x is independent of variable y given a list of conditioning varNames z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Checks the independence of x _||_ y | z
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns an independence result that states whether x _||_ y | z and what the p-value of the test is.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Tests x _||_ y | z with CORD and returns the result.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Tests x _||_ y | z with CORD and returns the result.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Tests for the conditional independence of two nodes, x and y, given a set of conditioning nodes z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Checks whether the partial correlation of x and y given z is small enough to call negligible, by the TOST criterion described in the class comment.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Checks the independence of two nodes given a conditioning set of nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Checks the independence of two nodes given a conditioning set and returns the result.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Determines whether x _||_ y | z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Checks for independence between two nodes given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestGin: Checks the independence of two given nodes, conditioned on a set of other nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Determines whether variable x is independent of variable y given a list of conditioning varNames z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Determines whether variable x is independent of variable y given a list of conditioning variables z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Checks independence by looking up facts in the list of facts supplied in the constructor.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Determines whether variable x is independent of variable y given a list of conditioning variables z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Determines whether two nodes are independent given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Checks the independence between two variables x and y given a conditioning set z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Checks the statistical independence between two variables x and y given a set of conditioning variables z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Checks the independence between two variables, given a set of conditioning variables.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Checks the independence of x and y given z, pooling over domains.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Determines independence between variables x and y, given the set of variables z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.Kci: Tests the conditional independence of two given variables (x and y) with respect to a set of conditioning variables (z) using the KCI (Kernel-based Conditional Independence) method.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Checks whether two nodes, x and y, are statistically independent given a set of conditioning nodes z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Checks the conditional independence between two variables given a conditioning set of variables using statistical testing.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.MsepTest: Checks the independence between two nodes with respect to a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Tests the conditional independence between two variables given a conditioning set, using a Python backend for statistical computations.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Tests the conditional independence between two variables given a conditioning set, using a Python backend for statistical computations.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.Rcit: Checks the statistical independence between two nodes x and y, optionally conditioned on a set of variables z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Checks the independence between two nodes given a set of additional nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Evaluates whether two nodes (variables) are independent given a set of conditioning nodes using a block-based conditional independence test.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Evaluates the statistical independence of two nodes given a conditioning set using nested TRFF likelihood-ratio fits.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Checks the independence between two nodes given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Checks the independence between two nodes x and y given a set of conditioning nodes z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Checks the independence between two variables x and y given a conditioning set z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Checks for independence between two nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Checks for independence between two nodes, given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Checks the independence between two nodes, given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Checks the independence between two nodes, given a set of conditioning nodes.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Checks the independence between two variables x and y given a conditioning set z.
checkIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
checkIndependence(Node, Node, Set) - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Determines the independence between two variables given a set of conditioning variables.
checkIndependenceForTargetNode(Node) - Method in class edu.cmu.tetrad.search.MarkovCheck: Retrieves the list of local independence facts for a given node.
checkJoint(Set, Node) - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Joint check for a set X (treat as "treatments") and a single outcome y.
CheckKnowledge - Class in edu.cmu.tetrad.search: Identifies violations of knowledge for a given graph.
checkLegalCpdag() - Method in class edu.cmu.tetrad.graph.Paths: Checks whether the given graph is a CPDAG (Completed Partially Directed Acyclic Graph).
checkMarkovBlanket(Graph) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Build the stacked product matrix from the data and graph, then run the joint check.
checkNestingOfFields() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Checks all of the classes in the serialization scope that implement TetradSerializable to make sure all of their fields are either themselves (a) primitive, (b) TetradSerializable, or (c) assignable from types designated as safely serializable by virtue of being included in the safelySerializableTypes array (see), or are arrays whose lowest order component types satisfy either (a), (b), or (c).
checkNonSinksAreAsChildren(Set, Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Whether any key cover appearing in Vs has one of the non-sinks among its children's variables (Python checkNonSinksAreAsChildren).
checkPairwise(Node, Node) - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Pairwise check for a single (x, y).
checkProbFileExists(String, int, int, int, int, String, int, double, double, String) - Method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: checkProbFileExists.
checkSerialVersionUid() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Checks all classes in the specified serialization scope that implement TetradSerializable to ensure proper usage of the `serialVersionUID` field.
checkValue(int) - Method in class edu.cmu.tetrad.data.DiscreteVariable: checkValue.
checkValue(Object) - Method in class edu.cmu.tetrad.data.AbstractVariable: Checks to see whether the passed value can be converted into a value for this variable.
checkValue(Object) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Checks to see whether the passed value can be converted into a value for this variable.
checkValue(Object) - Method in interface edu.cmu.tetrad.data.Variable: Checks to see whether the passed value can be converted into a value for this variable.
ChemMaskHarness - Class in edu.cmu.tetrad.search.harness: Stage 5 of the missing-data experiments: simulated data shaped like a real dataset, with the real dataset's own pattern of missing cells.
CHI_SQUARE - Enum constant in enum class edu.cmu.tetrad.search.test.ChiSquareTest.TestType: The chi-square test.
Chi2RankTest - Class in edu.cmu.tetrad.search.rlcd: The canonical-correlation chi-square rank test used by RLCD, translated from Chi2RankTest.py in the causal-learn implementation.
Chi2RankTest(double[][], int, double) - Constructor for class edu.cmu.tetrad.search.rlcd.Chi2RankTest: Constructs the test from a correlation matrix and a sample size directly, for callers that build the matrix themselves.
Chi2RankTest(DataSet) - Constructor for class edu.cmu.tetrad.search.rlcd.Chi2RankTest: Constructs the test from a data set.
Chi2RankTest(ICovarianceMatrix, double) - Constructor for class edu.cmu.tetrad.search.rlcd.Chi2RankTest: Constructs the test from a covariance or correlation matrix.
CHICKERING - Enum constant in enum class edu.cmu.tetrad.search.score.SemBicScore.RuleType: The standard linear, Gaussian BIC score.
chidist(double, int) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the one-tail probability of the Chi-squared distribution for doubles
childName - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Name of the child variable (head of the edge).
childName - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Name of the child variable (head of the edge).
childName - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: Name of the child variable (head of the deleted edge).
childProbsGiven(int, double[], int[]) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: For a discrete child, the generative class distribution p(Y | pa) given the parent values in contRow / discRow: exactly the distribution TrainedDagSimulatorGNM.generateChild(int, double[], int[], Random) samples from, including any lambdaParents mixing with base rates.
childrenPerGroup() - Method in record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Returns the value of the childrenPerGroup record component.
childrenPerGroup() - Method in record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Returns the value of the childrenPerGroup record component.
chiSq - Variable in class edu.cmu.tetrad.bayes.BayesProperties.LikelihoodRet: Represents the chi-squared statistic value resulting from a statistical test.
chisqCdf(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: chisqCdf.
chisqPdf(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: chisqPdf.
chisqQuantile(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: chisqQuantile.
chisqRand(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Chi square random generator.
chiSquare - Variable in class edu.cmu.tetrad.data.missing.MissingDataAudit.LittleResult: The chi-square statistic, d^2.
ChiSquare - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Fisher Z test.
ChiSquare - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
ChiSquare - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.IndependenceTestType: Constant for the ChiSquare independence test.
ChiSquare() - Constructor for class edu.cmu.tetrad.algcomparison.independence.ChiSquare: Initializes a new instance of the ChiSquare class.
ChiSquareTest - Class in edu.cmu.tetrad.search.test: Calculates chi-square or g-square for a conditional cross-tabulation table for independence question 0 _||_ 1 | 2, 3, ...max by summing up chi-square and degrees of freedom for each conditional table in turn, where rows or columns that sum to less than a given threshold have been removed.
ChiSquareTest(DataSet, double, ChiSquareTest.TestType, List) - Constructor for class edu.cmu.tetrad.search.test.ChiSquareTest: Constructs a test using the given data set and significance level.
ChiSquareTest.CellTableType - Enum Class in edu.cmu.tetrad.search.test: The type of cell table to use.
ChiSquareTest.Result - Class in edu.cmu.tetrad.search.test: Simple class to store the parameters of the result returned by the G Square test.
ChiSquareTest.TestType - Enum Class in edu.cmu.tetrad.search.test: The type of test to perform.
chiSquareUpperQuantile(int, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The upper-tail quantile of the central chi-square(df), i.e.
ChoiceGenerator - Class in edu.cmu.tetrad.util: Generates (nonrecursively) all of the combinations of a choose b, where a, b are nonnegative integers and a >= b.
ChoiceGenerator(int, int) - Constructor for class edu.cmu.tetrad.util.ChoiceGenerator: Constructs a new choice generator for a choose b.
chol(SimpleMatrix) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the Cholesky decomposition of the given matrix and returns its lower triangular matrix.
cholesky(Matrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: cholesky.
choleskySimulation(CovarianceMatrix) - Static method in class edu.cmu.tetrad.data.DataUtils: choleskySimulation.
choose(int, int) - Static method in class edu.cmu.tetrad.util.MathUtils: choose.
chooseInverse(double) - Method in class edu.cmu.tetrad.util.Matrix: Calculates and returns the appropriate inverse of the matrix based on the provided lambda value: If lambda is 0.0, the inverse of the matrix is computed using the standard inversion method. If lambda is greater than 0.0, the matrix is regularized by adding lambda to the diagonal, ensuring numerical stability for near-singular matrices, and then the inverse is computed. If lambda is less than 0.0, the Moore-Penrose pseudoinverse is computed, which is useful for non-square or singular matrices. This method is designed to handle a variety of inverse computations depending on the requirements, such as regularization or handling singular matrices.
chooseMatrix(SimpleMatrix, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Regularizes the diagonal of the given matrix by adding a scaled identity matrix.
chosenRank - Variable in class edu.cmu.tetrad.search.ScoredClusterFinder.ClusterHit: argmax rank (should equal requested k)
ciAlpha - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Confidence level for constructing confidence intervals.
CiFact(int, int, int[]) - Constructor for record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Creates an instance of a CiFact record class.
ciHiDr - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the lower bound of the confidence interval for the DR effect size (DR) in the context of effect estimation.
ciHiOr - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the lower bound of the confidence interval for the odds ratio (OR) in the context of effect estimation.
ciLoDr - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the lower bound of the confidence interval for the DR effect size (DR) in the context of effect estimation.
ciLoOr - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the lower bound of the confidence interval for the odds ratio (OR) in the context of effect estimation.
CIRCLE - Enum constant in enum class edu.cmu.tetrad.graph.Endpoint: Circle endpoint.
CIRCLE - Enum constant in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: The circle; see LayoutUtil.circleLayout(Graph, NodeSize).
CircleConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for circles--i.e.
CircleConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: Constructs a new CircleConfusion object.
CircleConfusion(Graph, Graph, boolean) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: Constructs a new CircleConfusion object.
circleLayout(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes in the graph in a circle, using an estimate of each node's rendered size.
circleLayout(Graph, LayoutUtil.NodeSize) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the non-latent nodes in a circle just large enough that no two node boxes overlap and, for a circle of a few nodes, an edge between any two of them would be at least MIN_EDGE_LENGTH long from box to box (see MIN_EDGE_LENGTH_MAX_RADIUS), places each latent near its adjacent nodes without overlapping anything, and shifts the result so the bounding box of all node boxes sits at (LAYOUT_MARGIN, LAYOUT_MARGIN) — i.e.
CirclePrecision - Class in edu.cmu.tetrad.algcomparison.statistic: CirclePrecision is a class that implements the Statistic interface.
CirclePrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CirclePrecision: Constructs the statistic.
CircleRecall - Class in edu.cmu.tetrad.algcomparison.statistic: Implements the CircleRecall statistic, which calculates the circle recall value for a given true graph, estimated graph, and data model.
CircleRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CircleRecall: Constructs the statistic.
CiTestHarness - Class in edu.cmu.tetrad.search.harness: CI Test Harness for Type I error + p-value uniformity diagnostics.
CiTestHarness() - Constructor for class edu.cmu.tetrad.search.harness.CiTestHarness: Default constructor for the CiTestHarness class.
CiTestHarness.CiFact - Record Class in edu.cmu.tetrad.search.harness: Immutable data container that represents a CiFact consisting of two integer values and an array of integers.
CiTestHarness.Config - Record Class in edu.cmu.tetrad.search.harness: Represents a configuration record that encapsulates various parameters used for the generation and evaluation of facts.
CiTestHarness.Result - Class in edu.cmu.tetrad.search.harness: Represents the result of a computation or analysis process.
CiTestHarness.Uniformity - Record Class in edu.cmu.tetrad.search.harness: Represents the uniformity test results using Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests.
ClassifierBayesUpdaterDiscrete - Class in edu.cmu.tetrad.classify: This class contains a method classify which uses an instantiated Bayes net (BayesIm) provided in the constructor.
ClassifierBayesUpdaterDiscrete(BayesIm, DataSet) - Constructor for class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: The constructor sets the values of the private member variables.
ClassifierDiscrete - Interface in edu.cmu.tetrad.classify: Interface implemented by classes that do discrete classification.
ClassifierMbDiscrete - Class in edu.cmu.tetrad.classify: Performs a Bayesian classification of a test set based on a given training set.
ClassifierMbDiscrete(String, String, String, String, String, String, String) - Constructor for class edu.cmu.tetrad.classify.ClassifierMbDiscrete: Constructs a new ClassifierMbDiscrete object using the given training and test data, target variable, alpha value,
classify() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Computes and returns the cross-tabulation of observed versus estimated values of the target variable as described above.
classify() - Method in interface edu.cmu.tetrad.classify.ClassifierDiscrete: classify.
classify() - Method in class edu.cmu.tetrad.classify.ClassifierMbDiscrete: Classifies the test data by Bayesian updating.
clazz() - Method in class edu.cmu.tetrad.annotation.AnnotatedClass: Gets the class.
clean(double[], double[][]) - Static method in class edu.cmu.tetrad.search.utils.NonlinearityTests: Remove rows with NaNs in y or any X column.
CleanData(double[], double[][]) - Constructor for class edu.cmu.tetrad.search.utils.NonlinearityTests.CleanData: Constructs a CleanData object with the provided data arrays.
clear() - Method in class edu.cmu.tetrad.data.Knowledge: Removes explicit knowledge and tier information.
clear() - Method in class edu.cmu.tetrad.graph.Dag: clear.
clear() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Removes all nodes (and therefore all edges) from the graph.
clear() - Method in interface edu.cmu.tetrad.graph.Graph: Removes all nodes (and therefore all edges) from the graph.
clear() - Method in class edu.cmu.tetrad.graph.LagGraph: clear.
clear() - Method in class edu.cmu.tetrad.graph.SemGraph: clear.
clear() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Clears the graph by removing all vertices and edges.
clear() - Method in class edu.cmu.tetrad.search.MarkovCheck: Clears the results stored in the `resultsIndep` and `resultsDep` lists.
clear() - Method in class edu.cmu.tetrad.search.utils.MinimaxGroupCache: Clears the cached groups stored in the internal cache.
clear() - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Clears all entries from the internal sepset mapping.
clear() - Method in class edu.cmu.tetrad.util.LogUtils: Removes all streams from logging.
clear() - Method in class edu.cmu.tetrad.util.PagCache: Clears the internal cache, removing all stored entries.
clear() - Method in class edu.cmu.tetrad.util.TetradLogger: Removes all streams from the logger.
clearArchiveDirectory() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Clears the archive directory.
clearBookmarks() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Clears all bookmarks.
clearCache() - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Clears the family cache.
clearCache() - Method in class edu.cmu.tetrad.data.missing.TestwiseRows: Clears this dataset's cache.
clearCache() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Clears the district cache.
clearCache() - Method in class edu.cmu.tetrad.search.utils.AdTree: Clears the cache.
clearCache() - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Clears the cache.
clearCaches() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Clears all cached evaluations maintained by the object.
clearCellTable() - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: clearCellTable.
clearContinuousBounds() - Method in class edu.cmu.tetrad.data.Histogram: Clears the continuous bounds applied to the histogram's target variable.
clearEdges() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: clearEdges.
clearEdges() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: clearEdges.
clearEdges() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Removes all edges from the graph.
clearEdges() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Removes all edges from the graph.
clearInputOutputKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Clears any supplied input/output role knowledge.
clearInputOutputKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Clears any supplied input/output role knowledge.
clearInputOutputKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Clears any supplied input/output role knowledge.
clearRow(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Assigns random probability values to the child values of this row that add to 1.
clearRow(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Assigns random probability values to the child values of this row that add to 1.
clearRow(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Clears all values in the specified row of a table.
clearRow(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Assigns random probability values to the child values of this row that add to 1.
clearRow(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Assigns random probability values to the child values of this row that add to 1.
clearSelection() - Method in class edu.cmu.tetrad.data.BoxDataSet: Marks all variables as deselected.
clearSelection() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: clearSelection.
clearSelection() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: clearSelection.
clearSelection() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: clearSelection.
clearSelection() - Method in interface edu.cmu.tetrad.data.DataSet: Marks all variables as deselected.
clearSelection() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Clears the current selection of variables in the covariance matrix.
clearSelection() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Marks all variables as deselected.
clearTable(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Randomizes every row in the table for the given node index.
clearTable(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Randomizes every row in the table for the given node index.
clearTable(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Clears the table by clearing all rows for the given node.
clearTable(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Randomizes every row in the table for the given node index.
clearTable(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Randomizes every row in the table for the given node index.
clearTest() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Resets the internal state of the object by clearing key data structures and references.
ClFisherZPython - Class in edu.cmu.tetrad.algcomparison.independence: An IndependenceWrapper that runs the Fisher Z conditional independence test implemented in the Python causal-learn library, bridging it into Tetrad's IndependenceTest framework.
ClFisherZPython() - Constructor for class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython: Default constructor for the ClFisherZPython class.
CLI (optional) - Section in class edu.cmu.tetrad.search.AdjustmentHarness
clique(List) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: True iff the nodes in W form a clique in the current DAG.
Clique - Enum constant in enum class edu.cmu.tetrad.search.utils.ClusterSignificance.CheckType: Check to see if the cluster is a clique.
cliques(Graph) - Method in class edu.cmu.tetrad.sem.Ricf: cliques.
ClKciPython - Class in edu.cmu.tetrad.algcomparison.independence: An IndependenceWrapper that runs the Kernel-based Conditional Independence (KCI) test implemented in the Python causal-learn library, bridging it into Tetrad's IndependenceTest framework.
ClKciPython() - Constructor for class edu.cmu.tetrad.algcomparison.independence.ClKciPython: Default constructor for the ClKciPython class.
clone() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Returns a clone of this graph
clone() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Returns a clone of this graph
clone() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns a clone of this graph
close() - Method in class edu.cmu.tetrad.search.test.ProcessPythonCiService
close() - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Closes the underlying PythonCiService associated with this instance, releasing any resources and terminating any persistent connections.
close() - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Closes the underlying PythonCiService associated with this instance, releasing any resources and terminating any persistent connections.
closure(String) - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Returns the closure of the current fact set under the semigraphoid axioms (symmetry, decomposition, weak union, contraction), the graphoid axioms (semigraphoid + intersection), or the compositional graphoid axioms (graphoid + composition).
ClRcitPython - Class in edu.cmu.tetrad.algcomparison.independence: An IndependenceWrapper that runs the Randomized Conditional Independence Test (RCIT) implemented in the Python causal-learn library, bridging it into Tetrad's IndependenceTest framework.
ClRcitPython() - Constructor for class edu.cmu.tetrad.algcomparison.independence.ClRcitPython: Default constructor for the ClRcitPython class.
cluster() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: The full cluster this constraint speaks about: the determined variable together with its support.
cluster(double[][], int, int) - Static method in class edu.cmu.tetrad.search.unmix.KMeans: Performs k-means clustering on a given dataset.
cluster(Matrix) - Method in interface edu.cmu.tetrad.cluster.ClusteringAlgorithm: Clusters the given data set.
cluster(Matrix) - Method in class edu.cmu.tetrad.cluster.KMeans: Clusters the given data set.
CLUSTER_SIZES - Static variable in class edu.cmu.tetrad.util.Params: Constant CLUSTER_SIZES="clusterSizes"
clusterData - Variable in class edu.cmu.tetrad.search.unmix.UnmixResult: A list of datasets corresponding to individual clusters obtained from a clustering algorithm.
clusterGraphs - Variable in class edu.cmu.tetrad.search.unmix.UnmixResult: A list of optional graphical representations of clusters, where each graph corresponds to a specific cluster.
ClusteringAlgorithm - Interface in edu.cmu.tetrad.cluster: Represents a clustering algorithm to cluster some data.
ClusterScore(double, double, int, int) - Constructor for record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Creates an instance of a ClusterScore record class.
ClusterSignificance - Class in edu.cmu.tetrad.search.utils: Provides some methods to check significance of clusters for clustering algroithms.
ClusterSignificance(List, DataModel) - Constructor for class edu.cmu.tetrad.search.utils.ClusterSignificance: Constructs a new cluster significance object.
ClusterSignificance.CheckType - Enum Class in edu.cmu.tetrad.search.utils: Gives the options for checking significance of clusters--could check the significance using a regression model, or could check to see if the cluster is a clique, or could not do the check.
clusterWithRestarts(double[][], int, int, int) - Static method in class edu.cmu.tetrad.search.unmix.KMeans: Performs k-means clustering with multiple restarts and selects the best result based on the lowest within-cluster sum of squared errors (SSE).
coClusterFrequency(String, String) - Method in class edu.cmu.tetrad.search.rlcd.RlcdStabilitySummary: The fraction of runs in which two observed variables share a latent parent.
code() - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Returns the value of the code record component.
codes() - Static method in class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester: Returns the codes this suggester acts on.
COEF - Enum constant in enum class edu.cmu.tetrad.sem.ParamType: Enum representing the free parameter type for structural equation modeling (SEM) models.
COEF_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant COEF_HIGH="coefHigh"
COEF_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant COEF_LOW="coefLow"
COEF_SYMMETRIC - Static variable in class edu.cmu.tetrad.util.Params: Constant COEF_SYMMETRIC="coefSymmetric"
coefficientPValues(HybridCgModel.HybridCgIm, DataSet) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: Classical OLS t-test p-values for the linear coefficients of continuous children, one per (child, discrete-parent stratum, continuous parent), for table display.
coefficients() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns the value of the coefficients record component.
coefRange(double, double) - Method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Sets the uniform range for structural coefficients.
col() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns the value of the col record component.
col(int) - Method in class edu.cmu.tetrad.util.Matrix: Retrieves the specified column from a data structure as a Vector object.
collapseToLag0(boolean) - Method in class edu.cmu.tetrad.search.Pcmci.Builder: Sets whether the Pcmci algorithm should collapse all lags greater than zero to lag 0.
collapseTrailingCap - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: A configuration flag that determines whether trailing capital letters following a digit in mixed alphanumeric strings should be collapsed during processing.
collapseTrailingCap(boolean) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets whether trailing capital letters should be collapsed in the resulting configuration.
collider(Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns true iff [a, b, c] is a collider.
COLLIDER - Enum constant in enum class edu.cmu.tetrad.search.utils.GraphSearchUtils.CpcTripleType: A collider triple.
COLLIDER - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.VcPc.CpcTripleType: The triple is a collider.
COLLIDER - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.VcPcFast.CpcTripleType: Constant COLLIDER
COLLIDER_DISCOVERY_RULE - Static variable in class edu.cmu.tetrad.util.Params: Constant COLLIDER_DISCOVERY_RULE="colliderDiscoveryRule"
COLLIDER_ORIENTATION_STYLE - Static variable in class edu.cmu.tetrad.util.Params: Constant COLLIDER_ORIENTATION_STYLE="colliderOrientationStyle"
colliderAllowed(Graph, Node, Node, Node, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines if the collider is allowed.
colliderDiagnostic(boolean) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Enables or disables the collider audit (step 3).
colliderOrientedMsg(Node, Node, Node) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: colliderOrientedMsg.
colliderOrientedMsg(Node, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: colliderOrientedMsg.
colliderOrientedMsg(String, Node, Node, Node) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: colliderOrientedMsg.
colliderStyle(Cdnod.ColliderOrientationStyle) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the collider orientation strategy for the algorithm.
colliderStyle(CdnodBoss.ColliderOrientationStyle) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the CdnodBoss.ColliderOrientationStyle to be used by the Builder.
colluded() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the colluded record component.
COLON - Static variable in class edu.cmu.tetrad.data.DelimiterType: Constant COLON
color(EdgeShading.Hue, double) - Static method in class edu.cmu.tetrad.graph.EdgeShading: Returns the shade for a hue and intensity.
color(EdgeShading.Hue, double, boolean) - Static method in class edu.cmu.tetrad.graph.EdgeShading: As EdgeShading.color(Hue, double), for a light or a dark canvas.
coloredGraph(HybridCgModel.HybridCgIm) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths: Returns a copy of the IM's graph (sharing node objects, with new edge objects) whose edges carry a line color and a tooltip annotation reflecting their strength.
coloredGraph(HybridCgModel.HybridCgIm, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths: As HybridCgEdgeStrengths.coloredGraph(HybridCgIm), with the edge colors chosen for a light or a dark canvas; see EdgeShading.color(EdgeShading.Hue, double, boolean).
colorFor(HybridCgEdgeStrengths.Kind, double) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths: Maps a kind and an intensity in [0, 1] to a color via EdgeShading: positive linear to blue, negative linear to vermillion, mixed sign to reddish purple, tabular to bluish green.
colorFor(HybridCgEdgeStrengths.Kind, double, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths: As HybridCgEdgeStrengths.colorFor(Kind, double), for a light or a dark canvas.
column(int) - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Returns the sample variance (divisor n - 1) of one column over its non-missing rows, and the count of those rows, as a one-column family.
columnCount() - Method in interface edu.pitt.isp.sverchkov.data.DataTable: columnCount.
columnCount() - Method in class edu.pitt.isp.sverchkov.data.DataTableImpl: columnCount.
columnNames(DataSet) - Method in class edu.cmu.tetrad.data.LongToWide: Returns the names the wide columns would have, in order, without building the wide data set: the kept row keys (if any), then for each value variable one name per observed level of the column key.
CombinationGenerator - Class in edu.cmu.tetrad.util: Generates (nonrecursively) all of the combinations of objects, where the number of objects in each dimension is specified.
CombinationGenerator(int[]) - Constructor for class edu.cmu.tetrad.util.CombinationGenerator: Constructs a new combination of objects, choosing one object from each dimension.
CombinationIterator - Class in edu.cmu.tetrad.util: Iterates through all the posible combinations for a set of variables (each with a different number of possible values).
CombinationIterator(int[]) - Constructor for class edu.cmu.tetrad.util.CombinationIterator: Creates a combination set for a set of variables with the given number of maxValues
combinations(List, int) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: All r-combinations of the list, in lexicographic index order (as itertools.combinations).
combine - Variable in class edu.cmu.tetrad.search.CordEngine4: Determines the method of combining statistical measures for computations.
combine - Variable in class edu.cmu.tetrad.search.CordEngine5: Specifies the method used to combine values during a certain operation or computation.
combineContinuousData(List, double[][]) - Static method in class edu.cmu.tetrad.util.MultidataUtils: combineContinuousData.
combineDataset(List) - Static method in class edu.cmu.tetrad.util.MultidataUtils: combineDataset.
combineDiscreteDataToDiscreteVerticalData(List, List, int[][], int, int) - Static method in class edu.cmu.tetrad.util.MultidataUtils: combineDiscreteDataToDiscreteVerticalData.
combineMixedContinuousData(List, List, double[][], int, int) - Static method in class edu.cmu.tetrad.util.MultidataUtils: combineMixedContinuousData.
combineMixedDiscreteData(List, List, int[][], int, int) - Static method in class edu.cmu.tetrad.util.MultidataUtils: combineMixedDiscreteData.
combineVariables(List, List) - Static method in class edu.cmu.tetrad.util.MultidataUtils: Combine the list of variables from each of data model in the list into one variable list.
COMMA - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Comma.
COMMA - Enum constant in enum class edu.cmu.tetrad.util.TextTable.Delimiter: Constant COMMA
COMMA - Static variable in class edu.cmu.tetrad.data.DelimiterType: Constant COMMA
command() - Element in annotation interface edu.cmu.tetrad.annotation.Algorithm: Command of the algorithm.
command() - Element in annotation interface edu.cmu.tetrad.annotation.Score: The command to execute the score.
command() - Element in annotation interface edu.cmu.tetrad.annotation.TestOfIndependence: Command of the test.
committed(Graph, SepsetMap) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Records a commit: recomputes the committed state's uncertified pairs when the I-map gate is on.
CommonAncestorFalseNegativeBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
CommonAncestorFalseNegativeBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalseNegativeBidirected: Constructs a new instance of the statistic.
CommonAncestorFalsePositiveBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
CommonAncestorFalsePositiveBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalsePositiveBidirected: Constructs a new instance of the statistic.
CommonAncestorTruePositiveBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
CommonAncestorTruePositiveBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorTruePositiveBidirected: Constructs a new instance of the statistic.
commonCeiling(int, double) - Static method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Upper bounds set to a single ceiling for every column, for data recorded at the top of a common scale.
CommonMeasuredAncestorRecallBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
CommonMeasuredAncestorRecallBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CommonMeasuredAncestorRecallBidirected: Initializes a new instance of the CommonMeasuredAncestorRecallBidirected class.
compare(ComparisonParameters) - Static method in class edu.cmu.tetrad.study.performance.Comparison: Simulates data from model paramerizing the given DAG, and runs the algorithm on that data, printing out error statistics.
compare(ComparisonParameters) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: Simulates data from model parameterizing the given DAG, and runs the algorithm on that data, printing out error statistics.
COMPARE_GRAPH_ALGCOMP - Static variable in class edu.cmu.tetrad.util.Params: Constant COMPARE_GRAPH_ALGCOMP="compareGraphAlgcomp"
compareClusterGraphsCpdag(Graph, Graph) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Compares two clustering graphs by transforming them into CPDAGs (Completed Partially Directed Acyclic Graphs), and calculates the adjacency F1 score, arrow F1 score, and Structural Hamming Distance (SHD) between the graphs.
compareFromFiles(String, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: compareFromFiles.
compareFromFiles(String, Algorithms, Statistics, Parameters, long, TimeUnit) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: compareFromFiles.
compareFromFiles(String, String, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Compares algorithms.
compareFromFiles(String, String, Algorithms, Statistics, Parameters, long, TimeUnit) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Compares algorithms.
compareFromSavedResults(String, String, Simulations, Algorithms, Statistics, Parameters, PrintStream, PrintStream) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Re-runs statistics computation against saved graphs from a previous run, without re-running any algorithms.
compareFromSimulations(String, Simulations, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Compare simulation results using the provided parameters and write the comparison results to a file.
compareFromSimulations(String, Simulations, Algorithms, Statistics, Parameters, long, TimeUnit) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: compareFromSimulations.
compareFromSimulations(String, Simulations, String, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Compares the results obtained from simulations.
compareFromSimulations(String, Simulations, String, Algorithms, Statistics, Parameters, long, TimeUnit) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Compares algorithms.
compareFromSimulations(String, Simulations, String, PrintStream, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Compares the results of simulations and generates an output file.
compareFromSimulations(String, Simulations, String, PrintStream, PrintStream, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Compares the results of different simulations and algorithms.
compareTo(Edge) - Method in class edu.cmu.tetrad.graph.Edge: compareTo.
compareTo(IndependenceFact) - Method in class edu.cmu.tetrad.graph.IndependenceFact
compareTo(Node) - Method in interface edu.cmu.tetrad.graph.Node: Compares this node to the given node by name, using the shared display order for possibly-lagged names: unlagged names first, then lagged names in order of increasing lag, with natural ordering of base names within a lag group and the raw name as a final tiebreaker, so distinct names never compare equal.
compareTo(LatentLatentEvaluator.LatentPair) - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair
compareTo(ScoredGraph) - Method in class edu.cmu.tetrad.search.score.ScoredGraph: Returns a compare value for this scored graph compared ot the given scored graph.
compareTo(Bes.Arrow) - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Sorting by bump, high to low.
compareTo(Point) - Method in class edu.cmu.tetrad.util.Point: True iff the given object is a point with the same coordinates as this one.
compareTo(NaturalSort.LaggedNaturalKey) - Method in class edu.cmu.tetrad.util.NaturalSort.LaggedNaturalKey
compareTo(NaturalSort.NaturalKey) - Method in class edu.cmu.tetrad.util.NaturalSort.NaturalKey
compareTo(Object) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch.Gene
compareTo(Object) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor
CompareTwoGraphs - Class in edu.cmu.tetrad.algcomparison: Gives the comparison of a target graph to a reference graph that is implemented in the interface.
Comparison - Class in edu.cmu.tetrad.algcomparison: Script to do a comparison of a list of algorithms using a list of statistics and a list of parameters and their values.
Comparison - Class in edu.cmu.tetrad.study.performance: Does a comparison of algorithm results across algorithm type, sample sizes, etc.
Comparison() - Constructor for class edu.cmu.tetrad.algcomparison.Comparison: Initializes a new instance of the Comparison class.
Comparison.ComparisonGraph - Enum Class in edu.cmu.tetrad.algcomparison: An enum of comparison graphs types.
Comparison.TableColumn - Enum Class in edu.cmu.tetrad.study.performance: An enumeration of the columns in the comparison table.
Comparison2 - Class in edu.cmu.tetrad.study.performance: Does a comparison of algorithm results across algorithm type, sample sizes, etc.
Comparison2.TableColumn - Enum Class in edu.cmu.tetrad.study.performance: An enum of table columns.
ComparisonParameters - Class in edu.cmu.tetrad.study.performance: Created by jdramsey on 3/24/16.
ComparisonParameters() - Constructor for class edu.cmu.tetrad.study.performance.ComparisonParameters: Constructor for ComparisonParameters.
ComparisonParameters(ComparisonParameters) - Constructor for class edu.cmu.tetrad.study.performance.ComparisonParameters: Constructor for ComparisonParameters.
ComparisonParameters.Algorithm - Enum Class in edu.cmu.tetrad.study.performance: An enumeration of the algorithms that can be used for structure learning.
ComparisonParameters.DataType - Enum Class in edu.cmu.tetrad.study.performance: An enumeration of the data types that can be used for structure learning.
ComparisonParameters.IndependenceTestType - Enum Class in edu.cmu.tetrad.study.performance: An enumeration of the independence test types that can be used for structure learning.
ComparisonParameters.ResultType - Enum Class in edu.cmu.tetrad.study.performance: An enumeration of the result types that can be used for structure learning.
ComparisonResult - Class in edu.cmu.tetrad.study.performance: .
ComparisonResult(ComparisonParameters) - Constructor for class edu.cmu.tetrad.study.performance.ComparisonResult: Constructor for ComparisonResult.
ComparisonScript - Class in edu.cmu.tetrad.study.performance: Runs algorithm on data set (simulation is OK), printing out error statistics.
compatible(Edge, Edge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines if two edges are compatible.
complete() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns true if every refinement reached was estimated, so that the effect list is a complete account of what the PAG allows.
COMPLETE_CASES_FORCE_SINGULARITY - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The number of complete rows minus one is smaller than the number of continuous variables, so ANY covariance or correlation matrix computed on complete cases is singular by arithmetic, regardless of what the variables measure.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Starts with a complete o-o graph.
COMPLETE_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Starts with a complete o-o graph.
COMPLETE_RULE_SET_USED - Static variable in class edu.cmu.tetrad.util.Params: Constant COMPLETE_RULE_SET_USED="completeRuleSetUsed"
completeGraph(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: completeGraph.
completeGraph_allNodesAdjacentAndTypePreserved() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Three variables (X, Y, Z) each replicated across three lags (0, 1, 2).
Completeness - Section in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty
Completeness - Section in class edu.cmu.tetrad.search.PairwiseMarkovProperty
completeRows() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns the value of the completeRows record component.
completeRows() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns the value of the completeRows record component.
Complexity - Section in class edu.cmu.tetrad.search.PairwiseMarkovProperty
composition() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if composition holds--e.g., (X _||_ Y | Z) ∧ (X _||_ W | Z) ==> X _||_ (Y ∪ W) | Z
COMPOSITIONAL_GRAPHOID - Enum constant in enum class edu.cmu.tetrad.search.harness.GraphoidClosureHarness.ClosureType: Compositional graphoid closure.
compositionalGraphoid() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks whether the IM is a compositional graphoid.
compressedCorrelation(Vector, Vector) - Static method in class edu.cmu.tetrad.util.StatUtils: compressedCorrelation.
Computational considerations - Section in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous
compute(boolean) - Method in interface edu.cmu.tetrad.stat.correlation.Covariance: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.correlation.CovarianceMatrix: compute.
compute(boolean) - Method in interface edu.cmu.tetrad.stat.correlation.RealCovariance: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.correlation.RealCovarianceMatrix: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.correlation.RealCovarianceMatrixForkJoin: compute.
compute(boolean) - Method in interface edu.cmu.tetrad.stat.RealVariance: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.RealVarianceVector: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.RealVarianceVectorForkJoin: compute.
compute(boolean) - Method in interface edu.cmu.tetrad.stat.Variance: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.VarianceVector: compute.
compute(boolean) - Method in class edu.cmu.tetrad.stat.VarianceVectorForkJoin: compute.
compute(double) - Method in class edu.cmu.tetrad.search.utils.RandomFourier: Computes the value of the Fourier series at a given input x.
compute(double) - Method in class edu.cmu.tetrad.search.utils.RandomPiecewiseSpline: Compute the value of the spline function at a given x.
compute(double) - Method in class edu.cmu.tetrad.search.utils.RandomRBF: Computes the Radial Basis Function (RBF) value for the given input using pre-initialized centers, amplitudes, and width parameter (sigma).
compute(DataSet, Graph) - Method in interface edu.cmu.tetrad.sem.DagMetric: Computes and returns the result of a metric evaluation for a given dataset and directed acyclic graph (DAG).
compute(DataSet, Graph) - Method in interface edu.cmu.tetrad.sem.DagMetric.MetricComputer: Computes a metric value based on the given dataset and directed acyclic graph (DAG).
compute(DataSet, Graph, IndependenceWrapper, Parameters, ConditioningSetType, MarkovCheckNullReference.SimulatorType, int, int, long, TrainedDagSimulatorGNM.Params) - Static method in class edu.cmu.tetrad.search.MarkovCheckNullReference: Convenience overload taking an IndependenceWrapper and Parameters, matching how the Markov-check test is configured in algcomparison and in py-tetrad's TetradSearch.
compute(DataSet, Graph, Function, ConditioningSetType, MarkovCheckNullReference.SimulatorType, int, int, long, TrainedDagSimulatorGNM.Params) - Static method in class edu.cmu.tetrad.search.MarkovCheckNullReference: Computes the null reference using a test factory.
compute(Graph, Node, Node, String, int) - Method in interface edu.cmu.tetrad.search.GacTotalEffectElibility.AdjustmentSetProvider: Computes adjustment sets based on the provided graph, pair of nodes, graph type, and maximum path length.
compute(Graph, Set, Node, String, int) - Method in interface edu.cmu.tetrad.search.GacTotalEffectElibility.JointAdjustmentSetProvider: Computes a list of sets of nodes based on the provided graph, set of nodes, target node, graph type, and maximum path length.
compute(HybridCgModel.HybridCgIm) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths: Computes a strength for every edge of the IM's graph.
compute(HybridCgModel.HybridCgIm, DataSet) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: Computes a per-edge LRT significance for every edge of the IM's graph, with per-stratum variances for continuous children (shareVariance = false).
compute(HybridCgModel.HybridCgIm, DataSet, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: Computes a per-edge LRT significance for every edge of the IM's graph.
compute(HybridCgModel.HybridCgPm, DataSet, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: As HybridCgEdgeSignificance.compute(HybridCgIm, DataSet, boolean), from the parametric model alone.
computeAdjusted(double) - Method in class edu.cmu.tetrad.search.utils.RandomFourier: Computes the adjusted value of the Fourier series at the given input x.
computeAdjusted(double) - Method in class edu.cmu.tetrad.search.utils.RandomPiecewiseSpline: Adjusts the value of the spline function at a given x by subtracting the function's value at x = 0.
computeAdjusted(double) - Method in class edu.cmu.tetrad.search.utils.RandomRBF: Computes the adjusted value of the Radial Basis Function (RBF) for the given input.
computeAdjustmentSetsIfEligible(Node, Node, GacTotalEffectElibility.AdjustmentSetProvider) - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Optional convenience: compute adjustment sets using a provided provider, but only if eligibility is OK.
computeAllImpliedFacts(Graph, ConditioningSetType) - Static method in class edu.cmu.tetrad.search.MarkovCheck: Computes all implied independence facts for the given graph based on the specified conditioning set type.
computeAlphaStar() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Computes the adjusted alpha threshold (alphaStar) for controlling the False Discovery Rate (FDR) during independence tests.
computeBicDeltaK1K2(DataSet, EmUnmix.Config, ResidualRegressor, Function, Function) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Computes the Bayesian Information Criterion (BIC) for mixture models with K=1 and K=2 clusters, and calculates the BIC difference (delta) between them.
computeClosure(Set, List, GraphoidClosureHarness.ClosureType) - Static method in class edu.cmu.tetrad.search.harness.GraphoidClosureHarness: Computes the closure of a set of IndependenceFacts under the given graphoid assumption type, using the variables in the provided list as the full variable universe.
computeEdgeStrength(String, String, int) - Method in class edu.cmu.tetrad.sem.NNEstimator: Computes the intervention strength of a single edge (parentName → childName) in the sense of Janzing et al.
computeEntropyStats(double[][]) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Computes entropy statistics for a given responsibility matrix in the context of mixture models.
computeGroups(DataSet, int[], List, int, int) - Static method in class edu.cmu.tetrad.search.utils.MinimaxBinning: Computes within-bin groups for conditioning set Z using quantile binning.
computeImpliedFactsByVertex(Graph, ConditioningSetType) - Static method in class edu.cmu.tetrad.search.MarkovCheck: Computes the implied independence facts for EVERY vertex of graph at once, keyed by vertex name.
computeImpliedFactsForVertex(Graph, Node, ConditioningSetType) - Static method in class edu.cmu.tetrad.search.MarkovCheck: Computes the implied independence facts for a given vertex within the specified graph based on the provided conditioning set type.
computeImpliedFactsForVertex(Graph, Node, ConditioningSetType, Graph) - Static method in class edu.cmu.tetrad.search.MarkovCheck: Variant of MarkovCheck.computeImpliedFactsForVertex(Graph, Node, ConditioningSetType) accepting a MAG prepared once per graph by MarkovCheck.prepareMagForVertexFacts(Graph, ConditioningSetType).
computeInd(Node, Node, Node...) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Computes the independence score between two nodes (x and y) conditioned on a set of other nodes (z).
computeJointAdjustmentSetsIfEligible(Set, Node, GacTotalEffectElibility.JointAdjustmentSetProvider) - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Same idea as GacTotalEffectElibility.computeAdjustmentSetsIfEligible(Node, Node, AdjustmentSetProvider) but for joint.
computeLowerTriangle(boolean) - Method in interface edu.cmu.tetrad.stat.correlation.Covariance: computeLowerTriangle.
computeLowerTriangle(boolean) - Method in class edu.cmu.tetrad.stat.correlation.CovarianceMatrix: computeLowerTriangle.
computeLowerTriangle(boolean) - Method in interface edu.cmu.tetrad.stat.correlation.RealCovariance: computeLowerTriangle.
computeLowerTriangle(boolean) - Method in class edu.cmu.tetrad.stat.correlation.RealCovarianceMatrix: computeLowerTriangle.
computeLowerTriangle(boolean) - Method in class edu.cmu.tetrad.stat.correlation.RealCovarianceMatrixForkJoin: computeLowerTriangle.
computeOLS(SimpleMatrix, SimpleMatrix, double) - Static method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Computes the Ordinary Least Squares (OLS) regression coefficients with L2 regularization.
computeOLS(SimpleMatrix, SimpleMatrix, double) - Static method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Computes the Ordinary Least Squares (OLS) solution for a linear system.
computeOLS(SimpleMatrix, SimpleMatrix, double) - Static method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Computes the Ordinary Least Squares (OLS) solution for a linear system.
computePartialEdgeStrength(String, String, int) - Method in class edu.cmu.tetrad.sem.NNEstimator: Computes the partial edge strength of X → Y: the held-out predictive gain from X once Y's other parents are accounted for, on the same k folds NNEstimator.crossValidate(int) uses.
computePValue(double[], double[]) - Method in interface edu.cmu.tetrad.search.RawMarginalIndependenceTest: Computes the p-value for the statistical test of marginal independence between the two given variables represented by the input arrays.
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.FfCi: Computes the p-value for the statistical test of independence between two variables.
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Computes the p-value for the statistical test of independence between two variables.
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Computes the marginal p-value between two raw columns by building a two-column dataset and running the marginal version of this test on it.
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Computes the p-value for the statistical test of independence between two variables.
computePValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.Kci: Computes the p-value for testing the independence of two variables represented by the input arrays.
computePValue(double[], double[][]) - Method in interface edu.cmu.tetrad.search.RawMarginalIndependenceTest: Computes the p-value for the statistical test of marginal independence between a single variable and a multivariate set of variables.
computePValue(double[], double[][]) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Multivariate fallback: tests x against each column of Y and combines with Fisher's method.
computePValue(double[], double[][]) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Default multivariate fallback: run BFIT on each column of Y and combine with Fisher.
computePValue(TetradInt...) - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest: Computes the p-value for the given tetrads.
computePValue(TetradInt...) - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest3: Computes the p-value for the given tetrads.
computePValue(List) - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest: Computes the p-value for the given tetrads.
computePValue(List) - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest3: Computes the p-value for the given tetrads.
computeStdErrors(ISemIm) - Method in class edu.cmu.tetrad.sem.SemStdErrorEstimator: This method computes the information matrix or Hessian matrix of second order partial derivatives of the fitting function (4B_2 on page 135 of Bollen) with respect to the free freeParameters of the estimated SEM.
computeValue(double) - Method in class edu.cmu.tetrad.search.utils.RandomMonotonicPiecewiseLinear: Compute the value of the monotonic piecewise linear function at a given x.
computeValue(double) - Method in class edu.cmu.tetrad.search.utils.RandomPiecewiseLinear: Computes the value of the piecewise linear function for a given x-value.
computeWeights(double) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes and returns an array of weights based on the given target value.
computeWeights(double[]) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes and normalises the product-kernel conditioning weights for the given target values.
concatenate(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Concatenates the vectors rows[i], i = 0...rows.length, into a single vector.
concatenate(DataSet...) - Static method in class edu.cmu.tetrad.data.DataTransforms: concatenate.
concatenate(DataSet, DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: concatenate.
concatenate(Matrix...) - Static method in class edu.cmu.tetrad.data.DataTransforms: concatenate.
concatenate(List) - Static method in class edu.cmu.tetrad.data.DataTransforms: concatenate.
CONCURRENT_FAS - Static variable in class edu.cmu.tetrad.util.Params: Constant CONCURRENT_FAS="concurrentFAS"
CONDITIONAL_GAUSSIAN - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant CONDITIONAL_GAUSSIAN="Mixed Conditional Gaussian"
conditionalBivariate(double[], double[], double) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes a conditional bivariate result based on the provided input arrays and target value.
conditionalBivariate(double[], double[], double[]) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes a conditional bivariate result using the provided data arrays and weights.
conditionalBivariate(double[], double[], double[], double[], int, double, double, boolean) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes the conditional bivariate density estimate for the given data points.
conditionalBivariate(double[], double[], double[], int, double, double) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes the conditional bivariate distribution for the given data and targets.
conditionalBivariateParallel(double[], double[], double[]) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes a conditional bivariate result in parallel given input data arrays and weights.
conditionalChiSquare(DataSet, List, List, Node, Node, List, IndependenceFact, double) - Static method in class edu.cmu.tetrad.search.test.DiscreteIndependenceUtils: Conditional chi-square test for X _||_ Y | Z, stratifying on Z value tuples.
ConditionalCorrelationIndependence - Class in edu.cmu.tetrad.search.test: Checks conditional independence of variable in a continuous data set using Daudin's method.
ConditionalCorrelationIndependence(DataSet, int, double, int) - Constructor for class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Initializes a new instance of the ConditionalCorrelationIndependence class using the provided DataSet.
conditionalCorrelationProfile(double[], double[]) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes the conditional correlation profile between two input time series arrays.
conditionalCorrelationProfile(double[], double[], double[], int, int) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes a conditional correlation profile between two response variables (Y1 and Y2) while sweeping through a range of values for a specified conditional variable.
conditionalCorrelationProfile(double[], double[], int) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes the conditional correlation profile of two given time series over a specified number of steps.
ConditionalGaussianBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Fisher Z test.
ConditionalGaussianBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.ConditionalGaussianBicScore: Initializes a new instance of the FisherZ class.
ConditionalGaussianLikelihood - Class in edu.cmu.tetrad.search.score: Implements a conditional Gaussian likelihood.
ConditionalGaussianLikelihood(DataSet) - Constructor for class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Constructs the score using a covariance matrix.
ConditionalGaussianLikelihood.Ret - Class in edu.cmu.tetrad.search.score: Gives return value for a conditional Gaussian likelihood, returning a likelihood value and the degrees of freedom for it.
ConditionalGaussianLrt - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Fisher Z test.
ConditionalGaussianLrt() - Constructor for class edu.cmu.tetrad.algcomparison.independence.ConditionalGaussianLrt: Initializes a new instance of the FisherZ class.
ConditionalGaussianScore - Class in edu.cmu.tetrad.search.score: Implements a conditional Gaussian BIC score for FGS, which calculates a BIC score for mixed discrete/Gaussian data using the conditional Gaussian likelihood function.
ConditionalGaussianScore(DataSet, double, boolean) - Constructor for class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Constructs the score.
ConditionalGaussianScore(DataSet, double, boolean, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Constructs the score from a dataset with an explicit missing-data specification.
ConditionalGaussianSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: A simulation method based on the conditional Gaussian assumption.
ConditionalGaussianSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Constructor for ConditionalGaussianSimulation.
ConditionalKDE - Class in edu.cmu.tetrad.util: Conditional Kernel Density Estimation with multivariate conditioning.
ConditionalKDE(double[]) - Constructor for class edu.cmu.tetrad.util.ConditionalKDE: Constructs a conditional kernel density estimator (KDE) with a single conditioning variable.
ConditionalKDE(double[][]) - Constructor for class edu.cmu.tetrad.util.ConditionalKDE: Constructor for the ConditionalKDE class.
ConditionalKDE(double[][], double[]) - Constructor for class edu.cmu.tetrad.util.ConditionalKDE: Constructs a conditional kernel density estimator (KDE) with specified conditioning variables and bandwidths.
ConditionalKDE.BatchResult - Class in edu.cmu.tetrad.util: Result of batchBivariateAllPairs(): provides indexed lookup so callers don't have to track which result corresponds to which pair.
ConditionalKDE.ConditionalResult1D - Class in edu.cmu.tetrad.util: Represents the result of a conditional density estimation in a one-dimensional space.
ConditionalKDE.ConditionalResult2D - Class in edu.cmu.tetrad.util: Represents the result of a 2D conditional density estimation.
conditionalMomentTest(double[], double[][], double) - Static method in class edu.cmu.tetrad.search.utils.NonlinearityTests: Performs a conditional moment test to assess the specification of a linear model.
Conditional testing - Section in class edu.cmu.tetrad.search.test.FfCi
conditionalUnivariate(double[], double) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes a conditional univariate result based on the provided parameters.
conditionalUnivariate(double[], double[]) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes the conditional univariate result based on the provided data and weights.
conditionalUnivariate(double[], double[], double[], int, double, boolean) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Full control overload — accepts precomputed weights.
conditionalUnivariate(double[], double[], int, double) - Method in class edu.cmu.tetrad.util.ConditionalKDE: Computes the conditional univariate result based on the provided data and parameters.
CONDITIONING_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant CONDITIONING_THRESHOLD="conditioningThreshold"
conditioningSet() - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Returns the value of the conditioningSet record component.
ConditioningSetType - Enum Class in edu.cmu.tetrad.search: The type of conditioning set to use for the Markov check.
ConditionResult(int, double, double, double, double, double, double, int, int) - Constructor for record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Creates an instance of a ConditionResult record class.
ConditionResult(int, List) - Constructor for record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.ConditionResult: Creates an instance of a ConditionResult record class.
conditions() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.StudyResult: Returns the value of the conditions record component.
conditions() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.StudyResult: Returns the value of the conditions record component.
condTargets - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: The conditional targets used in the density estimation.
condTargets - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The conditional targets used in the density estimation.
config(String) - Method in class edu.cmu.tetrad.util.LogUtils: config.
Config() - Constructor for class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Constructs a config with default thresholds.
Config() - Constructor for class edu.cmu.tetrad.data.audit.DataAudit.Config: Constructs a config with default thresholds.
Config() - Constructor for class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Default constructor for the Config class.
Config() - Constructor for class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Default constructor for Config.
Config() - Constructor for class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Default constructor for the Config class.
Config() - Constructor for class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: Constructs a new instance of the Config class with default settings.
Config(double, double, double, double, double, double, double, double) - Constructor for class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Constructs a config with the given thresholds.
Config(int, int, int, double, double, double, double, double, int, double, double, double) - Constructor for class edu.cmu.tetrad.data.audit.DataAudit.Config: Constructs a config with the given thresholds.
Config(int, int, int, double, double, double, double, double, int, double, double, double, int, double, double, int, String) - Constructor for class edu.cmu.tetrad.data.audit.DataAudit.Config: Constructs a config with the given thresholds, including the serial dependence settings.
Config(int, int, int, int, int, long, double[], int) - Constructor for record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Represents a configuration record that encapsulates various parameters used for the generation and evaluation of facts.
configuration(String) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Generates a configuration file containing information about available algorithms, statistics, independence tests, scores, and simulations.
configuration(String) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: configuration.
configurationActivated(TetradLoggerEvent) - Method in interface edu.cmu.tetrad.util.TetradLoggerListener: Invoked whenever a logger configuration is set on the TetradLogger and the logger is active (i.e., logging isn't turned off etc).
configurationDeactivated(TetradLoggerEvent) - Method in interface edu.cmu.tetrad.util.TetradLoggerListener: Invoked whenever a previously set logger config is resert or set to null and the logger is active (i.e., logging isn't turned off etc).
ConfigV1() - Constructor for class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Private constructor for the ConfigV1 class.
confirmClusters() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Promotes the rank-deficient sets found at the lowest pending rank to clusters, the set covering the most observed variables (members plus non-sinks) first, and marks the covers involved as leaves or non-leaves.
CONFLICT_RULE - Static variable in class edu.cmu.tetrad.util.Params: Constant CONFLICT_RULE="conflictRule"
CONNECTED - Static variable in class edu.cmu.tetrad.util.Params: Constant CONNECTED="connected"
CONNECTED_DAG - Static variable in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Connected DAG uniformly selected
connectedComponents() - Method in class edu.cmu.tetrad.graph.Paths: Returns a list of connected components in the graph.
connectedComponents(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Identifies all connected components in a given graph.
ConnectionFunction - Interface in edu.cmu.tetrad.sem: Created by IntelliJ IDEA.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.ColliderStyle: Represents the Conservative collider style in the graph learning process.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.search.Ccd.ColliderRule: Orient only when every separating set of (a, c) agrees about b; otherwise record the triple as ambiguous and leave both edges unoriented.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.search.Cdnod.ColliderOrientationStyle: Represents a collider orientation strategy in causal discovery that employs a conservative approach.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.search.CdnodBoss.ColliderOrientationStyle: Apply a conservative approach to collider orientation, favoring ambiguity when evidence for orientation is inconclusive.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.search.Fci.ColliderRule: Represents a more conservative approach to handling colliders, avoiding assumptions that may lead to incorrect inferences.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.search.Pc.ColliderOrientationStyle: Represents the collider orientation style for the Conservative PC algorithm.
CONSERVATIVE - Enum constant in enum class edu.cmu.tetrad.search.PcAR.ColliderOrientationStyle: Conservative PC (Ramsey, Zhang, Spirtes 2006): test all subsets of the adjacents of x and of y; orient as a collider only if z is in no separating set, as a noncollider only if z is in every separating set, and leave the triple unoriented (ambiguous) otherwise.
ConsistentDagIterator - Class in edu.cmu.tetrad.graph: Lazily enumerates the consistent DAG extensions of a DAG or CPDAG.
ConsistentDagIterator(Graph) - Constructor for class edu.cmu.tetrad.graph.ConsistentDagIterator: Enumerate distinct extensions, preferring the graph's own node order (forward).
ConsistentDagIterator(Graph, List, boolean, boolean) - Constructor for class edu.cmu.tetrad.graph.ConsistentDagIterator: Iterates over DAGs consistent with a given CPDAG.
CONSTANT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Constant CONSTANT=0
CONSTANT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.history.SimpleRandomizer: Indicates constant indegree.
CONSTANT_COLUMN - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A column is constant: it has at most one distinct value among its non-missing entries.
ConstantExpression - Class in edu.cmu.tetrad.calculator.expression: Represents a constant expression, that is an expression that always evaluates to the same value.
ConstantExpression(double) - Constructor for class edu.cmu.tetrad.calculator.expression.ConstantExpression: Constructs the constant expression given the value to use.
Constraint(String, List, double[], double, double) - Constructor for record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Creates an instance of a Constraint record class.
constraintFor(DataSet, String, double) - Static method in class edu.cmu.tetrad.data.audit.DeterministicClusters: Fits the equation writing the given variable as a function of the other continuous variables of the dataset.
constructCentralizedGramMatrix(List, DataSet, List) - Static method in class edu.cmu.tetrad.search.utils.KernelUtils: Constructs the centralized Gram matrix for a given vector valued sample.
constructGramMatrix(List, DataSet, List) - Static method in class edu.cmu.tetrad.search.utils.KernelUtils: Constructs Gram matrix for a given vector valued sample.
constructH(int) - Static method in class edu.cmu.tetrad.search.utils.KernelUtils: Constructs the projection matrix on 1/m
constructRandomMim(List, Integer, int, int, int, RandomMim.LatentLinkMode) - Static method in class edu.cmu.tetrad.graph.RandomMim: Constructs a random meta-graph as a Multiple Indicator Model (MIM) with specified structural constraints.
constructRandomMimic(List, Integer, int, int, int, int) - Static method in class edu.cmu.tetrad.graph.RandomMimic: Constructs a random MIMIC graph with specified structural constraints.
cont - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: A container for simulation results, including continuous and discrete values, variables, and true DAG.
cont - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: A two-dimensional array representing continuous values for all variables in a simulation result.
cont - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: A two-dimensional array representing continuous values for all variables in a simulation result.
cont - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: A two-dimensional array representing continuous values for all variables in a simulation result.
contains(GraphChange) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Contains is defined such that if the internal strucs of this GraphChange all individually contain the elements in the corresponding strucs of GraphChange gc, then this "contains" gc.
containsBidirectedEdge(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Checks if a given graph contains a bidirected edge.
containsCluster(Set, Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Whether Vs together with the non-sinks contain more than k elements from an existing k-cover's children (Python containsCluster).
containsEdge(KnowledgeEdge) - Method in class edu.cmu.tetrad.data.KnowledgeGroup: containsEdge.
containsEdge(Edge) - Method in class edu.cmu.tetrad.graph.Dag: Checks if the given edge is present in the graph.
containsEdge(Edge) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether this graph contains the given edge.
containsEdge(Edge) - Method in interface edu.cmu.tetrad.graph.Graph: Determines whether this graph contains the given edge.
containsEdge(Edge) - Method in class edu.cmu.tetrad.graph.LagGraph: Determines whether this graph contains the given edge.
containsEdge(Edge) - Method in class edu.cmu.tetrad.graph.SemGraph: Determines whether this graph contains the given edge.
containsEdge(Edge) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks if the given Edge object exists in the graph.
containsEmptyData() - Method in class edu.cmu.tetrad.data.DataModelList: Use this to check if the dataModelList only contains the default empty dataset that is being used to populat the empty spreadsheet - Added by Kevin
containsLatent(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: containsLatent.
containsMissingValue(DataSet) - Static method in class edu.cmu.tetrad.data.DataUtils: containsMissingValue.
containsMissingValue(Matrix) - Static method in class edu.cmu.tetrad.data.DataUtils: containsMissingValue.
containsNode(Node) - Method in class edu.cmu.tetrad.graph.Dag: Checks if the given Node object is contained in the graph.
containsNode(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether this graph contains the given node.
containsNode(Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines whether this graph contains the given node.
containsNode(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Determines whether this graph contains the given node.
containsNode(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Determines whether this graph contains the given node.
containsNode(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks if the graph contains a specific node.
containsParameter(Edge) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: containsParameter.
ContestedDeletion(Node, Node, Node, Set, String, boolean) - Constructor for record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Creates an instance of a ContestedDeletion record class.
Context - Interface in edu.cmu.tetrad.calculator.expression: Looks up the value of a variable.
contexts() - Method in class edu.cmu.tetrad.search.cdnod_pag.ChangeOracle: Retrieves the list of contexts associated with this ChangeOracle instance.
continuous() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Returns the value of the continuous record component.
Continuous - Enum constant in enum class edu.cmu.tetrad.data.DataType: Continuous.
Continuous - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.DataType: Constant for continuous data.
CONTINUOUS_FEW_VALUES - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A variable declared continuous takes only a small number of distinct observed values, suggesting it may be better treated as discrete, or that it was mechanically converted from a discrete source.
ContinuousDiscretizationSpec - Class in edu.cmu.tetrad.data: Specifies how a column (continuous or discrete) should be discretized.
ContinuousDiscretizationSpec(double[], List) - Constructor for class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Constructor for creating a ContinuousDiscretizationSpec object.
ContinuousDiscretizationSpec(double[], List, int) - Constructor for class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Creates a ContinuousDiscretizationSpec object with the given breakpoints, categories, and method.
ContinuousGeneral - Enum constant in enum class edu.cmu.tetrad.data.DataType: Continuous, general distriubtions
Continuous-only datasets - Section in class edu.cmu.tetrad.search.test.FfCi
ContinuousVariable - Class in edu.cmu.tetrad.data: Represents a real-valued variable.
ContinuousVariable(ContinuousVariable) - Constructor for class edu.cmu.tetrad.data.ContinuousVariable: Copy constructor.
ContinuousVariable(String) - Constructor for class edu.cmu.tetrad.data.ContinuousVariable: Constructs a new continuous variable with the given name.
contraction() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if contraction holds--e.g., (X _||_ Y | Z) ∧ (X _||_ W | Z ∪ Y) ==> X _||_ (Y ∪ W) | Z
Conventions - Section in interface edu.cmu.tetrad.search.is.IsScore
converged - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: True if the convergence tolerance was met.
converged() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns true if the RICF fit met its tolerance.
CONVERGENCE_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant CONVERGENCE_THRESHOLD="convergenceThreshold"
convert() - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: convert.
convert(boolean, boolean) - Method in class edu.cmu.tetrad.search.utils.MagToPag: This method does the conversion of MAG to PAG.
convert(String) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts a string spec of a graph--for example, "X1-->X2, X1---X3, X2o->X4, X3<->X4" to a Graph.
convertCovToCorr(Matrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Converts a covariance matrix to a correlation matrix in place; the same matrix is returned for convenience, but m is modified in the process.
convertCovToCorr(SimpleMatrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Converts a covariance matrix into a correlation matrix.
convertLowerTriangleToSymmetric(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Converts a matrix in lower triangular form to a symmetric matrix in square form.
convertNodes(Set, List) - Static method in class edu.cmu.tetrad.graph.MisclassificationUtils: convertNodes.
convertNumericalDiscreteToContinuous(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: convertNumericalDiscreteToContinuous.
convertToXml(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: convertToXml.
copy() - Method in class edu.cmu.tetrad.data.BoxDataSet: Returns a copy of this dataset.
copy() - Method in class edu.cmu.tetrad.data.ByteDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: copy.
copy() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: copy.
copy() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: copy.
copy() - Method in interface edu.cmu.tetrad.data.DataBox: copy.
copy() - Method in interface edu.cmu.tetrad.data.DataModel: copy.
copy() - Method in class edu.cmu.tetrad.data.DataModelList: copy.
copy() - Method in interface edu.cmu.tetrad.data.DataSet: Returns a copy of this dataset.
copy() - Method in class edu.cmu.tetrad.data.DoubleDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.FloatDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.IndependenceFacts: copy.
copy() - Method in class edu.cmu.tetrad.data.IntDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.Knowledge: Makes a shallow copy.
copy() - Method in class edu.cmu.tetrad.data.LongDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.MixedDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Returns a copy of this dataset.
copy() - Method in class edu.cmu.tetrad.data.ShortDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.TimeSeriesData: copy.
copy() - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: copy.
copy() - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: copy.
copy() - Method in interface edu.cmu.tetrad.graph.Graph: Creates and returns a copy of the current graph.
copy() - Method in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Creates a copy of this configuration object.
copy() - Method in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Creates and returns a copy of this configuration instance.
copy() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: copy.
copy() - Method in class edu.cmu.tetrad.util.Matrix: copy.
copy() - Method in class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: copy.
copy() - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig: Creates a copy of the TetradLoggerConfig object.
copy() - Method in class edu.cmu.tetrad.util.Vector: copy.
copy() - Method in class edu.pitt.csb.stability.DataGraphSearch: copy.
copyColumn(Node, DataSet, DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: copyColumn.
copyConstructor_preservesEdgesExactly() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: The copy constructor ReplicatingGraph(ReplicatingGraph) should produce an identical graph without doubling up edges.
copyNode(Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns a copy of the given node that is equal to it (same name, type, and, for a discrete variable, the same categories, so that it indexes the same column of a data set) and has the same position, but is a distinct object, so that a position later set on either is not seen through the other.
copyOf(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: copyOf.
copyOf(int[], int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: copyOf.
copySign(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the first floating-point argument with the sign of the second floating-point argument.
Cord - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for the CORD conditional independence test backed by Eric's engine (IndTestCordEric): an omnibus, orthogonal rank-score test of Y ⊥ Z | X that is sensitive to higher-moment, tail, and co-volatility dependence which a covariance-based test (e.g., Fisher Z) cannot see.
Cord() - Constructor for class edu.cmu.tetrad.algcomparison.independence.Cord: Constructs a new instance of the algorithm.
CORD_ENGINE - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_ENGINE="cordEngine"
CORD_LEARNING_RATE - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_LEARNING_RATE="cordLearningRate"
CORD_MAX_LEAF_NODES - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_MAX_LEAF_NODES="cordMaxLeafNodes"
CORD_NUM_ESTIMATORS - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_NUM_ESTIMATORS="cordNumEstimators"
CORD_NUM_THRESHOLDS - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_NUM_THRESHOLDS="cordNumThresholds"
CORD_SEED - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_SEED="cordSeed"
CORD_SYMMETRIC - Static variable in class edu.cmu.tetrad.util.Params: Constant CORD_SYMMETRIC="cordSymmetric"
CordEngine - Class in edu.cmu.tetrad.search.test: Standalone validation harness for the CORD port.
CordEngine - Interface in edu.cmu.tetrad.search: Interface for Cord engines.
CordEngine() - Constructor for class edu.cmu.tetrad.search.test.CordEngine: Constructs a new CordEngine instance.
CordEngine1 - Class in edu.cmu.tetrad.search: CordEngine1 is an implementation of a conditional independence testing engine based on the CORD methodology.
CordEngine1() - Constructor for class edu.cmu.tetrad.search.CordEngine1: Default constructor for the CordEngine1 class.
CordEngine1.Result - Class in edu.cmu.tetrad.search: Encapsulates the results of a conditional independence test for the hypothesis H0: Y is independent of Z given X.
CordEngine2 - Class in edu.cmu.tetrad.search: The CordEngine2 class acts as an implementation of the Cord statistical engine, which is designed for causal inference and related statistical procedures.
CordEngine2() - Constructor for class edu.cmu.tetrad.search.CordEngine2: Default constructor for the CordEngine2 class.
CordEngine2.Result - Class in edu.cmu.tetrad.search: Encapsulates the result of a statistical hypothesis test, storing the relevant test metrics and providing methods to evaluate the test outcome.
CordEngine3 - Class in edu.cmu.tetrad.search: CordEngine3 provides a suite of tools and algorithms for model building, evaluation, and statistical computations.
CordEngine3() - Constructor for class edu.cmu.tetrad.search.CordEngine3: Initializes a new instance of the CordEngine3 class.
CordEngine3.Result - Class in edu.cmu.tetrad.search: Represents the result of a statistical hypothesis test, encapsulating the p-value, test statistic, test status, sample size, and dimensionality of the conditioning variable.
CordEngine4 - Class in edu.cmu.tetrad.search: The CordEngine4 class is a data-driven machine learning engine designed for regression and classification tasks.
CordEngine4() - Constructor for class edu.cmu.tetrad.search.CordEngine4: Default constructor for the CordEngine4 class.
CordEngine4.Result - Class in edu.cmu.tetrad.search: Represents the result of a statistical test for conditional independence, specifically designed to assess the relationship between variables under the hypothesis H0: Y is conditionally independent of Z given X.
CordEngine5 - Class in edu.cmu.tetrad.search: The CordEngine5 class implements the CORD (Conditional Randomization Discovery) algorithm for testing the null hypothesis H0: Y is conditionally independent of Z given X.
CordEngine5() - Constructor for class edu.cmu.tetrad.search.CordEngine5: Constructs a new instance of the CordEngine5 class.
CordEngine5.Result - Class in edu.cmu.tetrad.search: The Result class represents the outcome of a statistical analysis, providing values such as aggregated p-values, test statistics, and metadata about the computation.
corr(Node, Node, List) - Method in class edu.cmu.tetrad.search.utils.PartialCorrelation: Calculates the partial correlation of x and y conditional on the nodes in z recursively.
CorrectnessGates - Class in edu.cmu.tetrad.search.utils: The two per-commit correctness gates shared by the latent-variable searches that remove edges from a Markov start under a legality certificate: the *-FCI family (StarFci and its subclasses), FCIT, and FCIT-ZM.
CorrectnessGates(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.utils.CorrectnessGates: Constructs the gates for a search using the given independence test.
correctnessRatio(int[][]) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: correctnessRatio.
CorrectSkeleton - Class in edu.cmu.tetrad.algcomparison.statistic: Outputs 1 if the skeleton is correct, 0 if not..
CorrectSkeleton() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.CorrectSkeleton: Initializes a new instance of the CorrectSkeleton class.
correlation(double[], double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: correlation.
correlation(double[], double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: correlation.
correlation(long[], long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: correlation.
correlation(long[], long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: correlation.
correlation(Vector, Vector) - Static method in class edu.cmu.tetrad.util.StatUtils: correlation.
CORRELATION_SHIFT - Enum constant in enum class edu.cmu.tetrad.search.Fask.TwoCycleRule: Compare CORRELATIONS, as in the original FASK (Sanchez-Romero et al.
CorrelationMatrix - Class in edu.cmu.tetrad.data: Stores a correlation matrix together with variable names and sample size; intended as a representation of a data set.
CorrelationMatrix(DataSet) - Constructor for class edu.cmu.tetrad.data.CorrelationMatrix: Constructs a new correlation matrix from the the given DataSet.
CorrelationMatrix(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.data.CorrelationMatrix: Constructs a new correlation matrix using the covariances in the given covariance matrix.
CorrelationMatrix(List, Matrix, int) - Constructor for class edu.cmu.tetrad.data.CorrelationMatrix: Constructs a correlation matrix data set using the given information.
CorrelationMatrixOnTheFly - Class in edu.cmu.tetrad.data: Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
CorrelationMatrixOnTheFly(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Constructs a new covariance matrix from the given data set.
CORRESPONDING - Enum constant in enum class edu.cmu.tetrad.graph.RandomMim.LatentLinkMode: Links are formed only between latents at the same index in each group.
corrExp(double[], double[], double[]) - Static method in class edu.cmu.tetrad.search.Fask: Calculates the correlation expectation: corrExp(x,y|z) = E(xy|z>0) / sqrt(E(x^2|z>0) E(y^2|z>0)).
cos(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the cosine of a given angle in radians.
cosh(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the hyperbolic cosine of a given value.
Cost, determinism, thread-safety - Section in class edu.cmu.tetrad.search.test.IndTestCord
Cost, determinism, thread-safety - Section in class edu.cmu.tetrad.search.test.IndTestCordEric
count(Map) - Method in class edu.pitt.isp.sverchkov.data.AdTree: Returns the number of rows in the data set.
COUNT - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: The number of long rows for the combination, as a continuous column (the value variable's own values are ignored; zero rows gives 0, not missing).
COUNT_MAP - Enum constant in enum class edu.cmu.tetrad.bayes.MlBayesIm.CptMapType: Represents a count-based CptMap type.
COUNT_SAMPLE - Enum constant in enum class edu.cmu.tetrad.search.test.ChiSquareTest.CellTableType: The count sample cell table.
countAdjErrors(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Counts the adjacencies that are in graph1 but not in graph2.
countColumn(File, char) - Static method in class edu.cmu.tetrad.util.DataUtility: countColumn.
countGroups() - Method in record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Returns the value of the countGroups record component.
countGroups() - Method in record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Returns the value of the countGroups record component.
countLine(File) - Static method in class edu.cmu.tetrad.util.DataUtility: countLine.
countMindChanges() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Returns the number of mind-changes between the previous decision epoch and the current one.
CountObjects(int[], int[][]) - Constructor for class edu.cmu.tetrad.search.is.BDeuScoreWOprior.CountObjects: Constructs a CountObjects instance with the given arrays representing object counts.
counts(A, Map) - Method in class edu.pitt.isp.sverchkov.data.AdTree: Returns the number of rows in the data set.
countValuesAtLeast(double) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns the number of stored (non-neutral) values at or above a threshold.
cov() - Method in record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Returns the value of the cov record component.
cov() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.CovAndCoefs: Returns the value of the cov record component.
cov(double[], double[], double[], double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: cov.
cov(Matrix) - Static method in class edu.cmu.tetrad.data.DataUtils: cov.
cov(SimpleMatrix) - Static method in class edu.cmu.tetrad.data.DataUtils: Computes the covariance matrix for the given data.
COV_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant COV_HIGH="covHigh"
COV_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant COV_LOW="covLow"
COV_SYMMETRIC - Static variable in class edu.cmu.tetrad.util.Params: Constant COV_SYMMETRIC="covSymmetric"
CovAndCoefs(Matrix, Matrix) - Constructor for record class edu.cmu.tetrad.search.score.SemBicScore.CovAndCoefs: Creates an instance of a CovAndCoefs record class.
COVAR - Enum constant in enum class edu.cmu.tetrad.sem.ParamType: Represents a free parameter type for structural equation modeling (SEM) models.
covariance() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns the value of the covariance record component.
covariance(double[], double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: covariance.
covariance(double[], double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: covariance.
covariance(int, int) - Method in interface edu.cmu.tetrad.data.Covariances: Returns the covariance at (i, j).
covariance(int, int) - Method in class edu.cmu.tetrad.data.CovariancesDoubleForkJoin: covariance.
covariance(long[], long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: covariance.
covariance(long[], long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: covariance.
Covariance - Enum constant in enum class edu.cmu.tetrad.data.DataType: Covariance.
Covariance - Interface in edu.cmu.tetrad.stat.correlation: Interface for computing covariances.
COVARIANCE_NOT_PSD - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A supplied covariance matrix has at least one negative eigenvalue (judged on the implied correlation matrix, for scale invariance): it is not the covariance matrix of any dataset.
COVARIANCE_NOT_SYMMETRIC - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A supplied covariance matrix is not symmetric within tolerance: some entry (i, j) differs from its transpose entry (j, i) by more than a small fraction of the largest absolute entry.
CovarianceAudit - Class in edu.cmu.tetrad.data.audit: A pre-search audit of a supplied covariance (or correlation) matrix.
CovarianceAudit(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.data.audit.CovarianceAudit: Audits the given covariance matrix with default thresholds.
CovarianceAudit(ICovarianceMatrix, CovarianceAudit.Config) - Constructor for class edu.cmu.tetrad.data.audit.CovarianceAudit: Audits the given covariance matrix with the given thresholds.
CovarianceAudit.Config - Class in edu.cmu.tetrad.data.audit: The thresholds used by a CovarianceAudit's checks.
covarianceGaussianCopula(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: Returns the Gaussian copula ("nonparanormal") correlation matrix of a continuous data set: for each pair of variables the sample Kendall's tau-b is computed and mapped to the latent Gaussian correlation by
CovarianceMatrix - Class in edu.cmu.tetrad.data: Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
CovarianceMatrix - Class in edu.cmu.tetrad.stat.correlation: Jan 25, 2016 2:13:26 PM
CovarianceMatrix(float[][]) - Constructor for class edu.cmu.tetrad.stat.correlation.CovarianceMatrix: Constructor for CovarianceMatrix.
CovarianceMatrix(CovarianceMatrix) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrix: Copy constructor.
CovarianceMatrix(DataSet) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrix: Constructs a new covariance matrix from the given data set.
CovarianceMatrix(DataSet, boolean) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrix: Constructor for CovarianceMatrix.
CovarianceMatrix(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrix: Constructor for CovarianceMatrix.
CovarianceMatrix(List, double[][], int) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrix: Constructor for CovarianceMatrix.
CovarianceMatrix(List, Matrix, int) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrix: Protected constructor to construct a new covariance matrix using the supplied continuous variables and the the given symmetric, positive definite matrix and sample size.
CovarianceMatrixOnTheFly - Class in edu.cmu.tetrad.data: Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
CovarianceMatrixOnTheFly(DataSet) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Constructs a new covariance matrix from the given data set.
CovarianceMatrixOnTheFly(DataSet, boolean) - Constructor for class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Constructor for CovarianceMatrixOnTheFly.
covarianceNonparanormalDrton(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: Deprecated.
This does NOT compute the nonparanormal (Gaussian copula) correlation estimator, despite the name. It stores raw Kendall's tau, omitting the inversion sin(pi * tau / 2) that maps a rank correlation back to the latent Gaussian correlation. Raw tau is severely attenuated relative to the correlation it is standing in for -- a latent rho of 0.9 returns about 0.71, and 0.5 returns about 0.34 -- so anything scored from this matrix systematically under-detects edges. It also uses tau-a rather than tau-b, so ties attenuate it further, and it is O(n^2) per variable pair. Use DataTransforms.covarianceGaussianCopula(DataSet) instead, which corrects all three. Retained only because removing a public method is a breaking change; nothing in Tetrad calls it.
Covariances - Interface in edu.cmu.tetrad.data: Some comemon methods for the various covariance implementations.
CovariancesDoubleForkJoin - Class in edu.cmu.tetrad.data: Computes covariances using the standard calculation.
CovariancesDoubleForkJoin(double[][], boolean) - Constructor for class edu.cmu.tetrad.data.CovariancesDoubleForkJoin: Constructor for CovariancesDoubleForkJoin.
covariates(Graph, Node, Node, List, double[][], int) - Static method in class edu.cmu.tetrad.search.Fask: Computes the covariates for a pair of nodes in a graph based on a set of orientation covariates determined up to a given maximum path length.
Cover - Class in edu.cmu.tetrad.search.rlcd: A cover in the sense of RLCD (Dong et al., ICLR 2024): a set of variable names that is treated as a single node group during the rank-deficiency search.
Cover(Collection, boolean, boolean, Boolean) - Constructor for class edu.cmu.tetrad.search.rlcd.Cover: Constructs a cover.
covered(SortedSet, SortedSet) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: covered.
coveredEdge(Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns true iff x->y or y->x is a covered edge.
CoverSets - Class in edu.cmu.tetrad.search.rlcd: Static set operations over Covers, translating the module-level helpers in Cover.py and misc.py of the causal-learn RLCD implementation.
covMatrix(double[], double[], double[][], double[], double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: covMatrix.
covRidgeRel - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Regularization parameter for covariance estimation.
covRidgeRel - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the annealing steps for the simulated annealing algorithm.
covRidgeRel - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: The relative weighting of the covariance ridge regularization term.
covs - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: The covariance matrices of the mixture components in the Gaussian Mixture Model (GMM).
covShrinkage - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Regularization parameter for covariance estimation.
covShrinkage - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the starting temperature for the simulated annealing algorithm.
covShrinkage - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Represents the shrinkage parameter used in estimating covariance matrices.
covType - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Specifies the covariance type to be used in the EmUnmix algorithm's Gaussian Mixture Model.
covType - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Specifies the type of covariance matrix to be used in the Gaussian Mixture Model (GMM) Expectation-Maximization (EM) algorithm.
covType - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: Specifies the type of covariance matrix used in the Gaussian Mixture Model (GMM).
covTypeOverride - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: If non-null, forces the given covariance type (FULL or DIAGONAL) for the Gaussian mixture, bypassing the automatic policy based on the per-cluster sample size.
Cpc - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Conservative PC (CPC).
Cpc() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: This class represents the constructor for the Cpc class.
Cpc(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: This class represents the constructor for the Cpc class.
CPC - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Constant for the CPC algorithm.
CPDAG - Enum constant in enum class edu.cmu.tetrad.graph.GraphUtils.GraphType: The CPDAG graph type.
CPDAG - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: A completed partially directed acyclic graph: the Markov-equivalence class of a DAG, with compelled edges directed and the rest left undirected.
CPDAG - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.ResultType: Constant for CPDAG result type.
CPDAG - Static variable in class edu.cmu.tetrad.util.Params: Constant CPDAG="cpdag"
CPDAG_of_the_true_DAG - Enum constant in enum class edu.cmu.tetrad.algcomparison.Comparison.ComparisonGraph: Constant for the CPDAG of the true DAG.
CPDAG_of_the_true_DAG - Enum constant in enum class edu.cmu.tetrad.algcomparison.TimeoutComparison.ComparisonGraph: The cpdag of the true dag.
CpdagCheckResult(boolean, Paths.CpdagViolation, String) - Constructor for record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Creates an instance of a CpdagCheckResult record class.
CpdagParentDistancesFromTrue - Class in edu.cmu.tetrad.search: CpdagParentDistancesFromTrue computes the distances between true edge strengths in a true DAG and the range of estimated edge strengths in an output CPDAG.
CpdagParentDistancesFromTrue() - Constructor for class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue: Constructs a new instance of the class.
CpdagParentDistancesFromTrue.DistanceType - Enum Class in edu.cmu.tetrad.search: The type of distance to calculate.
CptMap - Interface in edu.cmu.tetrad.bayes: An interface representing a map of probabilities or counts for nodes in a Bayesian network.
CptMapCounts - Class in edu.cmu.tetrad.bayes: Represents a conditional probability table (CPT) in a Bayes net.
CptMapCounts(int, int) - Constructor for class edu.cmu.tetrad.bayes.CptMapCounts: Constructs a new probability map, a map from a unique integer index for a particular node to the probability of that node taking on that value, where NaN's are not stored.
CptMapProbs - Class in edu.cmu.tetrad.bayes: Represents a conditional probability table (CPT) in a Bayes net.
CptMapProbs(double[][]) - Constructor for class edu.cmu.tetrad.bayes.CptMapProbs: Constructs a new probability map based on the given 2-dimensional array.
CptMapProbs(int, int) - Constructor for class edu.cmu.tetrad.bayes.CptMapProbs: Constructs a new probability map, a map from a unique integer index for a particular node to the probability of that node taking on that value, where NaN's are not stored.
CPU - Enum constant in enum class edu.cmu.tetrad.util.MillisecondTimes.Type: CPU time.
cpuTimeMillis() - Static method in class edu.cmu.tetrad.util.MillisecondTimes: cpuTimeMillis.
Cpw - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Implements the CPW (Cyclic Pairwise) algorithm for causal discovery from continuous, causally sufficient observational data.
Cpw() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Default constructor for the Cpw class.
Cpw(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Constructor for the Cpw class that initializes the algorithm with a specified independence test wrapper.
CpwLingamStudy - Class in edu.cmu.tetrad.search.harness: Simulation study of CPW on linear non-Gaussian acyclic model (LiNGAM) data, with FASK and DirectLiNGAM as references.
create(Class, IndependenceWrapper, ScoreWrapper) - Static method in class edu.cmu.tetrad.algcomparison.algorithm.AlgorithmFactory: Creates an algorithm.
create(Class, IndependenceWrapper, ScoreWrapper, Graph) - Static method in class edu.cmu.tetrad.algcomparison.algorithm.AlgorithmFactory: Creates an algorithm.
create(Class, Class, Class) - Static method in class edu.cmu.tetrad.algcomparison.algorithm.AlgorithmFactory: Creates an algorithm.
create(Class, Class, Class, Graph) - Static method in class edu.cmu.tetrad.algcomparison.algorithm.AlgorithmFactory: Creates an algorithm.
create(String, RandomGraph) - Static method in class edu.cmu.tetrad.algcomparison.simulation.SimulationUtils: Creates a simulation instance based on the specified simulation type and random graph.
create(String, RandomGraph, Knowledge) - Static method in class edu.cmu.tetrad.algcomparison.simulation.SimulationUtils: Creates a simulation instance based on the specified simulation type, random graph, and knowledge.
create(List) - Static method in class edu.cmu.tetrad.util.ParameterUtils: Create parameters with their default values.
createCellTable(BayesIm) - Static method in class edu.cmu.tetrad.bayes.StoredCellProbs: createCellTable.
createCellTable(MlBayesIm) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: createCellTable.
createCellTable(MlBayesImObs) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: createCellTable.
createContinuousVariables(String[]) - Static method in class edu.cmu.tetrad.data.DataUtils: createContinuousVariables.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Creates data models and their corresponding graphs based on the provided parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Creates data sets for simulation based on the given parameters and model reuse preference.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Creates data sets for simulation based on the given parameters and model reuse preference.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Creates simulated data and associated graphs based on the given parameters.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Creates data sets for simulation based on the given parameters and model reuse preference.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Creates a new data model for the simulation.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Creates a data set and simulates data.
createData(Parameters, boolean) - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Creates a data set and simulates data.
createDataSample(DataSet, RandomGenerator, int[], Parameters, double) - Static method in class edu.cmu.tetrad.data.DataSampling: Creates a resampled dataset from the given dataset based on the specified parameters.
createDataSamples(DataSet, RandomGenerator, Parameters) - Static method in class edu.cmu.tetrad.data.DataSampling: Create a list of dataset resampled from the given dataset.
createDataSamples(RandomGenerator, DataSet, Parameters) - Static method in class edu.cmu.tetrad.data.DataSampling: Create a list of dataset resampled from the given dataset.
createDisplayGraph(Graph, ResamplingEdgeEnsemble) - Static method in class edu.cmu.tetrad.util.GraphSampling: Create a graph for displaying and print out.
createExpression(Expression...) - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionDescriptor: Creates the actual expression that can be used to evaluate matters from the given expressions.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.Cyclic: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.ErdosRenyi: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.RandomForward: Creates a random graph by adding forward edges.
createGraph(Parameters) - Method in interface edu.cmu.tetrad.algcomparison.graph.RandomGraph: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.RandomMim: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.RandomMimic: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.RandomSingleFactorMim: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.RandomTwoFactorMim: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.ScaleFree: createGraph.
createGraph(Parameters) - Method in class edu.cmu.tetrad.algcomparison.graph.SingleGraph: createGraph.
createGraphWithHighProbabilityEdges(List) - Static method in class edu.cmu.tetrad.util.GraphSampling: Combine all the edges from the list of graphs onto one graph with the edge type that has the highest frequency probability.
createGraphWithoutNullEdges(Graph) - Static method in class edu.cmu.tetrad.util.GraphSampling: Create a graph from the given graph that contains no null edges.
createLagData(DataSet, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: Creates new time series dataset from the given one (fixed to deal with mixed datasets)
createLagData(DataSet, int, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: Creates a new time series dataset from the given one, expanding the provided within-lag Knowledge across all time lags.
createRandomCellTable() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: createRandomCellTable.
createRandomCellTable(List) - Static method in class edu.cmu.tetrad.bayes.StoredCellProbs: createRandomCellTable.
createReport(MimicBenchmarkResult, Parameters) - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkReport: Creates a readable report for the supplied benchmark result.
createReport(MimicBenchmarkResult, Parameters) - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkReportTdf: Creates a tab-delimited report for the supplied benchmark result and parameters.
createShiftedData(DataSet, int[]) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: createShiftedData.
crossTab(int, int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: This method computes the cross tablulation (table) of values of a gene and its possible parent.
crossTab(int, int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: This method computes the cross tablulation (table) of values of a gene and its possible parent.
crossTabulation() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Computes the "confusion matrix" of coefs of the number of cases associated with each combination of estimated and observed values in the test dataset.
crossTabulation() - Method in interface edu.cmu.tetrad.classify.ClassifierDiscrete: crossTabulation.
crossTabulation() - Method in class edu.cmu.tetrad.classify.ClassifierMbDiscrete: crossTabulation.
crossValidate(int) - Method in class edu.cmu.tetrad.sem.NNEstimator: Runs k-fold cross-validation and returns a CVReport with honest OOS metrics at both the node level and the whole-graph level.
cStar(LinkedList) - Static method in class edu.cmu.tetrad.search.Cstar: Returns a list of records for making a CSTaR table.
Cstar - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Cstar class.
Cstar - Class in edu.cmu.tetrad.search: Implements the CStaR algorithm (Stekhoven et al., 2012), which finds a CPDAG from the data and then tries all orientations of the undirected edges around each variable in the CPDAG to estimate a minimum bound on the causal effect for a given edge.
Cstar() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Constructor for Cstar.
Cstar(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Constructor for Cstar.
Cstar(IndependenceWrapper, ScoreWrapper, Parameters) - Constructor for class edu.cmu.tetrad.search.Cstar: Constructor.
CSTAR_CPDAG_ALGORITHM - Static variable in class edu.cmu.tetrad.util.Params: Constant CSTAR_CPDAG_ALGORITHM="cstarCpdagAlgorithm"
Cstar.CpdagAlgorithm - Enum Class in edu.cmu.tetrad.search: An enumeration of the options available for determining the CPDAG used for the algorithm.
Cstar.Record - Class in edu.cmu.tetrad.search: Represents a single record in the returned table for CSTaR.
Cstar.SampleStyle - Enum Class in edu.cmu.tetrad.search: An enumeration of the methods for selecting samples from the full dataset.
cu(double[], double[], double[]) - Static method in class edu.cmu.tetrad.search.Fask: E[x * y | condition > 0].
cumulativeProbability(double) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Calculates the cumulative probability of a given point.
cumulativeProbability(double, double) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Deprecated.
This method is deprecated and will be removed in a future release.
curatedStatistics() - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns a broader, curated set of statistics suitable for the stats list comparison, with duplicate abbreviations removed.
currentRepositoryVersion() - Static method in class edu.cmu.tetrad.util.Version: currentRepositoryVersion.
currentViewableVersion() - Static method in class edu.cmu.tetrad.util.Version: currentViewableVersion.
CUSTOM_NOISE_EXPRESSION - Static variable in class edu.cmu.tetrad.util.Params: Constant CUSTOM_NOISE_EXPRESSION="customNoiseExpression"
CUSTOM_NOISE_OPTION - Static variable in class edu.cmu.tetrad.util.Params: Constant CUSTOM_NOISE_OPTION="customNoiseOption"
CUTOFF_CONSTRAIN_SEARCH - Static variable in class edu.cmu.tetrad.util.Params: Constant CUTOFF_CONSTRAIN_SEARCH="cutoffConstrainSearch"
CUTOFF_DATA_SEARCH - Static variable in class edu.cmu.tetrad.util.Params: Constant CUTOFF_DATA_SEARCH="cutoffDataSearch"
CUTOFF_IND_TEST - Static variable in class edu.cmu.tetrad.util.Params: Constant CUTOFF_IND_TEST="cutoffIndTest"
CutoffFinder - Class in edu.cmu.tetrad.util: Provides a static method for finding the cutoff value for a symmetric probability distribution function about the origin.
cutoffsSummary() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Provides a summary of the current FDR cutoff parameters in a formatted string.
cvAdditiveVsRff(double[], double[][], int, double) - Static method in class edu.cmu.tetrad.search.utils.NonlinearityTests: Tests additivity of the conditional mean E(Y|X) by a nested cross-validated comparison: an additive ridge model (per-variable hinge features plus per-variable one-dimensional RFF features) versus the same additive design augmented with joint RFF (RBF random Fourier) features.
cvLinearVsNonlinear(double[], double[][], int, double) - Static method in class edu.cmu.tetrad.search.utils.NonlinearityTests: Performs cross-validation to compare linear ridge regression with nonlinear kernel ridge regression using Random Fourier Features (RFF).
CVReport - Class in edu.cmu.tetrad.sem: Result of k-fold cross-validation performed by NNEstimator#crossValidate(int).
CVReport(int, List, double) - Constructor for class edu.cmu.tetrad.sem.CVReport: Constructs an immutable cross-validation report, summarizing the results of k-fold cross-validation across multiple nodes.
Cyclic - Class in edu.cmu.tetrad.algcomparison.graph: Returns a cyclic graph build up from small cyclic graph components.
Cyclic() - Constructor for class edu.cmu.tetrad.algcomparison.graph.Cyclic: Initializes a new instance of the Cyclic class.
CYCLIC_COEF_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant CYCLIC_COEF_HIGH="cyclicCoefHigh"
CYCLIC_COEF_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant CYCLIC_COEF_LOW="cyclicCoefLow"
CYCLIC_COEF_STYLE - Static variable in class edu.cmu.tetrad.util.Params: Constant CYCLIC_COEF_STYLE="cyclicCoefStyle"
CYCLIC_CONSTRUCTED_FROM_SMALL_LOOPS - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant CYCLIC_CONSTRUCTED_FROM_SMALL_LOOPS="Cyclic (Constructed From Small Loops)"
CYCLIC_MAX_PROD - Static variable in class edu.cmu.tetrad.util.Params: Constant CYCLIC_MAX_PROD="cyclicMaxProd"
CYCLIC_RADIUS - Static variable in class edu.cmu.tetrad.util.Params: Constant CYCLIC_RADIUS="cyclicRadius"
CyclicSemScorer - Class in edu.cmu.tetrad.search: The CyclicSemScorer class is designed for evaluating the fit of a directed graph with respect to data using a structural equation modeling (SEM) framework.
CyclicSemScorer() - Constructor for class edu.cmu.tetrad.search.CyclicSemScorer: Default constructor for the CyclicSemScorer class.
CyclicSemScorer.ScoreResult - Record Class in edu.cmu.tetrad.search: Immutable result object representing the computed score from a scoring process.
CyclicSimResult(DataSet, SemIm) - Constructor for record class edu.cmu.tetrad.sem.SemIm.CyclicSimResult: Creates an instance of a CyclicSimResult record class.
CyclicStableUtils - Class in edu.cmu.tetrad.sem: Utilities to stabilize arbitrary directed graphs with cycles.

D

d - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: Represents the dimensionality of the data in the Gaussian Mixture Model (GMM).
D2 - Enum constant in enum class edu.cmu.tetrad.study.missingness.PooledDgLrtTest.Pooling: Li-Meng-Raghunathan-Rubin 1991: pools the chi-square statistics alone.
D3 - Enum constant in enum class edu.cmu.tetrad.study.missingness.PooledDgLrtTest.Pooling: Meng-Rubin 1992: pools via the statistic at averaged parameters.
Dag - Class in edu.cmu.tetrad.graph: Represents a directed acyclic graph--that is, a graph containing only directed edges, with no cycles.
Dag() - Constructor for class edu.cmu.tetrad.graph.Dag: Constructs a new directed acyclic graph (DAG).
Dag(Graph) - Constructor for class edu.cmu.tetrad.graph.Dag: Constructs a new directed acyclic graph from the given graph object.
Dag(List) - Constructor for class edu.cmu.tetrad.graph.Dag: Constructor for Dag.
DAG - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: A directed acyclic graph: every edge is directed and there is no directed cycle.
dagFromCpdag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
dagFromCpdag(Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
dagFromCpdag(Graph, boolean, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
dagFromCpdag(Graph, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: dagFromCpdag.
dagFromCpdag(Graph, Knowledge, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Returns a random DAG from the given CPDAG.
DagInCpcagIterator - Class in edu.cmu.tetrad.search.utils: Given a CPDAG, lists all the DAGs in that DPCAG.
DagInCpcagIterator(Graph) - Constructor for class edu.cmu.tetrad.search.utils.DagInCpcagIterator: The given CPDAG must be a CPDAG.
DagInCpcagIterator(Graph, Knowledge) - Constructor for class edu.cmu.tetrad.search.utils.DagInCpcagIterator: The given CPDAG must be a CPDAG.
DagInCpcagIterator(Graph, Knowledge, boolean, boolean) - Constructor for class edu.cmu.tetrad.search.utils.DagInCpcagIterator: The given CPDAG must be a CPDAG.
DagIterator - Class in edu.cmu.tetrad.search.utils: Given a graph, lists all DAGs that result from directing the undirected edges in that graph every possible way.
DagIterator(Graph) - Constructor for class edu.cmu.tetrad.search.utils.DagIterator: The given CPDAG must be a CPDAG.
Dagma - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: Implements the DAGMA algorithm.
Dagma - Class in edu.cmu.tetrad.search: Implements the DAGMA algorithm.
Dagma() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Dagma: Constructor for Dagma.
Dagma(DataSet) - Constructor for class edu.cmu.tetrad.search.Dagma: Constructor.
DagMetric - Interface in edu.cmu.tetrad.sem: Represents a functional interface for computing metrics on directed acyclic graphs (DAGs).
DagMetric.MetricComputer - Interface in edu.cmu.tetrad.sem: Represents a functional interface for computing a metric value from a DataSet and Graph.
DagMetricRegistry - Class in edu.cmu.tetrad.sem: Registry class that provides utility methods for working with DAG metrics.
DagMetricResult - Record Class in edu.cmu.tetrad.sem: Represents the result of a metric related to a Directed Acyclic Graph (DAG), including its name, value, an associated note, and an indication of whether a higher or lower value is considered better.
DagMetricResult(String, double, String) - Constructor for record class edu.cmu.tetrad.sem.DagMetricResult: Constructs an instance of DagMetricResult with the specified name, value, and note.
DagMetricResult(String, double, String, DagMetricResult.Better) - Constructor for record class edu.cmu.tetrad.sem.DagMetricResult: Creates an instance of a DagMetricResult record class.
DagMetricResult.Better - Enum Class in edu.cmu.tetrad.sem: Represents the evaluation criteria for a metric, describing whether a higher value, a lower value, or neither is considered "better" for the given context.
DagMetrics - Class in edu.cmu.tetrad.sem: Utility class that provides a set of predefined metrics for evaluating Directed Acyclic Graphs (DAGs).
DagScorer - Class in edu.cmu.tetrad.sem: Estimates a SemIm given a CovarianceMatrix and a SemPm.
DagScorer - Interface in edu.cmu.tetrad.search.utils: Interface for a method that scores a DAG.
DagScorer(DataSet) - Constructor for class edu.cmu.tetrad.sem.DagScorer: Constructs a new SemEstimator that uses the specified optimizer.
DagScorer(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.sem.DagScorer: Constructs a new SemEstimator that uses the specified optimizer.
DagScores - Class in edu.cmu.tetrad.sem: Utility class containing various scoring methods for Directed Acyclic Graphs (DAGs).
DagSepsets - Class in edu.cmu.tetrad.search.utils: Determines sepsets, collider, and noncolliders by examining d-separation facts in a DAG.
DagSepsets(Graph) - Constructor for class edu.cmu.tetrad.search.utils.DagSepsets: Constructs a new DagSepsets object for the given DAG.
dagToCpdag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Returns the completed partially directed acyclic graph (CPDAG) for a given directed acyclic graph (DAG).
dagToMag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a Directed Acyclic Graph (DAG) to a Maximal Ancestral Graph (MAG) by adding arrows to the edges.
dagToPag(Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the DagToPag algorithm.
dagToPag(Graph, boolean, int) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG).
dagToPag(Graph, Knowledge, boolean, int) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the provided DAG and knowledge.
DAO_ERROR_DISTRIBUTION - Static variable in class edu.cmu.tetrad.util.Params: Constant DAO_ERROR_DISTRIBUTION="daoErrorDistribution"
DAO_RANDOMIZE_ORDER - Static variable in class edu.cmu.tetrad.util.Params: Constant DAO_RANDOMIZE_ORDER="daoRandomizeOrder"
DAO_SF_IN - Static variable in class edu.cmu.tetrad.util.Params: Constant DAO_SF_IN="daoSfIn"
DAO_SF_OUT - Static variable in class edu.cmu.tetrad.util.Params: Constant DAO_SF_OUT="daoSfOut"
DAO_SIMULATION - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant DAO_SIMULATION="DAO Simulation"
DaoSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: Implements the DAG-adaptation of the Onion (DaO) simulation method for generating linear data from a DAG, with exogenous errors drawn from one of the reference implementation's five variance-matched families (Gaussian by default; Exponential, Gumbel, Laplace, or Uniform via daoErrorDistribution), as described in:
DaoSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation: Constructs a DaoSimulation using the supplied RandomGraph to produce the true DAG on each run.
DaoSimulation(Graph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation: Convenience constructor that fixes a single, predetermined true DAG.
data() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Result: Returns the value of the data record component.
data() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns the value of the data record component.
data(DataSet) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the data set for the builder.
data(DataSet) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Provide the DataSet on which the search will be run.
Data - Section in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython
Data - Section in class edu.cmu.tetrad.algcomparison.independence.ClKciPython
Data - Section in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython
DATA_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant DATA_TYPE="dataType"
dataAndIndex(DataSet, double[], String) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the data, index array, and potentially a custom name for the builder.
dataAndIndex(DataSet, double[], String) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Provide X and a continuous change index C to append as the last column.
DataAudit - Class in edu.cmu.tetrad.data.audit: A pre-search audit of a data matrix.
DataAudit(DataSet) - Constructor for class edu.cmu.tetrad.data.audit.DataAudit: Audits the given dataset with default thresholds.
DataAudit(DataSet, DataAudit.Config) - Constructor for class edu.cmu.tetrad.data.audit.DataAudit: Audits the given dataset with the given thresholds.
DataAudit.Config - Class in edu.cmu.tetrad.data.audit: Thresholds for the audit's checks, with defaults.
DataBox - Interface in edu.cmu.tetrad.data: Stores a 2D array of data.
DataConvertUtils - Class in edu.cmu.tetrad.util: Dec 15, 2018 11:10:30 AM
DataFilter - Interface in edu.cmu.tetrad.data: Interface implemented by classes whose purpose is to generate modifications of data sets.
DataForCalibrationRfci - Class in edu.cmu.tetrad.calibration: DataForCalibrationRfci class.
DataGraphSearch - Class in edu.pitt.csb.stability: Created by ajsedgewick on 9/4/15.
DataGraphSearch(double...) - Constructor for class edu.pitt.csb.stability.DataGraphSearch: Constructor for DataGraphSearch.
DataModel - Interface in edu.cmu.tetrad.data: Interface implemented by classes, instantiations of which can serve as data models in Tetrad.
DataModelList - Class in edu.cmu.tetrad.data: Stores a list of data models and keeps track of which one is selected.
DataModelList() - Constructor for class edu.cmu.tetrad.data.DataModelList: Constructor for DataModelList.
DataModelList(DataModelList) - Constructor for class edu.cmu.tetrad.data.DataModelList: Constructor for DataModelList.
DataSampling - Class in edu.cmu.tetrad.data: A utility for resampling dataset.
dataSet() - Method in class edu.cmu.tetrad.search.blocks.BlockSpec: Returns the data set associated with this block specification.
dataSet() - Method in record class edu.cmu.tetrad.sem.SemIm.CyclicSimResult: Returns the value of the dataSet record component.
dataSet() - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Returns the value of the dataSet record component.
DataSet - Interface in edu.cmu.tetrad.data: Implements a rectangular data set, in the sense of being a dataset with a fixed number of columns and a fixed number of rows, the length of each column being constant.
DATASET_WEIGHTED - Enum constant in enum class edu.cmu.tetrad.search.score.ImagesScore.WeightingScheme: Weighted average of the component scores after rescaling each by (mean N) / N_k, so that each dataset's vote is independent of its sample size.
DataSetHelper - Class in edu.cmu.tetrad.util: Utility class for creating a DataSet instance from various inputs.
DataSetProbs - Class in edu.cmu.tetrad.bayes: Estimates maximum likelihood probabilities directly from data on the fly.
DataSetProbs(DataSet) - Constructor for class edu.cmu.tetrad.bayes.DataSetProbs: Creates a cell count table for the given data set.
DataTable<N,V> - Interface in edu.pitt.isp.sverchkov.data: Data table implementation.
dataTableFromFile(File) - Static method in class edu.pitt.isp.sverchkov.data.DataTools: Reads a data table from a file.
DataTableImpl<N,V> - Class in edu.pitt.isp.sverchkov.data: Data table implementation.
DataTableImpl(List) - Constructor for class edu.pitt.isp.sverchkov.data.DataTableImpl: Constructor for DataTableImpl.
DataTools - Class in edu.pitt.isp.sverchkov.data: Data tools.
DataTransforms - Class in edu.cmu.tetrad.data: DataTransforms class.
dataType() - Element in annotation interface edu.cmu.tetrad.annotation.Algorithm: Description of the algorithm.
dataType() - Element in annotation interface edu.cmu.tetrad.annotation.Score: The data types that the score can be applied to.
dataType() - Element in annotation interface edu.cmu.tetrad.annotation.TestOfIndependence: Type of the test.
DataType - Enum Class in edu.cmu.tetrad.data: The type of the data set--continuous if all continuous variables, discrete if all discrete variables; otherwise, mixed.
DataUtility - Class in edu.cmu.tetrad.util: Fast data loader for continuous or discrete data.
DataUtils - Class in edu.cmu.tetrad.data: Some static utility methods for dealing with data sets.
DataWriter - Class in edu.cmu.tetrad.data: Provides static methods for saving data to files.
DAVIES_IMHOF - Enum constant in enum class edu.cmu.tetrad.search.test.FfCiContinuous.Approx: Represents the Davies-Imhof approximation method.
DAVIES_IMHOF - Enum constant in enum class edu.cmu.tetrad.search.test.Rcit.Approx: Represents the Davies-Imhof approximation
daviesP(double, double[]) - Static method in class edu.cmu.tetrad.search.test.QuadraticFormPValues: Davies/Imhof-style inversion for Q = sum_i lambda_i * Z_i^2 (Z_i~N(0,1)).
daviesP(double, double[], double, int, int) - Static method in class edu.cmu.tetrad.search.test.QuadraticFormPValues: Calculates the right-tail p-value P(Q >= stat) using a method from Davies and Imhof.
dcgToCcdPag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Returns the graph CCD is meant to recover from a directed cyclic graph (DCG): the cyclic PAG of its d-separation equivalence class, with CCD's underline and dotted-underline annotations.
Dci - Class in edu.cmu.tetrad.search.work_in_progress: Implements the DCI (Distributed Causal Inference) algorithm for learning causal structure over a set of variable from multiple datasets that each may only measure proper overlapping subsets of that sets, or datasets with some variables in common and others not.
Dci(List) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Dci: Constructor for Dci.
Dci(List, ResolveSepsets.Method) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Dci: Constructor for Dci.
dd - Enum constant in enum class edu.cmu.tetrad.graph.Edge.Property: Definitely direct.
DE_COUPLING - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_COUPLING="deCoupling"
DE_DERIVED_NOISE - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_DERIVED_NOISE="deDerivedNoise"
DE_EMIT_CONFIG_COLUMN - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_EMIT_CONFIG_COLUMN="deEmitConfigColumn"
DE_INTERACTION - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_INTERACTION="deInteraction"
DE_MAX_LEVELS - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_MAX_LEVELS="deMaxLevels"
DE_MIN_LEVELS - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_MIN_LEVELS="deMinLevels"
DE_NUM_DERIVED - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_NUM_DERIVED="deNumDerived"
DE_NUM_FACTORS - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_NUM_FACTORS="deNumFactors"
DE_NUM_RESPONSES - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_NUM_RESPONSES="deNumResponses"
DE_RESPONSE_NOISE - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_RESPONSE_NOISE="deResponseNoise"
DE_SELECTION - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_SELECTION="deSelection"
DE_SORT_BY_CONFIGURATION - Static variable in class edu.cmu.tetrad.util.Params: Constant DE_SORT_BY_CONFIGURATION="deSortByConfiguration"
DECLARED - Enum constant in enum class edu.cmu.tetrad.search.utils.EdgePriors.Origin: Supplied directly by the caller.
decode(String) - Static method in record class edu.cmu.tetrad.data.LogTransformSpec: Decodes a string produced by LogTransformSpec.encode(java.util.Map).
decomposition() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if decomposition holds, e.g., X _||_ (Y ∪ W) | Z ==> (X _||_ Y | Z) ∧ (X _||_ W | Z)
DEDUP_BY_CPDAG - Static variable in class edu.cmu.tetrad.util.Params: Constant DEDUP_BY_CPDAG="dedupByCpdag"
deduplicate(Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: Removes any cover whose variables are a strict subset of another cover's variables (Python deduplicate): {L1, {L1, L3}} becomes {{L1, L3}}.
deepCopy() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Creates a deep copy of this graph in which every node is a newly constructed object with the same name, type, position, and variable type as the original.
DEFAULT_COEF_HIGH - Static variable in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Default upper bound for structural coefficients.
DEFAULT_COEF_LOW - Static variable in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Default lower bound for structural coefficients.
DEFAULT_MAX_ITERS - Static variable in class edu.cmu.tetrad.sem.RicfEjml: Default maximum number of sweeps (matches the Python default).
DEFAULT_STRATEGY - Static variable in class edu.cmu.tetrad.search.RecursiveBlocking: The default strategy used for processing or executing tasks in the system.
DEFAULT_TOL - Static variable in class edu.cmu.tetrad.sem.RicfEjml: Default convergence tolerance (matches the Python default).
DEFAULT_VAR_HIGH - Static variable in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Default upper bound for error variances.
DEFAULT_VAR_LOW - Static variable in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Default lower bound for error variances.
defaultCategory(int) - Static method in class edu.cmu.tetrad.data.DataUtils: defaultCategory.
defaultConfiguration(Graph, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Returns a default configuration of the FciOrientDataExaminationStrategy object.
defaultConfiguration(IndependenceTest, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Returns a default configuration of the FciOrientDataExaminationStrategy object.
DefaultEvent(String, String) - Constructor for class edu.cmu.tetrad.util.DefaultTetradLoggerConfig.DefaultEvent: Constructs the event.
defaultLayout(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes in the graph by the default layout (see LayoutUtil.setDefaultLayout(DefaultLayout)), with estimated node sizes.
defaultLayout(Graph, LayoutUtil.NodeSize) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes in the graph by the default layout (see LayoutUtil.setDefaultLayout(DefaultLayout)), with the given node sizes for the layouts that use them.
defaultLayoutConditional(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes using the default layout unless they all have positions already.
defaultMetricsFor(DataSet) - Static method in class edu.cmu.tetrad.sem.DagMetricRegistry: Returns the default metric list for a dataset type, in display order.
defaultMixedName - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Represents the default name used for blocks when no match is identified.
defaultMixedName(String) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets the default mixed name for this configuration.
defaults() - Static method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Creates and returns a new instance of the Config class initialized with default configuration values.
defaults() - Static method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Returns a new SemParameterizer pre-configured with the default coefficient and variance ranges.
defaults() - Static method in class edu.cmu.tetrad.search.unmix.CausalUnmixer: Creates and returns a default Config object with predefined settings.
defaults() - Static method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the default spec: discrete indicators for every variable whose missingness rate lies between 0.02 and 0.98, with tier knowledge attached and self-masking forbidden.
DefaultSetEndpointStrategy - Class in edu.cmu.tetrad.search.utils: The DefaultSetEndpointStrategy class implements the SetEndpointStrategy interface and provides a default strategy for setting the endpoint of an edge in a graph.
DefaultSetEndpointStrategy() - Constructor for class edu.cmu.tetrad.search.utils.DefaultSetEndpointStrategy: Creates a new instance of DefaultSetEndpointStrategy.
defaultStatistics() - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns the statistics shown by default in the stats list comparison, in display order.
DefaultTetradLoggerConfig - Class in edu.cmu.tetrad.util: Logger configuration.
DefaultTetradLoggerConfig(String...) - Constructor for class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: Constructs the config for the given event ids.
DefaultTetradLoggerConfig(List) - Constructor for class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: Constructs the config given the events in it.
DefaultTetradLoggerConfig.DefaultEvent - Class in edu.cmu.tetrad.util: A default implementation of the event.
DefiniteDirectedPathPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
DefiniteDirectedPathPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathPrecision: Initializes a new instance of the DefiniteDirectedPathPrecision class.
DefiniteDirectedPathRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
DefiniteDirectedPathRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathRecall: Initializes a new instance of the DefiniteDirectedPathRecall class.
definiteNonDescendent(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: added by ekorber, 2004/06/12
Definition - Section in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty
DEFLATION - Static variable in class edu.cmu.tetrad.search.FastIca: Extract components one-at-a-time.
defVisiblePag(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Returns true if the edge form A to B is a definitely visible edge in a PAG.
Degenerate cases - Section in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest
DegenerateGaussianBgeScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for DG-BGe, the BGe (Normal-Wishart marginal likelihood) score over the degenerate Gaussian indicator embedding that DG-BIC uses.
DegenerateGaussianBgeScore - Class in edu.cmu.tetrad.search.score: DG-BGe: the BGe score applied to the degenerate Gaussian embedding of DegenerateGaussianScore.
DegenerateGaussianBgeScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBgeScore: Constructs a new instance of the wrapper.
DegenerateGaussianBgeScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.DegenerateGaussianBgeScore: Constructs a DG-BGe score.
DegenerateGaussianBgeScore(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.DegenerateGaussianBgeScore: Constructs the score with an explicit missing-data specification; LISTWISE and TESTWISE are supported on data with missing values (see BasisFunctionBgeScore(DataSet, int, boolean, MissingDataSpec)).
DegenerateGaussianBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for degenerate Gaussian BIC score
DegenerateGaussianBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBicScore: Initializes a new instance of the DegenerateGaussianBicScore class.
DegenerateGaussianLrt - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for DG LRT.
DegenerateGaussianLrt() - Constructor for class edu.cmu.tetrad.algcomparison.independence.DegenerateGaussianLrt: Initializes a new instance of the DegenerateGaussianLrt class.
DegenerateGaussianScore - Class in edu.cmu.tetrad.search.score: =This implements the degenerate Gaussian BIC score for FGES.
DegenerateGaussianScore(DataSet, boolean, double) - Constructor for class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Constructs the score using a dataset.
DegenerateGaussianScore(DataSet, boolean, double, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Constructs the score from a dataset with an explicit missing-data specification.
degree(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the maximum degree of a graph.
degreesOfFreedom() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the degreesOfFreedom record component.
DELETED_PAIR_BATTERY - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.CommitGate: The legality-plus-separator gate, plus verification against the independence test of every separation of the removed pair that the candidate MAG entails with conditioning sets of size at most batteryZMax.
DELETED_PAIR_BATTERY - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.CommitGate: The legality-plus-separator gate, plus verification against the independence test of every separation of the removed pair that the candidate MAG entails with conditioning sets of size at most batteryZMax.
DELETED_PAIR_BATTERY - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.CommitGate: The legality-plus-separator gate, plus verification against the independence test of every separation of the removed pair that the candidate MAG entails with conditioning sets of size at most batteryZMax.
DELETED_PAIR_BATTERY - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.CommitGate: The legality-plus-separator gate, plus verification against the independence test of every separation of the removed pair that the candidate MAG entails with conditioning sets of size at most batteryZMax.
Deletion(String, String, double, double, int, int, int) - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: Constructs a deletion record.
DELIMITER - Static variable in class edu.pitt.isp.sverchkov.data.DataTools: Constant DELIMITER=", "
DELIMITER_REGEX - Static variable in class edu.pitt.isp.sverchkov.data.DataTools: Constant DELIMITER_REGEX=" *, *"
DelimiterType - Class in edu.cmu.tetrad.data: Type-safe enum of delimiter types for parsing data.
DelimiterUtils - Class in edu.cmu.tetrad.util: Jun 20, 2017 12:09:05 PM
delta - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.BicDelta: Represents the difference between the Bayesian Information Criterion (BIC) values for mixture models with K=1 and K=2 clusters.
delta() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Computes the difference between the number of violations after and the baseline violations.
DeltaSextadTest - Class in edu.cmu.tetrad.search.utils: Implements a test for simultaneously zero sextads in the style of Bollen, K.
DeltaSextadTest(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.DeltaSextadTest: Constructs a test using a given data set.
DeltaSextadTest(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.utils.DeltaSextadTest: Constructs a test using the given covariance matrix.
DeltaTetradTest - Class in edu.cmu.tetrad.search.utils: Refactored implementation of the Delta Tetrad Test for Confirmatory Tetrad Analysis (CTA).
DeltaTetradTest(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.DeltaTetradTest: Constructor for Gaussian data.
DeltaTetradTest(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.utils.DeltaTetradTest: Constructor for a covariance matrix.
DeltaTetradTest2 - Class in edu.cmu.tetrad.search.utils: Implements a test for simultaneously zero sextads in the style of Bollen, K.
DeltaTetradTest2(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.DeltaTetradTest2: Constructs a test using a given data set.
DeltaTetradTest2(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.utils.DeltaTetradTest2: Constructs a test using the given covariance matrix.
DeltaTetradTest3 - Class in edu.cmu.tetrad.search.utils: Refactored implementation of the Delta Tetrad Test for Confirmatory Tetrad Analysis (CTA).
DeltaTetradTest3(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.DeltaTetradTest3: Constructor for Gaussian data.
DeltaTetradTest3(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.utils.DeltaTetradTest3: Constructor for a covariance matrix.
demean(double[][], Vector) - Static method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: demean.
demix() - Method in class edu.cmu.tetrad.search.work_in_progress.Demixer: Runs the E-M algorithm iteratively until the weights array converges.
Demixer - Class in edu.cmu.tetrad.search.work_in_progress: Uses expectation-maximization to sort a a data set with data sampled from two or more multivariate Gaussian distributions into its component data sets.
Demixer(DataSet, int) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Demixer: Constructor.
DemixerMMLKun - Class in edu.cmu.tetrad.search.work_in_progress: Created by user on 2/27/18.
DemixerMMLKun() - Constructor for class edu.cmu.tetrad.search.work_in_progress.DemixerMMLKun: Constructor for DemixerMMLKun.
density - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: The density values at the grid points.
density - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The density values at the specified grid points.
density(double) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Calculates the probability density function (PDF) of a given point.
DensityEst - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
DensityEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.DensityEst: Initializes a new instance of the DensityEst class.
DensityTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
DensityTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.DensityTrue: Initializes a new instance of the DensityTrue class.
depDecisions - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: A 3D array representing the decisions or classifications derived from dependent facts.
dependenceFactMsg(Node, Node, Set, double) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: dependenceFactMsg.
dependent - Enum constant in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference.OP: The operation is dependent.
DEPENDENT - Enum constant in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCCausalInference.OP: The operation is dependent.
DependentWildBootstrapUsage - Class in edu.cmu.tetrad.search.harness: Hand-run harness comparing the i.i.d.
depFacts - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: A list of dependent (Type II) facts.
depPvals - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: A two-dimensional array representing p-values associated with dependent (Type II) facts.
DeprecationUtils - Class in edu.cmu.tetrad.util: Utility for checking whether classes are marked @Deprecated.
depth - Variable in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: Constant depth=5
depth(int) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the maximum size of conditioning sets considered during the search.
depth(int) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the maximum size of conditioning sets considered during sepset enumeration in the collider-orientation step.
DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant DEPTH="depth"
describe(DataSet) - Static method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: The variable names of the data, for messages.
describeActive() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: One line describing the active gates, for a search's start-of-run log.
description() - Element in annotation interface edu.cmu.tetrad.annotation.Score: The description of the score.
description() - Element in annotation interface edu.cmu.tetrad.annotation.TestOfIndependence: Description of the test.
description() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: A one-line summary.
description() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: A one-line summary.
description() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Returns the value of the description record component.
description() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns the value of the description record component.
description() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Returns the value of the description record component.
description() - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Provides a textual description of the CandidateEdit operation.
deserializeArchivedVersions() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Deserializes examplars stored in archives in getArchiveDirectory().
deserializeCurrentDirectory() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Deserializes all files in the given directory, as a test to make sure they can all be deserialized.
Design - Section in class edu.cmu.tetrad.search.harness.MissingDataRecoveryHarness
Design - Section in class edu.cmu.tetrad.search.harness.MixedMissingDataHarness
DESIGNED_EXPERIMENT - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant DESIGNED_EXPERIMENT="Designed Experiment"
DesignedExperimentSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: Simulates data with the anatomy of a designed physical experiment (the archetype is the NASA Airfoil Self-Noise dataset), in contrast to an observational sample.
DesignedExperimentSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Constructs a DesignedExperimentSimulation.
det() - Method in class edu.cmu.tetrad.util.Matrix: det.
detachNodes(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns a copy of the graph whose nodes are fresh objects (see GraphUtils.copyNode(Node)), equal to the originals and in the same positions, with the same edges (including their properties, edge type probabilities, and display attributes), underlines, ambiguous triples, and graph attributes.
detail() - Method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Returns the value of the detail record component.
determinant(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: determinant.
determineClusters() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: From the saved rank-deficient sets of the lowest recorded rank, merges any pair of sets that overlap in at least min(|A|, |B|) − 1 covers and were found with the same non-sinks (Python determineClusters).
determined() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns the value of the determined record component.
determines(Node, Set) - Method in interface edu.cmu.tetrad.search.Fas.DeterminismGuard: Determines whether a node v is determined by a set of nodes S.
determines(List, Node) - Method in class edu.cmu.tetrad.search.IndTestIod: Determines whether the variables in z determine x.
determines(List, Node) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Determines whether a given list of nodes (z) allows the determination of another node (y) based on specific criteria within the scoring framework.
determines(List, Node) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Determines if variable y is determined by variables z.
determines(List, Node) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Determines whether the given set of nodes z implies or determines the state or value of the node y based on certain criteria.
determines(List, Node) - Method in interface edu.cmu.tetrad.search.is.IsScore: Determines whether the given set of nodes Z determines the target node y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.BDeuScore: Determines whether a set of nodes z determines a specific node y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.EbicScore: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns true iff the score determines the edge between the given nodes.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.FfMl: Determines whether a given node y is conditionally independent of other nodes z based on the local score calculation.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.GicScores: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Determines whether a specific node is conditionally independent of a target node given a set of parent nodes, based on the local score.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Determines whether the given variable y is conditionally independent of the set of variables z based on a computed score.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Determines whether the given node yNode is influenced by the list of nodes z based on a calculated local score.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Determines if a node is determined by a set of nodes.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.MvpScore: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Determines if an edge has an effect based on the given bump value.
determines(List, Node) - Method in interface edu.cmu.tetrad.search.score.Score: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns true is the variables in z determine the variable y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Determines whether variable x is independent of variable y given a list of conditioning nodes.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Determines whether a given list of nodes (z) determines a node (y).
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Determines whether the given set of nodes, z, has a deterministic relationship with the specified node, x.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Determines whether the z nodes determine the x node.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Determines if a given list of conditioning nodes (z) determines the value of a specific node (x).
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Determines whether variable x is independent of variable y given a list of conditioning variables z.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Determines if the given list of nodes (z) determines the specified node (y).
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Determines whether variable x is independent of variable y given a list of conditioning variables z.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Determines whether two nodes are independent given a set of conditioning nodes.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Determines if a variable xVar can be determined by a list of conditioning variables zList.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Determines the independence between a set of variables z and a variable x.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.MsepTest: Determines if a node is m-separated from a set of conditioning nodes.
determines(List, Node) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Determines the result of an independence test between a set of variables and a target variable.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Determines whether the given variables are conditionally independent.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Determines if a given set of nodes z determines the node y.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Determines if Node y is determined by the given list of Nodes z.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Determines if there exists a causal relationship between the nodes in z and node x.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Determines if a given Node is present in a List of Nodes.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Returns true iff the score determines the edge between x and y.
determines(List, Node) - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Determines the independence between a set of variables and a target variable.
determines(List, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Determines the independence between a list of conditioning variables (z) and a target variable (x).
determines(Set, Node) - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns true if y is determined by z, per the wrapped test.
determines(Set, Node) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns true if y is determined the variable in z.
determines(Set, Node) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Determines whether variable x is independent of a set of variables _z.
determines(Set, Node) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Determines whether variable x is independent of a set of variables _z.
determines(Set, Node) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Determines whether a given set of nodes, z, determines another node, y.
determines(Set, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Returns true if y is determined the variable in z.
determines(Set, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Returns true if y is determined the variable in z.
DETERMINISM_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant DETERMINISM_THRESHOLD="determinismThreshold"
determinismDetected(Set, Node) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: determinismDetected.
DeterminismRemovalSuggester - Class in edu.cmu.tetrad.data.audit: Turns the determinism findings of a DataAudit into a list of variables whose removal would resolve them, for a user to review.
DeterminismRemovalSuggester.Suggestion - Record Class in edu.cmu.tetrad.data.audit: One suggested removal.
DETERMINISTIC_RELATION - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A variable is constant, within tolerance, inside every multi-row cell of the joint values of a small set of other few-valued variables: on the analyzed rows, the variable is a function of that set (up to tolerance).
DeterministicClusters - Class in edu.cmu.tetrad.data.audit: Fits and reports the linear equations behind deterministic (and near-deterministic) relations among the continuous variables of a dataset.
DeterministicClusters.Constraint - Record Class in edu.cmu.tetrad.data.audit: One fitted constraint: determined = sum(coefficients * support) + intercept.
DeterministicClusters.Fitter - Class in edu.cmu.tetrad.data.audit: A reusable equation fitter for one dataset: the correlation matrix and its ridge-regularized inverse are computed once, and each equation costs a few small solves.
df - Variable in class edu.cmu.tetrad.data.missing.MissingDataAudit.LittleResult: The degrees of freedom: the sum over patterns of the number of observed variables, minus the number of variables.
df() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns the value of the df record component.
df() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns the value of the df record component.
DgBicTest - Class in edu.cmu.tetrad.algcomparison.independence: The SemBicTest class implements the IndependenceWrapper interface and represents a test for independence based on SEM BIC algorithm.
DgBicTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.DgBicTest: Constructs a new instance of the SEM BIC test.
diag() - Method in class edu.cmu.tetrad.util.Matrix: diag.
diag() - Method in class edu.cmu.tetrad.util.Vector: diag.
diag(Vector) - Static method in class edu.cmu.tetrad.util.Vector: Generates a diagonal matrix using the elements of the given vector.
DIAGONAL - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.CovarianceMode: Represents the diagonal covariance mode.
DIAGONAL - Enum constant in enum class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.CovarianceType: Represents the DIAGONAL covariance type for the Gaussian Mixture Model (GMM).
dieToss(int) - Static method in class edu.cmu.tetrad.util.StatUtils: dieToss.
diff(int[], int[]) - Static method in class edu.cmu.tetrad.util.RankTests: Computes the difference between two arrays, returning an array of elements that are present in the first array but not in the second.
differByPerturbation(int, int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: This method determines whether the levels for a given gene differ between two perturbations p0 and p1 (rows of the perturbation matrix).
difference(DataSet, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: Calculates the dth difference of the given data.
DIFFERENT_GRAPHS - Static variable in class edu.cmu.tetrad.util.Params: Constant DIFFERENT_GRAPHS="differentGraphs"
differExpressions(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: differExpressions.
Digraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua: Simple implementation of a directed Graph edges are just represented by float values (a zero == no edge) stored in a matrix.
Digraph(Digraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Copy constructor.
Digraph(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Creates a OldDigraph reading it from file fname.
Digraph(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Creates a OldDigraph with gName name, and n nodes.
DijkstraEdge(Node, int) - Constructor for class edu.cmu.tetrad.search.FciOrientDijkstra.DijkstraEdge: Creates a new DijkstraEdge.
DijkstraEdge(Node, int) - Constructor for class edu.cmu.tetrad.search.utils.R5R9Dijkstra.DijkstraEdge: Represents an edge connecting two nodes in Dijkstra's algorithm.
dimX - Variable in class edu.cmu.tetrad.search.CordEngine1.Result: Represents the dimensionality of the conditioning set X used in a conditional independence test.
dimX - Variable in class edu.cmu.tetrad.search.CordEngine2.Result: Represents the dimensionality of the conditioning variable X in a statistical test.
dimX - Variable in class edu.cmu.tetrad.search.CordEngine3.Result: Represents the dimensionality of the conditioning variable X in a statistical hypothesis test.
dimX - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: The dimensionality of the conditioning set X used in the statistical test.
dimX - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: Represents the dimensionality in the X direction of the problem or data being analyzed.
directedEdge(Node, Node) - Static method in class edu.cmu.tetrad.graph.Edges: Constructs a new directed edge from nodeA to nodeB (-->).
directedPaths(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds all directed paths from node1 to node2 with a maximum length.
DirectLingam - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: Direct LiNGAM.
DirectLingam - Class in edu.cmu.tetrad.search: Implements the DirectLiNGAM algorithm for learning a linear non-Gaussian acyclic model.
DirectLingam() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Constructor for DirectLingam.
DirectLingam(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Constructor for DirectLingam.
DirectLingam(DataSet, Score) - Constructor for class edu.cmu.tetrad.search.DirectLingam: Constructs a DirectLiNGAM search object from a data set and a score.
DirectLingam(List, Score) - Constructor for class edu.cmu.tetrad.search.DirectLingam: Constructs a DirectLiNGAM search object from several data sets over the same variables and a score.
directProduct(double[][], double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Computes the direct (Kronecker) outerProduct.
DirichletBayesIm - Class in edu.cmu.tetrad.bayes: Stores Dirichlet pseudocounts for the distributions of each variable conditional on particular combinations of its parent values and, together with Bayes Pm and Dag, provides methods to manipulate these tables.
DirichletBayesIm(DirichletBayesIm) - Constructor for class edu.cmu.tetrad.bayes.DirichletBayesIm: Copy constructor.
DirichletDataSetProbs - Class in edu.cmu.tetrad.bayes: Estimates probabilities directly from data on the fly using maximum likelihood method.
DirichletDataSetProbs(DataSet, double) - Constructor for class edu.cmu.tetrad.bayes.DirichletDataSetProbs: Creates a cell count table for the given data set.
DirichletEstimator - Class in edu.cmu.tetrad.bayes: Estimates a DirichletBayesIm from a DirichletBayesIm (the prior) and a data set.
DirichletEstimator() - Constructor for class edu.cmu.tetrad.bayes.DirichletEstimator: Creates a new instance of DirichletEstimator.
disabled() - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Creates a disabled tracer instance.
Disagreement(Node, Node, Node, boolean, CdnodBoss.ColliderOrientationStyle, Set, double) - Constructor for record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Creates an instance of a Disagreement record class.
DISALLOW - Enum constant in enum class edu.cmu.tetrad.search.Pc.AllowBidirected: Represents the option to disallow bidirected edges in a graph.
DISALLOW - Enum constant in enum class edu.cmu.tetrad.search.PcAR.AllowBidirected: Represents the option to disallow bidirected edges in a graph.
disallowComplement(int, int) - Method in class edu.cmu.tetrad.bayes.Proposition: Without changing the status of the specified category, disallows all other categories for the given variable.
disc - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: A 2D array representing the discrete codes for all variables in the simulation.
disc - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Discrete codes for all variables in a simulation result.
disc - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Discrete codes for all variables in a simulation result.
disc - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Discrete codes for all variables in a simulation result.
discover() - Method in interface edu.cmu.tetrad.search.blocks.BlockDiscoverer: Discovers and returns clusters or blocks of indices based on the underlying block discovery algorithm.
discover() - Method in class edu.cmu.tetrad.search.blocks.BpcBlockDiscoverer: Discovers and returns the specification of blocks (clusters of indices) within the dataset using the Bayesian Partitioning for Causal Discovery (BPC) algorithm.
discover() - Method in class edu.cmu.tetrad.search.blocks.FofcBlockDiscoverer: Discovers clusters or "blocks" of indices in the dataset using the FOFC algorithm.
discover() - Method in class edu.cmu.tetrad.search.blocks.FtfcBlockDiscoverer: Discovers clusters or "blocks" of indices in the dataset using the FOFC algorithm.
discover() - Method in class edu.cmu.tetrad.search.blocks.GffcBlockDiscoverer: Discovers clusters or "blocks" of indices in the dataset using the FOFC algorithm.
discover() - Method in class edu.cmu.tetrad.search.blocks.TscTestBlockDiscoverer: Discovers and identifies blocks of variables using the TSC (Testing Strong Causal structures) algorithm.
Discovery stage - Section in class edu.cmu.tetrad.search.Tsc
discrete - Variable in class edu.cmu.tetrad.sem.NodeAdequacySummary: Indicates whether the node is discrete or continuous.
discrete() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Returns the value of the discrete record component.
Discrete - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
Discrete - Enum constant in enum class edu.cmu.tetrad.data.DataType: Discrete.
Discrete - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.DataType: Constant for discrete data.
DISCRETE - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Discrete.
DISCRETE_INTERACTION_ORDER - Static variable in class edu.cmu.tetrad.util.Params: Constant DISCRETE_INTERACTION_ORDER="discreteInteractionOrder"
DISCRETE_LRT - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Discrete LRT.
DISCRETE_MANY_LEVELS - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A discrete variable has many observed levels.
DISCRETE_VARIATIONAL - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Discrete Variational.
DiscreteBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Discrete BIC test.
DiscreteBicScore - Class in edu.cmu.tetrad.search.score: Calculates the discrete BIC score.
DiscreteBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.DiscreteBicScore: Initializes a new instance of the DiscreteBicScore class.
DiscreteBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.DiscreteBicScore: Constructs the score using a dataset.
DiscreteBicScore(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.DiscreteBicScore: Constructs the score from a dataset with an explicit missing-data specification.
DiscreteBicScoreAdTree - Class in edu.cmu.tetrad.search.score: Calculates the discrete BIC score.
DiscreteBicScoreAdTree(DataSet) - Constructor for class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Constructs the score using a dataset.
discreteChild - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: true if the child variable is discrete.
discreteChild - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: true if the variable is discrete.
discreteChild - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: true if the child variable is discrete.
discreteChild - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: true if the child variable is discrete.
discreteChild - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Indicates whether the node is a discrete child.
discreteChild - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Whether the node represents a discrete variable.
discreteChild - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Whether the node represents a discrete variable.
discreteChild - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Whether the node represents a discrete variable.
DiscreteDiscretizationSpec - Class in edu.cmu.tetrad.data: Specifies how a column (continuous or discrete) should be discretized.
DiscreteDiscretizationSpec(int[], List) - Constructor for class edu.cmu.tetrad.data.DiscreteDiscretizationSpec: Constructor for DiscreteDiscretizationSpec.
DiscreteIndependenceUtils - Class in edu.cmu.tetrad.search.test: Utility methods for discrete conditional independence testing.
discreteIndicators() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the value of the discreteIndicators record component.
DiscreteScore - Interface in edu.cmu.tetrad.search.score: Gives an interface that can be used by various discrete scores.
discreteSerializableInstance() - Static method in class edu.cmu.tetrad.data.DataUtils: A discrete data set used to construct some other serializable instances.
DiscreteVariable - Class in edu.cmu.tetrad.data: Represents a discrete variable as a range of integer-valued categories 0, 1, ..., m - 1, where m is the number of categories for the variable.
DiscreteVariable(DiscreteVariable) - Constructor for class edu.cmu.tetrad.data.DiscreteVariable: Copy constructor.
DiscreteVariable(String) - Constructor for class edu.cmu.tetrad.data.DiscreteVariable: Builds a discrete variable with the given name and an empty list of categories.
DiscreteVariable(String, int) - Constructor for class edu.cmu.tetrad.data.DiscreteVariable: Builds a qualitative variable with the given name and number of categories.
DiscreteVariable(String, List) - Constructor for class edu.cmu.tetrad.data.DiscreteVariable: Builds a qualitative variable with the given name and array of possible categories.
DiscreteVariableType - Class in edu.cmu.tetrad.data: Type-safe enum of discrete variable types.
DiscretizationSpec - Interface in edu.cmu.tetrad.data: A continuous or discrete discretization spec (see).
discretize() - Method in class edu.cmu.tetrad.data.Discretizer: discretize.
discretize(double[], double[], String, List) - Static method in class edu.cmu.tetrad.data.Discretizer: Discretizes the continuous data in the given column using the specified cutoffs and category names.
discretize(DataSet, int, boolean) - Static method in class edu.cmu.tetrad.data.DataTransforms: discretize.
DISCRETIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant DISCRETIZE="discretize"
discretizeFor(Node, Node, double) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Discretizes the given value of a continuous parent node for a specific discrete child node into a bin index based on predefined cutpoints.
Discretizer - Class in edu.cmu.tetrad.data: Discretizes individual columns of discrete or continuous data.
Discretizer(DataSet) - Constructor for class edu.cmu.tetrad.data.Discretizer: Constructs a new discretizer that discretizes every variable as binary, using evenly distributed values.
Discretizer(DataSet, Map) - Constructor for class edu.cmu.tetrad.data.Discretizer: Constructor for Discretizer.
Discretizer.Discretization - Class in edu.cmu.tetrad.data: A discretization specification for a continuous variable.
DiscriminatingPath - Class in edu.cmu.tetrad.search.utils: Represents a discriminating path in a graph.
DiscriminatingPath(Node, Node, Node, Node, LinkedList, boolean) - Constructor for class edu.cmu.tetrad.search.utils.DiscriminatingPath: Constructs a DiscriminatingPath object with the specified parameters.
DiscriminatingTestOddsEstimator(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.PcAR.DiscriminatingTestOddsEstimator: Constructs the estimator.
DishModel - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Models the manner in which gene models are initialized differentially depending on the dishes that the cells are in.
DishModel(int, double) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Constructor for DishModel.
display() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: A readable dump of the current dictionary (Python misc.display).
display(SortedSet) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: display.
DisplayNameHandler - Class in edu.cmu.tetrad.study.gene.tetrad.gene.graph: Translates display names of lagged variables (e.g.
displayParents() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Displays the parents.
dissimilarity(Vector, Vector) - Method in interface edu.cmu.tetrad.cluster.metrics.Dissimilarity: dissimilarity.
dissimilarity(Vector, Vector) - Method in class edu.cmu.tetrad.cluster.metrics.SquaredErrorLoss: Calculates the dissimilarity between two vectors using the Euclidean dissimilarity metric.
Dissimilarity - Interface in edu.cmu.tetrad.cluster.metrics: Interface for a dissilimarity metric.
DIST - Enum constant in enum class edu.cmu.tetrad.sem.ParamType: Represents a free parameter type for structural equation modeling (SEM) models.
distance(LinkedList, double) - Method in class edu.cmu.tetrad.search.Ida: Returns the distance between the effects and the true effect.
distance(LinkedList, double) - Method in class edu.cmu.tetrad.search.PdagPagIda: Computes the minimum distance between a true causal effect and a set of estimated effects.
DistanceCorrTest() - Constructor for class edu.cmu.tetrad.search.test.IndTestGin.DistanceCorrTest: Constructs an instance of the DistanceCorrTest.
distances(Graph, Graph) - Method in class edu.cmu.tetrad.simulation.Gdistance: distances.
distances(FciOrientDijkstra.Graph, Node, boolean, Map) - Static method in class edu.cmu.tetrad.search.FciOrientDijkstra: Finds shortest distances from a start node to all other nodes in a graph.
distances(FciOrientDijkstra.Graph, Node, Node, Map, boolean) - Static method in class edu.cmu.tetrad.search.FciOrientDijkstra: Finds shortest distances from a x node to all other nodes in a graph.
distances(R5R9Dijkstra.Graph, boolean, Node, Node, boolean) - Static method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra: Finds shortest distances from a x node to all other nodes in a dijkstraGraph, subject to the following constraints.
distinct(Node...) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines whether three Node objects are distinct.
DISTORT_POST_NONLINEAR - Static variable in class edu.cmu.tetrad.util.Params: Constant DISTORT_POST_NONLINEAR="distortPostNonlinear"
DISTORT_PRE_NOISE - Static variable in class edu.cmu.tetrad.util.Params: /** Constant AM_DISTORT_PRE_ERROR="amDistortPreError"
Distribution - Interface in edu.cmu.tetrad.util.dist: Interface for a statistical distribution from which random values can be drawn.
DistributionSampler - Class in edu.cmu.tetrad.sem: The Sampler class provides functionality for sampling values from a specified probability distribution.
DistributionSampler(RealDistribution) - Constructor for class edu.cmu.tetrad.sem.DistributionSampler: Constructs a Sampler instance with the specified probability distribution.
district(Node) - Method in class edu.cmu.tetrad.graph.Paths: Retrieves the set of nodes that belong to the same district as the given node.
district(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the district of a given node in a graph.
Dm - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Algorithm-comparison wrapper for the Murray-Watters/Glymour style Detect-Mimic search, DM.
Dm - Class in edu.cmu.tetrad.search.mimic: Implements a Murray-Watters/Glymour style Detect-Mimic search ("DM").
Dm() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Constructs the algorithm with a default Fisher Z test.
Dm(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Constructs the algorithm with the supplied independence wrapper.
Dm(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.mimic.Dm: Constructs a new DM search.
DmFcit - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: DM-FCIT, removed in Tetrad 8.0.0.
DmFcit() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: No-arg constructor.
DmPc - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: DM-PC, removed in Tetrad 8.0.0.
DmPc() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: No-arg constructor.
DmPcHybrid - Class in edu.cmu.tetrad.search.mimic: A hybrid detect-mimic heuristic.
DmPcHybrid(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.mimic.DmPcHybrid: Constructs the search from an independence test.
DmPcRobust - Class in edu.cmu.tetrad.search.mimic: A more robust detect-mimic heuristic for MIMIC-like structures.
DmPcRobust(IndependenceTest, Knowledge) - Constructor for class edu.cmu.tetrad.search.mimic.DmPcRobust: Constructs a new instance of the DmPcRobust class using the specified independence test and prior knowledge.
DmRunner - Class in edu.cmu.tetrad.search.mimic: Benchmark runner for the DM algorithm.
DmRunner() - Constructor for class edu.cmu.tetrad.search.mimic.DmRunner: Constructs a new instance of the DmRunner.
DMSearch - Class in edu.cmu.tetrad.search.work_in_progress: Implements the DM search.
DMSearch() - Constructor for class edu.cmu.tetrad.search.work_in_progress.DMSearch: Constructor for DMSearch.
DMSearch.LatentStructure - Class in edu.cmu.tetrad.search.work_in_progress: Structure to hold latent structure.
DO_COLLIDER_ORIENTATION - Static variable in class edu.cmu.tetrad.util.Params: Constant DO_COLLIDER_ORIENTATION="doColliderOrientation"
DO_HIGHER_RANK_EXPANSION - Static variable in class edu.cmu.tetrad.util.Params: Constant DO_HIGHER_RANK_EXPANSION="doHigherRankExpansion"
DO_LEGALITY_GATING - Static variable in class edu.cmu.tetrad.util.Params: Constant DO_LEGALITY_GATING="doLegalityGating"
DO_ONE_EQUATION_ONLY - Static variable in class edu.cmu.tetrad.util.Params: Constant DO_ONE_EQUATION_ONLY="doOneEquationOnly"
DO_POSSIBLE_DSEP - Static variable in class edu.cmu.tetrad.util.Params: Constant DO_POSSIBLE_DSEP="doPossibleDsep"
doDiscriminatingPathOrientation(DiscriminatingPath, int, int, Graph, Set) - Method in interface edu.cmu.tetrad.search.utils.R0R4Strategy: Does a discriminating path orientation based on an examination of the data.
doDiscriminatingPathOrientation(DiscriminatingPath, int, int, Graph, Set) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Does a discriminating path orientation.
dof - Variable in class edu.cmu.tetrad.bayes.BayesProperties.LikelihoodRet: Represents the degrees of freedom (dof) used in statistical tests or model evaluations.
dof() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Returns the value of the dof record component.
dof(Graph) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: The number of penalized parameters of the graph: its number of edges.
doFdrLoop(IGraphSearch, boolean, double, double, boolean) - Static method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Executes a loop for controlling the false discovery rate (FDR) as part of a graph search process.
doLayout() - Method in class edu.cmu.tetrad.graph.LayoutUtil.FruchtermanReingoldLayout: Lays out the graph.
doLayout() - Method in class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Lays out the graph.
DOMAIN - Enum constant in enum class edu.cmu.tetrad.graph.NodeVariableType: The node variable type not intervened on.
DOMAIN_COLUMN - Static variable in class edu.cmu.tetrad.util.Params: Constant DOMAIN_COLUMN="domainColumn"
dot(Vector) - Method in class edu.cmu.tetrad.util.Vector: dot.
dotProduct(Vector) - Method in class edu.cmu.tetrad.util.Vector: dotProduct.
DOUBLE_REMOVE - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Remove two edges.
DoubleDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of double data.
DoubleDataBox(double[][]) - Constructor for class edu.cmu.tetrad.data.DoubleDataBox: Constructs a new data box using the given 2D double data array as data.
DoubleDataBox(int, int) - Constructor for class edu.cmu.tetrad.data.DoubleDataBox: Constructs an 2D double array consisting entirely of missing values (Double.NaN).
draw(SemIm, List) - Static method in class edu.cmu.tetrad.sem.SemStartValues: Draws scale-aware random starting values for the given free parameters.
drawBlockResamples(DataSet, int[], int, double, boolean, long) - Static method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Draws seed-controlled block resamples: each resample is the concatenation of the rows of percent * G groups drawn from the G distinct groups, with or without replacement, where a group is the set of rows sharing a label in groups.
drawResamples(DataSet, int, double, boolean, long) - Static method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Draws seed-controlled resamples of the given dataset: row-index samples of size percent * n, with or without replacement.
drawsPerConfig - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Number of draws of the child per configuration, for each of the two conditions.
driver() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Returns the value of the driver record component.
DropoutZeroDeletionStudy - Class in edu.cmu.tetrad.study.missingness: Replication harness for the zero-deletion ("testwise deletion of zeros") proposal of Dai, Ng, Luo, Spirtes, Stojanov and Zhang, "Gene Regulatory Network Inference in the Presence of Dropouts: A Causal View" (ICLR 2024).
dropped() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns the value of the dropped record component.
droppedNames(MissingnessThreshold.Effect) - Static method in class edu.cmu.tetrad.data.missing.MissingnessThreshold: The names a threshold would drop, in the order MissingnessThreshold.effectOf(DataSet, MissingDataAudit, double) reports them.
dsep(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Returns D-SEP(x, y) for a maximal ancestral graph G (or inducing path graph G, as in Causation, Prediction and Search).
dsep(Node, Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns D-SEP(x, y) for a MAG G.
dsep0(Node, Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns D-SEP(x, y) for a MAG G (or inducing path graph G, as in Causation, Prediction and Search).
dsep2(Node, Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns D-SEP(x, y) for a MAG G.
dsepReachability(Node, Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns D-SEP(x, y) for a MAG G.
DUPLICATE_COLUMNS - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: Two columns are exact affine functions of one another on the rows where both are observed (identical columns, complementary indicators such as y = 1 - x, or rescaled copies).

E

E - Static variable in class edu.cmu.tetrad.calculator.expression.ConstantExpression: Constant expression for e.
E - Static variable in class edu.cmu.tetrad.util.TMath: The mathematical constant e, which is the base of the natural logarithm.
E(double[], double[], double[]) - Static method in class edu.cmu.tetrad.search.Fask: E(xy | z>0).
E(double[], double[], double[], double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: E.
earlierMissingGivenLater() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the earlierMissingGivenLater record component.
earlyStopping - Variable in class edu.cmu.tetrad.search.CordEngine1: A flag to indicate whether the early stopping mechanism is enabled during iterative training processes.
earlyStopping - Variable in class edu.cmu.tetrad.search.CordEngine2: Indicates whether early stopping is enabled for iterative model training processes.
earlyStopping - Variable in class edu.cmu.tetrad.search.CordEngine3: Indicates whether the early stopping criterion is enabled for the training process.
earlyStopping - Variable in class edu.cmu.tetrad.search.CordEngine4: Indicates whether early stopping is enabled during model training.
earlyStopping - Variable in class edu.cmu.tetrad.search.CordEngine5: A flag indicating whether early stopping is enabled.
EB - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The EB rule.
EBIC_GAMMA - Static variable in class edu.cmu.tetrad.util.Params: Constant EBIC_GAMMA="ebicGamma"
EbicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for linear, Gaussian Extended BIC score (Chen and Chen).
EbicScore - Class in edu.cmu.tetrad.search.score: Implements the extended BIC (EBIC) score.
EbicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.EbicScore: Initializes a new instance of the EbicScore class.
EbicScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.score.EbicScore: Constructs the score using a covariance matrix.
EbicScore(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.EbicScore: Constructs the score using a covariance matrix.
edf() - Method in record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Returns the value of the edf record component.
edf() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Returns the value of the edf record component.
Edge - Class in edu.cmu.tetrad.graph: Represents an edge node1 *-# node2 where * and # are endpoints of type Endpoint--that is, Endpoint.TAIL, Endpoint.ARROW, or Endpoint.CIRCLE.
Edge(Edge) - Constructor for class edu.cmu.tetrad.graph.Edge: Constructor for Edge.
Edge(Node, Node, Endpoint, Endpoint) - Constructor for class edu.cmu.tetrad.graph.Edge: Constructs a new edge by specifying the nodes it connects and the endpoint types.
Edge(Node, Node, Endpoint, Endpoint, boolean) - Constructor for class edu.cmu.tetrad.graph.Edge: Constructs an Edge by specifying two nodes, their corresponding endpoints, and whether to flip the direction of the edge if it is pointing backwards.
Edge.Property - Enum Class in edu.cmu.tetrad.graph: A property of an edge.
Edge cases - Section in class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer
edgeCount_contemporaneous_exactlyThree() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: With 3 lag-slices and shift=0, one addUndirectedEdge call should produce exactly 3 edges total (one per slice).
edgeCount_crossLag_exactlyTwo() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: With 3 lags, a cross-lag edge (shift=-1) starting at lag 1 can mirror to lag 2 only → 2 edges total.
edgeDrawsPerConfig - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Number of draws of the child per parent configuration when computing edge strength (NNEstimator.computeEdgeStrength(String, String, int)).
EdgeListGraph - Class in edu.cmu.tetrad.graph: Stores a graph a list of lists of edges adjacent to each node in the graph, with an additional list storing all of the edges in the graph.
EdgeListGraph() - Constructor for class edu.cmu.tetrad.graph.EdgeListGraph: Constructs a new (empty) EdgeListGraph.
EdgeListGraph(EdgeListGraph) - Constructor for class edu.cmu.tetrad.graph.EdgeListGraph: Constructor for EdgeListGraph.
EdgeListGraph(Graph) - Constructor for class edu.cmu.tetrad.graph.EdgeListGraph: Constructs a EdgeListGraph using the nodes and edges of the given graph.
EdgeListGraph(Graph, boolean) - Constructor for class edu.cmu.tetrad.graph.EdgeListGraph: Constructs a copy of the given graph, optionally with its own node objects.
EdgeListGraph(List) - Constructor for class edu.cmu.tetrad.graph.EdgeListGraph: Constructs a new graph, with no edges, using the given variable names.
edgeMisclassificationCounts(Graph, Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Computes the misclassification counts for each edge in the given graphs.
edgeMisclassifications(double[][], NumberFormat) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Generates a textual representation of the edge misclassifications based on the provided counts and number format.
edgeMisclassifications(int[][]) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the misclassifications of edges based on the given counts.
edgeMisclassifications(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.MisclassificationUtils: edgeMisclassifications.
edgeNullRefits - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Number of refits used for the refit-noise null: the child's mechanism is retrained with the same parents under a new seed, and the same conditional MMD² is computed between the original and the refit.
edgeOrientedMsg(String, Edge) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: edgeOrientedMsg.
EdgePriorFacade - Class in edu.cmu.tetrad.search.harness: A small, Python-friendly facade over the edge-prior machinery, intended to be driven from JPype.
EdgePriorFacadeCheck - Class in edu.cmu.tetrad.search.harness: The EdgePriorFacadeCheck class is a testing utility designed to evaluate the behavior of the EdgePriorFacade and its associated functionalities.
EdgePriorFacadeCheck() - Constructor for class edu.cmu.tetrad.search.harness.EdgePriorFacadeCheck: Constructs an instance of the EdgePriorFacadeCheck class.
EdgePriors - Class in edu.cmu.tetrad.search.utils: An immutable, symmetric, name-keyed store of per-edge prior quantities, for use with EdgePriorScore and EdgePriorTest.
EdgePriors.Origin - Enum Class in edu.cmu.tetrad.search.utils: Where a store's numbers came from.
EdgePriors.Semantics - Enum Class in edu.cmu.tetrad.search.utils: What the stored numbers mean.
EdgePriorsAnchorCheck - Class in edu.cmu.tetrad.search.harness: The `EdgePriorsAnchorCheck` class provides a test suite for verifying the functional correctness of the anchoring behavior of the `EdgePriors` class.
EdgePriorsAnchorCheck() - Constructor for class edu.cmu.tetrad.search.harness.EdgePriorsAnchorCheck: Default constructor for the EdgePriorsAnchorCheck class.
EdgePriorsCheck - Class in edu.cmu.tetrad.search.harness: The EdgePriorsCheck class is a testing suite for verifying the behavior and correctness of score adjustments and independence tests with edge priors.
EdgePriorsCheck() - Constructor for class edu.cmu.tetrad.search.harness.EdgePriorsCheck: Default constructor for the EdgePriorsCheck class.
EdgePriorScore - Class in edu.cmu.tetrad.search.score: Wraps a Score and adds an independent Bernoulli prior on each adjacency.
EdgePriorScore(Score, EdgePriors) - Constructor for class edu.cmu.tetrad.search.score.EdgePriorScore: Constructs a prior-adjusted score.
EdgePriorScoreFacade - Class in edu.cmu.tetrad.search.harness: A small, Python-friendly facade over the edge-prior machinery for score-based search, intended to be driven from JPype.
EdgePriorsUsage - Class in edu.cmu.tetrad.search.harness: The EdgePriorsUsage class provides a sequence of computational steps for exploring the effects of priors on edge decisions in genomic data analysis.
EdgePriorsUsage() - Constructor for class edu.cmu.tetrad.search.harness.EdgePriorsUsage: Default constructor for the EdgePriorsUsage class.
EdgePriorTest - Class in edu.cmu.tetrad.search.test: Wraps an IndependenceTest and gives each pair of variables its own significance level, alpha_ij = w_ij * alpha, by testing the weighted p-value p_ij / w_ij against a common alpha.
EdgePriorTest(IndependenceTest, EdgePriors, double) - Constructor for class edu.cmu.tetrad.search.test.EdgePriorTest: Constructs a prior-adjusted test.
edgeRepeats - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Number of independent repeats of the edge-strength computation, each with its own draw of parent configurations, noise, and randomized parent values.
EdgeReplicationPolicy - Interface in edu.cmu.tetrad.graph: Strategy interface: given a seed edge E on graph G, return the full set of mirrored edges that should be kept in sync with E.
Edges - Class in edu.cmu.tetrad.graph: This factory class produces edges of the types commonly used for Tetrad-style graphs.
edgesAfter() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the edgesAfter record component.
EdgeShading - Class in edu.cmu.tetrad.graph: A small color-vision-safe palette for shading graph edges by the sign and strength of an estimated effect.
EdgeShading.Hue - Enum Class in edu.cmu.tetrad.graph: The hue families.
EdgeStat(double, double, double, int, int, int, FaskPool.Origin) - Constructor for record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Creates an instance of a EdgeStat record class.
EdgesToString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Returns a string representation of the set of edges in this graph
EdgesToString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns a string representation of the set of edges in this graph
EdgeStrengthResult - Class in edu.cmu.tetrad.sem: Result of a single edge-strength computation performed by NNEstimator.computeEdgeStrength(String, String, int).
EdgeTypeProbability - Class in edu.cmu.tetrad.graph: Apr 13, 2017 3:56:46 PM
EdgeTypeProbability() - Constructor for class edu.cmu.tetrad.graph.EdgeTypeProbability: Constructor for EdgeTypeProbability.
EdgeTypeProbability(EdgeTypeProbability.EdgeType, double) - Constructor for class edu.cmu.tetrad.graph.EdgeTypeProbability: Constructor for EdgeTypeProbability.
EdgeTypeProbability(EdgeTypeProbability.EdgeType, List, double) - Constructor for class edu.cmu.tetrad.graph.EdgeTypeProbability: Constructor for EdgeTypeProbability.
EdgeTypeProbability.EdgeType - Enum Class in edu.cmu.tetrad.graph: An enumeration of the different types of edges.
edgeworthP(double, double[], boolean) - Static method in class edu.cmu.tetrad.search.test.QuadraticFormPValues: Edgeworth / Cornish–Fisher tail.
edit() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the edit record component.
editApplied(VertexRepairSearch.CandidateEdit, Graph) - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.RepairListener: Called each time a candidate edit is successfully applied to the graph.
edu.cmu.tetrad.algcomparison - package edu.cmu.tetrad.algcomparison
edu.cmu.tetrad.algcomparison.algorithm - package edu.cmu.tetrad.algcomparison.algorithm
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag - package edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.algcomparison.algorithm.multi - package edu.cmu.tetrad.algcomparison.algorithm.multi
edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag - package edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pag - package edu.cmu.tetrad.algcomparison.algorithm.oracle.pag
edu.cmu.tetrad.algcomparison.algorithm.other - package edu.cmu.tetrad.algcomparison.algorithm.other
edu.cmu.tetrad.algcomparison.algorithm.pairwise - package edu.cmu.tetrad.algcomparison.algorithm.pairwise
edu.cmu.tetrad.algcomparison.examples - package edu.cmu.tetrad.algcomparison.examples
edu.cmu.tetrad.algcomparison.graph - package edu.cmu.tetrad.algcomparison.graph
edu.cmu.tetrad.algcomparison.independence - package edu.cmu.tetrad.algcomparison.independence
edu.cmu.tetrad.algcomparison.score - package edu.cmu.tetrad.algcomparison.score
edu.cmu.tetrad.algcomparison.simulation - package edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.algcomparison.statistic - package edu.cmu.tetrad.algcomparison.statistic
edu.cmu.tetrad.algcomparison.statistic.utils - package edu.cmu.tetrad.algcomparison.statistic.utils
edu.cmu.tetrad.algcomparison.sweep - package edu.cmu.tetrad.algcomparison.sweep
edu.cmu.tetrad.algcomparison.utils - package edu.cmu.tetrad.algcomparison.utils
edu.cmu.tetrad.annotation - package edu.cmu.tetrad.annotation
edu.cmu.tetrad.bayes - package edu.cmu.tetrad.bayes
edu.cmu.tetrad.calculator - package edu.cmu.tetrad.calculator
edu.cmu.tetrad.calculator.expression - package edu.cmu.tetrad.calculator.expression
edu.cmu.tetrad.calculator.parser - package edu.cmu.tetrad.calculator.parser
edu.cmu.tetrad.calibration - package edu.cmu.tetrad.calibration
edu.cmu.tetrad.classify - package edu.cmu.tetrad.classify
edu.cmu.tetrad.cluster - package edu.cmu.tetrad.cluster
edu.cmu.tetrad.cluster.metrics - package edu.cmu.tetrad.cluster.metrics
edu.cmu.tetrad.data - package edu.cmu.tetrad.data
edu.cmu.tetrad.data.audit - package edu.cmu.tetrad.data.audit
edu.cmu.tetrad.data.missing - package edu.cmu.tetrad.data.missing
edu.cmu.tetrad.data.simulation - package edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.estimate.v1 - package edu.cmu.tetrad.estimate.v1
edu.cmu.tetrad.graph - package edu.cmu.tetrad.graph
edu.cmu.tetrad.hybridcg - package edu.cmu.tetrad.hybridcg
edu.cmu.tetrad.regression - package edu.cmu.tetrad.regression
edu.cmu.tetrad.regression.v1 - package edu.cmu.tetrad.regression.v1
edu.cmu.tetrad.search - package edu.cmu.tetrad.search: This package contains classes for causal graph search algorithms.
edu.cmu.tetrad.search.blocks - package edu.cmu.tetrad.search.blocks
edu.cmu.tetrad.search.cdnod_pag - package edu.cmu.tetrad.search.cdnod_pag
edu.cmu.tetrad.search.harness - package edu.cmu.tetrad.search.harness
edu.cmu.tetrad.search.harness.tsbi - package edu.cmu.tetrad.search.harness.tsbi
edu.cmu.tetrad.search.harness.tsc - package edu.cmu.tetrad.search.harness.tsc
edu.cmu.tetrad.search.is - package edu.cmu.tetrad.search.is
edu.cmu.tetrad.search.mimic - package edu.cmu.tetrad.search.mimic
edu.cmu.tetrad.search.ntad_test - package edu.cmu.tetrad.search.ntad_test
edu.cmu.tetrad.search.rlcd - package edu.cmu.tetrad.search.rlcd
edu.cmu.tetrad.search.score - package edu.cmu.tetrad.search.score: This package contains classes for scoring causal graph models.
edu.cmu.tetrad.search.test - package edu.cmu.tetrad.search.test: This package contains classes for testing causal graph search algorithms.
edu.cmu.tetrad.search.unmix - package edu.cmu.tetrad.search.unmix
edu.cmu.tetrad.search.utils - package edu.cmu.tetrad.search.utils: This package contains utility classes for causal graph search algorithms.
edu.cmu.tetrad.search.work_in_progress - package edu.cmu.tetrad.search.work_in_progress: A package for algorithms that are not ready for prime time.
edu.cmu.tetrad.sem - package edu.cmu.tetrad.sem
edu.cmu.tetrad.simulation - package edu.cmu.tetrad.simulation
edu.cmu.tetrad.stat - package edu.cmu.tetrad.stat
edu.cmu.tetrad.stat.correlation - package edu.cmu.tetrad.stat.correlation
edu.cmu.tetrad.study - package edu.cmu.tetrad.study
edu.cmu.tetrad.study.examples.conditions - package edu.cmu.tetrad.study.examples.conditions
edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu - package edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu
edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua - package edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua
edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker - package edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker
edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal - package edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal
edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin - package edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin
edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util - package edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util
edu.cmu.tetrad.study.gene.tetrad.gene.graph - package edu.cmu.tetrad.study.gene.tetrad.gene.graph: Contains an editable display graph for (small) lag graphs.
edu.cmu.tetrad.study.gene.tetrad.gene.history - package edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements a time-series simulation engine suitable for time-series gene expression studies.
edu.cmu.tetrad.study.gene.tetrad.gene.simexp - package edu.cmu.tetrad.study.gene.tetrad.gene.simexp: Implements a collection of manual simulation experiments.
edu.cmu.tetrad.study.gene.tetrad.gene.simulation - package edu.cmu.tetrad.study.gene.tetrad.gene.simulation: Contains classes for generating simulations of expression levels over a collection of genes.
edu.cmu.tetrad.study.gene.tetrad.gene.util - package edu.cmu.tetrad.study.gene.tetrad.gene.util: Contains some utility classes needed for the edu.cmu.tetrad.gene.* packages.
edu.cmu.tetrad.study.gene.tetradapp.model - package edu.cmu.tetrad.study.gene.tetradapp.model
edu.cmu.tetrad.study.missingness - package edu.cmu.tetrad.study.missingness
edu.cmu.tetrad.study.performance - package edu.cmu.tetrad.study.performance
edu.cmu.tetrad.test - package edu.cmu.tetrad.test
edu.cmu.tetrad.util - package edu.cmu.tetrad.util
edu.cmu.tetrad.util.dist - package edu.cmu.tetrad.util.dist
edu.pitt.csb.mgm - package edu.pitt.csb.mgm
edu.pitt.csb.stability - package edu.pitt.csb.stability
edu.pitt.dbmi.algo.bayesian.constraint.inference - package edu.pitt.dbmi.algo.bayesian.constraint.inference
edu.pitt.dbmi.algo.bayesian.constraint.search - package edu.pitt.dbmi.algo.bayesian.constraint.search
edu.pitt.dbmi.algo.resampling - package edu.pitt.dbmi.algo.resampling
edu.pitt.isp.sverchkov.data - package edu.pitt.isp.sverchkov.data
effect() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns the value of the effect record component.
effect() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns the value of the effect record component.
Effect(double, List, int, int, int) - Constructor for record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Creates an instance of a Effect record class.
Effect(int, int, double) - Constructor for record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Canonical constructor, validating the indices and the weight.
EFFECTIVE_SAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant EFFECTIVE_SAMPLE_SIZE="effectiveSampleSize"
effectiveSampleSize(DataSet, int[], IndependenceFact) - Static method in class edu.cmu.tetrad.search.utils.PerFactEss: Computes the per-fact effective sample size for X _||_ Y | Z under the given block structure.
effectiveSampleSize(DataSet, int[], Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.PerFactEss: Computes the per-fact effective sample size for x _||_ y | z under the given block structure.
effectiveSampleSize(DataSet, MissingDataSpec) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: The effective sample size to use for penalized scores when the analysis runs on a covariance matrix estimated from incomplete data, per the spec's MissingDataSpec.EffectiveSampleSizeMode.
EffectiveSampleSizeSettable - Interface in edu.cmu.tetrad.util: Interface for classes that can set and retrieve the effective sample size.
effectOf(DataSet, MissingDataAudit, double) - Static method in class edu.cmu.tetrad.data.missing.MissingnessThreshold: The effect of a single threshold.
effects() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns the value of the effects record component.
Efficiency - Section in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty
effN - Variable in class edu.cmu.tetrad.util.RowCorrelationEffN.Result: The effective sample size, calculated as N / (1 + (N-1) * rho_hat).
Elapsed - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The elapsed time.
Elapsed - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant Elapsed
ElapsedCpuTime - Class in edu.cmu.tetrad.algcomparison.statistic: Records the elapsed time of the algorithm in seconds.
ElapsedCpuTime() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ElapsedCpuTime: Initializes a new instance of the ElapsedCpuTime class.
EM_COVARIANCE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Estimate the mean and covariance matrix once by expectation-maximization under a saturated Gaussian model (see EmCovarianceEstimator) and run the analysis on the estimated covariance matrix as if it were complete data.
EmBayesEstimator - Class in edu.cmu.tetrad.bayes: Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
EmBayesEstimator(BayesIm, DataSet) - Constructor for class edu.cmu.tetrad.bayes.EmBayesEstimator: Constructor for EmBayesEstimator.
EmBayesEstimator(BayesPm, DataSet) - Constructor for class edu.cmu.tetrad.bayes.EmBayesEstimator: Provides methods for estimating a Bayes IM from an existing BayesIM and a discrete dataset using EM (Expectation Maximization).
EmBayesProperties - Class in edu.cmu.tetrad.bayes: Calculates some scores for Bayes nets as a whole.
EmBayesProperties(DataSet, Graph) - Constructor for class edu.cmu.tetrad.bayes.EmBayesProperties: Constructor for EmBayesProperties.
EmBayesProperties.Estimator - Interface in edu.cmu.tetrad.bayes: Interface for an estimator.
embeddedData() - Method in record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Returns the value of the embeddedData record component.
EmbeddedData(DataSet, DataSet, Map) - Constructor for record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Creates an instance of a EmbeddedData record class.
embedding() - Method in record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Returns the value of the embedding record component.
Embedding - Class in edu.cmu.tetrad.search.utils: The Embedding class provides utilities for transforming datasets into embedded representations through basis expansions and one-hot encoding.
Embedding.EmbeddedData - Record Class in edu.cmu.tetrad.search.utils: Represents the embedded data result, holding the original dataset, the transformed embedded dataset, and a mapping between the indices of original variables and their corresponding transformed variables.
embeddingBlockSizes() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: The number of embedded components per variable, in variable order: the basis-function count for a continuous variable, the number of categories minus one for a discrete one (after any adaptive pruning).
emCovariance() - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec for EM covariance estimation with default EM settings.
EmCovarianceEstimator - Class in edu.cmu.tetrad.data: Estimates the mean and covariance matrix of a continuous dataset with missing values by maximum likelihood, under a saturated multivariate Gaussian model, using the EM algorithm.
EmCovarianceEstimator(DataSet) - Constructor for class edu.cmu.tetrad.data.EmCovarianceEstimator: Constructs an estimator for the given continuous dataset, whose missing values are represented as Double.NaN.
EmCovarianceEstimator.Result - Class in edu.cmu.tetrad.data: The result of an EM run: the estimated mean and covariance, together with convergence information.
emCovarianceIfRequested(DataModel, Parameters, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: Implements the "em" missing-data policy at the wrapper level for covariance-consuming scores that have an ICovarianceMatrix constructor but no native MissingDataSpec handling (EBIC, GIC, Poisson prior, Zhang-Shen bound, and the like).
EmCovarianceRankTest - Class in edu.cmu.tetrad.search.rlcd: A rank test over a covariance matrix estimated from incomplete data as a whole -- by EM under the missing-at-random assumption (EmCovarianceEstimator), or by pairwise deletion -- with a sample size chosen per query from the pairwise-complete counts of the columns that query mentions.
EmCovarianceRankTest(ICovarianceMatrix, int[][], int) - Constructor for class edu.cmu.tetrad.search.rlcd.EmCovarianceRankTest: Constructs the test over an estimated covariance matrix with the mean-pairwise sample-size rule.
EmCovarianceRankTest(ICovarianceMatrix, int[][], int, MissingDataSpec.EffectiveSampleSizeMode, double) - Constructor for class edu.cmu.tetrad.search.rlcd.EmCovarianceRankTest: Constructs the test over an estimated covariance matrix.
emEstimate(double[][], int, double, double) - Static method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Estimates the mean and covariance of a multivariate normal by EM from data with missing values, coded as Double.NaN.
emMaxIters - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Maximum number of iterations allowed for the Expectation-Maximization (EM) algorithm.
emMaxIters - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Maximum number of iterations allowed for the Expectation-Maximization (EM) algorithm during the computation process.
EmpiricalCdf - Class in edu.cmu.tetrad.sem: Only the cumulativeProbability, density, setShift methods are implemented.
EmpiricalCdf(List) - Constructor for class edu.cmu.tetrad.sem.EmpiricalCdf: Constructor for EmpiricalCdf.
EmpiricalDistributionForExpression - Class in edu.cmu.tetrad.sem: Returns a sample empirical distribution for a particular expression.
EmpiricalDistributionForExpression(GeneralizedSemPm, Node, Context) - Constructor for class edu.cmu.tetrad.sem.EmpiricalDistributionForExpression: Constructor for EmpiricalDistributionForExpression.
empiricalHSIC(Matrix, Matrix, int) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y
empiricalHSICincompleteCholesky(Matrix, Matrix, int) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y using incomplete Cholesky decomposition to approximate Gram matrices
empiricalHSICincompleteCholesky(Matrix, Matrix, Matrix, int) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y given Z using incomplete Cholesky decomposition to approximate Gram matrices
Empirical performance - Section in class edu.cmu.tetrad.search.Tsc
EMPTY - Enum constant in enum class edu.cmu.tetrad.search.harness.VertexRepairSimulation.StartingGraph: Start from an empty graph (no edges).
EmptyConfig(boolean) - Constructor for class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: Constructor for EmptyConfig.
emptyCount() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns the value of the emptyCount record component.
emptyGraph(int) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Creates an empty graph with the specified number of nodes.
emTol - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Convergence tolerance for the Expectation-Maximization (EM) algorithm.
EmUnmix - Class in edu.cmu.tetrad.search.unmix: The EmUnmix class provides functionality for applying the Expectation-Maximization (EM) algorithm on residual signatures derived from a dataset to fit Gaussian mixtures.
EmUnmix() - Constructor for class edu.cmu.tetrad.search.unmix.EmUnmix: Default constructor for EmUnmix.
EmUnmix.Config - Class in edu.cmu.tetrad.search.unmix: Configuration class for the EmUnmix algorithm, providing parameters and settings to control the behavior of the unmixing process.
EmUtils - Class in edu.cmu.tetrad.search.unmix: A utility class for operations commonly used in the Expectation-Maximization (EM) algorithm.
enabled(PrintStream) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Creates an enabled tracer instance.
encode(Map) - Static method in record class edu.cmu.tetrad.data.LogTransformSpec: Encodes a map of specs as a single string, for storage in a Parameters object and hence in a saved session.
endConflictCollection() - Static method in class edu.cmu.tetrad.search.RecursiveBlocking: Ends collection on this thread and returns the marks recorded since RecursiveBlocking.beginConflictCollection().
Endpoint - Enum Class in edu.cmu.tetrad.graph: A enumeration of the endpoint types that are permitted in Tetrad-style graphs: null (-), arrow (->), circle (-o), start (-*), and null (no endpoint).
endpointMisclassification(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.MisclassificationUtils: endpointMisclassification.
endpointMisclassification(List, Graph, Graph) - Static method in class edu.cmu.tetrad.study.performance.PerformanceTests: endpointMisclassification.
ensureColumns(int, List) - Method in class edu.cmu.tetrad.data.BoxDataSet: Ensures that the dataset has at least columns columns.
ensureColumns(int, List) - Method in interface edu.cmu.tetrad.data.DataSet: Ensures that the dataset has at least columns columns.
ensureColumns(int, List) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Ensures that the dataset has at least columns columns.
ensureLoggingForVerbose(boolean) - Method in class edu.cmu.tetrad.util.TetradLogger: Ensures logging is on when a caller has asked for verbose output, so that a verbose search cannot be silently swallowed by a logger that some other part of the process turned off.
ensureRows(int) - Method in class edu.cmu.tetrad.data.BoxDataSet: Ensures that the dataset has at least rows rows.
ensureRows(int) - Method in interface edu.cmu.tetrad.data.DataSet: Ensures that the dataset has at least rows rows.
ensureRows(int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Ensures that the dataset has at least rows rows.
ensureSymmetry() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if symmetry holds--i.e., X _||_ Y | Z ==> Y _||_ X | Z
ensureTiers(int) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Ensures that tiers 0..tier exist.
ensureTriviality() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Sets whether triviality as assumed.
entropy - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The entropy.
entropy(int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: This method computes the entropy of a binary signal stored in an int array.
entropy(int[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: entropy.
entropy(int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: This method computes the entropy of a binary signal stored in an int array.
entropy(int, double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the entropy of a distribution based on the provided data values and number of bins.
entropy(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: This method implements the same definition of entropy as above but this specialized version is intended to be used by the mutualInformation method (viz).
entropy(int, int) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: entropy.
entropy(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: This method implements the same definition of entropy as above but this specialized version is intended to be used by the mutualInformation method (viz).
EntropyStats(double, double, double) - Constructor for class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.EntropyStats: Constructs an instance of EntropyStats with the specified mean entropy and confidence fractions.
env() - Method in class edu.cmu.tetrad.search.cdnod_pag.ChangeOracle: Retrieves the first context (environment) from the list of contexts.
EOF - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: End of file.
epochs - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Training passes over the data per node.
epochs - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Specifies the number of training epochs to be used in simulations or iterative optimization tasks.
epochs - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Specifies the number of training iterations or epochs, which represent one complete pass through the entire training dataset during model training.
epochs - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Number of training epochs for the simulator.
epochs - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Number of training epochs for the simulator.
EQ - Enum constant in enum class edu.cmu.tetrad.search.test.IndTestBlocksLemma10.EqualityMode: Deprecated.

Represents the equality mode for the Lemma-10 criterion, indicating exact equality.
EQ - Enum constant in enum class edu.cmu.tetrad.sem.ParamComparison: An enum representing the "EQ" comparison type for a parameter in SEM estimation.
EQ - Enum constant in enum class edu.cmu.tetrad.sem.ParamConstraintType: The EQ represents a parameter constraint type EQ (equal).
EQUAL_FREQUENCY - Enum constant in enum class edu.cmu.tetrad.hybridcg.HybridCgCutpoints.Method: Divides the data into bins such that each bin contains an approximately equal number of data points.
EQUAL_INTERVALS - Enum constant in enum class edu.cmu.tetrad.hybridcg.HybridCgCutpoints.Method: Divides the range of data into bins of equal size.
equalCounts(Node, int) - Method in class edu.cmu.tetrad.data.Discretizer: Sets the given node to discretized using evenly distributed values using the given number of categories.
equalIntervals(Node, int) - Method in class edu.cmu.tetrad.data.Discretizer: Sets the given node to discretized using evenly spaced intervals using the given number of categories.
equals(double[][], double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Tests two matrices for equality.
equals(double[][], double[][], double) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Tests to see whether two matrices are equal within the given tolerance.
equals(double[], double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Tests two vectors for equality.
equals(double[], double[], double) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Tests to see whether two vectors are equal within the given tolerance.
equals(ScoredGraph) - Method in class edu.cmu.tetrad.search.score.ScoredGraph: Returns true if the scoreed graph and this scored graph are equal.
equals(Matrix, double) - Method in class edu.cmu.tetrad.util.Matrix: equals.
equals(Object) - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.annotation.AnnotatedClass
equals(Object) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns true iff this Bayes net is equal to the given Bayes net.
equals(Object) - Method in class edu.cmu.tetrad.bayes.BayesPm
equals(Object) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns true iff this Bayes net is equal to the given Bayes net.
equals(Object) - Method in class edu.cmu.tetrad.bayes.Evidence
equals(Object) - Method in class edu.cmu.tetrad.bayes.Manipulation
equals(Object) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Determines whether the specified object is equal to this Bayes net.
equals(Object) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns true iff this Bayes net is equal to the given Bayes net.
equals(Object) - Method in class edu.cmu.tetrad.bayes.Proposition
equals(Object) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns true iff this Bayes net is equal to the given Bayes net.
equals(Object) - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.data.BoxDataSet: Checks if the given object is equal to this dataset.
equals(Object) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Tests whether this variable is equal to the given variable.
equals(Object) - Method in class edu.cmu.tetrad.data.DataModelList
equals(Object) - Method in interface edu.cmu.tetrad.data.DataSet: Checks if the given object is equal to this dataset.
equals(Object) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Tests whether this variable is equal to the given variable.
equals(Object) - Method in record class edu.cmu.tetrad.data.JoinDatasets.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.data.Knowledge: Compares this Knowledge object with the specified object for equality.
equals(Object) - Method in class edu.cmu.tetrad.data.KnowledgeEdge
equals(Object) - Method in class edu.cmu.tetrad.data.KnowledgeGroup
equals(Object) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Compares this structure with the given object for equality of name, tiers, and within-tier flags.
equals(Object) - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.LongToWide.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Spec: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Checks if the given object is equal to this dataset.
equals(Object) - Method in class edu.cmu.tetrad.graph.Dag: Compares this Graph object with the specified object for equality.
equals(Object) - Method in class edu.cmu.tetrad.graph.Edge
equals(Object) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether this graph is equal to some other graph, in the sense that they contain the same nodes and the sets of edges defined over these nodes in the two graphs are isomorphic typewise.
equals(Object) - Method in interface edu.cmu.tetrad.graph.Graph: Determines whether this graph is equal to some other graph, in the sense that they contain the same nodes and the sets of edges defined over these nodes in the two graphs are isomorphic typewise.
equals(Object) - Method in class edu.cmu.tetrad.graph.GraphNode: Tests whether this variable is equal to the given variable.
equals(Object) - Method in class edu.cmu.tetrad.graph.IndependenceFact
equals(Object) - Method in interface edu.cmu.tetrad.graph.Node: Tests whether this variable is equal to the given variable.
equals(Object) - Method in class edu.cmu.tetrad.graph.NodePair
equals(Object) - Method in class edu.cmu.tetrad.graph.OrderedPair
equals(Object) - Method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.graph.SemGraph: Determines whether this graph is equal to some other graph, in the sense that they contain the same nodes and the sets of edges defined over these nodes in the two graphs are isomorphic typewise.
equals(Object) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Compares this Graph object with the specified object for equality.
equals(Object) - Method in class edu.cmu.tetrad.graph.Triple
equals(Object) - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.search.blocks.BlockSpec: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.ParseResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Uniformity: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.StudyResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.ConditionResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.StudyResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentSignature: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.PcAR.OrientationClash: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.Mark: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.search.rlcd.Cover
equals(Object) - Method in record class edu.cmu.tetrad.search.rlcd.LatentGroups.AdjacencyResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.SemBicScore.CovAndCoefs: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.NestedFits: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Eval: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Returns whether this object is equal to another.
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.MinimaxBinningConfig: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath
equals(Object) - Method in class edu.cmu.tetrad.search.utils.SepsetMap
equals(Object) - Method in class edu.cmu.tetrad.search.utils.Sextad
equals(Object) - Method in class edu.cmu.tetrad.search.utils.Tetrad
equals(Object) - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Compares this TetradInt object with the specified object for equality.
equals(Object) - Method in class edu.cmu.tetrad.search.utils.TetradNode
equals(Object) - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch
equals(Object) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange
equals(Object) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.GraphWithPValue: equals.
equals(Object) - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci
equals(Object) - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad
equals(Object) - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.sem.ParameterPair: Checks if this ParameterPair object is equal to the specified object.
equals(Object) - Method in class edu.cmu.tetrad.sem.SemEvidence: This method checks if the given object is equal to the current instance of SemEvidence.
equals(Object) - Method in record class edu.cmu.tetrad.sem.SemIm.CyclicSimResult: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class edu.cmu.tetrad.sem.SemManipulation: Compares the current instance with the specified object for equality.
equals(Object) - Method in class edu.cmu.tetrad.sem.SemProposition: Compares this SemProposition object with the specified object for equality.
equals(Object) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedParent
equals(Object) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor
equals(Object) - Method in class edu.cmu.tetrad.util.PointXy
equals(Object) - Method in class edu.cmu.tetrad.util.Vector
equals(Object) - Method in class edu.cmu.tetrad.util.Version
equation() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Renders the constraint as a fitted equation, e.g.
Equation - Class in edu.cmu.tetrad.calculator.expression: Represents an equation of the form Variable = Expression, where the Variable represents a column in some dataset.
Equation(String, Expression, String) - Constructor for class edu.cmu.tetrad.calculator.expression.Equation: Constructor for Equation.
EQUATION - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Equation.
EQUIVALENCE_MARGIN - Static variable in class edu.cmu.tetrad.util.Params: Constant EQUIVALENCE_MARGIN="equivalenceMargin"
EQUIVALENCE_MARGIN_MULTIPLIER - Static variable in class edu.cmu.tetrad.util.Params: Constant EQUIVALENCE_MARGIN_MULTIPLIER="equivalenceMarginMultiplier"
EquivalenceTest - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for the equivalence (TOST) test on the Fisher Z partial correlation.
EquivalenceTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.EquivalenceTest: Constructs a new instance of the test.
equivalentAlpha(double, int) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The level at which a penalty discount's threshold sits, the inverse of AlphaCalibration.equivalentPenaltyDiscount(double, int): alpha = P(chi-square(1) > c ln N).
equivalentPenalty() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns the value of the equivalentPenalty record component.
equivalentPenaltyDiscount(double, int) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The penalty discount whose one-degree-of-freedom acceptance threshold sits at the same level, so that a score-based and a test-based search are being run at comparable strictness per pair: c = Q_chi2(1)(1 - alpha) / ln N.
ERDOS_RENYI_DAG - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant ERDOS_RENYI_DAG="Erdos-Renyi DAG (Fixed Edge Probability"{trunked}
ErdosRenyi - Class in edu.cmu.tetrad.algcomparison.graph: Creates a random graph by the Erdos-Renyi method (probabiliy of edge fixed, # edges not).
ErdosRenyi() - Constructor for class edu.cmu.tetrad.algcomparison.graph.ErdosRenyi: Initializes a new instance of the ErdosRenyi class.
erf(double) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the error function for a double
error(String) - Method in class edu.cmu.tetrad.util.TetradLogger: Logs an error, this will log the message regardless of any configuration information.
ERROR - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant ERROR
ERROR - Enum constant in enum class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Severity: Represents a critical issue that requires immediate attention.
errorEval(Graph, Graph) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: errorEval.
ERRORS_NORMAL - Static variable in class edu.cmu.tetrad.util.Params: Constant ERRORS_NORMAL="errorsNormal"
ess - Variable in class edu.cmu.tetrad.util.EssLikePython.Result: Represents the effective sample size (ESS) calculated as N / (1 + (N-1)*avgRowCorr).
EssLikePython - Class in edu.cmu.tetrad.util: The EssLikePython class provides methods for estimating the Effective Sample Size (ESS) of a dataset using procedures that closely mimic functionality found my Python script.
EssLikePython() - Constructor for class edu.cmu.tetrad.util.EssLikePython: Default constructor for the EssLikePython class.
EssLikePython.Result - Class in edu.cmu.tetrad.util: Encapsulates the results of an estimation process, including average row correlation, effective sample size (ESS), and the number of rows used in the computation.
estimate() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Runs EM and returns the estimated covariance matrix.
estimate() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: The estimated covariance matrix, computed on first call.
estimate() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the estimate record component.
estimate() - Method in class edu.cmu.tetrad.sem.SemEstimator: Runs the estimator on the data and SemPm passed in through the constructor.
estimate() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbs: Runs the estimator on the data and SemPm passed in through the constructor.
estimate(double[][], double[][], double) - Static method in class edu.cmu.tetrad.search.utils.EstimateRank: Estimate rank from data.
estimate(int[], int[], double[][], int, double) - Static method in class edu.cmu.tetrad.search.utils.EstimateRank: Estimate rank from covariance matrix.
estimate(BayesPm, DataSet) - Method in interface edu.cmu.tetrad.bayes.EmBayesProperties.Estimator: estimate.
estimate(BayesPm, DataSet) - Method in class edu.cmu.tetrad.bayes.MlBayesEstimator: Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
estimate(BayesPm, DataSet) - Method in class edu.cmu.tetrad.bayes.MlBayesEstimatorOld: 33 Estimates a Bayes IM using the variables, graph, and parameters in the given Bayes PM and the data columns in the given data set.
estimate(DirichletBayesIm, DataSet) - Static method in class edu.cmu.tetrad.bayes.DirichletEstimator: estimate.
estimate(DataSet) - Static method in class edu.cmu.tetrad.data.missing.PairwiseCovarianceEstimator: Estimates the covariance matrix of the dataset's columns.
estimate(HybridCgModel.HybridCgPm, DataSet, Parameters) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEstimator: Estimates the parameters of a Hybrid Conditional Gaussian (HybridCG) Model using the given parameter model (pm), data set, and optional parameters.
estimate(GeneralizedSemPm, DataSet) - Method in class edu.cmu.tetrad.sem.GeneralizedSemEstimator: Maximizes likelihood equation by equation.
estimate(SimpleMatrix, int, int) - Static method in class edu.cmu.tetrad.util.RowCorrelationEffN: Estimates average pairwise row correlation (by sampling pairs) and returns Neff = N / (1 + (N-1)*rhoHat).
estimateAteBinaryVectorV1(DataSet, String, int[], Node, Set, AdjustmentEffectEstimatorV1.ConfigV1) - Static method in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1: Estimates the Average Treatment Effect (ATE) for a binary treatment variable provided as a vector.
estimateAteV1(DataSet, Node, Node, Set, AdjustmentEffectEstimatorV1.ConfigV1) - Static method in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1: v1: Estimate ATE using a provided adjustment set (already found by RA, Perkovic criterion, etc.).
estimateCovariance(DataSet) - Static method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Convenience method: estimates and returns the covariance matrix for the given dataset using default settings.
estimateCovariance(DataSet, MissingDataSpec) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: The covariance matrix of a dataset with missing values under a policy that estimates one: by EM with the spec's ridge, tolerance and iteration limit under EM_COVARIANCE, or by pairwise deletion (PairwiseCovarianceEstimator) under PAIRWISE_COVARIANCE.
estimatedNodeSize() - Static method in class edu.cmu.tetrad.graph.LayoutUtil: A size estimate for use when no display sizes are available: about 8 px per character of the name plus padding, with the same 60 px floor and ~30 px height the workbench's measured node boxes use.
estimateLikePython(SimpleMatrix, int, boolean) - Static method in class edu.cmu.tetrad.util.EssLikePython: Estimates the effective sample size (ESS) and average row correlation (avgRowCorr) from a given dataset, by column-standardizing, sampling rows, and computing row-wise correlations.
estimatePermutationRank(double[][], int[], int[], double, int) - Static method in class edu.cmu.tetrad.util.RankTests: Estimates rank using the permutation Wilks test, suitable for linear non-Gaussian data.
estimateRank(int[], int[], double) - Method in class edu.cmu.tetrad.util.StratifiedRankTests: Estimates the rank of Σ_X,Y|T as the smallest r whose null hypothesis rank ≤ r is not rejected at the given level, mirroring RankTests.estimateWilksRankFast(SimpleMatrix, int[], int[], int, double).
EstimateRank - Class in edu.cmu.tetrad.search.utils: Estimates the rank of a matrix.
estimateRankConditioned(int[], int[], int[], double) - Method in class edu.cmu.tetrad.util.StratifiedRankTests: Estimates the rank of Σ_X,Y|Z,T, conditioning on the variable set Z within each stratum by Schur complement and pooling over strata, mirroring RankTests.estimateWilksRankConditioned(SimpleMatrix, int[], int[], int[], int, double).
estimatesCovariance() - Method in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Whether the policy estimates one covariance matrix from the incomplete data and analyzes it as complete data: EM_COVARIANCE or PAIRWISE_COVARIANCE.
estimateW(DataSet, int, double, double) - Static method in class edu.cmu.tetrad.search.LingD: Estimates the weight matrix W using the Fast Independent Component Analysis (FastICA) algorithm.
estimateW(DataSet, int, double, double, boolean) - Static method in class edu.cmu.tetrad.search.LingD: Estimates the weight matrix (W) using the Fast Independent Component Analysis (FastICA) algorithm.
estimateWilksRank(SimpleMatrix, int[], int[], int, double) - Static method in class edu.cmu.tetrad.util.RankTests: Estimates the regularized canonical correlation analysis (rCCA) rank by sequentially testing the rank using Wilks' Lambda statistic.
estimateWilksRankConditioned(SimpleMatrix, int[], int[], int[], int, double) - Static method in class edu.cmu.tetrad.util.RankTests: Estimates the Wilks rank for variables X and Y conditioned on variables Z using the given covariance matrix and parameters.
estimateWilksRankFast(SimpleMatrix, int[], int[], int, double) - Static method in class edu.cmu.tetrad.util.RankTests: Estimates the rank of a matrix using the Wilks test and a Bartlett ÏÂ² approximation.
euclideanNorm() - Method in class edu.cmu.tetrad.util.Vector: Calculates the Euclidean norm (magnitude) of the vector.
eval(double, double) - Method in interface edu.cmu.tetrad.search.utils.Kernel: Evaluates the kernel at two points in the input space
eval(double, double) - Method in class edu.cmu.tetrad.search.utils.KernelGaussian: Evaluates the kernel at two points in the input space
eval(IndependenceFact) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Evaluates the independence fact using a cached or computed result.
Eval(boolean, double) - Constructor for record class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Eval: Creates an instance of a Eval record class.
evalAll(Collection, CachedIndependenceQueries.Dedup) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Evaluates a collection of independence facts and returns a list of evaluation results.
evalEdges(Graph, Set, Set) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: evalEdges.
evaluate(double) - Method in class edu.cmu.tetrad.search.utils.GaussianProcessRBF: Evaluates the simulated function at a given point using interpolation.
evaluate(double) - Method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunction1D: Evaluates the random function for a given input.
evaluate(double) - Method in class edu.cmu.tetrad.util.RandomPiecewiseLinearBijective: Evaluates the piecewise linear bijective function for a given input value.
evaluate(double) - Method in class edu.cmu.tetrad.util.TaylorSeries: Evaluates the Taylor series at a given point x by summing the terms up to the length of the derivative array.
evaluate(double[]) - Method in class edu.cmu.tetrad.search.utils.LinearFunctionND: Evaluates the linear function for a given input vector.
evaluate(double[]) - Method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptron: Evaluates the MLP for a given input vector.
evaluate(double[]) - Method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunctionND: Evaluates the random function for a given input vector.
evaluate(double[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Evaluates the term.
evaluate(double[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Evaluates the term.
evaluate(double[]) - Method in class edu.cmu.tetrad.util.MultiDimensionalTaylorDegree2: Evaluates a second-degree Taylor series expansion at a given point.
evaluate(Context) - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: Evaluates the expression using the given context
evaluate(Context) - Method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: Evaluates the expression using the given context
evaluate(Context) - Method in interface edu.cmu.tetrad.calculator.expression.Expression: Evaluates the expression using the given context
evaluate(Context) - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: Evaluates the expression using the given context
evaluate(Graph, Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator: Evaluates the estimated graph against the true graph and returns a report object.
evaluate(MimicModel, Graph) - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluator: Evaluates an estimated graph against the true model.
evaluateAdjusted(double[]) - Method in class edu.cmu.tetrad.search.utils.LinearFunctionND: Evaluates the linear function with the given input vector and calculates the resulting adjusted value for intercept == 0.0.
evaluateAdjusted(double[]) - Method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunctionND: Evaluates the adjusted output of the function for a given input vector.
evaluateGeneric(Context) - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: evaluateGeneric.
EvaluationExpression - Class in edu.cmu.tetrad.calculator.expression: An equation expression.
EvaluationExpression(VariableExpression, String) - Constructor for class edu.cmu.tetrad.calculator.expression.EvaluationExpression: Constructor for EvaluationExpression.
EVENLY_DISTRIBUTED_INTERVALS - Static variable in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Constant EVENLY_DISTRIBUTED_INTERVALS=2
EVENLY_DISTRIBUTED_VALUES - Static variable in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: The types of discretization
evenSplitVector(double, int) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: evenSplitVector.
Evidence - Class in edu.cmu.tetrad.bayes: Stores information for a variable source about evidence we have for each variable as well as whether each variable has been manipulated.
Evidence(Evidence) - Constructor for class edu.cmu.tetrad.bayes.Evidence: Copy constructor.
Evidence(Evidence, VariableSource) - Constructor for class edu.cmu.tetrad.bayes.Evidence: Constructor for Evidence.
Evidence(Proposition) - Constructor for class edu.cmu.tetrad.bayes.Evidence: Wraps the proposition.
EVIDENCE_DEFAULT - Enum constant in enum class edu.cmu.tetrad.search.Fask.Origin: The external graph's compelled orientation was kept as the default.
EVIDENCE_DEFAULT - Enum constant in enum class edu.cmu.tetrad.search.FaskPool.Origin: The majority compelled orientation of the evidence graphs was kept as the default.
evidenceAgainst() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the evidenceAgainst record component.
evidenceFor() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the evidenceFor record component.
exact(long, int) - Static method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: The exact chi-square null for a class of the given df.
EXACT_LINEAR_DEPENDENCE - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The correlation matrix of the continuous variables is singular (rank-deficient): some continuous variable is an exact linear function of the others on the analyzed rows.
Example - Section in class edu.cmu.tetrad.search.AdjustmentHarness
ExampleCompareFromFiles - Class in edu.cmu.tetrad.algcomparison.examples: An example script to load in data sets and graphs from files and analyze them.
ExampleCompareFromFiles - Class in edu.cmu.tetrad.study.examples.conditions: An example script to load in data sets and graphs from files and analyze them.
ExampleCompareSimulation - Class in edu.cmu.tetrad.algcomparison.examples: An example script to simulate data and run a comparison analysis on it.
ExampleCompareSimulation - Class in edu.cmu.tetrad.study.examples.conditions: An example script to simulate data and run a comparison analysis on it.
ExampleCompareSimulation2 - Class in edu.cmu.tetrad.algcomparison.examples: An example script to simulate data and run a comparison analysis on it.
ExampleCompareSimulationDiscrete - Class in edu.cmu.tetrad.study.examples.conditions: An example script to simulate data and run a comparison analysis on it.
ExampleFirstInflection - Class in edu.cmu.tetrad.study.examples.conditions: An example script to simulate data and run a comparison analysis on it.
ExampleNonlinearSave - Class in edu.cmu.tetrad.algcomparison.examples: An example script to save out data files and graphs from a simulation.
ExampleSave - Class in edu.cmu.tetrad.algcomparison.examples: An example script to save out data files and graphs from a simulation.
ExampleSave - Class in edu.cmu.tetrad.study.examples.conditions: An example script to save out data files and graphs from a simulation.
ExampleStars - Class in edu.cmu.tetrad.study.examples.conditions: An example script to simulate data and run a comparison analysis on it.
EXCLUDE - Enum constant in enum class edu.cmu.tetrad.search.blocks.SingletonClusterPolicy: Represents the policy to exclude single variables.
EXCLUDE_SELECTION_BIAS - Static variable in class edu.cmu.tetrad.util.Params: Constant EXCLUDE_SELECTION_BIAS="excludeSelectionBias"
excludeContextsFromS(boolean) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Configures whether context variables are excluded from conditioning sets during collider orientation.
excludeContextsFromS(boolean) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Configures whether context variables are excluded from conditioning sets in the collider audit.
exhaustiveSearch(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealSearch: This method computes m/e values for all single regulators, pairs and triples at a given time lag.
exhaustiveSearch(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealSearch: This method computes m/e values for all single regulators, pairs and triples at a given time lag.
exhaustiveSearch(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealSearch: This method computes m/e values for all single regulators, pairs and triples between two time lags (inclusively).
exhibitsSelectionBias(Graph, VertexRepairSearch.AdjustmentGraphType) - Static method in class edu.cmu.tetrad.search.VertexRepairSearch: Returns true if g exhibits selection bias under graphType, the static form of the predicate used internally by this search.
existsCombination() - Method in class edu.cmu.tetrad.bayes.Proposition: existsCombination.
existsCommonAncestor(Graph, Edge) - Static method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorTruePositiveBidirected: Returns true if there is a common ancestor of X and Y in the graph.
existsCommonAncestor(Graph, Edge) - Static method in class edu.cmu.tetrad.algcomparison.statistic.NumCommonMeasuredAncestorBidirected: existsCommonAncestor.
existsDirectedCycle() - Method in class edu.cmu.tetrad.graph.Paths: existsDirectedCycle.
existsDirectedPath(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Checks if a directed path exists between two nodes in a graph.
existsDirectedPath(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Checks if a directed path exists between two nodes within a certain depth.
existsDirectedPath(Node, Node, Pair) - Method in class edu.cmu.tetrad.graph.Paths: Checks if a directed path exists between two nodes in a graph, ignoring a specified edge.
existsDirectedPathFromTo(Graph, Node, Node) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNoMeasureAncestors: existsDirectedPathFromTo.
existsEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Determines whether the edge to 'factor' at time lag 0 from 'laggedFactor' exists in the graph.
existsEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Determines whether the edge to 'factor' at time lag 0 from 'laggedFactor' exists in the graph.
existsEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Determines whether the edge to 'factor' at time lag 0 from 'laggedFactor' exists in the graph.
existsEdge(String, LaggedFactor) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Determines whether the edge to 'factor' at time lag 0 from 'laggedFactor' exists in the graph.
existsEdgeCoef(Node, Node) - Method in class edu.cmu.tetrad.sem.SemIm: Determines whether an edge coefficient exists between two given nodes.
existsFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Determines whether the given factor exists in the graph.
existsFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Determines whether the given factor exists in the graph.
existsFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Determines whether the given factor exists in the graph.
existsFactor(String) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Determines whether the given factor exists in the graph.
existsIn(Graph) - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Determines whether a discriminating path exists in the given graph.
existsInducingPath(Node, Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether an inducing path exists between two nodes in a graph.
existsInducingPathBFS(Node, Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: Breadth-first version of the âinducing-path exists?â test.
existsInducingPathDFS(Node, Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether an inducing path exists between node1 and node2, given a set O of observed nodes and a set sem of conditioned nodes.
existsInducingPathInto(Node, Node, Graph, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.TsDagToPag: existsInducingPathInto.
existsInducingPathVisit(Node, Node, Node, Node, Set, LinkedList) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether an inducing path exists between two nodes in a graph.
existsInducingPathVisitts(Graph, Node, Node, Node, Node, LinkedList, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.TsDagToPag: existsInducingPathVisitts.
existsLatentCommonAncestor(Graph, Edge) - Static method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorTruePositiveBidirected: existsLatentCommonAncestor.
existsMissingValue() - Method in class edu.cmu.tetrad.data.BoxDataSet: existsMissingValue.
existsMissingValue() - Method in interface edu.cmu.tetrad.data.DataSet: existsMissingValue.
existsMissingValue() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: existsMissingValue.
existsPotentiallyDirectedPath(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether one node is an ancestor of another.
existsPotentiallyDirectedPath(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines whether there is a potentially directed path from node1 to node2.
existsPotentiallyDirectedPath(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: existsPotentiallyDirectedPath.
existsPotentiallyDirectedPath(Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: existsPotentiallyDirectedPath.
existsTrek(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether a trek exists between two nodes in the graph.
exp - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The exp.
exp(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the absolute value of a double value using the specified implementation.
EXP - Static variable in class edu.cmu.tetrad.search.FastIca: Neg-entropy nonlinearity: exp.
expandLatents(BlockSpec) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Expand ranks -> per-latent variables named Lk-1..Lk-r.
expandTemplate(String, GeneralizedSemPm, Node) - Method in class edu.cmu.tetrad.sem.TemplateExpander: Returns the expanded template, which needs to be checked to make sure it can be used.
Expectations - Section in class edu.cmu.tetrad.search.harness.MissingDataRecoveryHarness
expectedFalseEdges() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns the value of the expectedFalseEdges record component.
expectedFalseEdges() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns the value of the expectedFalseEdges record component.
expectedFalseEdges(double, Map, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The expected number of spurious edges, sum over df of count(df) * alpha(c, df).
expectedFalseEdges(int, double, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The expected number of spurious adjacencies at a given level: M alpha.
expectedFalseEdgesAt(double) - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The expected number of spurious edges over the whole search at a given penalty discount.
expectedFalseEdgesFitted(double, Map, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: Expected spurious edges under fitted nulls: sum over df classes of pairs * alpha(c, df, fit).
experiment(String, int, int, int, double, double, boolean, boolean, double, double, String, int) - Method in class edu.cmu.tetrad.study.RBExperiments: Performs an experiment to estimate the structure and parameters of a Bayesian network using various methods.
Experimental - Interface in edu.cmu.tetrad.util: Tags a file as experimental so it's not listed in the configuration.
Experimental - Annotation Interface in edu.cmu.tetrad.annotation: Oct 17, 2017 11:40:50 AM
EXPLAINING_SUBSET_FRACTION - Static variable in class edu.cmu.tetrad.data.audit.DataAudit: Fraction of the full R^2 that a greedily chosen predictor subset must reach for the subset to be reported as accounting for the dependence in NEAR_DETERMINISM_LINEAR messages.
EXPLAINING_SUBSET_MAX_SIZE - Static variable in class edu.cmu.tetrad.data.audit.DataAudit: Maximum number of predictors the greedy explaining-subset search will select.
explainingSubset(Matrix, int, double, double[]) - Static method in class edu.cmu.tetrad.data.audit.DataAudit: Greedy forward selection on a correlation matrix, used to localize a NEAR_DETERMINISM_LINEAR finding: for column a, predictors are added one at a time, each step choosing the column that most increases the R^2 of a on the selected set, stopping when the subset R^2 reaches DataAudit.EXPLAINING_SUBSET_FRACTION of fullR2 or the set reaches DataAudit.EXPLAINING_SUBSET_MAX_SIZE predictors.
ExploreComparison - Class in edu.cmu.tetrad.study.performance: Runs algorithm on data set (simulation is OK), printing out error statistics.
expm1(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the natural logarithm of a double value using the specified implementation.
Exponential - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
Exponential(double) - Constructor for class edu.cmu.tetrad.util.dist.Exponential: Constructor for Exponential.
Expression - Interface in edu.cmu.tetrad.calculator.expression: Represents a mathematical expression.
ExpressionDescriptor - Interface in edu.cmu.tetrad.calculator.expression: Represents a definition for some expression.
ExpressionDescriptor.Position - Enum Class in edu.cmu.tetrad.calculator.expression: An enum of positions that an expression can occur in.
ExpressionInitializationException - Exception Class in edu.cmu.tetrad.calculator.expression: Thrown if the expression can't be parsed, for instance, if it has the wrong number of arguments.
ExpressionInitializationException(String) - Constructor for exception class edu.cmu.tetrad.calculator.expression.ExpressionInitializationException: Constructor for ExpressionInitializationException.
ExpressionLexer - Class in edu.cmu.tetrad.calculator.parser: Parses the tokens of an expression.
ExpressionLexer(CharSequence) - Constructor for class edu.cmu.tetrad.calculator.parser.ExpressionLexer: Constructor for ExpressionLexer.
ExpressionManager - Class in edu.cmu.tetrad.calculator.expression: Manager for expressions, this includes all implementations of expression descriptors for the calculator.
ExpressionParser - Class in edu.cmu.tetrad.calculator.parser: Parses a string into a tree-like expression.
ExpressionParser() - Constructor for class edu.cmu.tetrad.calculator.parser.ExpressionParser: Constructrs a parser that has no allowable parameters.
ExpressionParser(Collection, ExpressionParser.RestrictionType) - Constructor for class edu.cmu.tetrad.calculator.parser.ExpressionParser: Constructs the parser given a collection of allowable parameters.
ExpressionParser.RestrictionType - Enum Class in edu.cmu.tetrad.calculator.parser: The type of restriction on parameters.
ExpressionSampler - Class in edu.cmu.tetrad.sem: The ExpressionSampler class provides functionality for parsing, storing, and evaluating mathematical expressions.
ExpressionSampler(String) - Constructor for class edu.cmu.tetrad.sem.ExpressionSampler: Constructs a new ExpressionSampler with the specified mathematical expression.
ExpressionSignature - Interface in edu.cmu.tetrad.calculator.expression: Represents the signature of the expression, for example sqrt(number).
expUnstandardized - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The exp unstandardized.
expUnstandardizedInverted - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The exp unstandardized inverted.
EXTERNAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

Use an external graph.
ExternalAlgorithm - Class in edu.cmu.tetrad.algcomparison.algorithm: Tags an an algorithm that loads up external graphs for inclusion in reports.
ExternalAlgorithm() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: Constructor for ExternalAlgorithm.
externalMutationIsRepairedInPlace() - Method in class edu.cmu.tetrad.util.PagCacheTest: Verifies that external mutations of a PAG object retrieved from the cache are repaired directly within the same object instance (in-place), ensuring that the object identity is preserved.
externalParents(Graph, Collection) - Static method in class edu.cmu.tetrad.search.GRegression: Returns the external parents of a set of nodes: the union of their parents, minus the set itself.
EXTRA_EDGE_REMOVAL_STEP - Static variable in class edu.cmu.tetrad.util.Params: Constant EXTRA_EDGE_REMOVAL_STEP="extraEdgeRemovalStep"
ExtraCategoryInterpolator - Class in edu.cmu.tetrad.bayes: Returns a data set in variables for columns with missing values are augmented with an extra category that represents the missing values, with missing values being reported as belong this category.
ExtraCategoryInterpolator() - Constructor for class edu.cmu.tetrad.bayes.ExtraCategoryInterpolator: Constructs a new instance of the algorithm.
extractClusterNames(Graph) - Static method in class edu.cmu.tetrad.search.harness.tsc.TrueClusterExtractor: Extracts true clusters and returns them as sets of variable names rather than Node objects.
extractClusters(Graph) - Static method in class edu.cmu.tetrad.search.harness.tsc.TrueClusterExtractor: Extracts true latent clusters from graph.
extractFactor_Display(String) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.DisplayNameHandler: Parses the given string representing a lagged factor and return the part that represents the factor.
extractLag_Display(String) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.DisplayNameHandler: Extracts the lag from the lagged factor name string.
extractLatentLeaders(Graph) - Static method in class edu.cmu.tetrad.search.harness.tsc.TrueClusterExtractor: Extracts the primary latent node for each group in the same order as TrueClusterExtractor.extractClusters(Graph).
extractSubMatrix(SimpleMatrix, int[], int[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Extracts a submatrix from the specified matrix by selecting the rows and columns indicated by the provided indices.
extractSubMatrix(SimpleMatrix, int, int, int, int) - Static method in class edu.cmu.tetrad.util.StatUtils: Extracts a submatrix from the given matrix within the specified row and column bounds.
extractToUserCache(String, String) - Static method in class edu.cmu.tetrad.util.PythonResource: Extracts a classpath resource to a stable cache directory (not a temp file), so a long-lived Python process can safely run it.
extractTrueStructural(Graph) - Static method in class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer: Extracts the true structural graph (latent-to-latent edges only) from a full MIM graph produced by RandomMim.
ExtraLatentStructureAlgorithm - Interface in edu.cmu.tetrad.algcomparison.algorithm: A tagging interface for algorithms that can be used for latent structure search.
EXTREME_VARIANCE_SCALE - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The ratio of the largest to the smallest positive variance on the diagonal of a supplied covariance matrix is extreme.

F

F_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant F_DEGREE="fDegree"
f1() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns the F1 score (harmonic mean of precision and recall), or NaN if either component is NaN or both are zero.
F1Adj - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for adjacencies.
F1Adj() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.F1Adj: Constructs a new instance of the algorithm.
F1All - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for adjacencies.
F1All() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.F1All: Constructs a new instance of the algorithm.
F1Arrow - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for arrowheads.
F1Arrow() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.F1Arrow: Constructs a new instance of the algorithm.
F1Circle - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for circles.
F1Circle() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.F1Circle: Constructs a new instance of the algorithm.
F1 computation - Section in class edu.cmu.tetrad.search.harness.tsc.SimulationStudy
F1Tail - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for tails.
F1Tail() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.F1Tail: Constructs a new instance of the algorithm.
FA_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant FA_THRESHOLD="fa_threshold"
factKey(IndependenceFact) - Static method in class edu.cmu.tetrad.search.VertexRepairSearch: Builds a canonical fact-key for deduplication.
FactorAnalysis - Class in edu.cmu.tetrad.algcomparison.algorithm.other: Factor analysis.
FactorAnalysis - Class in edu.cmu.tetrad.search: Implements the classical Factor Analysis algorithm.
FactorAnalysis() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis: Constructs a new instance of the algorithm.
FactorAnalysis(DataSet) - Constructor for class edu.cmu.tetrad.search.FactorAnalysis: Constructor.
FactorAnalysis(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.FactorAnalysis: Constructor.
FactoredBayesStructuralEM - Class in edu.cmu.tetrad.bayes: Implements the procedure Factored-Bayesian-SEM found on page 6 of "The Bayesian Structural EM Algorithm" by Nir Friedman.> 0
FactoredBayesStructuralEM(DataSet, BayesPm) - Constructor for class edu.cmu.tetrad.bayes.FactoredBayesStructuralEM: Constructor for FactoredBayesStructuralEM.
factorial(int) - Static method in class edu.cmu.tetrad.util.MathUtils: Calculates the factorial of a non-negative integer n.
factorial(int) - Static method in class edu.cmu.tetrad.util.StatUtils: factorial.
factoryUtil_replicatingFlagFalse_returnsEdgeListGraph() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: GraphFactoryUtil.newGraph(nodes, false) should return a plain EdgeListGraph.
factoryUtil_replicatingFlagTrue_returnsReplicatingGraph() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: GraphFactoryUtil.newGraph(nodes, true) should return a ReplicatingGraph.
factsForUniformZ(Graph, Node, Set) - Static method in class edu.cmu.tetrad.search.MarkovCheck: Generates a list of independence facts for a given node with respect to a given graph and a conditioning set.
fail() - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec that refuses data containing missing values.
fail(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a failure message prefixed with a cross mark emoji if the tracer is enabled.
FAIL - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: Throw an exception naming the number of duplicated combinations.
FAIL - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Refuse to analyze data containing missing values; an exception should be thrown.
failedCount() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the failedCount record component.
failToReject(int[], int[], int, double) - Method in class edu.cmu.tetrad.search.rlcd.Chi2RankTest
failToReject(int[], int[], int, double) - Method in class edu.cmu.tetrad.search.rlcd.EmCovarianceRankTest
failToReject(int[], int[], int, double) - Method in class edu.cmu.tetrad.search.rlcd.PooledRankTest
failToReject(int[], int[], int, double) - Method in interface edu.cmu.tetrad.search.rlcd.RankTester: Tests the null hypothesis that the cross-covariance of columns pcols and qcols has rank at most r.
failToReject(int[], int[], int, double) - Method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: Tests the null hypothesis that the cross-covariance of the two column sets, within each domain, has rank at most r, by comparing the pooled statistic with the chi-square critical value at the pooled degrees of freedom.
failToReject(int[], int[], int, double) - Method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest
FAITHFULNESS_ASSUMED - Static variable in class edu.cmu.tetrad.util.Params: Constant FAITHFULNESS_ASSUMED="faithfulnessAssumed"
FalseNegativesAdjacencies - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
FalseNegativesAdjacencies() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FalseNegativesAdjacencies: Constructs a new instance of the algorithm.
FalsePositiveAdjacencies - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
FalsePositiveAdjacencies() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FalsePositiveAdjacencies: Constructs a new instance of the algorithm.
falseToTrueRatio() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns the value of the falseToTrueRatio record component.
falseToTrueRatio() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns the value of the falseToTrueRatio record component.
family(int[]) - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Returns the sample covariance matrix (divisor n - 1) of the given columns over the rows complete on all of them, in the order the columns are given, together with the number of such rows.
family(int[], List) - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: As TestwiseCovariance.family(int[]), but restricted to the given candidate rows (null means all rows).
Family(Matrix, int, List) - Constructor for record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Creates an instance of a Family record class.
Fas - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Fast Adjacency Search (FAS)--i.e., the PC adjacency step, which is used in many algorithms.
Fas - Class in edu.cmu.tetrad.search: Implements the Fast Adjacency Search (FAS), which is the adjacency search of the PC algorithm (see).
Fas() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Constructor for Fas.
Fas(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Constructor for Fas.
Fas(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Fas: Constructs a new instance of the Fas algorithm using the specified independence test.
FAS_RULE - Static variable in class edu.cmu.tetrad.util.Params: Constant FAS_RULE="fasRule"
FAS_STABLE - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

Fast Adjacency Search (FAS) with the stable option.
Fas.BlockedDeletion - Record Class in edu.cmu.tetrad.search: A candidate deletion FAS declined because every accepting separating set it found at the depth in question was contaminated by determinism (see Fas.DeterminismGuard), and no clean alternate of the same size existed.
Fas.DeterminismGuard - Interface in edu.cmu.tetrad.search: Functional hook: return true if S functionally determines v (Var(v|S) = 0, i.e.
Fasd - Class in edu.cmu.tetrad.search: Adjusts FAS (see) for the deterministic case by refusing to removed edges based on conditional independence tests that are judged to be deterministic.
Fasd(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Fasd: Constructs a new FastAdjacencySearch.
FasDci - Class in edu.cmu.tetrad.search.work_in_progress: Implements a modified version of the the "fast adjacency search" for use in the Distributed Causal Inference (DCI) algorithm.
FasDci(Graph, IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.FasDci: Constructs a new FastAdjacencySearch for DCI.
FasDci(Graph, IndependenceTest, ResolveSepsets.Method, List, List, SepsetMapDci, SepsetMapDci) - Constructor for class edu.cmu.tetrad.search.work_in_progress.FasDci: Constructs a new FastAdjacencySearch for DCI with independence test pooling to resolve inconsistencies.
Fask - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: FASK algorithm.
Fask - Class in edu.cmu.tetrad.search: Implements the FASK (Fast Adjacency Skewness) algorithm.
Fask() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Constructor for Fask.
Fask(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Constructs a new Fask object with the given ScoreWrapper.
Fask(DataSet, Score) - Constructor for class edu.cmu.tetrad.search.Fask: Constructs a new Fask instance with the specified data set and score.
FASK_ADJACENCY_METHOD - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_ADJACENCY_METHOD="faskAdjacencyMethod"
FASK_DELTA - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_DELTA="faskDelta"
FASK_LEFT_RIGHT_RULE - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_LEFT_RIGHT_RULE="faskLeftRightRule"
FASK_NONEMPIRICAL - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_NONEMPIRICAL="faskNonempirical"
FASK_OVERRIDE_BLOCK_LENGTH - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_OVERRIDE_BLOCK_LENGTH="faskOverrideBlockLength"
FASK_OVERRIDE_BOOTSTRAPS - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_OVERRIDE_BOOTSTRAPS="faskOverrideBootstraps"
FASK_POOL_ADJACENCY - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_POOL_ADJACENCY="faskPoolAdjacency"
FASK_POOL_ADJACENCY_ORIENTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_POOL_ADJACENCY_ORIENTATIONS="faskPoolAdjacencyOrientations"
FASK_POOL_EXTERNAL_ADJACENCY_FRACTION - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_POOL_EXTERNAL_ADJACENCY_FRACTION="faskPoolExternalAdjacencyFraction"
FASK_POOL_EXTERNAL_ORIENTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant FASK_POOL_EXTERNAL_ORIENTATIONS="faskPoolExternalOrientations"
Fask.LeftRight - Enum Class in edu.cmu.tetrad.search: An enumeration representing directional and functional types.
Fask.Origin - Enum Class in edu.cmu.tetrad.search: The provenance of an orientation decision, recorded per edge in Fask.getEdgeOrigins().
Fask.TwoCycleRule - Enum Class in edu.cmu.tetrad.search: The discrepancy statistic the two-cycle test compares the full sample against its sign-truncated half-samples with.
FASK1 - Enum constant in enum class edu.cmu.tetrad.search.Fask.LeftRight: Use FASK1.
FASK1 - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

The original FASK left-right rule.
FASK2 - Enum constant in enum class edu.cmu.tetrad.search.Fask.LeftRight: Use FASK2.
FASK2 - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

The modified FASK left-right rule.
FaskConcatenated - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Wraps the IMaGES algorithm for continuous variables.
FaskConcatenated() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Constructor for FaskConcatenated.
FaskConcatenated(ScoreWrapper, IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Constructor for FaskConcatenated.
FaskCpwOrientationHarness - Class in edu.cmu.tetrad.search.harness: Harness for comparing FASK and CPW edge-orientation accuracy across all five pairwise left-right rules on random acyclic linear SEMs with Gumbel(0,1) errors.
FaskLeftRightHarness - Class in edu.cmu.tetrad.search.harness: Harness for tuning and comparing FASK left-right pairwise orientation rules on random residualized linear SEMs with skewed errors.
faskLeftRightV1(double[], double[], boolean, double) - Static method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Calculates the left-right ratio using the Fask method version 1.
faskLeftRightV2(double[], double[], boolean, double) - Static method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Calculates the left-right judgment for two arrays of double values.
FaskOrig - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: Deprecated.
FaskOrig - Class in edu.cmu.tetrad.search: Deprecated.
FaskOrig() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Constructor for Fask.
FaskOrig(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Constructor for Fask.
FaskOrig(DataSet, Score, IndependenceTest) - Constructor for class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Constructor.
FaskOrig.AdjacencyMethod - Enum Class in edu.cmu.tetrad.search: Deprecated.

Enumerates the alternatives to use for finding the initial adjacencies for FASK.
FaskOrig.LeftRight - Enum Class in edu.cmu.tetrad.search: Deprecated.

Enumerates the options left-right rules to use for FASK.
FaskPool - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Wraps the pooled-FASK multi-dataset algorithm for continuous variables.
FaskPool - Class in edu.cmu.tetrad.search: Orients a common adjacency graph over multiple datasets by pooling the FASK left-right statistic across the datasets, rather than voting on per-dataset orientations as FaskVote does.
FaskPool() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Constructor for FaskPool.
FaskPool(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Constructor for FaskPool.
FaskPool(List, ScoreWrapper) - Constructor for class edu.cmu.tetrad.search.FaskPool: Constructs a pooled-FASK search from the supplied datasets and score wrapper.
FaskPool.EdgeStat - Record Class in edu.cmu.tetrad.search: Pooled statistics for one edge.
FaskPool.Origin - Enum Class in edu.cmu.tetrad.search: The provenance of an orientation decision, recorded per edge in FaskPool.getEdgeStats().
FaskPool.Weighting - Enum Class in edu.cmu.tetrad.search: The weighting scheme for pooling per-dataset left-right statistics.
FaskPw - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: FASK-PW (pairwise).
FaskPw() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Constructor for FaskPw.
FaskPw(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Constructor for FaskPw.
FaskTwoCycleRuleHarness - Class in edu.cmu.tetrad.search.harness: Size and power of FASK's two-cycle test, Fask.twoCycleTest(double[], double[], double[][], double), under both Fask.TwoCycleRule settings, on two-variable systems with six noise shapes.
FaskVote - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Wraps the MultiFask algorithm for continuous variables.
FaskVote - Class in edu.cmu.tetrad.search: Implements a simple voting scheme for orienting a common adjacency graph using FASK.
FaskVote() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Constructor for FaskVote.
FaskVote(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Constructor for FaskVote.
FaskVote(List, ScoreWrapper) - Constructor for class edu.cmu.tetrad.search.FaskVote: Constructs a FASK-voting search from the supplied datasets, score wrapper, and independence wrapper.
FAST - Enum constant in enum class edu.cmu.tetrad.util.TMath.Impl: Represents the fast implementation strategy for mathematical computations.
FAST_ICA_A - Static variable in class edu.cmu.tetrad.util.Params: Constant FAST_ICA_A="fastIcaA"
FAST_ICA_MAX_ITER - Static variable in class edu.cmu.tetrad.util.Params: Constant FAST_ICA_MAX_ITER="fastIcaMaxIter"
FAST_ICA_TOLERANCE - Static variable in class edu.cmu.tetrad.util.Params: Constant FAST_ICA_TOLERANCE="fastIcaTolerance"
FastIca - Class in edu.cmu.tetrad.search: FastICA (real-valued) translated for Tetrad.
FastIca(Matrix, int) - Constructor for class edu.cmu.tetrad.search.FastIca: Constructs a FastIca object with the given data matrix and number of components.
FastICA - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The FastICA rule.
FastIca.IcaResult - Record Class in edu.cmu.tetrad.search: A record that represents the result of the Independent Component Analysis (ICA) process.
FBetaAdj - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for adjacencies.
FBetaAdj() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: Constructs a new instance of the algorithm.
fCdf(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: F CDF.
Fci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: The Fast Causal Inference (FCI) algorithm.
Fci - Class in edu.cmu.tetrad.search: The Fci class implements the Fast Causal Inference (FCI) algorithm for discovering causal structures from data.
Fci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Constructor.
Fci(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Constructor.
Fci(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Fci: Constructs an instance of the Fci algorithm using the specified independence test.
Fci(IndependenceTest, List) - Constructor for class edu.cmu.tetrad.search.Fci: Constructs an instance of the Fci algorithm using the specified independence test and a subset of variables to include in the search.
FCI - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Constant for the FCI algorithm.
Fci.ColliderRule - Enum Class in edu.cmu.tetrad.search: The ColliderRule enum defines the rules or strategies used to handle collider structures in causal inference or related algorithms.
FciCyclicPw - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: FCI-CPW: Run FCI with internally constructed PW-forbidden knowledge, then orient edges using a pairwise left-right rule on standardized data in cases that are safe under no-selection-bias cyclic semantics: Tail–tail (—) edges Orient per pairwise rule. Tail–circle (—o) edges If pairwise prefers x→y, set x→y (symmetrically, if prefers y→x, set y→x). Circle–circle (o–o) edges If pairwise prefers x→y, set x o→y (symmetrically for y→x).
FciCyclicPw() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Default constructor for the FciCyclicPw class.
FciCyclicPw(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Constructor for the FciCyclicPw class that initializes the algorithm with a specified independence test wrapper.
FciIod - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Runs FCI on multiple datasets using the IOD pooled dataset independence test.
FciIod() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Constructor for FciIod.
FciIod(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Constructor for FciIod.
FciMax - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: FCI-Max.
FciMax() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Constructor.
FciMax(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Constructor.
FciOrient - Class in edu.cmu.tetrad.search.utils: Performs the final orientation steps of the FCI algorithms, which is a useful tool to use in a variety of FCI-like algorithms.
FciOrient(R0R4Strategy) - Constructor for class edu.cmu.tetrad.search.utils.FciOrient: Initializes a new instance of the FciOrient class with the specified R4Strategy.
fciOrientbk(Knowledge, Graph, List, boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Orient the edges of a graph based on the given knowledge.
FciOrientDijkstra - Class in edu.cmu.tetrad.search: A simple implementation of Dijkstra's algorithm for finding the shortest path in a graph.
FciOrientDijkstra.DijkstraEdge - Class in edu.cmu.tetrad.search: Represents a node in Dijkstra's algorithm.
FciOrientDijkstra.Graph - Class in edu.cmu.tetrad.search: Represents a graph for Dijkstra's algorithm.
Fcit - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: This class represents the FCI Targeted Testing (FCIT) algorithm, which is variant of the *-FCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.
Fcit - Class in edu.cmu.tetrad.search: The FCI Targeted Testing (FCIT) algorithm: learns a PAG from observational data with latent variables.
Fcit() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: This class represents a FCIT algorithm.
Fcit(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: FCIT is a class that represents a FCIT algorithm.
Fcit(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.Fcit: FCIT constructor.
FCIT_CORRECTNESS - Static variable in class edu.cmu.tetrad.util.Params: Constant FCIT_CORRECTNESS="fcitCorrectness".
FCIT_STARTS_WITH - Static variable in class edu.cmu.tetrad.util.Params: Constant FCIT_STARTS_WITH="fcitStartsWith"
Fcit.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
Fcit2 - Class in edu.cmu.tetrad.search: The FCI Targeted Testing (FCIT) algorithm: learns a PAG from observational data with latent variables.
Fcit2(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.Fcit2: FCIT constructor.
Fcit2.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitBenchmarkHarness - Class in edu.cmu.tetrad.search.harness: The FcitBenchmarkHarness class serves as a benchmarking tool for evaluating various causal inference algorithms.
FcitBenchmarkHarness() - Constructor for class edu.cmu.tetrad.search.harness.FcitBenchmarkHarness: Default constructor for the FcitBenchmarkHarness class.
FcitBenchmarkHarness100 - Class in edu.cmu.tetrad.search.harness: The FcitBenchmarkHarness100 class serves as a benchmarking tool for evaluating various causal discovery algorithms on synthetic data.
FcitBenchmarkHarness100() - Constructor for class edu.cmu.tetrad.search.harness.FcitBenchmarkHarness100: Constructs an instance of the FcitBenchmarkHarness100 class.
FcitKeepKnowledgeOrientations - Class in edu.cmu.tetrad.search: The FCI Targeted Testing (FCIT) algorithm: learns a PAG from observational data with latent variables.
FcitKeepKnowledgeOrientations(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: FCIT constructor.
FcitKeepKnowledgeOrientations.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitMagMarkov - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: This class represents the FCI Targeted Testing (FCIT) algorithm, which is variant of the *-FCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.
FcitMagMarkov - Class in edu.cmu.tetrad.search: A Markov-preserving variant of FcitZm.
FcitMagMarkov() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: This class represents a FCIT algorithm.
FcitMagMarkov(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: FCIT is a class that represents a FCIT algorithm.
FcitMagMarkov(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitMagMarkov: FCIT constructor.
FcitMagMarkov.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitMarkov - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: This class represents the FCI Targeted Testing (FCIT) algorithm, which is variant of the *-FCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.
FcitMarkov - Class in edu.cmu.tetrad.search: A Markov-preserving variant of Fcit.
FcitMarkov() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: This class represents a FCIT algorithm.
FcitMarkov(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: FCIT is a class that represents a FCIT algorithm.
FcitMarkov(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitMarkov: FCIT constructor.
FcitMarkov.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitSepsets - Class in edu.cmu.tetrad.search.utils: FCIT's separator search: recursive blocking with an enumerated not-followed set, spanned over all subsets of the common neighbours of the pair.
FcitSepsets.SepsetResult - Record Class in edu.cmu.tetrad.search.utils: A found separator together with the p-value of the test that confirmed it.
FcitSepsets.SepsetSearch - Record Class in edu.cmu.tetrad.search.utils: Three-valued search outcome.
FcitSl - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: This class represents the FCI Targeted Testing (FCIT) algorithm, which is variant of the *-FCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.
FcitSl - Class in edu.cmu.tetrad.search: FCIT-SL ("Step Lemma"): FCIT-ZM extended with a Step-Lemma-guided representative search.
FcitSl() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: This class represents a FCIT algorithm.
FcitSl(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: FCIT is a class that represents a FCIT algorithm.
FcitSl(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitSl: FCIT constructor.
FCITSL_ALLOW_CLASS_ESCAPE - Static variable in class edu.cmu.tetrad.util.Params: Constant FCITSL_ALLOW_CLASS_ESCAPE="fcitSlAllowClassEscape"
FcitSl.CommitGate - Enum Class in edu.cmu.tetrad.search: Commit gate.
FcitSl.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitSl2 - Class in edu.cmu.tetrad.search: FCIT-SL ("Step Lemma"): FCIT-ZM extended with a Step-Lemma-guided representative search.
FcitSl2(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitSl2: FCIT constructor.
FcitSl2.CommitGate - Enum Class in edu.cmu.tetrad.search: Commit gate.
FcitSl2.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitSlKeepKnowledgeOrientations - Class in edu.cmu.tetrad.search: KEEP-KNOWLEDGE VARIANT. This class is the knowledge-orientation-keeping variant of FcitSl, parallel to StarFciKeepKnowledgeOrientations and FcitKeepKnowledgeOrientations.
FcitSlKeepKnowledgeOrientations(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: FCIT constructor.
FcitSlKeepKnowledgeOrientations.CommitGate - Enum Class in edu.cmu.tetrad.search: Commit gate.
FcitSlKeepKnowledgeOrientations.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitSlScoreCheck - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: FCIT-SL-Score-Check: the FCIT-SL algorithm with a MAG BIC SCORE CHECK applied to every candidate the search adjudicates.
FcitSlScoreCheck - Class in edu.cmu.tetrad.search: FCIT-SL-SCORE-CHECK: FcitSl with a MAG SCORE CHECK on every candidate the search adjudicates.
FcitSlScoreCheck() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: This class represents a FCIT algorithm.
FcitSlScoreCheck(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: FCIT is a class that represents a FCIT algorithm.
FcitSlScoreCheck(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitSlScoreCheck: FCIT constructor.
FcitSlScoreCheck.CommitGate - Enum Class in edu.cmu.tetrad.search: Commit gate.
FcitSlScoreCheck.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitZm - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: This class represents the FCI Targeted Testing (FCIT) algorithm, which is variant of the *-FCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.
FcitZm - Class in edu.cmu.tetrad.search: FCIT with a MAG-side legality gate.
FcitZm() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: This class represents a FCIT algorithm.
FcitZm(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: FCIT is a class that represents a FCIT algorithm.
FcitZm(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitZm: FCIT constructor.
FcitZm.COMMIT_ROUTE - Enum Class in edu.cmu.tetrad.search: How an accepted deletion is carried forward past the MAG legality gate.
FcitZm.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitZm2 - Class in edu.cmu.tetrad.search: FCIT with a MAG-side legality gate.
FcitZm2(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitZm2: FCIT constructor.
FcitZm2.COMMIT_ROUTE - Enum Class in edu.cmu.tetrad.search: How an accepted deletion is carried forward past the MAG legality gate.
FcitZm2.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
FcitZmKeepKnowledgeOrientations - Class in edu.cmu.tetrad.search: FCIT with a MAG-side legality gate.
FcitZmKeepKnowledgeOrientations(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: FCIT constructor.
FcitZmKeepKnowledgeOrientations.COMMIT_ROUTE - Enum Class in edu.cmu.tetrad.search: How an accepted deletion is carried forward past the MAG legality gate.
FcitZmKeepKnowledgeOrientations.START_WITH - Enum Class in edu.cmu.tetrad.search: Enumeration representing different start options.
fdr - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: False discovery rate method
fdr - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The fdr method.
fdr(double, List) - Static method in class edu.cmu.tetrad.util.StatUtils: fdr.
fdr(double, List, boolean, boolean) - Static method in class edu.cmu.tetrad.util.StatUtils: fdr.
FDR_Q - Static variable in class edu.cmu.tetrad.util.Params: Constant FDR_Q="fdrQ"
fdrCutoff(double, List, boolean) - Static method in class edu.cmu.tetrad.util.StatUtils: fdrCutoff.
fdrCutoff(double, List, boolean, boolean) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the cutoff value for p-values using the FDR method.
fdrCutoff(double, List, int[], boolean, boolean) - Static method in class edu.cmu.tetrad.util.StatUtils: fdrCutoff.
fdrQ(List, int) - Static method in class edu.cmu.tetrad.util.StatUtils: fdrQ.
Feature construction - Section in class edu.cmu.tetrad.search.test.Rcit
FfCi - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for FFCI test with a reduced parameter set.
FfCi - Class in edu.cmu.tetrad.search.test: A kernel-based conditional independence (CI) test for datasets containing any mix of continuous and discrete variables, implementing the Feature-Function CI (FF-CI) framework.
FfCi() - Constructor for class edu.cmu.tetrad.algcomparison.independence.FfCi: Default constructor for the FfCi class.
FfCi(DataSet) - Constructor for class edu.cmu.tetrad.search.test.FfCi: Constructs a new FfCi instance for conducting conditional independence tests on a dataset that may contain both continuous and discrete variables.
FfCi(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.test.FfCi: Constructs a new FfCi instance for conducting conditional independence tests on a dataset that may contain both continuous and discrete variables.
FfCi(DataSet, Parameters, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.FfCi: Constructs an instance with an explicit missing-data specification.
FfCi1 - Class in edu.cmu.tetrad.search.test: FF-CI-Mixed: Feature-Function CI test for mixed continuous/discrete data.
FfCi1(DataSet) - Constructor for class edu.cmu.tetrad.search.test.FfCi1: Constructs an instance of FfCiMixed with the specified dataset and default parameters.
FfCi1(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.test.FfCi1: Constructs a new FfCiMixed instance for conducting conditional independence tests on a dataset that may contain both continuous and discrete variables.
FfCiContinuous - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for FF-CI-Continuous test.
FfCiContinuous - Class in edu.cmu.tetrad.search.test: FF-CI (Fast Fourier / Feature-based Conditional Independence Test) for continuous data.
FfCiContinuous() - Constructor for class edu.cmu.tetrad.algcomparison.independence.FfCiContinuous: `Kci` constructor.
FfCiContinuous(DataSet) - Constructor for class edu.cmu.tetrad.search.test.FfCiContinuous: Constructs FF-CI with parameters.
FfCiContinuous(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.FfCiContinuous: Constructs FF-CI with an explicit missing-data specification.
FfCiContinuous.Approx - Enum Class in edu.cmu.tetrad.search.test: Represents different approximation methods that can be used for statistical or mathematical computations.
FfCiContinuous.FeatureType - Enum Class in edu.cmu.tetrad.search.test: Enum representing the feature generation methods for random Fourier features (RFF) and orthogonal random features (ORF).
FfCordContinuous - Class in edu.cmu.tetrad.search.score: FF-DML: Feature-Function Distributional Marginal Likelihood score (GP form, continuous)
FfCordContinuous(DataSet) - Constructor for class edu.cmu.tetrad.search.score.FfCordContinuous: Constructs a KffMl instance for performing kernel-based statistical analysis on the given dataset.
FfCordContinuous.Channel - Enum Class in edu.cmu.tetrad.search.score: Response channels for the distributional score.
FfCordContinuous.FeatureType - Enum Class in edu.cmu.tetrad.search.score: Represents the types of features that can be used in random feature mappings.
FfDmlContinuous - Class in edu.cmu.tetrad.search.score: FF-DML: Feature-Function Distributional Marginal Likelihood score (GP form, continuous)
FfDmlContinuous(DataSet) - Constructor for class edu.cmu.tetrad.search.score.FfDmlContinuous: Constructs a KffMl instance for performing kernel-based statistical analysis on the given dataset.
FfDmlContinuous.Channel - Enum Class in edu.cmu.tetrad.search.score: Response channels for the distributional score.
FfDmlContinuous.FeatureType - Enum Class in edu.cmu.tetrad.search.score: Represents the types of features that can be used in random feature mappings.
ffml() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the FFML score for a given dataset and DAG.
FfMl - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for FF-ML MIxed Marginal Likelihood Score.
FfMl - Class in edu.cmu.tetrad.search.score: FFML: Feature-Function Marginal Likelihood score for mixed (continuous + discrete) parents
FfMl() - Constructor for class edu.cmu.tetrad.algcomparison.score.FfMl: Constructs a new instance of the SemBicScore.
FfMl(DataSet) - Constructor for class edu.cmu.tetrad.search.score.FfMl: Constructs an instance of FfMlMixed for the given dataset.
FFML_FF_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant FFML_FF_FEATURES="ffmlFfFeatures"
FFML_INTERACTION_LAMBDA - Static variable in class edu.cmu.tetrad.util.Params: Constant FFML_INTERACTION_LAMBDA="ffmlInteractionLambda" Weight of the discrete-continuous interaction (product-kernel) term in the FFML score; 0 = additive (BF/DG-style) discrete handling.
FFML_RIDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant FFML_RIDGE="ffmlRidge"
FfMl.FeatureType - Enum Class in edu.cmu.tetrad.search.score: Represents the type of features used in the model.
FfMlContinuous - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for FF Marginal Likelihood Score.
FfMlContinuous - Class in edu.cmu.tetrad.search.score: FF-ML: Feature-Function Marginal Likelihood score (GP form, continuous)
FfMlContinuous() - Constructor for class edu.cmu.tetrad.algcomparison.score.FfMlContinuous: Constructs a new instance of the SemBicScore.
FfMlContinuous(DataSet) - Constructor for class edu.cmu.tetrad.search.score.FfMlContinuous: Constructs a KffMl instance for performing kernel-based statistical analysis on the given dataset.
FfMlContinuous.FeatureType - Enum Class in edu.cmu.tetrad.search.score: Represents the types of features that can be used in random feature mappings.
Fges - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: FGES (the heuristic version).
Fges - Class in edu.cmu.tetrad.search: Implements the Fast Greedy Equivalence Search (FGES) algorithm.
Fges() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Constructor for Fges.
Fges(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Constructor for Fges.
Fges(Score) - Constructor for class edu.cmu.tetrad.search.Fges: Constructor.
FGES - Enum constant in enum class edu.cmu.tetrad.search.Cstar.CpdagAlgorithm: The FGES algorithm.
FGES - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

FGES with the BIC score.
FGES - Enum constant in enum class edu.cmu.tetrad.search.harness.VertexRepairSimulation.StartingGraph: Run FGES and use its output as the starting graph.
FGES - Enum constant in enum class edu.cmu.tetrad.search.Rlcd.Stage1Method: FGES with the SEM BIC score (closest to the GES stage of causal-learn).
FGES - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Constant for the FGES algorithm.
FGES_FCI - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Constant for the GFCI algorithm.
FgesConcatenated - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Requires that the parameter 'randomSelectionSize' be set to indicate how many datasets should be taken at a time (randomly).
FgesConcatenated(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Constructor for FgesConcatenated.
FgesConcatenated(ScoreWrapper, boolean) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Constructor for FgesConcatenated.
FgesConcatenated(ScoreWrapper, Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Constructor for FgesConcatenated.
FgesMb - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: FGES-MB (the heuristic version).
FgesMb - Class in edu.cmu.tetrad.search: Implements the Fast Greedy Equivalence Search (FGES) algorithm.
FgesMb() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Constructor for FgesMb.
FgesMb(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Constructor for FgesMb.
FgesMb(Score) - Constructor for class edu.cmu.tetrad.search.FgesMb: Constructor.
FgesOrienter - Class in edu.cmu.tetrad.search.utils: This Orients a given undirected graph such that the edges in the graph are a superset of the edges in the oriented graph, using FGES method.
FgesOrienter(DataSet) - Constructor for class edu.cmu.tetrad.search.utils.FgesOrienter: The data set must either be all continuous or all discrete.
Fgls - Enum constant in enum class edu.cmu.tetrad.sem.ScoreType: The FGLS score
fifthMoment - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The fifth moment.
Figure4Harness - Class in edu.cmu.tetrad.search.harness: Clark's Figure 4, both components in one run, against the current PcAR + seeded-repair stack.
Figure4Harness() - Constructor for class edu.cmu.tetrad.search.harness.Figure4Harness: Default constructor for the Figure4Harness class.
FILE_OUT_PATH - Static variable in class edu.cmu.tetrad.util.Params: Constant FILE_OUT_PATH="fileOutPath"
fillIn(Graph, Node[]) - Static method in class edu.cmu.tetrad.bayes.GraphTools: Apply Tarjan and Yannakakis (1984) fill in algorithm for graph triangulation.
fillRowFromData(DataSet, int, double[], int[]) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Copies one observed row into the parallel continuous / discrete row arrays used by the mechanisms, so a child can be generated conditional on an observed parent configuration.
filter(DataSet) - Method in class edu.cmu.tetrad.bayes.ExtraCategoryInterpolator: Interpolates the given data set, producing a data set with no missing values.
filter(DataSet) - Method in class edu.cmu.tetrad.bayes.ModeInterpolator: Interpolates the given data set, producing a data set with no missing values.
filter(DataSet) - Method in class edu.cmu.tetrad.data.CaseExpander: Interpolates the given data set, producing a data set with no missing values.
filter(DataSet) - Method in interface edu.cmu.tetrad.data.DataFilter: Interpolates the given data set, producing a data set with no missing values.
filter(DataSet) - Method in class edu.cmu.tetrad.data.MeanInterpolator: Interpolates the given data set, producing a data set with no missing values.
filterByAnnotation(List, Class) - Method in class edu.cmu.tetrad.annotation.AbstractAnnotations: Filter annotated classes by annotation type.
filterByAnnotations(Class, List) - Static method in class edu.cmu.tetrad.annotation.AnnotatedClassUtils: Filters a list of annotated classes by the given annotation.
filterClusters(Predicate) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Removes, from the clusters determined for the current round, those that fail a test.
filterOutByAnnotation(List, Class) - Method in class edu.cmu.tetrad.annotation.AbstractAnnotations: Filter out annotated classes by annotation type.
filterOutDeprecated(List) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Filters out classes annotated with the Deprecated annotation from the provided list.
filterOutExperimental(List) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Filters out algorithms that are not for the given data type.
filterOutExperimental(List) - Method in class edu.cmu.tetrad.annotation.ScoreAnnotations: filterOutExperimental.
filterOutExperimental(List) - Method in class edu.cmu.tetrad.annotation.TestOfIndependenceAnnotations: filterOutExperimental.
finalOrientation(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Determines the final orientation of the given graph based on the rule set being used.
finalOrientation(Graph, boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Orients the graph (in place) according to rules in the graph (FCI step D).
find(DataSet, double) - Static method in class edu.cmu.tetrad.data.audit.DeterministicClusters: Finds deterministic constraints among the continuous variables of the dataset: while some continuous variable has residual fraction below the threshold given the other active continuous variables, its (screened) support is pruned to a minimal set still meeting the threshold, the constraint is recorded, and the variable is removed from the active set -- so multiple and overlapping constraints are each reported once.
FIND_ONE_FACTOR_CLUSTERS - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: FOFC algorithm
FIND_TWO_FACTOR_CLUSTERS - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: FTFC algorithm.
findChildren(Cover, boolean) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: The children of a cover (Python findChildren).
findChildrenOfAllSubSets(Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: The union of children over all keys whose variables lie within the variables of Ls (Python findChildrenOfAllSubSets).
findChokePoint(Node, Node, Map) - Method in class edu.cmu.tetrad.search.utils.MsepVertexCutFinder: Finds the choke points between a source node and a sink node within a given graph, considering a specific ancestor map and utilizing a d-separation-aware approach.
findCliques(int[][], int) - Static method in class edu.cmu.tetrad.graph.Paths.AllCliquesAlgorithm: Find all cliques in a graph.
findClusters() - Method in class edu.cmu.tetrad.search.Fofc: Runs the search and returns a graph of clusters with the ir respective latent parents.
findClusters() - Method in class edu.cmu.tetrad.search.Ftfc: Runs the search and returns a graph of clusters with the ir respective latent parents.
findClusters() - Method in class edu.cmu.tetrad.search.Gffc: Runs the search and returns a graph of clusters with the ir respective latent parents.
findClusters() - Method in class edu.cmu.tetrad.search.Tsc: Identifies clusters of variables and associates each cluster with a rank.
findClusters(int, int) - Method in class edu.cmu.tetrad.search.ScoredClusterFinder: Find all clusters of size 'size' inside Vsub whose RCCA-BIC score is maximized at rank 'targetRank' when contrasted with D = Vsub \ C.
findClusters(LatentGroups) - Method in class edu.cmu.tetrad.search.rlcd.RlcdClusterSearch: Runs the full stage-2 search on the structure (Python findClusters followed by the finish step).
findClustersAtRank(List, int, int) - Method in class edu.cmu.tetrad.search.Tsc: Calculates clusters of integers based on the given variables, size, and rank.
findClustersAtRank(List, int, int, double) - Method in class edu.cmu.tetrad.search.Tsc: Identifies clusters of variables at alpha specified rank using the supplied alpha.
findComponents() - Method in class edu.cmu.tetrad.search.FastIca: Run FastICA and return preprocessed X, whitening K, unmixing W, and sources S.
findDdpSepsetRecursive(IndependenceTest, Graph, Node, Node, int, int, int, PreserveMarkov, long) - Static method in class edu.cmu.tetrad.search.RecursiveDiscriminatingPathRule: Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate, with no independence-check counter and the given timeout.
findDdpSepsetRecursive(IndependenceTest, Graph, Node, Node, int, int, int, PreserveMarkov, IndependenceCheckCounter) - Static method in class edu.cmu.tetrad.search.RecursiveDiscriminatingPathRule: Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate, with an independence-check counter and no timeout.
findDdpSepsetRecursive(IndependenceTest, Graph, Node, Node, int, int, int, PreserveMarkov, IndependenceCheckCounter, long) - Static method in class edu.cmu.tetrad.search.RecursiveDiscriminatingPathRule: Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate.
findDdpSepsetRecursive(IndependenceTest, Graph, Node, Node, int, int, int, PreserveMarkov, IndependenceCheckCounter, long, boolean) - Static method in class edu.cmu.tetrad.search.RecursiveDiscriminatingPathRule: Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, with control over how the set is chosen when more than one candidate renders x and y independent.
findDescendants(Cover, boolean) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: All descendants of a cover under LatentGroups.findChildren(Cover, boolean) (Python findDescendants).
findHittingSet(List) - Static method in class edu.cmu.tetrad.search.work_in_progress.IonHittingSet: takes a List of HashSets of GraphChanges, and returns a List of GraphChanges.
findImpureClusters() - Method in class edu.cmu.tetrad.search.Tgin.TginOrientationStages: Deprecated.

Groups original latents into connected components joined by undirected edges.
FindingCode - Enum Class in edu.cmu.tetrad.data.audit: Machine-readable codes for the findings a DataAudit can emit.
findMb(Node) - Method in class edu.cmu.tetrad.search.GrowShrink: Finds the Markov blanket (MB) for a given target node.
findMb(Node) - Method in interface edu.cmu.tetrad.search.IMbSearch: Given the target, this returns all the nodes in the Markov Blanket.
findMb(Node) - Method in class edu.cmu.tetrad.search.PcMb: Given the target, this returns all the nodes in the Markov Blanket.
findMb(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.Iamb: Given the target, this returns all the nodes in the Markov Blanket.
findMb(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IambnPc: Given the target, this returns all the nodes in the Markov Blanket.
findMb(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.InterIamb: Given the target, this returns all the nodes in the Markov Blanket.
findMb(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.Mmmb: Given the target, this returns all the nodes in the Markov Blanket.
findMConnectingPaths(Graph, Node, Node, Collection) - Static method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath: Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z.
findMConnectingPathsOfLength(Graph, Node, Node, Collection, Integer) - Static method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath: Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z for a particular path length.
findMeasuredSubset(Cover) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: The observed covers whose names appear among the cover's variables (Python findMeassuredSubset).
findOptimalAssignment() - Method in class edu.cmu.tetrad.search.utils.HungarianAlgorithm: find an optimal assignment
findParents(Collection, boolean, boolean) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Finds the parents of a set of covers: all keys having any of them as a child (Python findParents), deduplicated.
findPathToTarget(Graph, Node, Node, Node, Set, Set, int, Set, Map) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose: Finds and returns a path from node 'a' to the target node 'b' in a given graph considering various constraints.
findPathToTarget(Graph, Node, Node, Node, Set, Set, int, Set, Map, RecursiveBlockingVerbose.Tracer) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose: Recursive method to find and evaluate the path between two nodes in a graph.
findPathToTargetVisit(Graph, Node, Node, Node, Set, Set, int, Set, Map) - Static method in class edu.cmu.tetrad.search.RecursiveBlocking1: Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.
findSepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Returns the sepset that contains the greedy test for variables x and y in the given graph.
findSubcovers(Cover, boolean) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: All covers (keys and observed covers) whose variables are a strict subset of the cover's (Python findSubcovers).
findTerm(int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Finds the first term matching the given profile.
findViolation(Knowledge, Collection, Collection) - Static method in class edu.cmu.tetrad.search.rlcd.TierPrecedence: Whether some member of ancestors is in a strictly later tier than some member of descendants.
fine(String) - Method in class edu.cmu.tetrad.util.LogUtils: fine.
finer(String) - Method in class edu.cmu.tetrad.util.LogUtils: finer.
finest(String) - Method in class edu.cmu.tetrad.util.LogUtils: finest.
FIRST - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: Keep the first row in file order.
FirstInflection - Class in edu.cmu.tetrad.algcomparison.algorithm: First inflection point.
FirstInflection(Algorithm, String, double, double, double) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.FirstInflection: Constructor for FirstInflection.
fishDep() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the fishDep record component.
fisher - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Fisher's method
fisher - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The fisher method.
FISHER_EPSILON - Static variable in class edu.cmu.tetrad.util.Params: Constant FISHER_EPSILON="fisherEpsilon"
fisher2 - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Fisher's method
fisher2 - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The fisher method with only available p values.
FisherZ - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Fisher Z test.
FisherZ - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.IndependenceTestType: Constant for the FisherZ independence test.
FisherZ() - Constructor for class edu.cmu.tetrad.algcomparison.independence.FisherZ: Constructs a new instance of the algorithm.
fishInd() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the fishInd record component.
fit() - Method in class edu.cmu.tetrad.sem.NNEstimator: Trains one neural network per node (given its parents) on the observed data.
fit() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Fit one mechanism per node given its parents in the supplied DAG.
fit() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Fit one mechanism per node given its parents in the supplied DAG.
fit() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Fit one mechanism per node given its parents in the supplied DAG.
fit() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Fit one mechanism per node given its parents in the supplied DAG.
fit(double[][], GaussianMixtureEM.Config) - Static method in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM: Fits a Gaussian Mixture Model (GMM) to the input data using the Expectation-Maximization (EM) algorithm.
fit(DataSet) - Method in class edu.cmu.tetrad.search.Lingam: Fits a dataset to estimate a weight matrix using the LiNGAM algorithm.
fit(DataSet) - Method in class edu.cmu.tetrad.search.LingD: Convenience: estimate W via FastICA, then enumerate B̂ candidates.
fit(DataSet, Node, List) - Method in class edu.cmu.tetrad.search.unmix.LinearQRRegressor: Fits the linear regression model to the given data.
fit(DataSet, Node, List) - Method in interface edu.cmu.tetrad.search.unmix.ResidualRegressor: Fits a local mechanism for the target variable using its parents from the provided dataset.
fitAndSimulate(int) - Method in class edu.cmu.tetrad.sem.NNEstimator: Convenience method: fits the model and immediately simulates sampleSize rows.
FitConGraphResult(DoubleMatrix2D, double, int, int) - Constructor for class edu.cmu.tetrad.sem.Ricf.FitConGraphResult: The result.
fitNullsByPermutation(double[][], List, int, long) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: Estimates the null of the per-pair LRT for every df class by permutation, for a score built on a Gaussian likelihood over blocks of columns (SEM BIC with one column per variable; Basis Function or Degenerate Gaussian BIC with one embedded block per variable).
fitNullsByPermutation(DataSet, int, long) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Estimates, by permutation on the raw data, the null distribution of this score's gain for adding one parent, per degrees-of-freedom class.
Fitter(DataSet, double) - Constructor for class edu.cmu.tetrad.data.audit.DeterministicClusters.Fitter: Constructs the fitter, computing means, standard deviations, and the correlation matrix once.
fixDirections(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Processes the given graph by fixing the directions of edges to ensure consistency, flipping edges where necessary, and optionally preserving ancillary graph information.
fixLatentErrorVariances() - Method in class edu.cmu.tetrad.sem.SemPm: fixLatentErrorVariances.
fixLatents1(int, Graph) - Static method in class edu.cmu.tetrad.graph.RandomGraph: fixLatents1.
fixLatents4(int, Graph) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Adjusts the latent nodes in the given graph based on the specified number of latent confounders.
fixOneLoadingPerLatent() - Method in class edu.cmu.tetrad.sem.SemPm: fixOneLoadingPerLatent.
FloatDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of float data.
FloatDataBox(float[][]) - Constructor for class edu.cmu.tetrad.data.FloatDataBox: Constructs a new data box using the given 2D float data array as data.
floor(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the largest (closest to positive infinity) double value that is less than or equal to the argument and is equal to a mathematical integer.
Flop - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: FLOP (Fast Learning of Order and Parents).
Flop - Class in edu.cmu.tetrad.search: Implements the FLOP (Fast Learning of Order and Parents) algorithm.
Flop() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Flop: Constructs a new FLOP algorithm.
Flop(DataSet) - Constructor for class edu.cmu.tetrad.search.Flop: Constructs a FLOP search over the given continuous dataset.
Flop(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.Flop: Constructs a FLOP search over the given covariance matrix.
FLOP_NUM_RESTARTS - Static variable in class edu.cmu.tetrad.util.Params: Constant FLOP_NUM_RESTARTS="flopNumRestarts"
FlopInitialOrder - Class in edu.cmu.tetrad.search.utils: Computes the principled initial variable order proposed in Section 4.1 of Wienobst, Henckel, and Weichwald (2026), "Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning" (the FLOP paper), for use with order-based permutation searches such as BOSS or FLOP.
flush() - Method in class edu.cmu.tetrad.util.TetradLogger: Flushes the writers.
Fml - Enum constant in enum class edu.cmu.tetrad.sem.ScoreType: The FML score
fnAdj() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the fnAdj record component.
fnAhd() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the fnAhd record component.
fofc(DataSet, double, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: Creates a new instance of a FofcBlockDiscoverer, which is an implementation of the BlockDiscoverer interface.
fofc(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: FOFC over a pooled covariance matrix; see FofcBlockDiscoverer.
fofc(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean, String) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: Creates a FOFC block discoverer that runs in stratified (domain-pooled) mode when a domain column is named; see FofcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean, String).
Fofc - Class in edu.cmu.tetrad.search: Generalized Find Factor Clusters (GFFC).
Fofc(CorrelationMatrix, double, int) - Constructor for class edu.cmu.tetrad.search.Fofc: Constructs an instance of the Fofc class from a correlation matrix, for use when the correlations do not come from a single raw data set--for example, the pooled correlations of several imputations of a data set with missing values.
Fofc(DataSet, double, int) - Constructor for class edu.cmu.tetrad.search.Fofc: Constructs an instance of the Fofc class using the given dataset, significance level, and equivalent sample size.
FOFC_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant FOFC_ALPHA="fofcAlpha"
FofcBlockDiscoverer - Class in edu.cmu.tetrad.search.blocks: A concrete implementation of the BlockDiscoverer interface that discovers clusters or "blocks" of indices using the FOFC algorithm.
FofcBlockDiscoverer(DataSet, double, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.FofcBlockDiscoverer: Constructs a new instance of FofcBlockDiscoverer, which is used to discover clusters or "blocks" of indices in a dataset based on the FOFC algorithm.
FofcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.FofcBlockDiscoverer: Constructs a FofcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
FofcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean, String) - Constructor for class edu.cmu.tetrad.search.blocks.FofcBlockDiscoverer: Constructs a FofcBlockDiscoverer that runs in stratified (domain-pooled) mode when a domain column is named: the rows are split by that column, which is removed from the variable list, and every rank decision pools the within-domain Bartlett statistics across domains (the Phase 1 upgrade of LCD-NOD, Dong et al., ICML 2026).
FofcRunner - Class in edu.cmu.tetrad.search.harness.tsc: AlgorithmRunner implementation for the FOFC (Find One Factor Clusters) algorithm.
FofcRunner(double) - Constructor for class edu.cmu.tetrad.search.harness.tsc.FofcRunner: Creates a FOFC runner with the given significance level.
forbid_latent_common_causes - Enum constant in enum class edu.cmu.tetrad.annotation.AlgType: If an algorithm forbids latent common causes.
forbidArrowheadsInto(Node) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Prohibits arrowheads into the given node (it is never used in the Y role).
forbidArrowheadsInto(Collection) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Prohibits arrowheads into the given nodes (they are never used in the Y role).
FORBIDDEN - Static variable in class edu.cmu.tetrad.data.KnowledgeGroup: Constant FORBIDDEN=2
forbiddenEdgesIterator() - Method in class edu.cmu.tetrad.data.Knowledge: Iterator over the KnowledgeEdge's representing forbidden edges.
forbiddenViolations(Graph, Knowledge) - Static method in class edu.cmu.tetrad.search.CheckKnowledge: Returns a sorted list of edges that violate the given knowledge.
forbidSelfMasking() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the value of the forbidSelfMasking record component.
forDataSet(DataSet) - Static method in class edu.cmu.tetrad.data.missing.TestwiseRows: Returns the (shared, cached) instance for the given dataset, creating it if necessary.
format(BlockSpec) - Static method in class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec: Formats the given BlockSpec object into a textual representation.
FORMAT - Static variable in class edu.cmu.tetrad.hybridcg.HybridCgIo: The format tag written to and required of every file.
formatErrorsArray(double[], String) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: formatErrorsArray.
forMatrix(Matrix) - Static method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: The shared instance for the given raw data matrix, created on first request.
forMatrix(Matrix) - Static method in class edu.cmu.tetrad.data.missing.TestwiseRows: Returns the (shared, cached) instance for the given raw data matrix, in which missing entries are NaN, creating it if necessary.
forward(NDManager, NDArray) - Method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronDjl: Computes the forward pass of the neural network for a given input.
found() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: True iff a blocking set was found.
fpAdj() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the fpAdj record component.
fpAhd() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the fpAhd record component.
fPdf(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: fPdf.
fQuantile(double, double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: fQuantile.
fracClipped - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the fraction of observations that were clipped during the effect estimation process.
fracConf80 - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.EntropyStats: Represents the fraction of data points in the clustering result where the maximum responsibility value assigned to a cluster for a data point (r_ik) is greater than or equal to 0.80.
fracConf90 - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.EntropyStats: Represents the fraction of data points in the clustering result where the maximum responsibility value assigned to a cluster for a data point (r_ik) is greater than or equal to 0.90.
fracDepDep() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the fracDepDep record component.
fracDepDep() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the fracDepDep record component.
fracDepInd() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the fracDepInd record component.
fracDepInd() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the fracDepInd record component.
fracNodesBeatBaseline - Variable in class edu.cmu.tetrad.sem.CVReport: Fraction of non-root nodes whose OOS metric beats their respective baseline (R² > 0 for continuous; xent improvement > 0 for discrete).
fracNodesImproved - Variable in class edu.cmu.tetrad.sem.AdequacyReport: Represents the fraction
fracPositiveDeriv - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Fraction of held-out rows on which the local derivative was strictly positive, among rows with a finite derivative.
FractionDependentUnderAlternative - Class in edu.cmu.tetrad.algcomparison.statistic: Estimates whether the p-values under the null are Uniform usign the Markov Checker.
FractionDependentUnderAlternative() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: Constructor for FractionDependentUnderAlternative.
FractionDependentUnderAlternative(double) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: Constructor for FractionDependentUnderAlternative.
FractionDependentUnderNull - Class in edu.cmu.tetrad.algcomparison.statistic: Estimates whether the p-values under the null are Uniform usign the Markov Checker.
FractionDependentUnderNull() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: Constructor for FractionDependentUnderNull.
FractionDependentUnderNull(double) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: Constructor for FractionDependentUnderNull.
fractionMissingInformation() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the fractionMissingInformation record component.
fractionResidual() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns the value of the fractionResidual record component.
fRand(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: F distribution random generator.
from() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.Mark: Returns the value of the from record component.
from(String) - Static method in class edu.cmu.tetrad.util.NaturalSort.LaggedNaturalKey: Creates a LaggedNaturalKey from the given string.
from(String) - Static method in class edu.cmu.tetrad.util.NaturalSort.NaturalKey: Creates a NaturalKey from the given string.
fromAnnotation(String[], String[], double[][], double) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Builds a locus score prior from a per-(SNP, trait) annotation, on the raw log-odds scale: beta_jk = tau * standardised(annotation)_jk.
fromAnnotation(String[], String[], double[][], double, double) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Builds a locus prior from a per-(SNP, trait) annotation.
fromAnnotation(String[], String[], double[][], double, double, AnnotationNormalization) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Builds a locus prior from a per-(SNP, trait) annotation under a selectable normalisation.
fromAnnotation(String[], String[], double[][], double, AnnotationNormalization) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Builds a locus score prior from a per-(SNP, trait) annotation under a selectable normalisation, on the raw log-odds scale: beta_jk = tau * adjusted(annotation)_jk.
fromAnnotationTollScaled(String[], String[], double[][], double, double, int) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Builds a locus score prior on the toll-fraction scale, so that one tau is comparable across PC, BOSS, and FGES despite their very different operating points.
fromAnnotationTollScaled(String[], String[], double[][], double, double, int, AnnotationNormalization) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Builds a locus score prior on the toll-fraction scale under a selectable normalisation: beta_jk = tau * adjusted(annotation)_jk * lambda * log(n) / 2.
fromDataSet(DataSet, String) - Static method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: Constructs the test from a single data set containing a discrete domain column, which is removed from the variable list and used to split the rows; see StratifiedRankTests.split(DataSet, Node).
fromDataSets(List) - Static method in class edu.cmu.tetrad.util.StratifiedRankTests: Constructs the test from one data set per stratum.
fromJson(String) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgIo: Reconstructs a model from JSON text produced by HybridCgIo.toJson(HybridCgIm) (or written by hand or from Python in the same format).
fromLogOdds(String[], double[][]) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Builds a locus score prior directly from a symmetric matrix of prior log-odds, for callers who want to construct beta themselves rather than derive it from an annotation.
fromLogOdds(String[], double[][], double, boolean) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Builds a locus prior directly from a symmetric matrix of prior log-odds, for callers who want to construct beta themselves.
fromMatrix(List, double[][], EdgePriors.Semantics) - Static method in class edu.cmu.tetrad.search.utils.EdgePriors: Builds a store from a dense symmetric matrix and the variable names it is indexed by.
fromMatrix(List, double[][], EdgePriors.Semantics, boolean) - Static method in class edu.cmu.tetrad.search.utils.EdgePriors: Builds a store from a dense symmetric matrix, optionally permitting zero weights.
fromPag(Graph) - Static method in class edu.cmu.tetrad.graph.LegEnumerator: Seeds the enumeration from the canonical Zhang MAG of a PAG, which is a LEG.
fromParameters(Parameters) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: Builds a MissingDataSpec from algcomparison/GUI/py-tetrad parameters (see the MISSING_* constants in Params), or returns null--meaning "legacy default behavior"--if the policy parameter is absent or "default".
fromR(List, int, double[][], int[][]) - Static method in class edu.cmu.tetrad.util.DataSetHelper: Creates a new DataSet instance from the specified inputs, including continuous and discrete data along with variable definitions.
fromSample(long, double[]) - Static method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Fits kappa and nu to a sample of null LRT values.
FRUCHTERMAN_REINGOLD - Enum constant in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: Fruchterman-Reingold; see LayoutUtil.FruchtermanReingoldLayout.
fruchtermanReingoldLayout(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: fruchtermanReingoldLayout.
fruchtermanReingoldLayout(Graph, double) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Fruchterman-Reingold with a chosen natural edge length: the distance at which the textbook pull along an edge and push between its two endpoints balance.
FruchtermanReingoldLayout(Graph) - Constructor for class edu.cmu.tetrad.graph.LayoutUtil.FruchtermanReingoldLayout: Constructs a new FruchtermanReingoldLayout for the given graph.
ftfc(DataSet, double, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: Constructs a FtfcBlockDiscoverer instance, which implements the FTFC algorithm for discovering clusters or "blocks" of indices in a dataset.
ftfc(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: FTFC over a pooled covariance matrix; see FtfcBlockDiscoverer.
Ftfc - Class in edu.cmu.tetrad.search: Generalized Find Factor Clusters (GFFC).
Ftfc(CorrelationMatrix, double, int) - Constructor for class edu.cmu.tetrad.search.Ftfc: Constructs an instance of the Ftfc class from a correlation matrix, for use when the correlations do not come from a single raw data set--for example, the pooled correlations of several imputations of a data set with missing values.
Ftfc(DataSet, double, int) - Constructor for class edu.cmu.tetrad.search.Ftfc: Constructs an instance of Ftfc.
FtfcBlockDiscoverer - Class in edu.cmu.tetrad.search.blocks: A concrete implementation of the BlockDiscoverer interface that discovers clusters or "blocks" of indices using the FTFC algorithm.
FtfcBlockDiscoverer(DataSet, double, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.FtfcBlockDiscoverer: Constructs a new instance of FofcBlockDiscoverer, which is used to discover clusters or "blocks" of indices in a dataset based on the FOFC algorithm.
FtfcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.FtfcBlockDiscoverer: Constructs a FtfcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
FtfcRunner - Class in edu.cmu.tetrad.search.harness.tsc: AlgorithmRunner implementation for the FOFC (Find One Factor Clusters) algorithm.
FtfcRunner(double) - Constructor for class edu.cmu.tetrad.search.harness.tsc.FtfcRunner: Creates a FOFC runner with the given significance level.
full() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.NestedFits: Returns the value of the full record component.
FULL - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.CovarianceMode: Represents the full covariance mode.
FULL - Enum constant in enum class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.CovarianceType: Represents the FULL covariance type for the Gaussian Mixture Model (GMM).
FULL_N - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataSpec.EffectiveSampleSizeMode: Use the full number of rows of the dataset.
FULL_OUTER - Enum constant in enum class edu.cmu.tetrad.data.JoinDatasets.JoinType: Keep every row of both sides; unmatched rows get missing values in the other side's columns.
fullEmbedding(DataSet, int) - Static method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Computes the unpruned embedding layout for a single data set, for callers that need to combine or validate basis-column decisions across data sets.
fullScore(Graph) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Scores the graph with a single RICF fit of the whole graph, no decomposition and no cache.
fullSigmaSafetyMargin - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Safety margin for full sigma estimation.
fullyConnect(Endpoint) - Method in class edu.cmu.tetrad.graph.Dag: Fully connects the given endpoint.
fullyConnect(Endpoint) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Removes all edges from the graph and fully connects it using #-# edges, where # is the given endpoint.
fullyConnect(Endpoint) - Method in interface edu.cmu.tetrad.graph.Graph: Removes all edges from the graph and fully connects it using #-# edges, where # is the given endpoint.
fullyConnect(Endpoint) - Method in class edu.cmu.tetrad.graph.LagGraph: Removes all edges from the graph and fully connects it using #-# edges, where # is the given endpoint.
fullyConnect(Endpoint) - Method in class edu.cmu.tetrad.graph.SemGraph: Removes all edges from the graph and fully connects it using #-# edges, where # is the given endpoint.
fullyConnect(Endpoint) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Fully connects the given endpoint to all other endpoints in the graph.
Function - Interface in edu.cmu.tetrad.util: Interface for a single-argument, double-valued function that can be passed to mathematical routines.
futureNodeVisit(Graph, Node, LinkedList, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: futureNodeVisit.
futureNodeVisit(Graph, Node, LinkedList, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: futureNodeVisit.
futureNodeVisit(Graph, Node, LinkedList, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.VcPc: futureNodeVisit.
futureNodeVisit(Graph, Node, LinkedList, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: futureNodeVisit.
futureNodeVisit(Graph, Node, LinkedList, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: futureNodeVisit.

G

g(double) - Static method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Calculates the logarithm of the hyperbolic cosine of the maximum of x and 0.
G_SQUARE - Enum constant in enum class edu.cmu.tetrad.search.test.ChiSquareTest.TestType: The G-square test.
GacTotalEffectElibility - Class in edu.cmu.tetrad.search: Library-side helper for "Adjustment & Total Effects" style workflows.
GacTotalEffectElibility(Graph, String, int) - Constructor for class edu.cmu.tetrad.search.GacTotalEffectElibility: Constructs a new GacTotalEffectElibility instance with the specified graph, graph type, and maximum path length.
GacTotalEffectElibility.AdjustmentSetProvider - Interface in edu.cmu.tetrad.search: Optional hook for computing adjustment sets.
GacTotalEffectElibility.Eligibility - Class in edu.cmu.tetrad.search: Immutable eligibility result.
GacTotalEffectElibility.JointAdjustmentSetProvider - Interface in edu.cmu.tetrad.search: Optional hook for joint adjustment sets.
GacTotalEffectElibility.Status - Enum Class in edu.cmu.tetrad.search: High-level eligibility states for total-effect computation.
gamma - Variable in class edu.cmu.tetrad.search.CordEngine5: A fixed-gamma quantile value used to represent the median.
gamma(double) - Static method in class edu.cmu.tetrad.util.StatUtils: GAMMA FUNCTION (From DStat, used by permission).
Gamma - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
GAMMA - Enum constant in enum class edu.cmu.tetrad.search.test.FfCiContinuous.Approx: Represents the gamma approximation method.
GAMMA - Enum constant in enum class edu.cmu.tetrad.search.test.Rcit.Approx: Represents the gamma
GAMMA_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant GAMMA_HIGH="gammaHigh"
GAMMA_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant GAMMA_LOW="gammaLow"
gammaApprox_independent_vs_dependent() - Method in class edu.cmu.tetrad.search.KciSmokeTest: Tests the accuracy and behavior of the gamma approximation method for conditional independence testing using the Kci framework.
gammaCdf(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: compute complementary gamma cdf by its continued fraction expansion
gammaMin - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the lower bound for the "adaptive" search process.
gammaPdf(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: gammaPdf.
gammaQuantile(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: gammaQuantile.
gammaRand(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Gamma random generator.
gammaSatterthwaiteP(double, double[]) - Static method in class edu.cmu.tetrad.search.test.QuadraticFormPValues: Gamma (Satterthwaite–Welch) approximation using first two moments.
GAP - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: GAP test, for FTFC.
gate(DataModel, Parameters, boolean, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: The wrapper-level missing-data gate, called at the top of every algcomparison test and score wrapper's getTest/getScore method.
gate(DataModel, Parameters, Set, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: The finer-grained form of MissingDataUtils.gate(DataModel, Parameters, boolean, String), for components that support some native policies but not others (e.g., the Chi-Square test performs test-wise deletion natively but has no EM-covariance route, while Fisher Z supports both).
gate(BlockSpec, Parameters, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: The block-spec variant of MissingDataUtils.gate(DataModel, Parameters, boolean, String), for wrappers that construct block tests and scores from a BlockSpec rather than from the data model handed to getTest/getScore.
gate(BlockSpec, Parameters, Set, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: As MissingDataUtils.gate(BlockSpec, Parameters, String), for block components that implement some policies natively; see MissingDataUtils.gate(DataModel, Parameters, java.util.Set, String) for the meaning of the native set.
Gaussian - Annotation Interface in edu.cmu.tetrad.annotation: Data are normal distributed.
GAUSSIAN - Enum constant in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NoiseModel: A noise model based on the Gaussian (normal) distribution.
GAUSSIAN - Enum constant in enum class edu.cmu.tetrad.search.test.Kci.KernelType: Represents the Gaussian (RBF) kernel, commonly used for non-linear transformations.
GAUSSIAN - Enum constant in enum class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Multiplier: Standard normal multipliers.
GAUSSIAN_FACTOR - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Gaussian Factor.
GAUSSIAN_PROCESS_STRUCTURAL_EQUATION_MODEL - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant NON_LINEAR_STRUCTURAL_EQUATION_MODEL="GP Nonlinear Structural Equation Model"
GAUSSIAN_PVALUE - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: This one will work and does a good job for medium sized models.
GAUSSIAN_SCORE - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: This is very slow.
GAUSSIAN_SCORE_ITERATE - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Even slower.
GAUSSIAN_SCORE_MARKS - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: This will work and does a good job for small models, no more than 4 latents.
GaussianCategoricalIm(GeneralizedSemPm) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: GaussianCategoricalIm.
GaussianCategoricalIm(GeneralizedSemPm, boolean) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: This method is needed to normalize edge parameters for an Instantiated Mixed Model Generates edge parameters for c-d and d-d edges from a single weight, abs(w), drawn by the normal IM constructor.
GaussianCategoricalPm(Graph, String) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: GaussianCategoricalPm.
GaussianMixtureEM - Class in edu.cmu.tetrad.search.unmix: Lightweight Gaussian Mixture EM with full or diagonal covariance.
GaussianMixtureEM() - Constructor for class edu.cmu.tetrad.search.unmix.GaussianMixtureEM: Constructs a new instance of the GaussianMixtureEM class.
GaussianMixtureEM.Config - Class in edu.cmu.tetrad.search.unmix: Configuration class for the Gaussian Mixture Model (GMM) Expectation-Maximization (EM) algorithm.
GaussianMixtureEM.CovarianceType - Enum Class in edu.cmu.tetrad.search.unmix: Specifies the type of covariance matrix used in the Gaussian Mixture Model (GMM) implemented by the GaussianMixtureEM class.
GaussianMixtureEM.Model - Class in edu.cmu.tetrad.search.unmix: Represents the Gaussian Mixture Model (GMM) computed and used in the GaussianMixtureEM class.
gaussianPdf(double[], double[], double[][], GaussianMixtureEM.CovarianceType) - Static method in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM: Computes the probability density function (PDF) of a multivariate Gaussian distribution.
GaussianPower - Class in edu.cmu.tetrad.util.dist: GaussianPower class.
GaussianPower(double) - Constructor for class edu.cmu.tetrad.util.dist.GaussianPower: Constructor for GaussianPower.
GaussianProcessRBF - Class in edu.cmu.tetrad.search.utils: GaussianProcessRBF simulates a Gaussian Process (GP) with a Radial Basis Function (RBF) kernel.
GaussianProcessRBF(double[], double, double, double) - Constructor for class edu.cmu.tetrad.search.utils.GaussianProcessRBF: Constructor for Gaussian Process simulation using an RBF kernel.
Gcm - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for KCI test.
Gcm - Class in edu.cmu.tetrad.search.test: Generalized Covariance Measure (GCM) conditional independence test.
Gcm() - Constructor for class edu.cmu.tetrad.algcomparison.independence.Gcm: `Kci` constructor.
Gcm(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.Gcm: Constructs a new instance of the Gcm class using the provided continuous data set and the specified alpha value.
GCM_CONTROL_FUNCTION - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_CONTROL_FUNCTION="gcmControlFunction"
GCM_MULTIPLIER_SAMPLES - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_MULTIPLIER_SAMPLES="gcmMultiplierSamples"
GCM_REGRESSOR_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_REGRESSOR_TYPE="gcmRegressorType"
GCM_RFF_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_RFF_FEATURES="gcmRffFeatures"
GCM_RFF_SIGMA - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_RFF_SIGMA="gcmRffSigma"
GCM_RIDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_RIDGE="gcmRidge"
GCM_Z_TRUNCATION_LIMIT - Static variable in class edu.cmu.tetrad.util.Params: Constant GCM_Z_TRUNCATION_LIMIT="gcmZTruncationLimit"
Gcm.RegressorType - Enum Class in edu.cmu.tetrad.search.test: Represents the type of regressor to be used in a statistical or machine learning model.
Gdistance - Class in edu.cmu.tetrad.simulation: Created by Erich on 7/3/2016.
Gdistance(DataSet, double, double, double) - Constructor for class edu.cmu.tetrad.simulation.Gdistance: Constructor for Gdistance.
GdistanceApply - Class in edu.cmu.tetrad.simulation: Created by Erich on 7/14/2016.
GdistanceRandom - Class in edu.cmu.tetrad.simulation: Created by Erich on 8/6/2016.
GdistanceRandom(DataSet) - Constructor for class edu.cmu.tetrad.simulation.GdistanceRandom: Constructor for GdistanceRandom.
GdistanceRandomApply - Class in edu.cmu.tetrad.simulation: Created by Erich on 8/6/2016.
GdistanceTest - Class in edu.cmu.tetrad.simulation: Created by Erich on 7/6/2016.
GdistanceUtils - Class in edu.cmu.tetrad.simulation: Created by Erich on 8/11/2016.
GE - Enum constant in enum class edu.cmu.tetrad.search.test.IndTestBlocksLemma10.EqualityMode: Deprecated.

Represents the inequality mode "greater than or equal to" for the Lemma-10 criterion.
Gene(int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch.Gene: Constructor for Gene.
GeneHistory - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements the basic machinery used by all history objects.
GeneHistory(Initializer, UpdateFunction) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Constructs a new history with the given initializer and the given update function.
GenePm - Class in edu.cmu.tetrad.study.gene.tetradapp.model: Implements a parametric gene model.
GenePm(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetradapp.model.GenePm: Construct a new gene pm, wrapping the given lag graph.
General - Annotation Interface in edu.cmu.tetrad.annotation: Data with variables of general distribution.
GENERAL_ADDITIVE_MODEL - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant ADDITIVE_NOISE_MODEL="Additive Noise SEM"
GENERAL_NOISE_SEM - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant CAUSAL_PERCEPTRON_NETWORK="Causal Peceptron Network (CPN)"
GENERAL_SEM_ERROR_TEMPLATE - Static variable in class edu.cmu.tetrad.util.Params: Constant GENERAL_SEM_ERROR_TEMPLATE="generalSemErrorTemplate"
GENERAL_SEM_FUNCTION_TEMPLATE_LATENT - Static variable in class edu.cmu.tetrad.util.Params: Constant GENERAL_SEM_FUNCTION_TEMPLATE_LATENT="generalSemFunctionTemplateLatent"
GENERAL_SEM_FUNCTION_TEMPLATE_MEASURED - Static variable in class edu.cmu.tetrad.util.Params: Constant GENERAL_SEM_FUNCTION_TEMPLATE_MEASURED="generalSemFunctionTemplateMeasured"
GENERAL_SEM_PARAMETER_TEMPLATE - Static variable in class edu.cmu.tetrad.util.Params: Constant GENERAL_SEM_PARAMETER_TEMPLATE="generalSemParameterTemplate"
GENERAL_STRUCTURAL_EQUATION_MODEL - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant GENERAL_STRUCTURAL_EQUATION_MODEL="General Structural Equation Model"
GeneralAdditiveModel - Class in edu.cmu.tetrad.algcomparison.simulation: Wrapper for the neural-net Generalized Additive Model (GAM) simulator.
GeneralAdditiveModel - Class in edu.cmu.tetrad.sem: GeneralAdditiveModel
GeneralAdditiveModel(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Constructs a GeneralAdditiveModel instance with the specified random graph.
GeneralAdditiveModel(Graph, int, Sampler) - Constructor for class edu.cmu.tetrad.sem.GeneralAdditiveModel: Constructs a new generalized additive model simulator.
GeneralAndersonDarlingTest - Class in edu.cmu.tetrad.data: Implements the Anderson-Darling test against the given CDF, with P values calculated as in R's ad.test method (in package nortest).
GeneralAndersonDarlingTest(List, RealDistribution) - Constructor for class edu.cmu.tetrad.data.GeneralAndersonDarlingTest: Constructs an Anderson-Darling test for the given column of data.
GeneralizedSemEstimator - Class in edu.cmu.tetrad.sem: Estimates a Generalized SEM I'M given a Generalized SEM PM and a data set.
GeneralizedSemEstimator() - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemEstimator: Constructs a new GeneralizedSemEstimator.
GeneralizedSemEstimator.MyContext - Class in edu.cmu.tetrad.sem: Context.
GeneralizedSemIm - Class in edu.cmu.tetrad.sem: Represents a generalized SEM-instantiated model.
GeneralizedSemIm(GeneralizedSemPm) - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemIm: Constructs a new GeneralizedSemIm from the given GeneralizedSemPm by picking values for each of the freeParameters from their initial distributions.
GeneralizedSemIm(GeneralizedSemPm, SemIm) - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemIm: Initializes a GeneralizedSemIm object by applying values to free parameters from the given GeneralizedSemPm and SemIm.
GeneralizedSemPm - Class in edu.cmu.tetrad.sem: Parametric model for a Generalized SEM model.
GeneralizedSemPm(Graph) - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemPm: Constructs a BayesPm from the given Graph, which must be convertible first into a ProtoSemGraph and then into a SemGraph.
GeneralizedSemPm(SemGraph) - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemPm: Constructs a new SemPm from the given SemGraph.
GeneralizedSemPm(GeneralizedSemPm) - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemPm: Initializes a new instance of the GeneralizedSemPm class by copying the properties of the provided GeneralizedSemPm object.
GeneralizedSemPm(SemPm) - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemPm: Constructs a new GeneralizedSemPm object based on the given SemPm object.
GeneralNoiseSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a Causal Perceptron Network.
GeneralNoiseSimulation - Class in edu.cmu.tetrad.sem: General-noise simulator: X_j = f_j(Pa(X_j), e_j)
GeneralNoiseSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Constructs a SemSimulation object with the given RandomGraph object.
GeneralNoiseSimulation(Graph, int, Sampler, int[], double, Function) - Constructor for class edu.cmu.tetrad.sem.GeneralNoiseSimulation: Constructs a GeneralNoiseSimulation instance based on the provided parameters.
GeneralNoiseSimulation(Graph, int, Sampler, int[], double, Function, boolean, double) - Constructor for class edu.cmu.tetrad.sem.GeneralNoiseSimulation: Constructs a GeneralNoiseSimulation instance based on the provided parameters.
GeneralSemSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: The GeneralSemSimulation class represents a simulation using a generalized structural equation model (SEM).
GeneralSemSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Constructs a GeneralSemSimulation object with the given RandomGraph object.
GeneralSemSimulation(GeneralizedSemIm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Constructs a GeneralSemSimulation object with the given GeneralizedSemIm object.
GeneralSemSimulation(GeneralizedSemPm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Initializes a GeneralSemSimulation with the given GeneralizedSemPm object.
GeneralSemSimulationSpecial1 - Class in edu.cmu.tetrad.algcomparison.simulation: This was used for a simulation to test the FTFC and FOFC algorithm and contains some carefully selected functions to test nonlinearity and non-Gaussianity.
GeneralSemSimulationSpecial1(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Constructor for GeneralSemSimulationSpecial1.
generate() - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Generates a random graph.
generate() - Method in class edu.cmu.tetrad.sem.GeneralAdditiveModel: Generates a simulated dataset according to the structure of the graph and the specified parameters of the General Additive Model.
generate(int, int, int, boolean, double, double, double) - Method in class edu.cmu.tetrad.search.mimic.RandomMimicGraphGenerator: Generates a random MIMIC model.
generate(Graph) - Static method in class edu.cmu.tetrad.bayes.ModelGenerator: This method takes an acyclic graph as input and returns a list of graphs each of which is a modification of the original graph with either an edge deleted, added or reversed.
generate(Parameters) - Method in class edu.cmu.tetrad.search.mimic.RandomMimicGraphGenerator: Generates a random MIMIC model using values read from the given parameters object.
generateAllResults() - Method in class edu.cmu.tetrad.search.MarkovCheck: Generates all results, for both the Markov and dependency checks, for each node in the graph given the parents of that node.
generateChild(int, double[], int[], Random) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Generates one value of node childIdx from its fitted mechanism, reading the parents from contRow / discRow and writing the result into the child's slot of the same arrays.
generateCpdagDags(Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Generates the list of DAGs in the given cpdag.
generateData() - Method in class edu.cmu.tetrad.sem.AdditiveNoiseSimulation: Generates a synthetic dataset by simulating data propagation through a graph with additive noise.
generateData() - Method in class edu.cmu.tetrad.sem.GeneralNoiseSimulation: Generates a dataset based on the current graph structure, incorporating random noise and a multi-layer perceptron (MLP) for each node in the graph.
generateData() - Method in class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear: Generates synthetic data based on a directed acyclic graph (DAG) with causal relationships and post-nonlinear causal mechanisms.
generateData() - Method in class edu.cmu.tetrad.sem.PostnonlinearSem: Generates synthetic data based on the directed acyclic graph (DAG) with post-nonlinear causal mechanisms.
generateMbDags(Graph, boolean, IndependenceTest, int, Node) - Static method in class edu.cmu.tetrad.search.utils.MbUtils: Generates the list of MB DAGs consistent with the MB CPDAG returned by the previous search.
generateMixedEdgeParams(double, int) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: generateMixedEdgeParams.
generateReportFromExternalAlgorithms(String, String, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Generates a report from external algorithms.
generateReportFromExternalAlgorithms(String, String, Algorithms, Statistics, Parameters, long, TimeUnit) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: generateReportFromExternalAlgorithms.
generateReportFromExternalAlgorithms(String, String, String, Algorithms, Statistics, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Generates a report from external algorithms based on the given parameters.
generateReportFromExternalAlgorithms(String, String, String, Algorithms, Statistics, Parameters, long, TimeUnit) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: generateReportFromExternalAlgorithms.
generateResults(boolean, boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Generates results based on the specified independence and clearing conditions.
generateSubsetMinimal(Collection, int) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: Generates all minimal subsets of the given covers whose variable cardinality exceeds k, where a subset is minimal if dropping its smallest-cardinality member brings the cardinality to at most k.
get(double[], double) - Static method in class edu.cmu.tetrad.util.TaylorSeries: Get the Taylor series with the given derivatives and center.
get(int) - Method in class edu.cmu.tetrad.data.DataModelList
get(int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the node at index j in pi.
get(int) - Method in class edu.cmu.tetrad.util.Vector: get.
get(int, int) - Method in interface edu.cmu.tetrad.bayes.CptMap: Retrieves the value at the specified row and column in the CptMap.
get(int, int) - Method in class edu.cmu.tetrad.bayes.CptMapCounts: Returns the probability of the node taking on the value specified by the given row and column.
get(int, int) - Method in class edu.cmu.tetrad.bayes.CptMapProbs: Returns the probability of the node taking on the value specified by the given row and column.
get(int, int) - Method in class edu.cmu.tetrad.data.ByteDataBox: get.
get(int, int) - Method in interface edu.cmu.tetrad.data.DataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.DoubleDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.FloatDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.IntDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.LongDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.MixedDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.ShortDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: get.
get(int, int) - Method in class edu.cmu.tetrad.util.ConditionalKDE.BatchResult: Retrieves a ConditionalResult2D object associated with the specified pair of indices.
get(int, int) - Method in class edu.cmu.tetrad.util.Matrix: get.
get(int, int) - Method in class edu.cmu.tetrad.util.MView: Retrieves the value at the specified row and column in the matrix view.
get(Node) - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Returns the parents of the node x.
get(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: get.
get(Node, Node) - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Retrieves the sepset previously set for {a, b}, or null if no such set was previously set.
get(Node, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: Retrieves the sepset previously set for {x, y}, or null if no such set was previously set.
get(String) - Method in class edu.cmu.tetrad.search.utils.IndependenceCheckCounter: Count for a single site.
get(String) - Method in class edu.cmu.tetrad.util.AlgorithmDescriptions: Gets the description of the algorithm.
get(String) - Method in class edu.cmu.tetrad.util.IndependenceTestDescriptions: get.
get(String) - Method in class edu.cmu.tetrad.util.ParamDescriptions: get.
get(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the object value of the given parameter, looking up its default in the ParamDescriptions map.
get(String) - Method in class edu.cmu.tetrad.util.ScoreDescriptions: get.
get(String, Object) - Method in class edu.cmu.tetrad.util.Parameters: Returns the object value of the given parameter, using the given default.
get(String, String) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns the stored value for the unordered pair {a, b}, or the neutral value if none.
getA() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the first node.
getA() - Method in class edu.cmu.tetrad.sem.ParameterPair: Method getNumObjects
getA() - Method in class edu.cmu.tetrad.util.ChoiceGenerator: Getter for the field a.
getA() - Method in class edu.cmu.tetrad.util.dist.Split: Getter for the field a.
getA() - Method in class edu.cmu.tetrad.util.SelectionGenerator: Getter for the field a.
getA() - Method in class edu.cmu.tetrad.util.SublistGenerator: Getter for the field a.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFn: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFp: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFpr: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTn: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTp: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTpr: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorF1: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFn: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFp: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFpr: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecisionCommonEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecallCommonEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTn: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTp: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiff: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedFP: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentExistsLatentCommonCause: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentPrecision: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTP: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CirclePrecision: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CircleRecall: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalseNegativeBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalsePositiveBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorTruePositiveBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonMeasuredAncestorRecallBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.CorrectSkeleton: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ElapsedCpuTime: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Adj: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1All: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Arrow: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Circle: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Tail: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.FalseNegativesAdjacencies: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.FalsePositiveAdjacencies: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.GraphExactlyRight: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaAverageSquaredDistance: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMaxSquaredDiffEstTrue: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMinSquaredDiffEstTrue: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgSquaredDifference: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMaximumSquaredDifference: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMinimumSquaredDifference: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst2: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedOrientationRatioEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliesLegalMag: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalseNegativeBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalsePositiveBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorRecallBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorTruePositiveBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalCpdag: Returns the abbreviation "CPDAG", representing a Legal CPDAG.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalPag: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphPrecision: This method returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphRecall
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagCgScore: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagDgScore: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagSemScore: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPasses: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPassesBestOf10: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingP: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingPBestOf10: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialP: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialPBestOf10: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFdrCutoffP: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH0: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH1: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffP: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffPBestOf10: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPasses: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPassesBestOf10: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrAdj: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrArrow: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.Maximal: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.MaximalityCondition: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH0: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH1: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoAlmostCyclicPathsCondition: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoCyclicPathsCondition: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoPotentiallyDirectedF1: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumAmbiguousTriples: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsNotInUnshieldedCollidersEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberCollidersEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInCollidersEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInUnshieldedCollidersEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberTailsEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberUnshieldedCollidersEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedBothNonancestorAncestor: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredDD: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredNL: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPD: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPL: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCommonMeasuredAncestorBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDefiniteDirectedEdgeAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeConfounded: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeNonAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeNonAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeNonAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleVisibleNonancestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectBidirected: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectDDAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectPDAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectVisibleEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCoveringAdjacenciesInPag: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyDirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyNotDirectedPaths: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeBnaMeasuredCounfounded: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNoMeasureAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNotAncNotRev: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeReversed: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedShouldBePartiallyDirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumEdgeInEstInTrue: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumGenuineAdjacenciesInPag: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectDDAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectPDAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectVisibleAncestors: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumLatentCommonAncestorBidirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumNondirectedEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumParametersEst: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPartiallyOrientedEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPotentiallyDirected: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumUndirectedEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdgeTrue: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationPrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationRecall: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyPrecision: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyRecall: Retrieves the abbreviation for the given statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ParameterColumn: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentAmbiguous: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentBidirectedEdges: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedEst: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedTrue: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueDistanceToAlpha: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueUniformityUnderNull: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPathF1: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPrecision: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedRecall: Retrieves the abbreviation for the SemidirectedRecall statistic.
getAbbreviation() - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.StructuralHammingDistance: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TailPrecision: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TailRecall: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesArrows: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesTails: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveArrow: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveTails: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionArrow: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionTails: Returns the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallArrows: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallTails: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveArrow: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveDirectedPathNonancestor: Retrieves the abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveTails: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalseNegative: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalsePositive: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCyclePrecision: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleRecall: The abbreviation for the statistic.
getAbbreviation() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleTruePositive: The abbreviation for the statistic.
getAbsTotalEffects(Node, Node) - Method in class edu.cmu.tetrad.search.Ida: This method calculates the absolute total effects of node x on node y.
getAbsTotalEffects(Node, Node) - Method in class edu.cmu.tetrad.search.PdagPagIda: Calculates the absolute total effects of one variable on another.
getAcceptedCommits() - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Number of commits adopted (trial copy passed strong legality after propagation).
getActiveNonSinkSet() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the active non-sink names.
getActiveSet() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the active set of covers.
getAcyclicTrimmedBHat(Matrix) - Method in class edu.cmu.tetrad.search.Lingam: Processes a given matrix W to generate a scaled and trimmed matrix B̂ that is guaranteed to correspond to an acyclic directed graph (DAG).
getAdequacyReport() - Method in class edu.cmu.tetrad.sem.NNEstimator: Returns the most recently fitted simulator, or null if
getAdjacencies() - Method in class edu.cmu.tetrad.search.Pcd: Returns the set of adjacent edges in the graph.
getAdjacencies() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Getter for the field adjacencies.
getAdjacencies() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns a list of adjacent node pairs in the current graph.
getAdjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: getAdjacencies.
getAdjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: getAdjacencies.
getAdjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: getAdjacencies.
getAdjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: getAdjacencies.
getAdjacency() - Method in class edu.cmu.tetrad.search.Rlcd: The signed adjacency over observed then latent variables as stage 2 left it, in causal-learn's encoding: A[i][j] = −1 and A[j][i] = 1 for i → j; −1 both ways for an undirected edge.
getAdjacencyInstability() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the StARS-style adjacency instability over the resamples, in [0, 0.5], or NaN if no resamples were run.
getAdjacencySubgraphWithTargetNode(Graph, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the subgraph over the adjacency of a target node for a DAG, CPDAG, MAG, or PAG.
getAdjacencyTest() - Method in class edu.cmu.tetrad.search.Ccd: Returns the separate adjacency-phase test, or null if the main test is used throughout.
getAdjacentNodes(Node) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the adjacent nodes of a given node in the graph.
getAdjacentNodes(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getAdjacentNodes.
getAdjacentNodes(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getAdjacentNodes.
getAdjacentNodes(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getAdjacentNodes.
getAdjacentNodes(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getAdjacentNodes.
getAdjacentNodes(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves a list of adjacent nodes for the given node.
getAdjacentNodes(Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the nodes adjacent to v.
getAdjCor() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the adjacency correct.
getAdjFn() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the adjacency false negatives.
getAdjFp() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the adjacency false positives.
getAdjPrec() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the adjaency precision.
getAdjPrecision() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: getAdjPrecision.
getAdjRec() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the adjacency recall.
getAdjRecall() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: getAdjRecall.
getAdPValues() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the Anderson-Darling p-values per continuous variable name, for those variables with enough non-missing values to test, unmodifiable.
getAhdCor() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the arrowhead correct.
getAhdFn() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the arrowhead false negatives.
getAhdFp() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the arrowhead false positives.
getAhdPrec() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the arrowhead precision.
getAhdRec() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the arrowhead recall.
getAic(int, int...) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Computes the Akaike Information Criterion (AIC) score for the given variable and its parent variables in a probabilistic graphical model such as a Bayesian network.
getAlgorithm() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field algorithm.
getAlgorithmDescription() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns a description of the algorithm swept.
getAlgorithmDescriptions() - Static method in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: Returns the algorithm descriptions.
getAlgorithmName() - Method in class edu.cmu.tetrad.search.GrowShrink: Returns "Grow Shrink".
getAlgorithmName() - Method in interface edu.cmu.tetrad.search.IMbSearch: The name of the algorithm.
getAlgorithmName() - Method in class edu.cmu.tetrad.search.PcMb: Returns "PC-MB."
getAlgorithmName() - Method in class edu.cmu.tetrad.search.work_in_progress.Iamb: getAlgorithmName.
getAlgorithmName() - Method in class edu.cmu.tetrad.search.work_in_progress.IambnPc: getAlgorithmName.
getAlgorithmName() - Method in class edu.cmu.tetrad.search.work_in_progress.InterIamb: getAlgorithmName.
getAlgorithmName() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmmb: getAlgorithmName.
getAlgorithmParameters(Algorithm) - Static method in class edu.cmu.tetrad.util.Params: getAlgorithmParameters.
getAlgorithms() - Method in class edu.cmu.tetrad.algcomparison.algorithm.Algorithms: Returns the list of algorithm.
getAllAttributes() - Method in class edu.cmu.tetrad.data.ContinuousVariable: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.data.DiscreteVariable: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.graph.Dag: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getAllAttributes.
getAllAttributes() - Method in interface edu.cmu.tetrad.graph.Graph: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.graph.GraphNode: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.graph.LagGraph: getAllAttributes.
getAllAttributes() - Method in interface edu.cmu.tetrad.graph.Node: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.graph.SemGraph: getAllAttributes.
getAllAttributes() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves all the attributes stored in the object.
getAllGraphsByDirectingUndirectedEdges(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Returns a list of all possible graphs obtained by directing undirected edges in the given graph.
getAllNbSet() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the stage-1 neighbours of the partition.
getAllowedValues() - Method in class edu.cmu.tetrad.util.ParamDescription: The legal values for an enumerated String parameter, in display order, or an empty list for free-text and non-String parameters.
getAllParents(Graph, Set) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: getAllParents.
getAllRows(int) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Returns a list of rows 1...sampleSize.
getAllSubsetsIndependenceFacts() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the set of independence facts used in the Markov check, for dseparation and dconnection separately.
getAllVariableNames() - Method in class edu.cmu.tetrad.search.Rlcd: Returns the variable names for the adjacency matrix.
getAlpha() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport: The significance level the elimination used.
getAlpha() - Method in class edu.cmu.tetrad.regression.LogisticRegression: Getter for the field alpha.
getAlpha() - Method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Returns the base significance level this prior is anchored at.
getAlpha() - Method in class edu.cmu.tetrad.search.harness.tsbi.TsbiRunner: Returns the significance level used by this runner.
getAlpha() - Method in class edu.cmu.tetrad.search.IndTestIod: Returns the alpha level for the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the alpha value used for threshold-based decisions in this object.
getAlpha() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the alpha value used in the model computation.
getAlpha() - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Retrieves the alpha value associated with this score.
getAlpha() - Method in interface edu.cmu.tetrad.search.score.InstanceSpecificScore: Retrieves the instance-specific weighting factor alpha.
getAlpha() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Retrieves the significance level (alpha) used by the underlying IndependenceTest.
getAlpha() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Returns the alpha of the base test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: Returns the model significance level being used for tests.
getAlpha() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the base significance level, before per-edge weighting.
getAlpha() - Method in class edu.cmu.tetrad.search.test.FfCi: Retrieves the alpha value.
getAlpha() - Method in class edu.cmu.tetrad.search.test.FfCi1: Retrieves the alpha value.
getAlpha() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Retrieves the alpha level used for statistical significance testing.
getAlpha() - Method in class edu.cmu.tetrad.search.test.Gcm
getAlpha() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Returns the significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Retrieves the alpha value currently set for this instance.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Retrieves the significance level (alpha) for the statistical tests performed by this instance.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the alpha significance level of the test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Returns the model significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Returns the significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Returns the significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the significance level (alpha) used for the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns the one-sided level of each of the two tests.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

This method returns the alpha significance cutoff value used in the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestGin: Returns the significance level for the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Returns the alpha value used for checking independence.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Returns the alpha of the component tests (they are required to agree; the first is reported).
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns the alpha parameter for the probabilistic test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Retrieves the significance level (alpha) used in the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns the significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Gets the model significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the value of the alpha threshold, which is generally used for statistical tests to determine the significance or rejection criteria.
getAlpha() - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Retrieves the significance level (alpha) used by the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Retrieves the significance level (alpha) used for statistical testing in this object.
getAlpha() - Method in class edu.cmu.tetrad.search.test.MsepTest: Returns an alpha level, 0.5.
getAlpha() - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Retrieves the significance level (alpha) used in conditional independence tests.
getAlpha() - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Retrieves the significance level (alpha) used in conditional independence tests.
getAlpha() - Method in class edu.cmu.tetrad.search.test.Rcit: Retrieves the value of the alpha parameter.
getAlpha() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Retrieves the significance level (alpha) for the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Returns the alpha value, the threshold for determining statistical significance.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Getter for the field alpha.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Getter for the field alpha.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Getter for the field alpha.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Retrieves the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Retrieves the alpha level of the Independence Test.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: getAlpha.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Gets the getModel significance level.
getAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Returns the significance level of the independence test.
getAlpha() - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
getAlpha() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field alpha.
getAlpha() - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Getter for the field alpha.
getAlpha(Node, Node) - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the effective significance level for the unordered pair {x, y}, namely w_xy * alpha, capped at 1.
getAlphaFromFdr() - Method in class edu.cmu.tetrad.search.test.AlphaReport: The level the false-edge budget allows.
getAlphaFromMinEffect() - Method in class edu.cmu.tetrad.search.test.AlphaReport: The level the effect-size criterion demands, taken over the hardest df class (the one needing the largest level), or 0 when disabled.
getAlphaMu() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the prior precision on the mean.
getAlphaMu() - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the prior precision on the mean.
getAlphaNeededByDf() - Method in class edu.cmu.tetrad.search.test.AlphaReport: The level each df class needs to detect the requested effect at the requested power.
getAlphaW() - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the Wishart degrees of freedom, alpha_w = p + alphaWOffset.
getAlphaWOffset() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the Wishart degrees-of-freedom offset above the embedded dimension.
getAlphaWOffset() - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the Wishart degrees of freedom offset above the number of variables.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.graph.Dag: Returns a set of ambiguous triples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in interface edu.cmu.tetrad.graph.Graph: getAmbiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.graph.LagGraph: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.graph.SemGraph: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the set of ambiguous triples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.graph.Underlines: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.search.PcMb: Returns the set of triples identified as ambiguous by the CPC algorithm during the most recent search.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field ambiguousTriples.
getAmbiguousTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field ambiguousTriples.
getAmbiguousTriplesFromGraph(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Retrieves the list of ambiguous triples from the given graph for a given node.
getAmenablePathsMag(Node, Node, int, Set) - Method in class edu.cmu.tetrad.graph.Paths: Amenable paths for MAGs: potentially directed paths from node1 to node2 whose first edge is directed out of node1 AND visible (Zhang 2008; required for adjustment amenability in MAGs per Perkovic et al.
getAmenablePathsPag(Node, Node, int, Set) - Method in class edu.cmu.tetrad.graph.Paths: Finds amenable paths from the given source node to the given destination node with a maximum length, for a PAG.
getAmenablePathsPdagMag(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds amenable paths from the given source node to the given destination node with a maximum length.
getAncestors(Node) - Method in class edu.cmu.tetrad.graph.Paths: Retrieves the ancestors of a specified `Node` in the graph.
getAncestors(Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: getAncestors.
getAncestors(List) - Method in class edu.cmu.tetrad.graph.Paths: Returns a list of all ancestors of the given nodes.
getAncestorsMap() - Method in class edu.cmu.tetrad.graph.Paths: Return a map from each node to its collection of ancestors.
getAncillaryGraph(String) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Returns the ancillary graph with the given name, or null if no ancillary graph by that name has been set.
getAndersonDarlingA2(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the Anderson-Darling A^2 statistic for the given list of results.
getAndersonDarlingA2(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the Anderson-Darling A2 value for a list of independence results.
getAndersonDarlingA2Star(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the Anderson-Darling A^2* statistic for the given list of results.
getAndersonDarlingP(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the Anderson-Darling p-value for the given list of results.
getAndersonDarlingP(List) - Static method in class edu.cmu.tetrad.search.VertexRepairSearch: Tests a list of p-values against the Anderson-Darling Test.
getAndersonDarlingPValue(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the Anderson-Darling p-value for a given list of independence results.
getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(IndependenceTest, Graph, Double, Double) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the Anderson-Darling test and classifies nodes as accepted or rejected based on the given threshold.
getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(IndependenceTest, Graph, Graph, Double, Double, Double) - Method in class edu.cmu.tetrad.search.MarkovCheck: Get accepts and rejects nodes for all nodes from Anderson-Darling test and generate the plot data for confusion statistics.
getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(IndependenceTest, Graph, Graph, Double, Double, Double) - Method in class edu.cmu.tetrad.search.MarkovCheck: Get accepts and rejects nodes for all nodes from Anderson-Darling test and generate the plot data for confusion statistics.
getAnnealStartT() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the starting temperature for annealing.
getAnnealSteps() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the number of annealing steps.
getAnnotatedClasses() - Method in class edu.cmu.tetrad.annotation.AbstractAnnotations: Get annotated classes.
getAnnotatedClasses(String, Class) - Static method in class edu.cmu.tetrad.annotation.AnnotatedClassUtils: Gets a list of annotated classes in the given package.
getAnnotation() - Method in class edu.cmu.tetrad.graph.Edge: Returns the annotation shown in the workbench tooltip for this edge, or null if none.
getApparentlyNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Getter for the field apparentlyNonadjacencies.
getApparentNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: getApparentNonadjacencies.
getApparentNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: getApparentNonadjacencies.
getApparentNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: getApparentNonadjacencies.
getApparentNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: getApparentNonadjacencies.
getArea(Function, double, double, int) - Static method in class edu.cmu.tetrad.util.Integrator: Finds the area under function f between x1 and x2 using Simpson's rule.
getArgument(int) - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionSignature: getArgument.
getArrowsFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getArrowsFn.
getArrowsFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getArrowsFp.
getArrowsTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getArrowsTn.
getArrowsTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getArrowsTp.
getASquared() - Method in class edu.cmu.tetrad.data.AndersonDarlingTest: Constructs an Anderson-Darling test for the given column of data.
getASquared() - Method in class edu.cmu.tetrad.data.GeneralAndersonDarlingTest: Getter for the field aSquared.
getASquared() - Method in class edu.cmu.tetrad.data.MultiGeneralAndersonDarlingTest: Getter for the field aSquared.
getaSquaredStar() - Method in class edu.cmu.tetrad.sem.GeneralizedSemEstimator: Returns the value of the field aSquaredStar.
getASquaredStar() - Method in class edu.cmu.tetrad.data.AndersonDarlingTest: Constructs an Anderson-Darling test for the given column of data.
getASquaredStar() - Method in class edu.cmu.tetrad.data.GeneralAndersonDarlingTest: Getter for the field aSquaredStar.
getASquaredStar() - Method in class edu.cmu.tetrad.data.MultiGeneralAndersonDarlingTest: Getter for the field aSquaredStar.
getAssociatedNode(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns the associated node for the given error node in the specified graph.
getAttemptedCommits() - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Number of candidate commits whose change/stability gates passed (each was tried on a copy).
getAttribute(String) - Method in class edu.cmu.tetrad.data.ContinuousVariable: getAttribute.
getAttribute(String) - Method in class edu.cmu.tetrad.data.DiscreteVariable: getAttribute.
getAttribute(String) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the value associated with the given key in the attribute map.
getAttribute(String) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getAttribute.
getAttribute(String) - Method in interface edu.cmu.tetrad.graph.Graph: getAttribute.
getAttribute(String) - Method in class edu.cmu.tetrad.graph.GraphNode: getAttribute.
getAttribute(String) - Method in class edu.cmu.tetrad.graph.LagGraph: getAttribute.
getAttribute(String) - Method in interface edu.cmu.tetrad.graph.Node: getAttribute.
getAttribute(String) - Method in class edu.cmu.tetrad.graph.SemGraph: getAttribute.
getAttribute(String) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the value associated with the specified key from the attributes map.
getAuc() - Method in class edu.cmu.tetrad.util.RocCalculator: Calculates the area under the ROC curve using a very clever Ramsey idea.
getAuditedTriples() - Method in class edu.cmu.tetrad.search.CdnodBoss: Number of unshielded triples (with a non-context at the center) audited in the most recent CdnodBoss.search(); zero if the audit was disabled.
getAverageEvaluations() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns average evaluations by algorithm name across all trials.
getAverageInputSimilarity() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Retrieves the average input similarity, representing the average similarity between matched inputs.
getAverageLatentSimilarity() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Retrieves the average latent similarity, representing the average similarity between matched latent pairs.
getAverageOutputSimilarity() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Retrieves the average output similarity, representing the average similarity between matched outputs.
getAverageSquaredDistance(List) - Method in class edu.cmu.tetrad.search.IdaCheck: Returns the average of the squared distances between the true total effects and the IDA effect ranges the list of node pairs indicated.
getAvgMaxSquaredDiffEstTrue(List) - Method in class edu.cmu.tetrad.search.IdaCheck: Returns the average of the squared differences between the maximum total effects and the true total effects for the list of node pairs indicated.
getAvgMinSquaredDiffEstTrue(List) - Method in class edu.cmu.tetrad.search.IdaCheck: Returns the average of the squared differences between the minimum total effects and the true total effects for the list of node pairs indicated.
getB() - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Returns the coefficient matrix for the search.
getB() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the second node.
getB() - Method in class edu.cmu.tetrad.sem.ParameterPair: Method getNumObjects
getB() - Method in class edu.cmu.tetrad.util.ChoiceGenerator: Getter for the field b.
getB() - Method in class edu.cmu.tetrad.util.dist.Split: Getter for the field b.
getBackdoorPaths(Node, Node, String, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds and returns all valid backdoor paths between two nodes in a graph.
getBandwidth() - Method in interface edu.cmu.tetrad.search.utils.Kernel: Gets kernel bandwidth
getBandwidth() - Method in class edu.cmu.tetrad.search.utils.KernelGaussian: getBandwidth.
getBandwidthMode() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the bandwidth estimation mode.
getBandwidthMode() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the current bandwidth mode used for computations.
getBasalExpression() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Getter for the field basalExpression.
getBase() - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Retrieves the base SemBicScore associated with this instance.
getBaseSeed() - Method in class edu.cmu.tetrad.search.score.FfMl: Returns the base seed value used for initializing or seeding operations.
getBaseTest() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Retrieves the underlying IndependenceTest instance used within the CachingIndependenceTest.
getBayesIm() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Getter for the field bayesIm.
getBayesIm() - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Returns the evidence for the updater.
getBayesIm() - Method in class edu.cmu.tetrad.bayes.Identifiability: The BayesIm that this updater bases its update on.
getBayesIm() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Retrieves the Bayesian network representation associated with this updater.
getBayesIm() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: The BayesIm that this updater bases its update on.
getBayesIm() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Retrieves the Bayesian network representation associated with this updater.
getBayesIm() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the Bayes IM being used.
getBayesIm(Element) - Method in class edu.cmu.tetrad.bayes.BayesXmlParser: Returns the BayesIm object represented by the given element.
getBayesIm(Element) - Method in class edu.cmu.tetrad.search.utils.BayesImParser: Takes an xml representation of a Bayes IM and reinstantiates the IM.
getBayesImObs() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Getter for the field bayesImObs.
getBayesIms() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the list of Bayes IMs.
getBayesImVars() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the variables of the BayesIM.
getBayesPm() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the underlying Bayes PM.
getBayesPm() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Getter for the field bayesPm.
getBayesPm() - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Getter for the field bayesPm.
getBayesPm() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Getter for the field bayesPm.
getBayesPm() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Getter for the field bayesPm.
getBayesPm() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getBayesPm.
getBestOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: The best order found by the last search, or null before any search.
getBestScore() - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: The score of the best graph found by the last search, or NaN before any search.
getBeta() - Method in class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: Getter for the field beta.
getBeta() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Retrieves the direct-effects matrix Beta, which is a key component in the model (Beta = I - IB where IB = I - Beta).
getBhat() - Method in class edu.cmu.tetrad.sem.Ricf.RicfResult: Returns the bhat matrix.
getBHat() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: Returns the B-Hat matrix found by the most recent search.
getBHat() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Retrieves the bHat matrix.
getBic() - Method in class edu.cmu.tetrad.bayes.BayesProperties: Retrieves the Bayesian Information Criterion (BIC) value for the current instance.
getBic() - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Calculates the BIC (Bayes Information Criterion) score for a BayesPM with respect to a given discrete data set.
getBic() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Deprecated.
This reflects thread-local per-call state. Prefer IndTestFisherZ.getResult(Node, Node, Set) and IndTestFisherZ.Result.bic(), or the pure IndTestFisherZ.bic(double, int).
getBic() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getBic.
getBic() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the BIC.
getBics() - Method in class edu.cmu.tetrad.search.Boss: Returns the BIC scores.
getBics() - Method in class edu.cmu.tetrad.search.BossTopK: Returns the BIC scores.
getBicScore() - Method in class edu.cmu.tetrad.sem.DagScorer: getBicScore.
getBicScore() - Method in interface edu.cmu.tetrad.sem.ISemIm: getBicScore.
getBicScore() - Method in interface edu.cmu.tetrad.sem.Scorer: getBicScore.
getBicScore() - Method in class edu.cmu.tetrad.sem.SemIm: getBicScore.
getBinomialPValue(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the binomial p-value based on the list of visible pairs.
getBinomialPValue_(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the Binomial p-value for the given list of results.
getBlockA() - Method in class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Returns the list of nodes in Block A associated with the NTAD analysis result.
getBlockB() - Method in class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Returns the list of nodes in Block B associated with the NTAD analysis result.
getBlocked() - Method in class edu.cmu.tetrad.search.Fas: Returns every deletion FAS declined to make because of the determinism guard, in the order encountered.
getBlockedDeletions() - Method in class edu.cmu.tetrad.search.PcAR: Passthrough to the internal Fas instance's Fas.getBlocked(): every deletion the adjacency phase declined to make because the only accepting separating sets it found were contaminated by determinism.
getBlocks() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Retrieves the list of blocks, where each block is represented as a list of integers.
getBlockSize() - Method in class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Retrieves the block size associated with the NTAD analysis result.
getBlockSpec() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Retrieves the block specification associated with the algorithm.
getBlockSpec() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Retrieves the block specification for the current instance.
getBlockSpec() - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Retrieves the block specification associated with this score object.
getBlockSpec() - Method in interface edu.cmu.tetrad.search.score.BlockScore: Retrieves the specification of the block associated with the score.
getBlockSpec() - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Retrieves the block specification associated with this object.
getBlockSpec() - Method in interface edu.cmu.tetrad.search.test.BlockTest: Retrieves the BlockSpec associated with this block-based independence test.
getBlockSpec() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
getBlockSpec() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes: Retrieves the block specification associated with this instance of IndTestBlocks.
getBlockSpec() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Retrieves the block specification associated with this instance.
getBMap(Graph) - Static method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Returns a map of nodes to bidirected edges for them.
getBoolean(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the boolean value of the given parameter, looking up its default in the ParamDescriptions map.
getBoolean(String, boolean) - Method in class edu.cmu.tetrad.util.Parameters: Returns the boolean value of the given parameter, using the given default.
getBooleanInfluenceRate() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Getter for the field booleanInfluenceRate.
getBootstrapGraphs() - Method in class edu.cmu.tetrad.algcomparison.algorithm.AbstractBootstrapAlgorithm: Returns the bootstrap graphs.
getBootstrapGraphs() - Method in interface edu.cmu.tetrad.algcomparison.algorithm.ReturnsBootstrapGraphs: Returns the bootstrap graphs.
getBootstrappingParameters(Algorithm) - Static method in class edu.cmu.tetrad.util.Params: getBootstrappingParameters.
getBootstrapSample(DataSet, int) - Static method in class edu.cmu.tetrad.data.DataTransforms: getBootstrapSample.
getBootstrapSample(DataSet, int, RandomGenerator) - Static method in class edu.cmu.tetrad.data.DataTransforms: Get dataset sampled with replacement.
getBootstrapSample(Matrix, int) - Static method in class edu.cmu.tetrad.data.DataTransforms: getBootstrapSample.
getBossPenaltyDiscount() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the BOSS penalty discount value.
getBreakpoints() - Method in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Retrieves the array of breakpoints used for discretization.
getBreakpoints() - Method in class edu.cmu.tetrad.data.SplitCasesSpec: Getter for the field breakpoints.
getBscD() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Returns the BSC-D score.
getBscI() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Returns the BSC-I score.
getBuckets() - Method in class edu.cmu.tetrad.search.GRegression: Returns the bucket decomposition used by this estimator.
getBump() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the bump.
getC() - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Getter for the field c.
getCacheHits() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: The number of district subproblems answered from the cache since construction or the last clear.
getCacheHits() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Number of eval() calls served from cache.
getCacheMisses() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: The number of district subproblems actually fitted since construction or the last clear.
getCacheMisses() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Number of eval() calls that reached the underlying test (cache misses).
getCardinality(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the cardinality (number of categories) for a discrete node at a given index.
getCases() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: Getter for the field cases.
getCategories() - Method in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Retrieves the list of categories for the discretized data.
getCategories() - Method in class edu.cmu.tetrad.data.DiscreteDiscretizationSpec: Getter for the field categories.
getCategories() - Method in class edu.cmu.tetrad.data.DiscreteVariable: Getter for the field categories.
getCategories(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the list of categories for a discrete node at a given index.
getCategory(int) - Method in class edu.cmu.tetrad.data.DiscreteVariable: getCategory.
getCategory(Node, int) - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the index'th value for the given node.
getCategory(Node, int) - Method in class edu.cmu.tetrad.bayes.Evidence: getCategory.
getCategoryIndex(Node, String) - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the index of the given category for the given node.
getCategoryIndex(String, String) - Method in class edu.cmu.tetrad.bayes.Evidence: getCategoryIndex.
getCategoryIndex(String, String) - Method in class edu.cmu.tetrad.bayes.Proposition: getCategoryIndex.
getCatRho() - Method in class edu.cmu.tetrad.search.score.FfMl: Retrieves the value of the catRho property.
getCatRho() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Retrieves the categorical similarity rho.
getCatRho() - Method in class edu.cmu.tetrad.search.test.FfCi: Retrieves the current categorical feature correlation coefficient (catRho) used in the mixed independence test.
getCatRho() - Method in class edu.cmu.tetrad.search.test.FfCi1: Retrieves the current categorical feature correlation coefficient (catRho) used in the mixed independence test.
getCausalOrder() - Method in class edu.cmu.tetrad.search.DirectLingam: Returns the causal order estimated by the last call to DirectLingam.search(), earliest (most nearly exogenous) first.
getCauseNode() - Method in class edu.cmu.tetrad.search.Cstar.Record: Returns the cause node associated with this record.
getCell(int) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Returns the cell in the table for the given index.
getCellIndex(int...) - Method in class edu.cmu.tetrad.search.utils.AdTree: Returns the cell index for the given coordinates.
getCellIndex(int...) - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Returns the cell index for the given coordinates.
getCellIndex(int...) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Returns the index of the cell in the table for the given coordinates.
getCellIndex(int[]) - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the index in the table for the cell with the given coordinates.
getCellNumber() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: While the simulate() method is being executed, returns the cell number of the cell currently being simulated in a particular dish, zero indexed.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.BayesImProbs: Calculates the probability in the given cell from the conditional probabilities in the BayesIm.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.CellTableProbs: Calculates the probability of a cell corresponding to the given variable values.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.DataSetProbs: getCellProb.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: getCellProb.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.IntAveDataSetProbs: getCellProb.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.StoredCellProbs: getCellProb.
getCellProb(int[]) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: getCellProb.
getCellTableType() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: The cell table type in use.
getCensoredCells() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: The number of cells treated as censored.
getCensoredCellsByColumn() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Per column, the number of cells treated as censored (above or below).
getCenter() - Method in class edu.cmu.tetrad.util.TaylorSeries: Retrieves the center point about which the Taylor series is expanded.
getCenteringFrame() - Static method in class edu.cmu.tetrad.util.JOptionUtils: getCenteringFrame.
getCenterX() - Method in class edu.cmu.tetrad.data.ContinuousVariable: Getter for the field centerX.
getCenterX() - Method in class edu.cmu.tetrad.data.DiscreteVariable: Getter for the field centerX.
getCenterX() - Method in class edu.cmu.tetrad.graph.GraphNode: Getter for the field centerX.
getCenterX() - Method in interface edu.cmu.tetrad.graph.Node: Returns the x coordinate of the center of this node.
getCenterY() - Method in class edu.cmu.tetrad.data.ContinuousVariable: Getter for the field centerY.
getCenterY() - Method in class edu.cmu.tetrad.data.DiscreteVariable: Getter for the field centerY.
getCenterY() - Method in class edu.cmu.tetrad.graph.GraphNode: Getter for the field centerY.
getCenterY() - Method in interface edu.cmu.tetrad.graph.Node: Returns the y coordinate of the center of this node.
getCfi() - Method in interface edu.cmu.tetrad.sem.ISemIm: getCfi.
getCfi() - Method in class edu.cmu.tetrad.sem.SemIm: getCfi.
getChangedEdges() - Method in class edu.cmu.tetrad.search.utils.MeekRules: Returns a complete set of all the edges that were changed in the course of orientation, as a map from the previous edges in the graph to the new, changed edges for the same node pair.
getChannels() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Returns the active response channels.
getChannels() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Returns the active response channels.
getChannelWeights() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Returns the per-channel weights.
getChannelWeights() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Returns the per-channel weights.
getCharSequence() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionLexer: Returns the CharSequence associated with this lexer.
getCheckMillis() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: Total time spent running the Markov checks on the null draws, in milliseconds.
getChildName() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: Name of the child variable (head of the deleted edge).
getChildren() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups.Entry: Returns the children of this entry's cover.
getChildren(int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the children of a node v.
getChildren(Node) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the children of a specified Node in the graph.
getChildren(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getChildren.
getChildren(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getChildren.
getChildren(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getChildren.
getChildren(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getChildren.
getChildren(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns a list of children nodes for the given node.
getChildren(Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the children of a node v.
getChildrenOfNonAtomicsSet() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the children of non-atomic covers.
getChipChipVariability() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the chip to chip variability.
getChipChipVariability() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getChipChipVariability.
getChisq() - Method in class edu.cmu.tetrad.bayes.BayesProperties: Retrieves the chi-squared (chisq) value for the current instance.
getChiSquare() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the chi Square value.
getChiSquare() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getChiSquare.
getChiSquare() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the chi square.
getChiSquare() - Method in class edu.cmu.tetrad.sem.DagScorer: getChiSquare.
getChiSquare() - Method in interface edu.cmu.tetrad.sem.ISemIm: getChiSquare.
getChiSquare() - Method in interface edu.cmu.tetrad.sem.Scorer: getChiSquare.
getChiSquare() - Method in class edu.cmu.tetrad.sem.SemIm: getChiSquare.
getChiSquareP(double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the p-value for the given chi-square statistic and degrees of freedom.
getClassifications() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the classification for the target variable.
getCliques(Node[], Graph) - Static method in class edu.cmu.tetrad.bayes.GraphTools: Get cliques in a decomposable graph.
getCliqueTree(Node[], Map, Map) - Static method in class edu.cmu.tetrad.bayes.GraphTools: getCliqueTree.
getCluster(int) - Method in class edu.cmu.tetrad.cluster.KMeans: getCluster.
getClustering() - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

The clustering used.
getClusters() - Method in interface edu.cmu.tetrad.cluster.ClusteringAlgorithm: getClusters.
getClusters() - Method in class edu.cmu.tetrad.cluster.KMeans: Getter for the field clusters.
getClusters() - Method in class edu.cmu.tetrad.search.Bpc: Identifies clusters of variables based on tetrad purity and pairwise dependence.
getCode() - Method in class edu.cmu.tetrad.data.audit.AuditFinding: Returns the machine-readable code for this finding.
getCoef() - Method in class edu.cmu.tetrad.regression.RegressionResult: getCoef.
getCoef() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: Getter for the field coef.
getCoefficient() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Returns the coefficient.
getCoefficient(int, int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Retrieves the coefficient value for a continuous child node.
getCoefficientMatrix() - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getCoefficientMatrix.
getCoefficientParameter(Node, Node) - Method in class edu.cmu.tetrad.sem.SemPm: getCoefficientParameter.
getCoefficientRange(Node, Node) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getCoefficientRange.
getCoefficients() - Method in class edu.cmu.tetrad.search.utils.LinearFunctionND: Returns the coefficients of the linear function.
getCoefs() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The array of regression coefficients.
getCollapsedVarGraph() - Method in class edu.cmu.tetrad.search.utils.TsUtils.VarResult: Getter for the field collapsedVarGraph.
getColliderPath() - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Retrieves the list of nodes that represents the collider path in the discriminating path.
getColliderRule() - Method in class edu.cmu.tetrad.search.Ccd: Returns Step B's collider rule.
getColliders() - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Return colliders
getCollidersFromGraph(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: getCollidersFromGraph.
getColliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field colliderTriples.
getColliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field colliderTriples.
getColliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field colliderTriples.
getColliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field colliderTriples.
getColliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field colliderTriples.
getColliderTriples(Graph) - Method in class edu.cmu.tetrad.search.Pc: Retrieves all colliders from the provided graph based on specific criteria.
getColliderTriples(Graph) - Method in class edu.cmu.tetrad.search.PcAR: Retrieves all colliders from the provided graph based on specific criteria.
getColPerm() - Method in class edu.cmu.tetrad.search.utils.PermutationMatrixPair: Getter for the field colPerm.
getColumn(int) - Method in class edu.cmu.tetrad.util.Matrix: getColumn.
getColumnIndex(Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: getColumn.
getColumnIndex(Node) - Method in interface edu.cmu.tetrad.data.DataSet: getColumn.
getColumnIndex(Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getColumn.
getColumnIndex(String) - Method in interface edu.cmu.tetrad.data.DataSet: Retrieves the column index for a variable by its name.
getColumnToTooltip() - Method in class edu.cmu.tetrad.data.BoxDataSet: Getter for the field columnToTooltip.
getColumnToTooltip() - Method in interface edu.cmu.tetrad.data.DataSet: Returns the map of column names to tooltips.
getColumnToTooltip() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Getter for the field columnToTooltip.
getComparisonGraph() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Returns the ComparisonGraph instance.
getComparisonGraph() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: The type of graph the results are compared to.
getComparisonGraph(Graph) - Method in interface edu.cmu.tetrad.algcomparison.algorithm.Algorithm: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Dagma: Retrieves the comparison graph for the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Returns a comparison graph based on the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Returns a comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Returns a comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Returns a comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the comparison graph of the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.FirstInflection: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Returns a comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam: Constructs and returns a comparison graph in the form of a completed partially directed acyclic graph (CPDAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Returns a CPDAG that is the comparison graph for the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Generates a comparison graph by converting a given graph into its completed partially directed acyclic graph (CPDAG) form.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Retrieves the comparison graph for the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Flop: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Generates a comparison causal graph derived from the input graph by converting it to a completed partially directed acyclic graph (CPDAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Returns the given graph for comparison purposes.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Retrieves the comparison graph for the given true directed graph: the cyclic PAG that BOSS-CCD is meant to recover, not the true graph itself.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Retrieves the comparison graph for the given true directed graph: the cyclic PAG that CCD is meant to recover, not the true graph itself.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Returns the comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Generates a comparison graph for the given graph by transforming it into a partial ancestral graph (PAG) representation via dagToPag.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Returns the comparison graph for the supplied graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Returns the comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Generates a comparison graph for the given graph by transforming it into a partially directed acyclic graph (PAG) representation.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Returns the comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Returns the comparison graph, the PAG of the true DAG.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Returns the comparison graph based on the true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvBossExperimental: Retrieves a comparison graph by transforming a true directed graph into a PAG.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Returns the comparison graph based on the true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Returns a comparison graph based on the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Retrieves the comparison graph from the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Returns the comparison graph created by converting a true directed graph into a partially directed acyclic graph (PAG).
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis: Returns an undirected graph used for comparison.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Glasso: Retrieves a comparison graph for the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Returns an undirected graph used for comparison.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Returns an undirected graph used for comparison.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

Returns a copy of the supplied graph for use as a comparison baseline.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd: The true graph itself is used for comparison, since the output includes latent nodes.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Returns a comparison graph based on the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Returns a comparison graph based on the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Returns a comparison graph for the given true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Generates a comparison graph based on the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Returns a comparison graph based on the true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Returns a comparison graph based on the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Returns a comparison graph based on the true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Returns a comparison graph based on the provided true directed graph.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Returns a comparison graph for the given true directed graph, if there is one.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.StabilitySelection: Returns that graph that the result should be compared to.
getComparisonGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.algorithm.StARS: Returns that graph that the result should be compared to.
getComparisonGraph(Graph, Parameters) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns a comparison graph based on the specified parameters.
getConditional(int, int[], int[]) - Method in class edu.cmu.tetrad.bayes.JunctionTreeInference: Computes the conditional probability distribution of a given node (variable) conditioned on its parent variables and their specified values within a Bayesian network.
getConditional(int, int[], int[]) - Method in class edu.cmu.tetrad.bayes.VariableEliminationInference: Conditional distribution P(node | parents=parentValues, evidence).
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.BayesImProbs: Calculates the conditional probability of an assertion given a condition.
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.CellTableProbs: Calculates the conditional probability of an assertion given a condition.
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.DataSetProbs: Calculates the conditional probability of an assertion given a condition in a Bayes information model (Bayes IM).
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: Calculates the conditional probability of an assertion given a condition in the context of a Bayes IM.
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.IntAveDataSetProbs: Calculates the conditional probability of an assertion given a condition.
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.StoredCellProbs: Calculates the conditional probability of an assertion given a condition.
getConditionalProb(Proposition, Proposition) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: Calculates the conditional probability of an assertion given a condition.
getConditioningNodes() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the nodes that are possible Z1,...,Zn for X _||_ Y | Z1,...,Zn.
getConditionNumber() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the condition number (largest over smallest eigenvalue) of the implied correlation matrix, positive infinity if the smallest eigenvalue is nonpositive, or NaN if the eigenvalues could not be computed.
getConditions() - Method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath: Getter for the field conditions.
getConfigurationStarts(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the starting row of each configuration block in the emitted data for the given simulation index: ascending, beginning with 0; a single block covering all rows when deSortByConfiguration is off.
getConnectivity() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: getConnectivity.
getConnectivity() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: getConnectivity.
getConnectivity() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Returns (a copy of) the sorted map from factors to lagged factors which internally encodes the update graph.
getConnectivity() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Returns (a copy of) the sorted map from factors to lagged factors which internally encodes the update graph.
getConstantColumns(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: getConstantColumns.
getContemporaneousGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the contemporaneous summary graph for the given run (equal to the true graph in non-serial mode).
getContestedDeletions() - Method in class edu.cmu.tetrad.search.PcAR: Returns every tier-1 positive recorded during the last PcAR.search() call, in the order detected.
getContext() - Method in class edu.cmu.tetrad.sem.ExpressionSampler: Retrieves the current Context instance associated with this ExpressionSampler.
getContinuousCorrelationMatrix() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the pairwise-complete Pearson correlation matrix of the continuous variables, in the order given by DataAudit.getContinuousNames(), or null if there are fewer than two continuous variables.
getContinuousData() - Method in class edu.cmu.tetrad.data.MixedDataBox: Getter for the field continuousData.
getContinuousData(String) - Method in class edu.cmu.tetrad.data.Histogram: Retrieves the continuous data for a specified variable from the dataset.
getContinuousDataSet(DataModel) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Returns the datamodel case to DataSet if it is continuous.
getContinuousNames() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the names of the continuous variables, in column order, excluding constant columns (see the class Javadoc), unmodifiable.
getContinuousParents(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the continuous parents of a node at a given index.
getContParentCutpointsForDiscreteChild(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the cutpoints for continuous parents of a discrete child node.
getControlPoints() - Method in class edu.cmu.tetrad.util.RandomPiecewiseLinearBijective: Retrieves the list of control points defining the piecewise linear bijective function.
getCoordinates(int) - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the array representing the combination of parent values for this row.
getCorrectResult() - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Getter for the field correctResult.
getCorrelationMatrix() - Method in class edu.cmu.tetrad.data.BoxDataSet: getCorrelationMatrix.
getCorrelationMatrix() - Method in interface edu.cmu.tetrad.data.DataSet: If this is a continuous data set, returns the correlation matrix.
getCorrelationMatrix() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getCorrelationMatrix.
getCorrelationMatrix(DataSet) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: getCorrelationMatrix.
getCorrelationRank() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the eigenvalue-based rank of the implied correlation matrix (the number of eigenvalues exceeding the configured relative tolerance times the largest eigenvalue), or -1 if unavailable.
getCorrespondingNodeIndex(int, BayesIm) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the index of the given node in the given BayesIm.
getCorrespondingNodeIndex(int, BayesIm) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the index of the given node in the given BayesIm.
getCorrespondingNodeIndex(int, BayesIm) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns the corresponding node index in the given BayesIm based on the node index in this BayesIm.
getCorrespondingNodeIndex(int, BayesIm) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the index of the given node in the given BayesIm.
getCorrespondingNodeIndex(int, BayesIm) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the index of the given node in the given BayesIm.
getCount(int) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Returns the count of the cell in the table for the given index.
getCount(int[]) - Method in class edu.cmu.tetrad.search.utils.AdTree: Returns the count for the cell at the given coordinates.
getCount(int[]) - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Returns the count for the cell at the given coordinates.
getCount(int[]) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Returns the count of the cell in the table for the given coordinates.
getCounts() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the counts.
getCov() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the covariance matrix associated with this independence test.
getCov() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the covariance matrix used in the computation.
getCov() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Returns the covariance matrix of the base test.
getCov() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the covariance matrix of the wrapped test.
getCov() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns the covariance matrix.
getCov() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the covariance matrix used by this instance.
getCov() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Returns the covariance matrix for the data sets.
getCov() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Returns the covariance matrix of the concatenated data.
getCov() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Returns the covariance matrix.
getCov() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns the covariance matrix.
getCov() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Retrieves the covariance matrix.
getCov() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

getCov.
getCov() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

getCov.
getCov() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: getCov.
getCov() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: getCov.
getCov(List, int[], int[], DataSet, Matrix) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: Computes the covariance matrix for the given subset of rows and columns in the provided data set.
getCovAndCoefs(int, int[], Matrix, ICovarianceMatrix, boolean, double) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the covariance matrix of the regression of the ith variable on its parents and the regression coefficients.
getCovAndCoefs(int, int[], Matrix, ICovarianceMatrix, double, List) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the covariance matrix of the regression of the ith variable on its parents and the regression
getCovarianceMatrix() - Method in class edu.cmu.tetrad.data.BoxDataSet: getCovarianceMatrix.
getCovarianceMatrix() - Method in interface edu.cmu.tetrad.data.DataSet: If this is a continuous data set, returns the covariance matrix.
getCovarianceMatrix() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getCovarianceMatrix.
getCovarianceMatrix() - Method in class edu.cmu.tetrad.search.is.IsGFci: Returns the covariance matrix associated with this instance.
getCovarianceMatrix(DataModel, boolean) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Returns the model cast to ICovarianceMatrix if already a covariance matric, or else returns the covariance matrix for a dataset.
getCovarianceMatrix(DataSet, boolean) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: getCovarianceMatrix.
getCovarianceMode() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the covariance mode.
getCovarianceParameter(Node, Node) - Method in class edu.cmu.tetrad.sem.SemPm: getCovarianceParameter.
getCovarianceRange(Node, Node) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getCovarianceRange.
getCovariances() - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the covariance matrix the score is computed from, or null under test-wise deletion (where there is no single covariance matrix; each family has its own).
getCovariances() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns the sample size.
getCovariances() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns the covariance matrix.
getCovariances() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the covariance matrix.
getCovariances() - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns the covariance matrix.
getCovariances() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Getter for the field covariances.
getCovMatrix() - Method in interface edu.cmu.tetrad.search.utils.TetradTest: getCovMatrix.
getCovMatrix() - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: getCovMatrix.
getCovMatrix() - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: getCovMatrix.
getCovMatrix() - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: getCovMatrix.
getCovMatrix() - Method in class edu.cmu.tetrad.sem.DagScorer: Getter for the field covMatrix.
getCovMatrix() - Method in interface edu.cmu.tetrad.sem.Scorer: getCovMatrix.
getCovMatrix() - Method in class edu.cmu.tetrad.sem.SemEstimator: Getter for the field covMatrix.
getCpcTripleType(Node, Node, Node, IndependenceTest, int, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: getCpcTripleType.
getCpdag() - Method in class edu.cmu.tetrad.search.Dagma: Retrieves the value of the cpdag field.
getCpdag() - Method in class edu.cmu.tetrad.search.PermutationSearch: Retrieves the value of cpdag.
getCpdag() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Retrieves the value of cpdag.
getCptMapType() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Retrieves the CptMapType for this instance.
getCptMapType() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: A flag indicating whether to use CptMaps or not.
getCurrentOffset() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionLexer: Getter for the field currentOffset.
getCutoff(Function, double, double, double) - Static method in class edu.cmu.tetrad.util.CutoffFinder: Assumes f is a positive symmetric function between x1 and x2 about 0.
getCvReport() - Method in class edu.cmu.tetrad.sem.NNEstimator: Retrieves the CVReport object.
getDag() - Method in interface edu.cmu.tetrad.bayes.BayesIm: $Description
getDag() - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the DAG.
getDag() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getDag.
getDag() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: getDag.
getDag() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getDag.
getDag() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getDag.
getDag() - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Returns the parent matrix for the graph.
getDag() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The DAG actually checked (after any CPDAG-to-DAG conversion).
getDag() - Method in class edu.cmu.tetrad.search.score.GraphScore: Returns a copy of the DAG being searched over.
getDag() - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Returns the DAG being analyzed.
getDag() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
getDag() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
getDag() - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Retrieves the Directed Acyclic Graph (DAG) produced by the Sepset algorithm.
getDag() - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
getDag() - Method in class edu.cmu.tetrad.sem.NNEstimator: Returns the DAG used for factorization.
getDag(List) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Returns the parent matrix for the graph.
getDag(List, Graph, boolean) - Static method in class edu.cmu.tetrad.graph.Paths: Generates a directed acyclic graph (DAG) based on the given list of nodes using Raskutti and Uhler's method.
getDagsInCpdagMeek(Graph, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Retrieves a list of directed acyclic graphs (DAGs) within the given completed partially directed acyclic graph (CPDAG) using the Meek rules.
getData() - Method in class edu.cmu.tetrad.data.Discretizer.Discretization: Retrieves the data associated with the discretization.
getData() - Method in class edu.cmu.tetrad.data.DoubleDataBox: Getter for the field data.
getData() - Method in class edu.cmu.tetrad.data.IntDataBox: Getter for the field data.
getData() - Method in class edu.cmu.tetrad.data.TimeSeriesData: getData.
getData() - Method in class edu.cmu.tetrad.search.IndTestIod
getData() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the data model associated with this independence test.
getData() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the data model used in the computation.
getData() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns the data set.
getData() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the data model.
getData() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Retrieves the data model from the test object.
getData() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Retrieves the data model associated with the underlying independence test.
getData() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the data model of the test.
getData() - Method in class edu.cmu.tetrad.search.test.FfCi: Retrieves the current data set associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.FfCi1: Retrieves the current data set associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Retrieves the dataset associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.Gcm
getData() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Retrieves the data model associated with this test.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Returns the original dataset.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Retrieves the original dataset represented by this instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Retrieves the data model associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Retrieves the data model associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
getData() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the data being analyzed.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Returns the data set being analyzed.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns the data.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the data model backing this test.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the data model backing this test.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the data set used for the independence test.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Retrieves the data model associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns the data this test reads.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Retrieves the data model associated with the wrapped IndependenceTest.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the data set associated with the current instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Returns the concatenated data.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Returns the concatenated data.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestGin: Returns the current regressor being used for the independence test.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Returns the data.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Returns the data set being analyzed.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Returns the facts supplied in the constructor, which constutite a data model.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Returns the data of the first component test; the pooled test has no single data set.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Returns the data.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns the data model associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Retrieves the dataset associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Returns the data used.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns the first of the per-domain data sets, anchoring the test to a data model.
getData() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Gets the data set used for the independence test.
getData() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the data model associated with the current instance.
getData() - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Retrieves the current data model associated with the independence test.
getData() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Retrieves the dataset associated with this object.
getData() - Method in class edu.cmu.tetrad.search.test.MsepTest: Returns the data set used for the test.
getData() - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Retrieves the data model associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Retrieves the data model associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.Rcit: Retrieves the dataset associated with this instance.
getData() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Retrieves the data model associated with this object.
getData() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Retrieves the data model associated with the current block specification.
getData() - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Retrieves the data model associated with the current test instance.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Getter for the field data.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Retrieves the dataset used in the independence test.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Retrieves the data set from the method.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

getData.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Returne the original data for the method.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Retrieves the original data used for the independence test.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Retrieve the data set used in the independence test.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: getData.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: Getter for the field data.
getData() - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Retrieves the data model associated with this test.
getData() - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
getData() - Method in class edu.cmu.tetrad.util.Matrix: Returns the wrapped SimpleMatrix.
getData() - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Retrieves the original dataset used for the independence test.
getDataBox() - Method in class edu.cmu.tetrad.data.BoxDataSet: Getter for the field dataBox.
getDataFile() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field dataFile.
getDataModel() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Retrieves the data model associated with the current instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Retrieves the data model associated with the current instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.FfMl: Retrieves the data model associated with this instance, encapsulating the data used for computations or analysis in the current context.
getDataModel() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Retrieves the data model associated with the current instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the data model associated with the HeavyTailSemBicScore instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Retrieves the data model associated with this instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the current data model associated with this instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Retrieves the data model used for scoring.
getDataModel() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the data model used for scoring.
getDataModel() - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Retrieves the current data model instance.
getDataModel() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the data model used for the score calculation.
getDataModel() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the data model.
getDataModel() - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Retrieves the dataset associated with this instance.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves the data model located at the specified index within the collection of data models.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Returns the data model at the specified index.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the number of data sets to simulate.
getDataModel(int) - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Retrieves the data model at the specified index from this simulation.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Returns the number of data sets to simulate.
getDataModel(int) - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Returns the number of data sets to simulate.
getDataSet() - Method in class edu.cmu.tetrad.bayes.CellTableProbs: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.bayes.DataSetProbs: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.bayes.FactoredBayesStructuralEM: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.bayes.IntAveDataSetProbs: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.data.Histogram: Retrieves the current DataSet instance associated with this object.
getDataSet() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the required effect/gain threshold for determining an effect edge.
getDataSet() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the training data set that is utilized within this instance.
getDataSet() - Method in interface edu.cmu.tetrad.search.is.IsScore: Retrieves the dataset associated with the scoring or inference process.
getDataSet() - Method in class edu.cmu.tetrad.search.score.BdeScore: Returns the DataSet associated with this method.
getDataSet() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Retrieves the dataset associated with this BdeuScore object.
getDataSet() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Returns the dataset.
getDataSet() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Returns the dataset.
getDataSet() - Method in interface edu.cmu.tetrad.search.score.DiscreteScore: Returns the dataset.
getDataSet() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns the dataset.
getDataSet() - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns the data set.
getDataSet() - Method in interface edu.cmu.tetrad.search.utils.TetradTest: getDataSet.
getDataSet() - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: getDataSet.
getDataSet() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: getDataSet.
getDataSet() - Method in class edu.cmu.tetrad.sem.DagScorer: Getter for the field dataSet.
getDataSet() - Method in interface edu.cmu.tetrad.sem.Scorer: getDataSet.
getDataSet() - Method in class edu.cmu.tetrad.sem.SemEstimator: Getter for the field dataSet.
getDataSet() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbs: Getter for the field dataSet.
getDataSets() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: The data sets being pooled for the calling thread: those registered by PooledScoreWrapper.setThreadDataSets(List), or the default list if none are registered.
getDataSets() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the list of data sets associated with this independence test.
getDataSets() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the list of data sets used in the computation.
getDataSets() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Returns the datasets of the base test.
getDataSets() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the datasets of the wrapped test.
getDataSets() - Method in class edu.cmu.tetrad.search.test.Gcm: Retrieves a list of data sets associated with the current instance.
getDataSets() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns the datasets for this test
getDataSets() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Retrieves the data sets used in the underlying independence test.
getDataSets() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves a list of data sets associated with this instance.
getDataSets() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns the per-domain data sets.
getDataSets() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Returns the data sets used for the independence test.
getDataSets() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Retrieves the list of datasets associated with this object.
getDataSets() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest
getDataSets() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Retrieves the list of data sets.
getDataSets() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Returns the datasets for this test
getDataSets() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Retrieves the data sets used in the independence test.
getDataType() - Method in interface edu.cmu.tetrad.algcomparison.algorithm.Algorithm: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Dagma: Retrieves the data type of the algorithm's output.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Returns the data type of the algorithm, which can be Continuous, Discrete, Mixed, Graph, Covariance, or All.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Retrieves the data type of the dataset.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Retrieves the data type of the dataset.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Returns the data type of the given method.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the data type of the algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.FirstInflection: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Returns the type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam: Retrieves the data type associated with the implementation.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Returns the data type of the algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Retrieves the data type associated with the current test instance.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Flop: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Retrieves the data type of the dataset required for the PCMCI algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Returns the data type associated with the TwoStep algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Retrieves the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Retrieves the data type that the search requires.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Retrieves the data type that the search requires.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Retrieves the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Retrieves the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Retrieves the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Retrieves the data type that the search requires.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Retrieves the data type that the search requires.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Retrieves the data type associated with the current instance of the algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Returns the data type supported by the underlying test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Retrieves the data type associated with the current instance of the algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Retrieves the data type required for the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Retrieves the data type required for the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Returns the data type required.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Returns the data type that this algorithm requires.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvBossExperimental: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Retrieves the data type associated with the method.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Glasso: Retrieves the data type required by the search algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Retrieves the data type required by the algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Retrieves the data type associated with this algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

GIN operates on continuous (non-Gaussian) data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Retrieves the data type of the dataset.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Returns the data type required for the search, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Retrieves the data type required by the algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Retrieves the data type of the current algorithm.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.StabilitySelection: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.algorithm.StARS: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisExpandedGcm: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionBlocksIndTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionLrt: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in interface edu.cmu.tetrad.algcomparison.independence.BlockIndependenceWrapper: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTest: Deprecated.

Retrieves the data type associated with this test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestLemma10: Deprecated.

Retrieves the data type associated with this independence test implementation.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestTs: Retrieves the data type associated with this independence test implementation.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.CciTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.ChiSquare: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython: Retrieves the data type associated with the test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.ClKciPython: Returns the data type associated with this implementation.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython: Retrieves the data type associated with the test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.ConditionalGaussianLrt: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.Cord: Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.DegenerateGaussianLrt: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.DgBicTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.EquivalenceTest: Returns the data type this test requires.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.FfCi: Provides the data type of the test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.FfCiContinuous: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.FisherZ: Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.Gcm: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.Gin: Retrieves the data type associated with this test, which indicates that the test operates on continuous data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.GSquare: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in interface edu.cmu.tetrad.algcomparison.independence.IndependenceWrapper: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.Kci: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.MinimaxCITest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.MultinomialLogisticRegressionWald: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.Mvplrt: Deprecated.

Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.PoissonBicTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.ProbabilisticTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestLemma10Singletons: Deprecated.

Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTs: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTsSingletons: Deprecated.

Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestWilkesSingletons: Deprecated.

Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.Rcit: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.SemBicTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.StratifiedRank: Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.TrekSeparationBlocksIndependence: Retrieves the data type of the independence test.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.independence.TRffLrTest: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBgeScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScoreTabular: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BdeuScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BfBlocksBicScore: Retrieves the data type associated with this score wrapper.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BgeScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Retrieves the data type associated with this score wrapper.
getDataType() - Method in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.ConditionalGaussianBicScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBgeScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBicScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.DiscreteBicScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.EbicScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.FfMl: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.FfMlContinuous: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.GicScores: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.HeavyTailSemBicScore: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.HuangMarginalLikelihoodScore: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Returns the data type associated with this scoring method.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Returns the data type associated with this score wrapper.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.KcvBicScore: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.KernelMarginalLikelihoodScore: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.MagDgBicScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.MSepScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.MVPBicScore: Deprecated.

Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.PoissonPriorScore: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.RankBicScore: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.RffBicScore: Returns the data type of the current score.
getDataType() - Method in interface edu.cmu.tetrad.algcomparison.score.ScoreWrapper: Returns the data type that the search requires, whether continuous, discrete, or mixed.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.SemBicScore: Returns the data type of the current score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.TRffBicScore: Retrieves the data type associated with this scoring mechanism.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.score.ZhangShenBoundScore: Returns the data type of the score.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Returns the data type associated with this model.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Returns the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the data type of the data.
getDataType() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Retrieves the data type of the data set.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Returns the data type the inner independence wrapper accepts; pooling does not change it.
getDataType() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Returns the data type the inner score wrapper accepts; pooling does not change it.
getDataType() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Returns the data type of the data.
getDataType() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field dataType.
getDatum(int, int) - Method in class edu.cmu.tetrad.data.TimeSeriesData: getDatum.
getDecayRate() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Getter for the field decayRate.
getDefaultLayout() - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Returns the layout used for graphs that have not been laid out.
getDefaultValue() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field defaultValue.
getDefiniteNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: getDefiniteNonadjacencies.
getDefiniteNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: getDefiniteNonadjacencies.
getDefiniteNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: getDefiniteNonadjacencies.
getDefiniteNonadjacencies() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: getDefiniteNonadjacencies.
getDegree() - Method in class edu.cmu.tetrad.graph.Dag: getDegree.
getDegree() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getDegree.
getDegree() - Method in interface edu.cmu.tetrad.graph.Graph: getDegree.
getDegree() - Method in class edu.cmu.tetrad.graph.LagGraph: getDegree.
getDegree() - Method in class edu.cmu.tetrad.graph.SemGraph: getDegree.
getDegree() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the degree of the graph.
getDegree(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns the maximum degree of a graph.
getDegree(Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns the degree of a given node in the graph.
getDegree(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getDegree.
getDegree(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getDegree.
getDegree(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getDegree.
getDegree(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getDegree.
getDegree(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the degree of a given node in the graph.
getDeletions() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport: The proposed deletions, in the order they were made.
getDeletions() - Method in class edu.cmu.tetrad.sem.PredictionPruneReport: The proposed deletions, sorted by child name and elimination step.
getDelta() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Returns the split threshold delta passed to the constructor.
getDemixedData() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: getDemixedData.
getDepth() - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.Pcd: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.PcMb: Returns the depth of the search--that is, the maximum number of variables conditioned on in any conditional independence test.
getDepth() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.Dci: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.FasDci: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field depth.
getDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field depth.
getDerivatives() - Method in class edu.cmu.tetrad.util.TaylorSeries: Retrieves a copy of the array containing the derivatives of the Taylor series.
getDescendants(Node) - Method in class edu.cmu.tetrad.graph.Paths: Returns a list of all descendants of the given node.
getDescendants(List) - Method in class edu.cmu.tetrad.graph.Paths: Retrieves the descendants of the given list of nodes.
getDescendantsMap() - Method in class edu.cmu.tetrad.graph.Paths: Return a map from each node to its collection of descendants.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.algorithm.Algorithm: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Dagma: Returns the description of the DAGMA algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Returns a short, one-line description of the FASK algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Returns a short, one-line description of the FASK algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Returns the description of the LiNGAM algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the description of the algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.FirstInflection: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Returns the description of this method.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam: Provides a description of the CAM algorithm's functionality in this implementation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Returns a description of the algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Provides a description of the CD-NOD algorithm using the associated configured IndependenceWrapper test or a default description if no test is configured.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Flop: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Provides a description for the PCMCI algorithm, including the configured independence test, if applicable.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Provides a description of the Two-Step algorithm, which is based on FastICA and supports cycles in continuous data.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Returns a description of the BOSS-FCI algorithm using the description of its independence test and score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Returns a description of the algorithm using the description of its independence test and score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Returns a description of the algorithm using the description of its independence test and score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Returns a description of the BOSS-FCI algorithm using the description of its independence test and score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Returns a description of the BOSS-FCI algorithm using the description of its independence test and score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Returns a description of the algorithm using the description of its independence test and score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Provides a textual description of the CPW algorithm, including its functionality and distinguishing features, such as the use of pairwise-derived forbidden knowledge and pairwise orientation for certain edges.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Returns a description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Provides a textual description of the FCI-CPW algorithm, including its functionality and distinguishing features, such as the use of pairwise-derived forbidden knowledge and pairwise orientation for certain edges.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Returns a description of the FGES-FCI algorithm using the description of the independence test and score associated with it.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Returns a description of the GFCI algorithm using the description of the independence test and score associated with it.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Returns a short description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Returns a short description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvBossExperimental: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Returns a description of the method.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Glasso: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Returns a description of the MimbuildBollen algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Provides a brief description of the "Mimbuild Pca" algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

Returns a human-readable description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Returns a description of the RSkew algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Returns a description of this method.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Returns the description of the algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Returns a description of the method.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Returns a description of the algorithm being used, including the initial graph if available.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Returns the description of the current algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Returns a description of the method.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Returns a description of the algorithm used.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Returns a description of the method.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.StabilitySelection: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.algorithm.StARS: Returns a short, one-line description of this algorithm.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.Cyclic: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.ErdosRenyi: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomForward: Returns a short, one-line description of this graph type.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.graph.RandomGraph: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomMim: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomMimic: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomSingleFactorMim: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomTwoFactorMim: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.ScaleFree: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.graph.SingleGraph: Returns a short, one-line description of this graph type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisExpandedGcm: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionBlocksIndTest: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionLrt: Returns a short of this independence test.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.independence.BlockIndependenceWrapper: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTest: Deprecated.

Returns a description of the test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestLemma10: Deprecated.

Returns a description of the "Blocks Test Lemma 10" independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestTs: Returns a description of the "Blocks Test Lemma 10" independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.CciTest: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.ChiSquare: Returns a short description of the Chi Square Test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython: Provides a textual description of the test implementation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.ClKciPython: Provides a description of the test being implemented by the ClKciPython class.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython: Provides a textual description of the test implementation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.ConditionalGaussianLrt: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.Cord: Retrieves the description of the CORD test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.DegenerateGaussianLrt: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.DgBicTest: Returns a short description of the test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.EquivalenceTest: Returns a short description of this test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.FfCi: Provides a description of the independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.FfCiContinuous: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.FisherZ: Retrieves the description of the Fisher Z test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.Gcm: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.Gin: Provides a description of the GIN (Generalized Independent Noise) residual-independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.GSquare: Returns a short of this independence test.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.independence.IndependenceWrapper: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.Kci: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.MinimaxCITest: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.MultinomialLogisticRegressionWald: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.Mvplrt: Deprecated.

Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.PoissonBicTest: Returns a short description of the test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.ProbabilisticTest: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestLemma10Singletons: Deprecated.

Retrieves the description of the Fisher Z test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTs: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTsSingletons: Deprecated.

Retrieves the description of the Fisher Z test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestWilkesSingletons: Deprecated.

Retrieves the description of the Fisher Z test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.Rcit: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.SemBicTest: Returns a short description of the test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.StratifiedRank: Retrieves the description of the test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.TrekSeparationBlocksIndependence: Retrieves the description of the Fisher Z test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.independence.TRffLrTest: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBgeScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScoreTabular: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BdeuScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BfBlocksBicScore: Provides a textual description of the score type.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BgeScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Provides a textual description of the score type.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.ConditionalGaussianBicScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBgeScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBicScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.DiscreteBicScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.EbicScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.FfMl: Returns the description of the Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.FfMlContinuous: Returns the description of the Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.GicScores: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.HeavyTailSemBicScore: Returns the description of the Sem BIC Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.HuangMarginalLikelihoodScore: Returns the description of the Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Provides a description of the scoring method used in this class.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Provides a description of the Instance-Specific BDeu score, a discrete Dirichlet–multinomial posterior predictive score for the specific instance.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.KcvBicScore: Returns the description of the Sem BIC Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.KernelMarginalLikelihoodScore: Returns the description of the Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.MagDgBicScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.MSepScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.MVPBicScore: Deprecated.

Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.PoissonPriorScore: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.RankBicScore: Returns the description of the Sem BIC Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.RffBicScore: Returns the description of the Sem BIC Score.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.score.ScoreWrapper: Returns a short of this independence test.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.SemBicScore: Returns the description of the Sem BIC Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.TRffBicScore: Provides a textual description of the current scoring mechanism.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.score.ZhangShenBoundScore: Returns the description of the Zhang-Shen Bound Score.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Provides a description of the Generalized Additive Model (GAM) with its associated random graph.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: getDescription.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonGaussianSemSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the description of the simulation.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFn: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFp: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFpr: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTn: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTp: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTpr: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorF1: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFn: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFp: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFpr: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecisionCommonEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecallCommonEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTn: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTp: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiff: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedFP: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentExistsLatentCommonCause: Returns a short description of the statistic, which is the percentage of bidirected edges for which a latent confounder exists.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentPrecision: Returns a short description of the statistic, which is the percentage of bidirected edges for which a latent confounder exists.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTP: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CirclePrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CircleRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalseNegativeBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalsePositiveBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorTruePositiveBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonMeasuredAncestorRecallBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.CorrectSkeleton: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ElapsedCpuTime: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Adj: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1All: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Arrow: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Circle: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Tail: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.FalseNegativesAdjacencies: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.FalsePositiveAdjacencies: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.GraphExactlyRight: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaAverageSquaredDistance: Retrieves the description for this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMaxSquaredDiffEstTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMinSquaredDiffEstTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgSquaredDifference: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMaximumSquaredDifference: Retrieves the description for this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMinimumSquaredDifference: Retrieves the description for this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst: A Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst2: A Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedOrientationRatioEst: A Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliesLegalMag: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalseNegativeBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalsePositiveBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorRecallBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorTruePositiveBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalCpdag: Provides a description of the output indicating whether the estimated graph is a Legal CPDAG.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalPag: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphRecall
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagCgScore: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagDgScore: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagSemScore: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPasses: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPassesBestOf10: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingP: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingPBestOf10: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialP: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialPBestOf10: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFdrCutoffP: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH0: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH1: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffP: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffPBestOf10: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPasses: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPassesBestOf10: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrAdj: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrArrow: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.Maximal: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.MaximalityCondition: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH0: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH1: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoAlmostCyclicPathsCondition: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoCyclicPathsCondition: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoPotentiallyDirectedF1: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumAmbiguousTriples: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsNotInUnshieldedCollidersEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberCollidersEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInCollidersEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInUnshieldedCollidersEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberTailsEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberUnshieldedCollidersEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedBothNonancestorAncestor: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredDD: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredNL: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPD: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPL: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCommonMeasuredAncestorBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDefiniteDirectedEdgeAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeConfounded: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeNonAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeNonAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeNonAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleVisibleNonancestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectDDAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectPDAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectVisibleEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCoveringAdjacenciesInPag: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyDirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyNotDirectedPaths: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeBnaMeasuredCounfounded: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNoMeasureAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNotAncNotRev: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeReversed: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedShouldBePartiallyDirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumEdgeInEstInTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumGenuineAdjacenciesInPag: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectDDAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectPDAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectVisibleAncestors: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumLatentCommonAncestorBidirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumNondirectedEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumParametersEst: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPartiallyOrientedEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPotentiallyDirected: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumUndirectedEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdgeTrue: Retrieves the description of the statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ParameterColumn: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentAmbiguous: Retrieves the description of the statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentBidirectedEdges: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedEst: Retrieves the description of the statistic: Proportion of semi(X, Y) in estimated graph for which there is no semi(Y, X) in true graph.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedTrue: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueDistanceToAlpha: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueUniformityUnderNull: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPathF1: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedRecall: Returns a short one-line description of this statistic.
getDescription() - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.StructuralHammingDistance: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TailPrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TailRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesArrows: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesTails: Retrieves a short one-line description of the statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveArrow: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveTails: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionArrow: Retrieves the description of the statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionTails: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallArrows: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallTails: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveArrow: Retrieves a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveDirectedPathNonancestor: Retrieves the description of the statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveTails: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalseNegative: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalsePositive: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCyclePrecision: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleRecall: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleTruePositive: Returns a short one-line description of this statistic.
getDescription() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Returns a description of the pooled test, formed by prefixing the inner wrapper's description with "Pooled".
getDescription() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Returns a description of the pooled score, formed by prefixing the inner wrapper's description with "Pooled (IMaGES)".
getDescription() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: getDescription.
getDescription() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: getDescription.
getDescription() - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Returns the description of the simulation.
getDescription() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Returns the description of the simulation.
getDescription() - Method in enum class edu.cmu.tetrad.graph.Paths.CpdagViolation: Retrieves the description of this CPDAG violation.
getDescription() - Method in class edu.cmu.tetrad.search.test.IndTestGin: Provides a string description of the current configuration of the independence test, including details about the regressor and backend used, and additional information related to permutations if applicable.
getDescription() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig.DefaultEvent: getDescription.
getDescription() - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig.Event: Returns the description of the event.
getDescriptorFromToken(String) - Method in class edu.cmu.tetrad.calculator.expression.ExpressionManager: getDescriptorFromToken.
getDescriptors() - Method in class edu.cmu.tetrad.calculator.expression.ExpressionManager: Getter for the field descriptors.
getDeterminismThreshold() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Getter for the field determinismThreshold.
getDf() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: The sparse-adjusted degrees of freedom at the time of removal.
getDf() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Returns the adjusted degrees of freedom, or -1 if the degrees of freedom cannot be determined.
getDf() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the degrees of freedom associated with the most recent call of isIndependent.
getDfPairCounts() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The df histogram: df to the number of null pairs with that df.
getDfPairCounts() - Method in class edu.cmu.tetrad.search.test.AlphaReport: The df histogram: df to the number of null pairs with that df.
getDiff() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Retrieves the final convergence metric (entrywise L1 change).
getDim(int) - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the dimension of the given variable.
getDimension() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getDimension.
getDimension() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getDimension.
getDimension() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getDimension.
getDimension() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the dimension of the covariance matrix.
getDimension() - Method in class edu.cmu.tetrad.util.StratifiedRankTests: Returns the dimension (number of variables) of the stratum matrices.
getDimension(int) - Method in interface edu.cmu.tetrad.data.CellTable: Returns the dimension of the specified variable in the cell table.
getDimension(int) - Method in class edu.cmu.tetrad.data.CellTableAdTree: Returns the dimensions of the given variable.
getDimension(int) - Method in class edu.cmu.tetrad.data.CellTableCountSample: Returns the dimensions of the given variable.
getDimension(int) - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the dimension of the given variable.
getDir() - Method in class edu.cmu.tetrad.search.Cstar: getDir.
getDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns the directed edge between the given nodes, if one exists in the graph.
getDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getDirectedEdge.
getDirectedEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: getDirectedEdge.
getDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getDirectedEdge.
getDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getDirectedEdge.
getDirectedEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the directed edge connecting two nodes in the graph.
getDirectedEdgeHead(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: For a directed edge, returns the node adjacent to the arrow endpoint.
getDirectedEdgeTail(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: For a directed edge, returns the node adjacent to the null endpoint.
getDisagreements() - Method in class edu.cmu.tetrad.search.CdnodBoss: Disagreements between the CPDAG's collider status and the CI test's verdict recorded in the most recent CdnodBoss.search().
getDiscreteData() - Method in class edu.cmu.tetrad.data.MixedDataBox: Getter for the field discreteData.
getDiscreteData() - Method in class edu.cmu.tetrad.search.utils.AdTree: Returns the pre-built discrete data array, for sharing across bootstrap instances.
getDiscreteData() - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Returns the pre-built discrete data array, for sharing across bootstrap instances.
getDiscreteDataSet(DataModel) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Returns the datamodel case to DataSet if it is discrete.
getDiscreteInteractionOrder() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the discrete interaction order.
getDiscreteParents(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the discrete parents of a node at a given index.
getDiscreteScore() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: getDiscreteScore.
getDishBumpStDev() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Getter for the field dishBumpStDev.
getDishDishVariability() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the standard deviation in percentage of random dish bump values away from 100%.
getDishDishVariability() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getDishDishVariability.
getDishModel() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Gets the dish model.
getDishNumber() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Returns the number of the getModel dish.
getDishNumber() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: While the simulate() method is being executed, returns the dish number of the cell currently being simulated, zero indexed.
getDisplayMax() - Method in class edu.cmu.tetrad.data.Histogram: Retrieves the maximum value used for display purposes in a continuous histogram.
getDisplayMin() - Method in class edu.cmu.tetrad.data.Histogram: Retrieves the minimum value used for display purposes in a continuous histogram.
getDisplayName() - Method in interface edu.cmu.tetrad.graph.Node: Returns the display name.
getDisplayString(LaggedFactor) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.DisplayNameHandler: Converts the given lagged factor into a display string.
getDisplayString(String, int) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.DisplayNameHandler: Uses the given factor and lag information to construct a display string.
getDist() - Method in class edu.cmu.tetrad.sem.EmpiricalDistributionForExpression: Generates an empirical cumulative distribution function (CDF) based on the evaluation of an expression over a repetitive sampling process.
getDistalEndpoint(Node) - Method in class edu.cmu.tetrad.graph.Edge: getDistalEndpoint.
getDistalNode(Node) - Method in class edu.cmu.tetrad.graph.Edge: Traverses the edge in an undirected fashion--given one node along the edge, returns the node at the opposite end of the edge.
getDistances(Graph, double[][], DataSet, CpdagParentDistancesFromTrue.DistanceType) - Method in class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue: Calculates the distance matrix for the edges in the given PDAG (outputPdag).
getDistinctQueries() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Distinct cached queries currently held (authoritative distinct-key count).
getDistribution() - Method in class edu.cmu.tetrad.sem.DistributionSampler: Retrieves the probability distribution encapsulated within this instance.
getDistribution() - Method in class edu.cmu.tetrad.sem.Parameter: Getter for the field distribution.
getDistribution(int) - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: Classifies a given case into a model, based on which model has the highest gamma value for that case.
getDmStructure() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Getter for the field dmStructure.
getDof() - Method in class edu.cmu.tetrad.bayes.BayesProperties: Retrieves the degrees of freedom (dof) value for the current instance.
getDof() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood.Ret: Returns the degrees of freedom.
getDof() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getDof.
getDof() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the degrees of freedom.
getDof() - Method in class edu.cmu.tetrad.sem.DagScorer: getDof.
getDof() - Method in interface edu.cmu.tetrad.sem.Scorer: getDof.
getDof() - Method in class edu.cmu.tetrad.sem.SemPm: getDof.
getDof() - Method in class edu.cmu.tetrad.sem.SemSandwichEstimator: Returns the model degrees of freedom used for the scaled test.
getDoF(int, int[]) - Method in class edu.cmu.tetrad.search.score.MvpLikelihood: Returns the score of the node at index i, given its parents.
getDomains() - Method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: Returns the per-domain data sets.
getDottedUnderlinedTriplesFromGraph(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Retrieves the list of dotted and underlined triples from the given graph, with the specified node as the middle node.
getDottedUnderlines() - Method in class edu.cmu.tetrad.graph.Dag: getDottedUnderlines.
getDottedUnderlines() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getDottedUnderlines.
getDottedUnderlines() - Method in interface edu.cmu.tetrad.graph.Graph: getDottedUnderlines.
getDottedUnderlines() - Method in class edu.cmu.tetrad.graph.LagGraph: getDottedUnderlines.
getDottedUnderlines() - Method in class edu.cmu.tetrad.graph.SemGraph: getDottedUnderlines.
getDottedUnderlines() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns a set of Triple objects representing dotted underlines.
getDottedUnderlines() - Method in class edu.cmu.tetrad.graph.Underlines: getDottedUnderlines.
getDouble(int, int) - Method in class edu.cmu.tetrad.data.BoxDataSet: getDouble.
getDouble(int, int) - Method in interface edu.cmu.tetrad.data.DataSet: getDouble.
getDouble(int, int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getDouble.
getDouble(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the double value of the given parameter, looking up its default in the ParamDescriptions map.
getDouble(String, double) - Method in class edu.cmu.tetrad.util.Parameters: Returns the object value of the given parameter, using the given default.
getDoubleData() - Method in class edu.cmu.tetrad.data.BoxDataSet: getDoubleData.
getDoubleData() - Method in interface edu.cmu.tetrad.data.DataSet: getDoubleData.
getDoubleData() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getDoubleData.
getDoubleMissingValue() - Static method in class edu.cmu.tetrad.data.ContinuousVariable: getDoubleMissingValue.
getDoubleValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Returns the value stored at element (r,c) as a double
getDoubleValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Returns the value stored at element (r,c) as a double
getDoubleValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Returns the value stored at element (r,c) as a double
getDoubleValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Returns the value stored at element (r,c) as a double
getDoubleValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Returns the value stored at element (r,c) as a double
getEBICprior() - Method in class edu.cmu.tetrad.search.score.MvpLikelihood: getEBICprior.
getEdge() - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Retrieves the edge associated with this CandidateEdit operation, if applicable.
getEdge() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: Getter for the field edge.
getEdge(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Returns the value of edge between nodes i and j
getEdge(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns the value of edge between nodes i and j
getEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the edge between two nodes in the graph.
getEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getEdge.
getEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: getEdge.
getEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getEdge.
getEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getEdge.
getEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the edge between the given nodes.
getEdgeCoef() - Method in class edu.cmu.tetrad.sem.DagScorer: Getter for the field edgeCoef.
getEdgeCoef() - Method in interface edu.cmu.tetrad.sem.Scorer: getEdgeCoef.
getEdgeCoef() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field edgeCoef.
getEdgeCoef(Edge) - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field edgeCoef.
getEdgeCoef(Node, Node) - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field edgeCoef.
getEdgeCoef(Node, Node) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Getter for the field edgeCoef.
getEdgeOrigins() - Method in class edu.cmu.tetrad.search.Fask: Returns the provenance of each orientation from the last search: whether it came from the left-right statistic alone, from a compelled external orientation kept as the default, from an override of such an orientation, or from the two-cycle test.
getEdgeParams(Node, Node, GeneralizedSemPm) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: getEdgeParams.
getEdgeProbabilityGraph() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the probability-annotated graph aggregating the resample searches, or null if no resamples were run.
getEdges() - Method in class edu.cmu.tetrad.data.KnowledgeGroup: getEdges.
getEdges() - Method in class edu.cmu.tetrad.graph.Dag: getEdges.
getEdges() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getEdges.
getEdges() - Method in interface edu.cmu.tetrad.graph.Graph: getEdges.
getEdges() - Method in class edu.cmu.tetrad.graph.LagGraph: getEdges.
getEdges() - Method in class edu.cmu.tetrad.graph.SemGraph: getEdges.
getEdges() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the set of edges in the graph.
getEdges() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns a list of edges for the current graph as a list of ordered pairs.
getEdges() - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Retrieves a list containing the edge associated with this CandidateEdit, if applicable.
getEdges() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Returns the edge matrix.
getEdges(Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns a list of edges connected to the given node.
getEdges(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getEdges.
getEdges(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getEdges.
getEdges(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getEdges.
getEdges(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getEdges.
getEdges(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the list of edges connected to the specified node.
getEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns a list of edges between the specified nodes in the graph.
getEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getEdges.
getEdges(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: getEdges.
getEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getEdges.
getEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getEdges.
getEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Finds all edges between two nodes.
getEdgesAdded() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the edges added.
getEdgesRemoved() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the edges removed.
getEdgeStats() - Method in class edu.cmu.tetrad.search.FaskPool: Returns the per-edge pooled statistics recorded during the last search, keyed by the edge added to the result graph.
getEdgeType() - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: Getter for the field edgeType.
getEdgeTypeProbabilities() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field edgeTypeProbabilities.
getEdgewiseComparisonString(Graph, Graph) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns an edgewise comparison of two graphs.
getEdgewiseComparisonString(String, Graph, String, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: getEdgewiseComparisonString.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.harness.tsbi.TsbiRunner: Returns the effective sample size used by this runner.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.MarkovCheck
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the effective sample size.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the effective sample size used in the score.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Retrieves the effective sample size used in the score calculation.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Retrieves the effective sample size used in computations.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Returns the effective sample size: the value set via ConditionalGaussianScore.setEffectiveSampleSize(int), or the number of rows in the dataset if none has been set.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.EbicScore
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Returns the effective sample size, which represents the number of observations adjusted for correlations or weighting within the sample data.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Returns the effective sample size, which represents the number of observations adjusted for correlations or weighting within the sample data.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.FfMl: Returns the effective sample size used in computations.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Returns the effective sample size, which represents the number of observations adjusted for correlations or weighting within the sample data.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.GicScores
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the effective sample size used in the statistical model.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Retrieves the effective sample size used in the scoring algorithm.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Calculates and returns the effective sample size, which is a measure of the number of independent observations in a dataset, potentially accounting for dependencies or weights.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the effective sample size used for scoring.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Returns the effective sample size, which represents the adjusted number of observations in the dataset after accounting for factors such as correlation or weighting.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Returns the effective sample size used in the score calculation.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.SemBicScore
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Retrieves the effective sample size used in the context of this instance.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.score.ZsbScore
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns the effective sample size: the value set via IndTestConditionalGaussianLrt.setEffectiveSampleSize(int), or, if none has been set, the number of rows currently in use (the restriction from IndTestConditionalGaussianLrt.setRows(List) if any, otherwise the full dataset).
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the effective sample size used for the independence test.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the effective sample size.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Retrieves the effective sample size for the independence test.
getEffectiveSampleSize() - Method in class edu.cmu.tetrad.search.Tsc: Returns the effective sample size.
getEffectiveSampleSize() - Method in interface edu.cmu.tetrad.util.EffectiveSampleSizeSettable: Returns the effective sample size.
getEffectNode() - Method in class edu.cmu.tetrad.search.Cstar.Record: Retrieves the effect node of the record.
getEffects() - Method in class edu.cmu.tetrad.search.Ida.NodeEffects: Returns the effects.
getEffects() - Method in class edu.cmu.tetrad.search.PdagPagIda.NodeEffects: Retrieves the list of effect values associated with the nodes.
getElapsed() - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Getter for the field elapsed.
getElapsedMillis() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the wall time in milliseconds for this setting.
getElapsedTime() - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

getElapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.Fci: Returns the elapsed time recorded by the algorithm or process.
getElapsedTime() - Method in class edu.cmu.tetrad.search.Fges: Returns the elapsed time of the search.
getElapsedTime() - Method in class edu.cmu.tetrad.search.FgesMb: Returns the elapsed time of the search.
getElapsedTime() - Method in class edu.cmu.tetrad.search.Icd: Retrieves the elapsed time recorded for the current operation or process.
getElapsedTime() - Method in class edu.cmu.tetrad.search.Pcd: Returns the elapsed time in milliseconds since the start of the method.
getElapsedTime() - Method in class edu.cmu.tetrad.search.PcMb: Returns the elapsed time of the most recent search.
getElapsedTime() - Method in class edu.cmu.tetrad.search.Rfci: Returns the elapsed time of the search.
getElapsedTime() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.Dci: Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: getElapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field elapsedTime.
getElapsedTime() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field elapsedTime.
getElapsedTime(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: getElapsedTime.
getElapsedTimeMillis() - Method in class edu.cmu.tetrad.search.is.IsGFci: Returns the elapsed time in milliseconds for the execution or process associated with this instance.
getElement(BayesIm) - Static method in class edu.cmu.tetrad.bayes.BayesXmlRenderer: Private constructor to prevent instantiation.
getElement(SemIm) - Static method in class edu.cmu.tetrad.sem.SemXmlRenderer: Converts a Sem Im into xml.
getEmbeddedData(DataSet, int, int, double) - Static method in class edu.cmu.tetrad.search.utils.Embedding: Generates an embedding of the given dataset based on the specified parameters.
getEmbeddedDimension() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the total number of embedded columns (the dimension of the Normal-Wishart prior).
getEmbedding() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns a copy of the embedding (variable index to embedded column indices) the score is using.
getEmbedding() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Returns a copy of the embedding this score is scoring against (variable index to embedded columns).
getEmMaxIterations() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The maximum number of EM iterations.
getEmMaxIters() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the maximum number of iterations for the EM algorithm.
getEmpiricalP() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The fraction of null draws with ad_ind at or below the real ad_ind.
getEmpiricalPFraction() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The fraction of null draws whose rejection fraction is at or above the real one (large fractions are bad, so the tail is on the high side).
getEmRidge() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The EM ridge.
getEmTolerance() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The EM convergence tolerance.
getEndpoint(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns the endpoint between two nodes in the graph.
getEndpoint(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getEndpoint.
getEndpoint(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: getEndpoint.
getEndpoint(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getEndpoint.
getEndpoint(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getEndpoint.
getEndpoint(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the endpoint between two nodes in the graph.
getEndpoint1() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field endpoint1.
getEndpoint2() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field endpoint2.
getEpsilon() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the epsilon value.
getEqualFrequencyBreakPoints(double[], int) - Static method in class edu.cmu.tetrad.data.Discretizer: getEqualFrequencyBreakPoints.
getErrCovar() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field errCovar.
getErrCovar(Node, Node) - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field errCovar.
getErrorCovar() - Method in class edu.cmu.tetrad.sem.DagScorer: Getter for the field errorCovar.
getErrorCovar() - Method in interface edu.cmu.tetrad.sem.Scorer: getErrorCovar.
getErrorCovariance(Node, Node) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getErrorCovariance.
getErrorDistribution(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Returns the error distribution for the factor'th factor.
getErrorDistribution(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Returns the error distribution for the factor'th factor.
getErrorDistribution(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Returns the error distribution for the factor'th factor.
getErrorNode(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getErrorNode.
getErrorNode(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the error node associated with the given node.
getErrorNodes() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the list of exogenous variableNodes.
getErrorNodes() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getErrorNodes.
getErrorPolicy() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Retrieves the error-handling policy currently used by the instance.
getErrorsTemplate() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the error template string.
getErrorVariance(Node) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getErrorVariance.
getErrVar(Node) - Method in class edu.cmu.tetrad.sem.SemIm: getErrVar.
getEss(ICovarianceMatrix) - Static method in class edu.cmu.tetrad.data.DataUtils: Returns the equivalent sample size, assuming all units are equally correlated and all unit variances are equal.
getEssMode() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The effective sample size mode.
getEstimatedGraph(String) - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the estimated graph for the given algorithm name, or null if none exists.
getEstimatedGraphs() - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the estimated graphs by algorithm name.
getEstimatedIm() - Method in class edu.cmu.tetrad.bayes.EmBayesEstimator: Getter for the field estimatedIm.
getEstimatedLatentCount() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Retrieves the estimated latent count value.
getEstimatedSem() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getEstimatedSem.
getEstimatedSem() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the estimated SEM IM.
getEstimatedSem() - Method in class edu.cmu.tetrad.sem.SemEstimator: Getter for the field estimatedSem.
getEstimatedSem() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbs: Getter for the field estimatedSem.
getEstimatedToTrueMatching() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the estimated-to-true latent matching.
getEstimator() - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Getter for the field estimator.
getEstSem() - Method in class edu.cmu.tetrad.sem.DagScorer: getEstSem.
getEstSem() - Method in interface edu.cmu.tetrad.sem.Scorer: getEstSem.
getEtaSquaredValues() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns eta-squared values for (discrete, continuous) pairs, keyed "discreteName|continuousName", unmodifiable.
getEvaluation(String) - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the evaluation for the given algorithm name, or null if none exists.
getEvaluations() - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the evaluations by algorithm name.
getEvidence() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Getter for the field evidence.
getEvidence() - Method in class edu.cmu.tetrad.bayes.Identifiability: Getter for the field evidence.
getEvidence() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Retrieves the evidence associated with this updater.
getEvidence() - Method in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Returns the manipulation that was used to manipulate the Bayes IM.
getEvidence() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: Getter for the field evidence.
getEvidence() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Getter for the field evidence.
getEvidence() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Retrieves the evidence associated with this updater.
getEvidence() - Method in class edu.cmu.tetrad.sem.SemUpdater: Getter for the field evidence.
getExampleNonsingular(ICovarianceMatrix, int) - Static method in class edu.cmu.tetrad.data.DataUtils: getExampleNonsingular.
getExogenous(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getExogenous.
getExpectedTrueEdges() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The expected number of true edges the budget is a fraction of, p * expectedDegree / 2.
getExpectedTrueEdges() - Method in class edu.cmu.tetrad.search.test.AlphaReport: The expected number of true edges, p * expectedDegree / 2.
getExplainedAway() - Method in class edu.cmu.tetrad.search.Rlcd: The number of rank deficiencies the last search explained away by observed variables, summed over partitions.
getExplainedAway() - Method in class edu.cmu.tetrad.search.rlcd.RlcdClusterSearch: The number of rank deficiencies this search found and then explained away by observed variables, so declined to make clusters of.
getExponent(double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the unbiased exponent used in the representation of the floating-point value.
getExpression() - Method in class edu.cmu.tetrad.calculator.expression.Equation: Getter for the field expression.
getExpression() - Method in class edu.cmu.tetrad.sem.ExpressionSampler: Retrieves the stored mathematical expression associated with this instance.
getExpressions() - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: getExpressions.
getExpressions() - Method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: getExpressions.
getExpressions() - Method in interface edu.cmu.tetrad.calculator.expression.Expression: getExpressions.
getExpressions() - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: getExpressions.
getExternalGraph() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Getter for the field externalGraph.
getExtraPairs() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the extra estimated pairs not present in the truth.
getF1StatsForTargetNodeAdjacencySubgraph(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates F1 statistics for a target node's adjacency subgraph in terms of adjacency, arrow types, circle types, and tail types.
getF1StatsForTargetNodeMBSubgraph(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Computes the F1 statistics for the Markov blanket subgraph with the target node in the given estimated graph and true graph.
getF1StatsForTargetNodeParentsSubgraph(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Computes the F1 statistics (F1-Adjacency, F1-Arrow, F1-Circle, F1-Tail) between the parents subgraph of a given target node in the estimated graph and the corresponding parents subgraph in the true graph.
getF1StatsForWholeGraph(Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the F1 statistics for the entire graph by comparing an estimated graph to the true graph.
getFact() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns the independence fact being stored.
getFactor() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Returns the factor.
getFactor() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedEdge: Getter for the field factor.
getFactor() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Returns the name of the lagged factor.
getFactor(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the (string name of) the factor at the given index.
getFactor(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the (string name of) the factor at the given index.
getFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: getFactors.
getFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: getFactors.
getFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Returns a SortedSet of the factors in this graph.
getFactors() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Returns a SortedSet of the factors in this graph.
getFacts() - Method in class edu.cmu.tetrad.data.IndependenceFacts: getFacts.
getFalseNegatives() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the number of false negatives.
getFalsePositives() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the number of false positives.
getFas() - Method in class edu.cmu.tetrad.search.Pc: Retrieves the Fas object.
getFas() - Method in class edu.cmu.tetrad.search.PcAR: Retrieves the Fas object.
getFdrCutoff(double, boolean, boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the FDR (False Discovery Rate) p-value cutoff for the given condition, using the Benjamini-Hochberg procedure (or Benjamini-Yekutieli if negative correlation is assumed).
getFdrCutoff(double, List, boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the FDR (False Discovery Rate) p-value cutoff for the given list of results, using the Benjamini-Hochberg procedure (or Benjamini-Yekutieli if negative correlation is assumed).
getFeatureType() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Retrieves the feature type for the current instance.
getFeatureType() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Retrieves the feature type for the current instance.
getFeatureType() - Method in class edu.cmu.tetrad.search.score.FfMl: Retrieves the feature type associated with this object.
getFeatureType() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Retrieves the feature type for the current instance.
getFeatureType() - Method in class edu.cmu.tetrad.search.test.FfCi: Retrieves the current feature type used in the mixed independence test.
getFeatureType() - Method in class edu.cmu.tetrad.search.test.FfCi1: Retrieves the current feature type used in the mixed independence test.
getFilename() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.StoredLagGraphParams: Returns the stored file.
getFinalStrategyUsingDsep(Graph, Knowledge, boolean, Map) - Static method in class edu.cmu.tetrad.search.utils.MagToPag: Returns the final strategy for finding a PAG using D-SEP.
getFindings() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the findings, in the order the checks run, unmodifiable.
getFindings() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the findings, in the order the checks run, unmodifiable.
getFindings(FindingCode) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the findings with the given code, in order, unmodifiable.
getFindings(FindingCode) - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the findings with the given code, in order, unmodifiable.
getFirst() - Method in class edu.cmu.tetrad.graph.NodePair: Getter for the field first.
getFirst() - Method in class edu.cmu.tetrad.graph.OrderedPair: Getter for the field first.
getFirstEdge() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move: Getter for the field edge.
getFirstLayer() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: getFirstLayer.
getFirstStepStored() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the index of the first step to actually be stored out.
getFirstStepStored() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getFirstStepStored.
getFisherCombinedP(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates and returns Fisher's combined p-value based on the specified dependence assumption.
getFisherCombinedPValue(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the combined p-value using Fisher's method for a given list of independence test results.
getFisherZ() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns the underlying Fisher Z test.
getFitDiff() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the final RICF parameter change.
getFitMillis() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: Time spent fitting the simulator, in milliseconds.
getFitReportText() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Human-readable report (one block per node).
getFitReportText() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Human-readable report (one block per node).
getFitReportText() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Human-readable report (one block per node).
getFitReportText() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Human-readable report (one block per node).
getFixedParameters() - Method in interface edu.cmu.tetrad.sem.ISemIm: getFixedParameters.
getFixedParameters() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field fixedParameters.
getFml() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getFml.
getFml() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the Fml.
getFml() - Method in class edu.cmu.tetrad.sem.DagScorer: The value of the maximum likelihood function for the getModel the model (Bollen 107).
getFml() - Method in interface edu.cmu.tetrad.sem.Scorer: getFml.
getFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.AdjacencyConfusion: Returns the false negative count.
getFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: False negatives.
getFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.BidirectedConfusion: Returns the number of false negatives.
getFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: False negatives.
getFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.LocalGraphConfusion: Returns the false negatives (FN) value of the LocalGraphConfusion object.
getFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.OrientationConfusion: Getter for the field fn.
getFnc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: False negatives for common edges.
getFnc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: False negatives for common edges.
getFocusBasePag() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the value of the focusBasePag property.
getFocusBasePag() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the value of the focusBasePag property.
getFocusBasePag() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the value of the focusBasePag property.
getFocusBasePag() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the value of the focusBasePag property.
getFocusBatteryZ() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the focus battery data structured as a list of sets of strings.
getFocusBatteryZ() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the focus battery data structured as a list of sets of strings.
getFocusBatteryZ() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the focus battery data structured as a list of sets of strings.
getFocusBatteryZ() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the focus battery data structured as a list of sets of strings.
getFocusEnumerated() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the value of the focusEnumerated property.
getFocusEnumerated() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the value of the focusEnumerated property.
getFocusEnumerated() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the value of the focusEnumerated property.
getFocusEnumerated() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the value of the focusEnumerated property.
getFocusInterimPag() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the value of the focusInterimPag property.
getFocusInterimPag() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the value of the focusInterimPag property.
getFocusInterimPag() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the value of the focusInterimPag property.
getFocusInterimPag() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the value of the focusInterimPag property.
getFocusSeedLog() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the log of focus seed activities.
getFocusSeedLog() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the log of focus seed activities.
getFocusSeedLog() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the log of focus seed activities.
getFocusSeedLog() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the log of focus seed activities.
getFocusSepsetLog() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the log of focus separation sets.
getFocusSepsetLog() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the log of focus separation sets.
getFocusSepsetLog() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the log of focus separation sets.
getFocusSepsetLog() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the log of focus separation sets.
getFocusTally() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the current focus tally, which is a mapping of keys to their corresponding integer values.
getFocusTally() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the current focus tally, which is a mapping of keys to their corresponding integer values.
getFocusTally() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the current focus tally, which is a mapping of keys to their corresponding integer values.
getFocusTally() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the current focus tally, which is a mapping of keys to their corresponding integer values.
getForbidden() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: Getter for the field forbidden.
getForceVisibility() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the nodes b for which an edge X -> b is treated as visible by fiat.
getFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.AdjacencyConfusion: Returns the false positive count.
getFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: False positives.
getFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.BidirectedConfusion: Returns the number of false positives.
getFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: False positives.
getFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.LocalGraphConfusion: Retrieves the value of false positives (FP) from the LocalGraphConfusion object.
getFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.OrientationConfusion: Getter for the field fp.
getFpc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: False positives for common edges.
getFpc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: False positives for common edges.
getFracImproved() - Method in class edu.cmu.tetrad.sem.AdequacyReport: Retrieves the fraction of nodes that demonstrated performance improvement relative to the baseline.
getFractionDependent(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the fraction of dependent judgments for the given list of results.
getFractionDependent(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the fraction of dependent results.
getFreeParameters() - Method in interface edu.cmu.tetrad.sem.ISemIm: getFreeParameters.
getFreeParameters() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field freeParameters.
getFreeParameters() - Method in class edu.cmu.tetrad.sem.SemPm: getFreeParameters.
getFreeParamValues() - Method in interface edu.cmu.tetrad.sem.ISemIm: getFreeParamValues.
getFreeParamValues() - Method in class edu.cmu.tetrad.sem.SemIm: getFreeParamValues.
getFrequencies() - Method in class edu.cmu.tetrad.data.Histogram: Calculates and returns frequency counts for the data associated with the target variable.
getFrom() - Method in class edu.cmu.tetrad.data.KnowledgeEdge: Getter for the field from.
getFromVariables() - Method in class edu.cmu.tetrad.data.KnowledgeGroup: getFromVariables.
getFullGraph() - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

The full graph discovered.
getFullSigmaSafetyMargin() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the full sigma safety margin.
getFullTierOrdering() - Method in class edu.cmu.tetrad.graph.SemGraph: getFullTierOrdering.
getFValue(int, double[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Calculates the new value for the F function, for the given factor, in light of the given history.
getGeneralizedSemPm() - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Retrieves the GeneralizedSemPm object associated with this GeneralizedSemIm.
getGesDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Getter for the field gesDepth.
getGraph() - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the directed acyclic graph (DAG) associated with this model.
getGraph() - Method in interface edu.cmu.tetrad.regression.Regression: getGraph.
getGraph() - Method in class edu.cmu.tetrad.regression.RegressionCovariance: Getter for the field graph.
getGraph() - Method in class edu.cmu.tetrad.regression.RegressionDataset: Getter for the field graph.
getGraph() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Retrieves the causal graph associated with this instance.
getGraph() - Method in class edu.cmu.tetrad.search.GRegression: Returns the MPDAG.
getGraph() - Method in class edu.cmu.tetrad.search.Icd: Retrieves the graph associated with the current instance.
getGraph() - Method in class edu.cmu.tetrad.search.IdaCheck: Returns a copy of the graph associated with this instance.
getGraph() - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Returns the current graph after search, if available.
getGraph() - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns a defensive copy of the full graph.
getGraph() - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Returns the current graph after search, if available.
getGraph() - Method in class edu.cmu.tetrad.search.score.ScoredGraph: Returns the graph.
getGraph() - Method in class edu.cmu.tetrad.search.test.MsepTest: Returns the underlying graph that is being used to calculate d-separation relationships.
getGraph() - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Returns the current working graph (may be updated during search).
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Getter for the field graph.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Getter for the field graph.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.GraphWithPValue: Getter for the field graph.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: The graph that's constructed during the search.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: The graph that's constructed during the search.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: The graph that's constructed during the search.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: The graph that's constructed during the search.
getGraph() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: The graph that's constructed during the search.
getGraph() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the structural model graph this SEM PM is using.
getGraph() - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Getter for the field graph.
getGraph() - Method in class edu.cmu.tetrad.sem.SemPm: Getter for the field graph.
getGraph(boolean) - Method in class edu.cmu.tetrad.search.Grasp: Returns the graph based on the specified parameters.
getGraph(boolean) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the DAG build for the current permutation, or its PDAG.
getGraph(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: getGraph.
getGraph(List, Map, boolean) - Static method in class edu.cmu.tetrad.search.PermutationSearch: Construct a graph given a specification of the parents for each node.
getGraph(List, Map, boolean) - Static method in class edu.cmu.tetrad.search.PermutationSearchTopK: Construct a graph given a specification of the parents for each node.
getGraph(List, Map, Knowledge, boolean) - Static method in class edu.cmu.tetrad.search.PermutationSearch: Constructs a graph given a specification of the parents for each node.
getGraph(List, Map, Knowledge, boolean) - Static method in class edu.cmu.tetrad.search.PermutationSearchTopK: Constructs a graph given a specification of the parents for each node, with graph replication disabled.
getGraph(List, Map, Knowledge, boolean, boolean) - Static method in class edu.cmu.tetrad.search.PermutationSearch: Constructs a graph given a specification of the parents for each node.
getGraph(List, Map, Knowledge, boolean, boolean) - Static method in class edu.cmu.tetrad.search.PermutationSearchTopK: Constructs a graph given a specification of the parents for each node.
getGraphComparison(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Just counts arrowhead errors--for cyclic edges counts an arrowhead at each node.
getGraphFile() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field graphFile.
getGraphLearner() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the graph learning method currently set for this instance.
getGraphName() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.RevealOutputGraph: Getter for the field graphName.
getGraphName() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealOutputGraph: Getter for the field graphName.
getGraphName() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealOutputGraph: Getter for the field graphName.
getGraphName() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns the name of the graph
getGraphName() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.OutputGraph: Returns the name of the graph
getGraphNum() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field graphNum.
getGraphRBD() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Returns the graph that was learned using the BSC-D method.
getGraphRBI() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Returns the graph that was learned using the BSC-I method.
getGraphType() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Retrieves the graph type associated with this instance.
getGraphWithoutXToY(Graph, Node, Node, GraphUtils.GraphType) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns a graph that is obtained by removing the edge from node x to node y from the input graph.
getGroups(DataSet, int[], List, int, int) - Method in class edu.cmu.tetrad.search.utils.MinimaxGroupCache: Computes and returns a group of bins based on the input data and parameters.
getGroups(DataSet, int[], List, MinimaxBinningConfig) - Method in class edu.cmu.tetrad.search.utils.MinimaxGroupCache: Computes and returns groups of data indices based on the input dataset, feature indices, rows to include, and binning configuration.
getGST(Node) - Method in class edu.cmu.tetrad.search.PermutationSearch: Retrieves the GrowShrinkTree (GST) associated with the given Node.
getGST(Node) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Retrieves the GrowShrinkTree (GST) associated with the given Node.
getH() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the cache of independence facts and their corresponding posterior probabilities.
getH() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves an unmodifiable map of independence facts and their associated probabilities.
getH() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns a map from independence facts to their posterior probabilities of independence.
getH() - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: getH.
getHiddenLayers() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Retrieves the configuration for the hidden layers.
getHigh() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: Getter for the field high.
getHighPValueAlpha() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Getter for the field highPValueAlpha.
getHistory() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the history that was set in the constructor.
getHistoryArray() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Returns the getModel history array.
getI() - Method in class edu.cmu.tetrad.search.utils.Sextad: Getter for the field i.
getI() - Method in class edu.cmu.tetrad.search.utils.Tetrad: Getter for the field i.
getI() - Method in class edu.cmu.tetrad.search.utils.TetradNode: Getter for the field i.
getI() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Getter for the field i.
getId() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig.DefaultEvent: getId.
getId() - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig.Event: Returns the ID of the event.
getIdaMinEffect(Node, Node) - Method in class edu.cmu.tetrad.search.IdaCheck: Gets the signed minimum absolute total effect value between two nodes.
getIdaType() - Method in class edu.cmu.tetrad.search.Ida: Retrieves the IDA_TYPE associated with this instance.
getIMinusBeta() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Retrieves the matrix IB where IB = I - Beta.
getImplCovar() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Getter for the field implCovar.
getImplCovar(boolean) - Method in interface edu.cmu.tetrad.sem.ISemIm: getImplCovar.
getImplCovar(boolean) - Method in class edu.cmu.tetrad.sem.SemIm: getImplCovar.
getImplCovar(List) - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field implCovar.
getImplCovarMeas() - Method in interface edu.cmu.tetrad.sem.ISemIm: getImplCovarMeas.
getImplCovarMeas() - Method in class edu.cmu.tetrad.sem.SemIm: getImplCovarMeas.
getImplCovarMeas() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Getter for the field implCovarMeas.
getImpliedCorrelationMatrix() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the correlation matrix implied by the (symmetrized) supplied covariance matrix, over the positive-variance variables in the order given by CovarianceAudit.getVariableNames(), or null if there are fewer than two such variables.
getImpliedFactCount() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the number of independence facts the graph implied under the conditioning set type at the last result generation, or -1 if results have not been generated (or were generated by the all-subsets route, which does not record this set).
getIms() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Retrieves the list of GeneralizedSemIm objects used for simulation.
getIms() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Retrieves the list of SemIm objects used for simulation.
getIms() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Retrieves the list of SemIm objects used for simulation.
getIndegree() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Getter for the field indegree.
getIndegree(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the maximum indegree in a given graph.
getIndegree(Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns the indegree of the specified node in the graph.
getIndegree(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getIndegree.
getIndegree(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getIndegree.
getIndegree(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getIndegree.
getIndegree(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getIndegree.
getIndegree(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the indegree of a given node in the graph.
getIndegreeType() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Getter for the field indegreeType.
getIndependenceFacts() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Returns the independence facts in the form 1:2|3 for use in various Tetrad algorithms.
getIndependenceNodes() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the nodes that are possible X and Y for X _||_ Y | Z1,...,Zn.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.GfciOld: Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.is.IsGFci: Retrieves the independence test associated with the current instance.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the independence test being used.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.Pcd: Retrieves the IndependenceTest used by this method.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.StarFci: Returns the independence test.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.StarFciB: Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.StarFciC: Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.StarFciD: Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.StarFciOriginal: Returns the independence test used in search.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field independenceTest.
getIndependenceTest() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field independenceTest.
getIndependenceTests() - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Retrieves the list of independence tests managed by this instance.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Retrieves the IndependenceWrapper associated with this object.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Returns the independence wrapper of the algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Retrieves the configured IndependenceWrapper instance associated with this object.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Retrieves the IndependenceWrapper associated with this object.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Retrieves the current instance of IndependenceWrapper associated with this object.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Returns the IndependenceWrapper associated with this BOSS-FCI algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Returns the IndependenceWrapper associated with this algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Returns the IndependenceWrapper associated with this algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Returns the IndependenceWrapper associated with this BOSS-FCI algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Returns the IndependenceWrapper associated with this BOSS-FCI algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Returns the IndependenceWrapper associated with this algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Returns the IndependenceWrapper object associated with this instance.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Returns the IndependenceWrapper object associated with this instance.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Retrieves the IndependenceWrapper object associated with this method.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Retrieves the current instance of the IndependenceWrapper used for conditional independence testing in the algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Retrieves the IndependenceWrapper object associated with this method.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Retrieves the current instance of the IndependenceWrapper used for conditional independence testing in the algorithm.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Retrieves the IndependenceWrapper object associated with this method.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Returns the independence wrapper associated with this instance.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Returns the independence wrapper associated with this instance.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Retrieves the IndependenceWrapper object associated with this method.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Returns the independence wrapper.
getIndependenceWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Retrieves the IndependenceWrapper associated with this algorithm.
getIndependenceWrapper() - Method in interface edu.cmu.tetrad.algcomparison.utils.TakesIndependenceWrapper: Returns the independence wrapper.
getIndex() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the index of the arrow.
getIndex() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch.Gene: getIndex.
getIndex() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedParent: Returns the index of the parent.
getIndex(int, IndexedParent) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the index of the parent of the given factor that is equal to the given IndexedParent, or -1 if the given IndexedParent is not equal to any parent.
getIndex(int, IndexedParent) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the index of the parent of the given factor that is equal to the given IndexedParent, or -1 if the given IndexedParent is not equal to any parent.
getIndex(DataModel) - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: getIndex.
getIndex(Endpoint) - Static method in class edu.cmu.tetrad.graph.MisclassificationUtils: getIndex.
getIndex(Node) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: Getter for the field index.
getIndex(String) - Method in class edu.cmu.tetrad.data.DiscreteVariable: getIndex.
getIndex(String) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: getIndex.
getIndex(String) - Static method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getIndex.
getIndex(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the index of the given String factor.
getIndex(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the index of the given String factor.
getIndex(String) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: getIndex.
getIndex(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the index of the given parent for the given factor.
getIndex(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the index of the given parent for the given factor.
getIndexedLagGraph() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: getIndexedLagGraph.
getIndexedLagGraph() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Returns the indexed connectivity.
getIndexedLagGraph() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Returns the indexed connectivity.
getIndexedLagGraph() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.UpdateFunction: getIndexedLagGraph.
getIndices() - Method in class edu.cmu.tetrad.util.IndexedMatrix: Getter for the field indices.
getInducingPath(Node, Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: This method calculates the inducing path between two measured nodes in a graph.
getInhibitExcite() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Returns the inhibit/excite.
getInitializer() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Returns the initializer.
getInitSync() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Determines whether initialization is synchronized.
getInner() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Returns the independence wrapper that is built on each pooled data set.
getInner() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Returns the score wrapper that is built on each pooled data set.
getInputNames() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns the currently specified input variable names.
getInputNames() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns the currently specified input variable names.
getInputNames() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns the currently specified input variable names.
getInputNodes() - Method in interface edu.cmu.tetrad.sem.ConnectionFunction: getInputNodes.
getInputParents(Node) - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns the measured input parents of the given latent in the true graph.
getInputs() - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns a defensive copy of the measured input list.
getInputs() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Getter for the field inputs.
getInputs(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: getInputs.
getInstance() - Static method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Gets the instance of this class.
getInstance() - Static method in class edu.cmu.tetrad.annotation.ScoreAnnotations: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.annotation.TestOfIndependenceAnnotations: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.calculator.expression.ExpressionManager: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.sem.TemplateExpander: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.util.AlgorithmDescriptions: Gets the instance of this class.
getInstance() - Static method in class edu.cmu.tetrad.util.IndependenceTestDescriptions: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.util.LogUtils: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.util.NumberFormatUtil: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.util.PagCache: Provides access to the singleton instance of the PagCache class.
getInstance() - Static method in class edu.cmu.tetrad.util.ParamDescriptions: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.util.RandomUtil: Retrieves the singleton instance of RandomUtil associated with the current thread.
getInstance() - Static method in class edu.cmu.tetrad.util.ScoreDescriptions: getInstance.
getInstance() - Static method in class edu.cmu.tetrad.util.TaskManager: Getter for the field instance.
getInstance() - Static method in class edu.cmu.tetrad.util.TetradLogger: Returns an instance of TetradLogger.
getInstance() - Static method in class edu.cmu.tetrad.util.TetradProperties: getInstance.
getInstanceRowCentered() - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Returns a copy of the centered instance row data associated with this instance.
getInt(int, int) - Method in class edu.cmu.tetrad.data.BoxDataSet: getInt.
getInt(int, int) - Method in interface edu.cmu.tetrad.data.DataSet: getInt.
getInt(int, int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getInt.
getInt(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the integer value of the given parameter, looking up its default in the ParamDescriptions map.
getInt(String, int) - Method in class edu.cmu.tetrad.util.Parameters: Returns the integer value of the given parameter, looking up its default in the ParamDescriptions map.
getIntegerDistribution(Context) - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: getIntegerDistribution.
getIntegerDistribution(Context) - Method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: getIntegerDistribution.
getIntegerDistribution(Context) - Method in interface edu.cmu.tetrad.calculator.expression.Expression: getIntegerDistribution.
getIntegerDistribution(Context) - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: getIntegerDistribution.
getInteractionLambda() - Method in class edu.cmu.tetrad.search.score.FfMl: Retrieves the weight of the discrete-continuous interaction (product-kernel) term.
getIntercept() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The intercept.
getIntercept() - Method in class edu.cmu.tetrad.search.utils.LinearFunctionND: Returns the intercept of the linear function.
getIntercept(Node) - Method in interface edu.cmu.tetrad.sem.ISemIm: getIntercept.
getIntercept(Node) - Method in class edu.cmu.tetrad.sem.SemIm: Calculates the intercept for a given node.
getIntersectionComparisonString(List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Generates a comparison string for the intersection of multiple graphs.
getInterval() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the interval (in time steps) between time steps stored out.
getInterval() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getInterval.
getInts(int[]) - Static method in class edu.cmu.tetrad.util.MathUtils: Converts a list of indices into a list of Integers representing a cluster.
getIsAlpha() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Retrieves the current value of the isAlpha property, which likely represents a parameter or constant used in scoring or computation within the class.
getIterations() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the number of RICF sweeps used.
getIterations() - Method in class edu.cmu.tetrad.search.Ion: getIterations.
getIterations() - Method in class edu.cmu.tetrad.sem.Ricf.RicfResult: Returns the number of iterations.
getIterations() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Retrieves the number of sweeps performed during the iterative fitting process.
getJ() - Method in class edu.cmu.tetrad.search.utils.Sextad: Getter for the field j.
getJ() - Method in class edu.cmu.tetrad.search.utils.Tetrad: Getter for the field j.
getJ() - Method in class edu.cmu.tetrad.search.utils.TetradNode: Getter for the field j.
getJ() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Getter for the field j.
getJitter() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the jitter value used in bandwidth estimation.
getJitter() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the jitter parameter.
getJitter() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the current jitter value used to perturb matrices for numerical stability in computations such as solving positive definite systems.
getJointMarginal(int[], int[]) - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Computes the joint marginal probability for the specified variables and their corresponding values.
getJointMarginal(int[], int[]) - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Returns the joint marginal probability of the given variables taking the given values, given the evidence.
getJointMarginal(int[], int[]) - Method in class edu.cmu.tetrad.bayes.Identifiability: getJointMarginal.
getJointMarginal(int[], int[]) - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Indicates whether this updater supports computing joint marginals.
getJointMarginal(int[], int[]) - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater
getJointMarginal(int[], int[]) - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Indicates whether this updater supports computing joint marginals.
getJointProbability(int[], int[]) - Method in class edu.cmu.tetrad.bayes.JunctionTreeInference: Computes the joint probability of a set of variables being in specified states according to the current Bayesian network model and evidence.
getJointProbability(int[], int[]) - Method in class edu.cmu.tetrad.bayes.VariableEliminationInference: Conditional joint marginal P(vars=values | evidence).
getJPD() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getJPD.
getK() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the current value of K.
getK() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Returns the number of top models requested (the k passed to the constructor).
getK() - Method in class edu.cmu.tetrad.search.utils.Sextad: Getter for the field k.
getK() - Method in class edu.cmu.tetrad.search.utils.Tetrad: Getter for the field k.
getK() - Method in class edu.cmu.tetrad.search.utils.TetradNode: Getter for the field k.
getK() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Getter for the field k.
getK() - Method in class edu.cmu.tetrad.util.PartialCorrelationPdf: Getter for the field k.
getKAddition() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the value of the k_addition parameter.
getKAddition() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the K addition value, which is used in the scoring mechanisms within the class to adjust the evaluation of certain configurations or parameters.
getKDeletion() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the value of the k_deletion parameter.
getKDeletion() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the K deletion value, which is used in the scoring mechanisms within the class to adjust the evaluation of certain configurations or parameters.
getKernelType() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the kernel type.
getKmax() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the maximum allowed value of K (Kmax).
getKmeansRestarts() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the number of k-means restarts.
getKmin() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the minimum allowed value of K.
getknowledge() - Method in interface edu.cmu.tetrad.search.utils.R0R4Strategy: Returns the knowledge object used by the strategy.
getknowledge() - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Retrieves the Knowledge object used by the FciOrientDataExaminationStrategy.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Retrieves the knowledge associated with this object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Retrieves the knowledge associated with this object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Retrieves the knowledge of the current instance.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Returns the knowledge of the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Retrieves the knowledge object associated with the current instance of Cdnod.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Retrieves the knowledge associated with this object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Retrieves the current knowledge associated with the TwoStep algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Retrieves the knowledge associated with the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Retrieves the knowledge associated with the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Retrieves the knowledge associated with the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Retrieves the knowledge associated with the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Retrieves the knowledge associated with the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Retrieves the knowledge associated with the algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Retrieves the knowledge object associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Retrieves the knowledge object associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Retrieves the knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Returns the background knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Retrieves the knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Retrieves the knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Retrieves the Knowledge object associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Retrieves the Knowledge object associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Returns the knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Returns the knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Retrieves the knowledge object associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Retrieves the knowledge associated with this method.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Retrieves the knowledge associated with this object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Retrieves the knowledge object associated with this algorithm.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Retrieves the knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Retrieves the knowledge associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Retrieves a copy of the current knowledge associated with this score wrapper.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied
getKnowledge() - Method in interface edu.cmu.tetrad.algcomparison.utils.AcceptsKnowledge: Returns a knowledge object.
getKnowledge() - Method in class edu.cmu.tetrad.data.BoxDataSet: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getKnowledge.
getKnowledge() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.data.DataModelList: Getter for the field knowledge.
getKnowledge() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the knowledge associated with the ICovarianceMatrix.
getKnowledge() - Method in class edu.cmu.tetrad.data.IndependenceFacts: Getter for the field knowledge.
getKnowledge() - Method in interface edu.cmu.tetrad.data.KnowledgeTransferable: getKnowledge.
getKnowledge() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.data.TimeSeriesData: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.Fci: Retrieves the knowledge structure maintained by this instance.
getKnowledge() - Method in class edu.cmu.tetrad.search.Fges: Returns the background knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.FgesMb: Returns the background knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.GfciOld: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.Icd: Retrieves the knowledge object associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.search.is.IsGFci: Retrieves the Knowledge object associated with this instance.
getKnowledge() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the knowledge object for the Markov checker.
getKnowledge() - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the tier knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.Pcd: Retrieves the Knowledge object used by this method.
getKnowledge() - Method in class edu.cmu.tetrad.search.PcMb: Returns The knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.Rfci: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.StarFci: Returns the background knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.StarFciB: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.StarFciC: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.StarFciD: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.StarFciOriginal: Returns the knowledge used in search.
getKnowledge() - Method in class edu.cmu.tetrad.search.utils.BesPermutation: Returns the knowledge that BES will use.
getKnowledge() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.utils.MagToPag: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.FasDci: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field knowledge.
getKnowledge() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field knowledge.
getKnowledge(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the design-implied knowledge for the given data model: tiers by role, factors (F) before derived quantities (D) before responses (R), with the CONFIG bookkeeping column, when emitted, in a tier of its own before all of these (its fixed-effects reading).
getKnowledge(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the design-implied knowledge for the given data model: tiers by role, context (C) before system (S) before indices (I) before outcomes (Y), with the SUBJECT bookkeeping column, when emitted, in a tier of its own before all of these (its fixed-effects reading).
getKnowledge(int) - Method in interface edu.cmu.tetrad.algcomparison.utils.ProvidesKnowledge: Returns the design-implied knowledge for the given data model, over that data model's variables.
getKnowledge(Graph) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: getKnowledge.
getKnowledge(Graph) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getKnowledge.
getKnowledge(Graph) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: getKnowledge.
getKnowledgeGroups() - Method in class edu.cmu.tetrad.data.Knowledge: Getter for the field knowledgeGroups.
getKReorientation() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the value of the k_reorientation parameter.
getKReorientation() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the K reorientation value, which is used in the scoring mechanisms within the class to adjust the evaluation of certain configurations or parameters.
getKsPValue(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the Kolmorogov-Smirnov p-value for the given list of results.
getKsPValue(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the Kolmogorov-Smirnov (KS) p-value for a list of independence test results.
getKsPValue(List) - Static method in class edu.cmu.tetrad.util.UniformityTest: Calculates the p-value of a list of points using the Kolmogorov-Smirnov test.
getKsPValue(List, double, double) - Static method in class edu.cmu.tetrad.util.UniformityTest: Calculates the p-value of a list of points using the Kolmogorov-Smirnov test.
getKurtosisCap() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the kurtosis cap value.
getKurtosisGamma() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the kurtosis (fourth standardized moment) of the gamma distribution.
getL() - Method in class edu.cmu.tetrad.search.utils.Sextad: Getter for the field l.
getL() - Method in class edu.cmu.tetrad.search.utils.Tetrad: Getter for the field l.
getL() - Method in class edu.cmu.tetrad.search.utils.TetradNode: Getter for the field l.
getL() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Getter for the field l.
getLag() - Method in class edu.cmu.tetrad.graph.TimeLagGraph.NodeId: Getter for the field lag.
getLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedParent: Returns the lag of the parent.
getLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Returns the number of time steps back for this lagged factor.
getLag(String) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: getLag.
getLag(String) - Static method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getLag.
getLag(String) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: getLag.
getLag0Nodes() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Getter for the field lag0Nodes.
getLag1Autocorrelations() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the lag-1 autocorrelation in file order per continuous variable name, for those variables with enough observed values to compute it, unmodifiable.
getLaggedFactor() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedEdge: Getter for the field laggedFactor.
getLaggedFactor(String) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.DisplayNameHandler: Parses the given string and returns the LaggedFactor it represents.
getLagGraph() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.GenePm: Gets the lag workbench that is wrapped.
getLags(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.RevealOutputGraph: Returns the lags of the parent variables for the given variable, provided parents have associated time lags; otherwise, returns null.
getLags(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Returns the lags of the parent variables for the given variable, provided parents have associated time lags; otherwise, returns null.
getLags(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealOutputGraph: Returns the lags of the parent variables for the given variable, provided parents have associated time lags; otherwise, returns null.
getLags(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealOutputGraph: Returns the lags of the parent variables for the given variable, provided parents have associated time lags; otherwise, returns null.
getLags(int) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.OutputGraph: Returns the lags of the parent variables for the given variable, provided parents have associated time lags; otherwise, returns null.
getLambda() - Method in class edu.cmu.tetrad.search.GRegression: Returns a copy of the block-recursive coefficient matrix Lambda, indexed like the covariance matrix: lambda[i][j] is the coefficient of X_i in the least-squares regression of X_j on the external parents of j's bucket, and zero if i is not such a parent.
getLambda() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the value of the lambda property.
getLambda() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the value of the lambda variable.
getLambda1() - Method in class edu.cmu.tetrad.search.Dagma: Retrieves the value of lambda1.
getLambdas() - Method in class edu.cmu.tetrad.data.Linearizer: Returns the fitted Yeo-Johnson lambdas, in variable order.
getLastA() - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Retrieves the last calculated matrix A.
getLastErrorMessage() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Returns the message of the most recent error swallowed by a deferred error policy (TREAT_AS_INDEPENDENT or TREAT_AS_DEPENDENT) while evaluating a fact, or null if none has occurred since the last setTest.
getLastExtrapolationFraction() - Method in class edu.cmu.tetrad.sem.NNEstimator: Returns the fraction of rows in the most recent simulation where at least one mechanism was asked to extrapolate: some continuous parent lay more than the GNM's zWarn1 training SDs from its training mean.
getLastImprovementRestart() - Method in class edu.cmu.tetrad.search.Flop: Returns the index of the last ILS restart that improved on the incumbent in the most recent search.
getLastP() - Method in class edu.cmu.tetrad.search.test.Gcm: Retrieves the p-value from the most recently performed independence test.
getLastR() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Deprecated.
This reflects thread-local per-call state. Prefer IndTestFisherZ.getResult(Node, Node, Set) and IndTestFisherZ.Result.r().
getLastT() - Method in class edu.cmu.tetrad.search.test.Gcm: Retrieves the value of the last computed test statistic (T) in the model.
getLatentCountError() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Returns the absolute difference in latent counts.
getLatentDict() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the latent dictionary.
getLatentEffects(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: getLatentEffects.
getLatentGroups() - Method in class edu.cmu.tetrad.search.Rlcd: Returns the per-partition latent structures.
getLatentLatentReport(String) - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the latent-latent adequacy report for the given algorithm name, or null if none exists.
getLatentLatentReports() - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the latent-latent adequacy reports by algorithm name.
getLatentNodes() - Method in class edu.cmu.tetrad.sem.SemPm: getLatentNodes.
getLatents() - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns a defensive copy of the latent list.
getLatents() - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Returns the current latent nodes after search, if available.
getLatents() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: getLatents.
getLatents(Graph) - Static method in class edu.cmu.tetrad.sem.ReidentifyVariables: getLatents.
getLatentsCov() - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

The covariance matrix over the latents that is implied by the clustering.
getLatentsCovariance() - Method in class edu.cmu.tetrad.search.MimbuildBollen: Retrieves the latent covariance matrix associated with the MimbuildBollen instance.
getLatentsCovariance() - Method in class edu.cmu.tetrad.search.MimbuildPca: Retrieves the latent covariance matrix, which encapsulates the covariances between the latent variables associated with the specified blocks in the PCA analysis.
getLatestFilePath() - Method in class edu.cmu.tetrad.util.TetradLogger: Getter for the field latestFilePath.
getLeaf() - Method in class edu.cmu.tetrad.search.rlcd.Cover: Returns the leaf flag.
getLearningRate() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the learning rate used for training nuisance models.
getLearningRate() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the nuisance learning rate used in the testing process.
getLegalityRejections() - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Number of commits rejected because the propagated trial failed strong legality.
getLengthWritten() - Method in interface edu.cmu.tetrad.util.TetradLogger.LogDisplayOutputStream: The total string length written to the text area.
getLhat() - Method in class edu.cmu.tetrad.sem.Ricf.RicfResult: Returns the "lhat" matrix.
getLik() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood.Ret: Returns the likelihood.
getLik(int, int[]) - Method in class edu.cmu.tetrad.search.score.MvpLikelihood: Returns the score of the node at index i, given its parents.
getLikelihood() - Method in class edu.cmu.tetrad.bayes.BayesProperties: Retrieves the likelihood value for the current instance.
getLikelihood(int, int[]) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Returns the likelihood of variable i conditional on the given parents, assuming the continuous mixedVariables index by i or by the parents are jointly Gaussian conditional on the discrete comparison.
getLikelihood(int, int[]) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Calculates the likelihood for the given variable and its parent variables based on the provided data and covariance matrices.
getLikelihood(int, int[], List) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: As ConditionalGaussianLikelihood.getLikelihood(int, int[]), but evaluated over exactly the given rows, passed explicitly so that concurrent callers (parallel FGES / BOSS scoring with testwise deletion) do not need to mutate shared state via ConditionalGaussianLikelihood.setRows(List).
getLikelihoodAndDof(int, int...) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Computes the likelihood and degrees of freedom (dof) for a given variable and its parent variables.
getLikelihoodRatio(int, int[], int[]) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Returns the likelihood-ratio ingredients for the nested comparison of target i given parents0 (the smaller model) versus target i given parents1 (the larger model; parents0 should be a subset of parents1).
getLikelihoodRatio(int, int[], int[], List) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: As ConditionalGaussianLikelihood.getLikelihoodRatio(int, int[], int[]), but evaluated over exactly the given rows, passed explicitly so that concurrent callers (the parallelized Markov checker, parallel constraint-based search) do not need to mutate shared state via ConditionalGaussianLikelihood.setRows(List).
getLikelihoodRatioP() - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Calculates the p-value of the graph with respect to the given data.
getLikelihoodRatioP(Graph) - Method in class edu.cmu.tetrad.bayes.BayesProperties: Likelihood-ratio p-value for BN vs.
getLinearizedTransformed(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: Returns a linearized ("un-warped") version of the dataset: each variable is passed through a monotone Yeo-Johnson transform chosen to make its relations to the other variables as linear as possible (post-nonlinear model un-warping).
getLineColor() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field lineColor.
getListOfExplicitlyForbiddenEdges() - Method in class edu.cmu.tetrad.data.Knowledge: getListOfExplicitlyForbiddenEdges.
getListOfExplicitlyRequiredEdges() - Method in class edu.cmu.tetrad.data.Knowledge: getListOfExplicitlyRequiredEdges.
getListOfForbiddenEdges() - Method in class edu.cmu.tetrad.data.Knowledge: getListOfForbiddenEdges.
getListOfRequiredEdges() - Method in class edu.cmu.tetrad.data.Knowledge: getListOfRequiredEdges.
getLocalPValues(IndependenceTest, List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the local p-values for a given independence test and a list of independence facts.
getLocalPValues(IndependenceTest, List, Double) - Method in class edu.cmu.tetrad.search.MarkovCheck: Get Local P values with shuffle threshold provided.
getLocation(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Gets the location.
getLocation(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Gets the location.
getLocation(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Gets the location.
getLocation(String) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Gets the location.
getLocations() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: getLocations.
getLocations() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: getLocations.
getLocations() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Getter for the field locations.
getLocations() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Gets the locations.
getLoggedEvents() - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: What went less than cleanly in the last call to impute: fallbacks to marginal hot-deck draws, predictors left out as constant, and fits that were possible only because of the ridge.
getLoggerForClass(Class) - Method in class edu.cmu.tetrad.util.TetradLogger: getLoggerForClass.
getLoggingDirectory() - Method in class edu.cmu.tetrad.util.TetradLogger: getLoggingDirectory.
getLoggingFilePrefix() - Method in class edu.cmu.tetrad.util.TetradLogger: getLoggingFilePrefix.
getLogLik() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the Gaussian log likelihood at the fit.
getLogLik() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Gaussian log-likelihood of the fitted model, evaluated at the implied covariance.
getLogLikelihood() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the observed-data log likelihood at the final parameter estimates.
getLogLikelihood() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The log likelihood of the regression
getLong(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the long value of the given parameter, looking up its default in the ParamDescriptions map.
getLong(String, long) - Method in class edu.cmu.tetrad.util.Parameters: Returns the long value of the given parameter, looking up its default in the ParamDescriptions map.
getLongDescription() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field longDescription.
getLow() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: Getter for the field low.
getLowerBoundDouble() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field lowerBoundDouble.
getLowerBoundInt() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field lowerBoundInt.
getLowerBoundLong() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field lowerBoundLong.
getLrScores() - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Returns a matrix of left-right scores for the search.
getM() - Method in class edu.cmu.tetrad.search.utils.Sextad: Getter for the field m.
getM() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Getter for the field m.
getMag() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the MAG that was fitted.
getMag() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Returns the wrapped MAG.
getMag() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Returns the wrapped MAG.
getMag() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Returns the wrapped MAG.
getManager() - Method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronDjl: Returns the NDManager used for managing computational resources.
getManipulatedBayesIm() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Getter for the field manipulatedBayesIm.
getManipulatedBayesIm() - Method in class edu.cmu.tetrad.bayes.Identifiability: Getter for the field manipulatedBayesIm.
getManipulatedBayesIm() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Retrieves the manipulated Bayesian network representation associated with this updater.
getManipulatedBayesIm() - Method in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Returns the manipulated Bayes IM.
getManipulatedBayesIm() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: Getter for the field manipulatedBayesIm.
getManipulatedBayesIm() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Retrieves the manipulated Bayesian network representation associated with this updater.
getManipulatedGraph() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: getManipulatedGraph.
getManipulatedGraph() - Method in class edu.cmu.tetrad.bayes.Identifiability: getManipulatedGraph.
getManipulatedGraph() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Retrieves the manipulated Bayesian network representation associated with this updater.
getManipulatedGraph() - Method in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Returns the manipulated graph.
getManipulatedGraph() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: getManipulatedGraph.
getManipulatedGraph() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Retrieves the manipulated Bayesian network representation associated with this updater.
getManipulatedGraph() - Method in class edu.cmu.tetrad.sem.SemUpdater: getManipulatedGraph.
getManipulatedSemIm() - Method in class edu.cmu.tetrad.sem.SemUpdater: getManipulatedSemIm.
getMargin() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns the largest |partial correlation| this test counts as negligible.
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Returns the marginal probability of the given variable taking the given value, given the evidence.
getMarginal(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Returns the marginal probability of the given variable taking the given value, given the evidence.
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.Identifiability: Returns the marginal probability of the given variable taking the given value, given the evidence.
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.JunctionTreeInference: Computes the marginal probability of a specific variable (node) being in a given category within the current Bayesian network model after accounting for evidence.
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Indicates whether this updater supports computing joint marginals.
getMarginal(int, int) - Method in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Returns the marginal probability of the given variable taking the given value, given the evidence.
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.VariableEliminationInference: Conditional marginal P(node=category | evidence).
getMarginal(int, int) - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Indicates whether this updater supports computing joint marginals.
getMarginals() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the marginals.
getMarkovAuditFailures() - Method in class edu.cmu.tetrad.search.PcAR: Returns the diagnostic Markov-audit flags from the last PcAR.search() call.
getMarkovBlanketSubgraphWithTargetNode(Graph, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the subgraph over the Markov blanket of a target node for a DAG, CPDAG, MAG, or PAG.
getMarkovCheckDescription() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns a description of the Markov-check test, or null if none was configured.
getMarkovCheckRecord() - Method in class edu.cmu.tetrad.search.MarkovCheck: Generates the results for the given set of independence facts as a single record.
getMarkovCheckRecordString() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the Markov check record as a string.
getMarkovCpdag() - Method in class edu.cmu.tetrad.search.GfciOld: Executes the FGES algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.Bfci: Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.BfciC: Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.BfciD: Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.Gfci: Executes the FGES algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration (paper, Alg.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.GraspFci
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.SpFci: Executes the SP algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Returns the initial Markov model for the search: a CPDAG that is Markov to the data (paper, Alg.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.StarFciGuaranteePag: Deprecated.

Bridges the renamed abstract method: delegates to StarFciGuaranteePag.getMarkovDag(boolean), the abstract method of this class before the rename, so existing subclasses keep working unchanged.
getMarkovCpdag(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
getMarkovDag(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Runs BOSS to obtain the Markov CPDAG (as in Bfci).
getMarkovDag(boolean) - Method in class edu.cmu.tetrad.search.StarFciGuaranteePag: Deprecated.
Override StarFci.getMarkovCpdag(boolean) on StarFci instead.
getMarkovDag(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Constructs and returns the Markov Directed Acyclic Graph (DAG) representing the probabilistic relationships between variables.
getMarkovStats() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the Markov-check statistics for the point graph, or null if no Markov-check test was configured.
getMatchedLatentCount() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Retrieves the matched latent count value.
getMatrix() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getMatrix.
getMatrix() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getMatrix.
getMatrix() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Getter for the field matrix.
getMatrix() - Method in interface edu.cmu.tetrad.data.Covariances: Returns the underlying covariance matrix.
getMatrix() - Method in class edu.cmu.tetrad.data.CovariancesDoubleForkJoin: getMatrix.
getMatrix() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the covariance matrix.
getMatrix(int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Getter for the field matrix.
getMax() - Method in class edu.cmu.tetrad.data.Histogram: Computes and returns the maximum value from a list of unconditioned continuous data points.
getMaxBandwidthRows() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the maximum number of rows to consider for bandwidth estimation.
getMaxBandwidthRows() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the maximum number of bandwidth rows considered during computations.
getMaxCoef() - Method in class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue: Returns the maximum estimated coefficients for each edge.
getMaxDegree() - Method in class edu.cmu.tetrad.search.Fges: The maximum of parents any nodes can have in the output pattern.
getMaxDegree() - Method in class edu.cmu.tetrad.search.FgesMb: The maximum of parents any nodes can have in the output pattern.
getMaxDegree() - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Calculates the maximum degree for the current dataset.
getMaxDegree() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the maximum degree of the variables in the dataset.
getMaxDegree() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the maximum degree allowed for a node in the context of this implementation.
getMaxDegree() - Method in class edu.cmu.tetrad.search.is.IsGFci: Retrieves the maximum degree for the graph search procedure.
getMaxDegree() - Method in interface edu.cmu.tetrad.search.is.IsScore: Retrieves the maximum allowable degree for a node in the given context or structure.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Retrieves the maximum degree from the underlying BIC score component.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Retrieves the maximum degree from the underlying BIC score component.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.BdeScore: Gets the maximum degree of the BDe Score.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Returns the maximum degree of the BDeuScore object.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Recommended max degree (same heuristic used elsewhere).
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.EbicScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the max degree.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Computes the maximum degree based on a logarithmic function that evaluates the current effective sample size.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Computes the maximum degree based on a logarithmic function that evaluates the current effective sample size.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.FfMl: Calculates the maximum degree based on the effective size (nEff).
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Computes the maximum degree based on a logarithmic function that evaluates the current effective sample size.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.GraphScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Calculates the maximum degree of the model based on the effective sample size (nEff).
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Computes the maximum degree based on a heuristic approach.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the maximum degree allowed for variables in the computation.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the maximum degree allowed for scoring.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.MvpScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the maximum degree for the score calculation.
getMaxDegree() - Method in interface edu.cmu.tetrad.search.score.Score: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the maximum degree of the score.
getMaxDegree() - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.SpFci: Returns The maximum indegree of the output graph.
getMaxDegree() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Returns the max degree, by default 1000.
getMaxDegree() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Returns the max degree, by default 1000.
getMaxDiscriminatingPathLength() - Method in class edu.cmu.tetrad.search.Rfci: Returns the maximum length of any discriminating path, or -1 of unlimited.
getMaxDiscriminatingPathLength() - Method in class edu.cmu.tetrad.search.SpFci: Returns the maximum length of any discriminating path, or -1 of unlimited.
getMaxDiscriminatingPathLength() - Method in class edu.cmu.tetrad.search.utils.FciOrient: Returns the maximum path length, or -1 if unlimited.
getMaxDiscriminatingPathLength() - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Retrieves the maximum length of any discriminating path.
getMaxEigenvalue() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the largest eigenvalue of the implied correlation matrix, or NaN if it could not be computed.
getMaximumCardinalityOrdering(Graph) - Static method in class edu.cmu.tetrad.bayes.GraphTools: Perform Tarjan and Yannakakis (1984) maximum cardinality search (MCS) to get the maximum cardinality ordering.
getMaxIndex() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Returns the highest variable index in any term.
getMaxIndex() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Returns the highest variable index in this term.
getMaxit() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Getter for the field maxit.
getMaxIterations() - Method in class edu.cmu.tetrad.cluster.KMeans: Return the maximum number of iterations, or -1 if the algorithm is allowed to run unconstrainted.
getMaxLag() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Getter for the field maxLag.
getMaxLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: getMaxLag.
getMaxLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: getMaxLag.
getMaxLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Maximum lag needed to fully represent the graph, which is the largest lag of any of the lagged factors stored in the graph.
getMaxLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: getMaxLag.
getMaxLag() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Maximum lag needed to fully represent the graph, which is the largest lag of any of the lagged factors stored in the graph.
getMaxLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Returns the max lag of the history.
getMaxLag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Returns the max lag of the history.
getMaxLag() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.UpdateFunction: getMaxLag.
getMaxLagAllowable() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: getMaxLagAllowable.
getMaxLagAllowable() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: getMaxLagAllowable.
getMaxLagAllowable() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Gets the maximum allowable lag.
getMaxLagAllowable() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Gets the maximum allowable lag.
getMaxLeafNodes() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the maximum number of leaf nodes in the nuisance tree.
getMaxLeafNodes() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the maximum number of leaf nodes allowed for the nuisance decision trees.
getMaxLengthAdjustment() - Method in class edu.cmu.tetrad.search.Ida: Returns the maximum length adjustment value.
getMaxMemUsage() - Method in class edu.cmu.tetrad.search.Ion: getMaxMemUsage.
getMaxNumShifts() - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Getter for the field maxNumShifts.
getMaxPathLength() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility: Retrieves the maximum allowable length of paths to consider in the computation.
getMaxPathLength() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the maximum path length used by the amenability and adjustment searches.
getMaxPathLength() - Method in class edu.cmu.tetrad.search.is.IsGFci: Retrieves the maximum path length for the graph search procedure.
getMaxShift() - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Getter for the field maxShift.
getMaxTierForbiddenWithin() - Method in class edu.cmu.tetrad.data.Knowledge: getMaxTierForbiddenWithin.
getMaxTotalEffect(Node, Node) - Method in class edu.cmu.tetrad.search.IdaCheck: Returns the maximum total effect value between two nodes.
getMconn() - Method in class edu.cmu.tetrad.search.MarkovCheck.AllSubsetsIndependenceFacts: Returns the set of m-connection facts.
getMConnectedVars(Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: Retrieves the set of nodes that are connected to the given node y and are also present in the set of nodes z.
getMConnectedVars(Node, Set, Map) - Method in class edu.cmu.tetrad.graph.Paths: getMConnectedVars.
getMean(int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Retrieves the mean value for a continuous child node.
getMean(Node) - Method in interface edu.cmu.tetrad.sem.ISemIm: getMean.
getMean(Node) - Method in class edu.cmu.tetrad.sem.SemIm: Calculates the mean value associated with a given Node.
getMeanImprovement() - Method in class edu.cmu.tetrad.sem.AdequacyReport: Retrieves the mean improvement of nodes over a baseline metric.
getMeanPairwiseCount() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The mean, over pairs of distinct columns, of the pairwise complete count (see MissingDataSpec.EffectiveSampleSizeMode.MEAN_PAIRWISE).
getMeanParameter(Node) - Method in class edu.cmu.tetrad.sem.SemPm: getMeanParameter.
getMeans() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the estimated variable means, in the order of the dataset's variables.
getMeans() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: The estimated means.
getMeans() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: getMeans.
getMeans() - Method in class edu.cmu.tetrad.sem.SemIm: getMeans.
getMeanStdDev(Node) - Method in interface edu.cmu.tetrad.sem.ISemIm: getMeanStdDev.
getMeanStdDev(Node) - Method in class edu.cmu.tetrad.sem.SemIm: Calculates the mean standard deviation for the given node.
getMeasuredData() - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the measured data.
getMeasuredData() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the measured data that is the result of a simulation, in the form of a three-dimensional double array.
getMeasuredData() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getMeasuredData.
getMeasuredNodes() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the list of measured variables.
getMeasuredNodes() - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the measured nodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns all measured variables, with inputs listed first and outputs second.
getMeasuredNodes() - Method in class edu.cmu.tetrad.sem.DagScorer: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns a list of measured nodes.
getMeasuredNodes() - Method in interface edu.cmu.tetrad.sem.ISemIm: getMeasuredNodes.
getMeasuredNodes() - Method in interface edu.cmu.tetrad.sem.Scorer: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.sem.SemIm: The list of measured nodes for the semPm.
getMeasuredNodes() - Method in class edu.cmu.tetrad.sem.SemPm: getMeasuredNodes.
getMeasuredNodes() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: The list of measured nodes for the semPm.
getMeasuredVarNames() - Method in class edu.cmu.tetrad.sem.SemPm: getMeasuredVarNames.
getMessage() - Method in class edu.cmu.tetrad.data.audit.AuditFinding: Returns the human-readable one-line message for this finding.
getMethod() - Method in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Returns the value of the method used for discretization.
getMin() - Method in class edu.cmu.tetrad.data.Histogram: Calculates and returns the minimum value from a list of unconditioned continuous data.
getMinClusterSize() - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

Clusters smaller than this size will be tossed out.
getMinCoef() - Method in class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue: Returns the minimum estimated coefficients for each edge.
getMinDepth() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: getMinDepth.
getMinEigenvalue() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the smallest eigenvalue of the implied correlation matrix, or NaN if it could not be computed (fewer than two positive-variance variables, or a failed decomposition).
getMinimum() - Method in class edu.cmu.tetrad.search.MimbuildBollen: Retrieves the minimum value associated with the MimbuildBollen class.
getMinPairwiseCount() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the smallest pairwise observed count, a conservative candidate for an effective sample size.
getMinPairwiseCount() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The minimum, over pairs of distinct columns, of the pairwise complete count.
getMinScale() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the minimum scale value.
getMinTotalEffect(Node, Node) - Method in class edu.cmu.tetrad.search.IdaCheck: Gets the minimum total effect value between two nodes.
getMisclassificationTable(Graph, Graph) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns a misclassification comparison of two graphs.
getMissingCells() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: The number of cells treated as ordinary missing values (NaN in an unbounded column).
getMissingCount(int) - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The number of missing entries for the given column.
getMissingDataAudit() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the delegated missingness audit, or null if the dataset has no missing values.
getMissingDataSpec() - Method in class edu.cmu.tetrad.search.Rlcd: The missing-data spec in force, or null.
getMissingPairs() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the true pairs missing from the estimate.
getMissingRate(int) - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The fraction of entries missing for the given column.
getMissingValueMarker() - Method in class edu.cmu.tetrad.data.AbstractVariable: getMissingValueMarker.
getMissingValueMarker() - Method in class edu.cmu.tetrad.data.ContinuousVariable: getMissingValueMarker.
getMissingValueMarker() - Method in class edu.cmu.tetrad.data.DiscreteVariable: getMissingValueMarker.
getMissingValueMarker() - Method in interface edu.cmu.tetrad.data.Variable: Returns the name of the variable.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Declares this score's native missing-value support: test-wise deletion.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.BgeScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Declares this score's native missing-value support: test-wise deletion.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Declares this score's native missing-value support: test-wise deletion.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.EbicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.FfMl: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.GicScores: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in interface edu.cmu.tetrad.search.score.Score: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Declares this score's native missing-value support: test-wise deletion.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.score.ZsbScore: Declares what this score can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.FfCi: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.Gcm: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Declares this test's native missing-value support: test-wise deletion.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.Kci: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.Rcit: Declares what this test can do, natively, with data containing missing values.
getMissingValueSupport() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Declares what this test can do, natively, with data containing missing values.
getMixedDataSet() - Method in class edu.cmu.tetrad.bayes.EmBayesEstimator: getMixedDataSet.
getMixedDataSet(DataModel) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Returns the datamodel case to DataSet if it is mixed.
getMlag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Getter for the field mlag.
getMlag() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraphParams: Returns the maximum lag.
getMmd2() - Method in class edu.cmu.tetrad.sem.AdequacyReport: Retrieves the squared Maximum Mean Discrepancy (MMD²) value, which quantifies the alignment between real and simulated data distributions.
getMnarData(Graph, int, int, double, int) - Static method in class edu.cmu.tetrad.search.utils.LgMnarDataSimulator: Generates a dataset with Missing Not At a Random (MNAR) data mechanism applied to specific variables in a graph.
getModel(Graph) - Static method in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty: Computes the complete OLMP model for the given ADMG.
getModel(Graph) - Static method in class edu.cmu.tetrad.search.OrderedLocalMarkovPropertySinkElimination: Computes the ordered local Markov property for a maximal ancestral graph (MAG).
getModel(Graph) - Static method in class edu.cmu.tetrad.search.PairwiseMarkovProperty: Computes the pairwise Markov property for the given ADMG.
getModelForNode(Graph, Node) - Static method in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty: Returns the subset of OLMP independence facts that have x as one endpoint (i.e., facts of the form x ⊥ y | Z or y ⊥ x | Z), normalised so that x is always the left-hand (getX()) node.
getModelForNode(Graph, Node) - Static method in class edu.cmu.tetrad.search.OrderedLocalMarkovPropertySinkElimination: Returns the subset of ordered-local-Markov independence facts whose "sink" is the given node x.
getModelForNode(Graph, Node) - Static method in class edu.cmu.tetrad.search.PairwiseMarkovProperty: Returns the subset of pairwise independence facts that involve node x (as either the left or right endpoint), normalised so that x is always the left-hand (getX()) node.
getModelGraph(int) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Returns the graph of the i-th top permutation (0 = highest scoring), built with the same graph-construction method used for the top model, honoring the current cpdag and replicating-graph flags and the current knowledge.
getModelList() - Method in class edu.cmu.tetrad.data.DataModelList: Getter for the field modelList.
getModelPermutation(int) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Returns the i-th top permutation (0 = highest scoring) as an array of indices into PermutationSearchTopK.getVariables().
getModelPValue(List) - Method in class edu.cmu.tetrad.search.utils.ClusterSignificance: Returns the p-value for the given cluster.
getModelScore() - Method in class edu.cmu.tetrad.search.Fges: Returns the score of the final search model.
getModelScore() - Method in class edu.cmu.tetrad.search.FgesMb: Returns the score of the final search model.
getModelScore(int) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Returns the total (higher-is-better) score of the i-th top permutation (0 = highest scoring).
getMsep() - Method in class edu.cmu.tetrad.search.MarkovCheck.AllSubsetsIndependenceFacts: Returns the set of m-separation facts.
getN() - Method in class edu.cmu.tetrad.data.Histogram: Returns the size of the conditioned data list.
getN() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: The available-case count of the family the test was run on.
getN() - Method in class edu.cmu.tetrad.regression.RegressionResult: Getter for the field n.
getN() - Method in class edu.cmu.tetrad.search.utils.Sextad: Getter for the field n.
getN() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Getter for the field n.
getN() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Getter for the field n.
getName() - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: Getter for the field name.
getName() - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionDescriptor: getName.
getName() - Method in class edu.cmu.tetrad.data.AbstractVariable: Getter for the field name.
getName() - Method in class edu.cmu.tetrad.data.BoxDataSet: Gets the name of the data set.
getName() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Gets the name of the covariance matrix.
getName() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Gets the name of the covariance matrix.
getName() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Gets the name of the covariance matrix.
getName() - Method in interface edu.cmu.tetrad.data.DataModel: getName.
getName() - Method in class edu.cmu.tetrad.data.DataModelList: Gets the name of the data model list.
getName() - Method in interface edu.cmu.tetrad.data.DataSet: getName.
getName() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Gets the name of the covariance matrix.
getName() - Method in class edu.cmu.tetrad.data.IndependenceFacts: Getter for the field name.
getName() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns the display name of this structure.
getName() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Gets the name of the data set.
getName() - Method in class edu.cmu.tetrad.data.TimeSeriesData: Getter for the field name.
getName() - Method in class edu.cmu.tetrad.graph.GraphNode: Getter for the field name.
getName() - Method in interface edu.cmu.tetrad.graph.Node: Returns the name of this node.
getName() - Method in class edu.cmu.tetrad.graph.TimeLagGraph.NodeId: Getter for the field name.
getName() - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimicRunner: Deprecated.

Returns the display name of the search algorithm.
getName() - Method in class edu.cmu.tetrad.search.mimic.DmRunner: Returns the display name of the search algorithm.
getName() - Method in interface edu.cmu.tetrad.search.mimic.MimicSearchRunner: Returns the display name of the search algorithm.
getName() - Method in class edu.cmu.tetrad.search.mimic.TrekMimicRunner: Returns the display name of the search algorithm.
getName() - Method in class edu.cmu.tetrad.sem.Parameter: Getter for the field name.
getName() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: getName.
getName() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Getter for the field name.
getName() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbComponent: Returns the name.
getName() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Returns name of this matrix
getName() - Method in class edu.cmu.tetrad.util.dist.Beta: Please don't make me say it...
getName() - Method in class edu.cmu.tetrad.util.dist.ChiSquare: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.Discrete: getName.
getName() - Method in interface edu.cmu.tetrad.util.dist.Distribution: Returns the name of the distribution.
getName() - Method in class edu.cmu.tetrad.util.dist.Exponential: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.Gamma: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.GaussianPower: Getter for the field name.
getName() - Method in class edu.cmu.tetrad.util.dist.Indicator: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.LogNormal: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.Normal: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.Poisson: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.SingleValue: Returns the name of the distribution.
getName() - Method in class edu.cmu.tetrad.util.dist.Split: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: getName.
getName() - Method in class edu.cmu.tetrad.util.dist.Uniform: getName.
getNameNoLag(Object) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: getNameNoLag.
getNameNoLag(Object) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getNameNoLag.
getNameNoLag(Object) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: getNameNoLag.
getNames() - Method in class edu.cmu.tetrad.util.ParamDescriptions: getNames.
getNeighbors(Node) - Method in class edu.cmu.tetrad.search.FciOrientDijkstra.Graph: Returns the neighbors of a node, reachable via DijkstraEdges in the grph.
getNeighbors(Node) - Method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Graph: Retrieves the filtered neighbors of a given node.
getNEstimators() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the number of boosting iterations per nuisance model.
getNEstimators() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the number of boosting iterations used per nuisance model.
getNewSemIm() - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: getNewSemIm.
getNewSemIm() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Getter for the field newSemIm.
getNewSemIm() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Getter for the field newSemIm.
getNextOffset() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionLexer: Getter for the field nextOffset.
getNextOffset() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionParser: getNextOffset.
getNLevels() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the number of Y-threshold levels (K) used in the CDF grid.
getNLevels() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the number of Y-threshold levels (nLevels) used in the testing process.
getNode() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: Getter for the field node.
getNode(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the name of the given node.
getNode(int) - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the node at the given index.
getNode(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the name of the given node.
getNode(int) - Method in class edu.cmu.tetrad.bayes.Evidence: getNode.
getNode(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Retrieves the node at the specified index.
getNode(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the name of the given node.
getNode(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the name of the given node.
getNode(int) - Method in class edu.cmu.tetrad.sem.SemEvidence: getNode.
getNode(int) - Method in class edu.cmu.tetrad.sem.SemManipulation: getNode.
getNode(Node) - Method in class edu.cmu.tetrad.search.IndTestIod: Return the node associated with the given variable in the graph.
getNode(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: getNode.
getNode(String) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the name of the given node.
getNode(String) - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the node by the given name.
getNode(String) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getNode.
getNode(String) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: getNode.
getNode(String) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getNode.
getNode(String) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getNode.
getNode(String) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the node in the graph with the specified name.
getNode(String) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getNode.
getNode(String) - Method in interface edu.cmu.tetrad.graph.Graph: getNode.
getNode(String) - Method in class edu.cmu.tetrad.graph.LagGraph: getNode.
getNode(String) - Method in class edu.cmu.tetrad.graph.SemGraph: getNode.
getNode(String) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves a Node from the graph based on the given name.
getNode(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the Node with the given name.
getNode(String, int) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: getNode.
getNode1() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field node1.
getNode2() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field node2.
getNodeA() - Method in class edu.cmu.tetrad.sem.Parameter: Getter for the field nodeA.
getNodeB() - Method in class edu.cmu.tetrad.sem.Parameter: Getter for the field nodeB.
getNodeDists(Graph) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: getNodeDists.
getNodeExpression(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: getNodeExpression.
getNodeExpressionString(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: getNodeExpressionString.
getNodeId(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: getNodeId.
getNodeIndex() - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the node index.
getNodeIndex() - Method in class edu.cmu.tetrad.sem.SemProposition: Returns the index of the node.
getNodeIndex(Node) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the index of the given node.
getNodeIndex(Node) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the index of the given node.
getNodeIndex(Node) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns the index of the given node in the nodes array.
getNodeIndex(Node) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the index of the given node.
getNodeIndex(Node) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the index of the given node.
getNodeIndex(Node) - Method in class edu.cmu.tetrad.sem.SemEvidence: getNodeIndex.
getNodeIndex(String) - Method in class edu.cmu.tetrad.bayes.Evidence: getNodeIndex.
getNodeIndex(String) - Method in class edu.cmu.tetrad.bayes.Proposition: getNodeIndex.
getNodeIndex(String) - Method in class edu.cmu.tetrad.sem.SemEvidence: getNodeIndex.
getNodeIndex(String) - Method in class edu.cmu.tetrad.sem.SemManipulation: getNodeIndex.
getNodeName(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.RevealOutputGraph: Returns the name of the variable at the given index.
getNodeName(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealOutputGraph: Returns the name of the variable at the given index.
getNodeName(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealOutputGraph: Returns the name of the variable at the given index.
getNodeName(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns the name of node i in this graph
getNodeName(int) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.OutputGraph: Returns the name of the variable at the given index.
getNodeNames() - Method in class edu.cmu.tetrad.graph.Dag: getNodeNames.
getNodeNames() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getNodeNames.
getNodeNames() - Method in interface edu.cmu.tetrad.graph.Graph: getNodeNames.
getNodeNames() - Method in class edu.cmu.tetrad.graph.LagGraph: getNodeNames.
getNodeNames() - Method in class edu.cmu.tetrad.graph.SemGraph: getNodeNames.
getNodeNames() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns a list of node names in the graph.
getNodeReports() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Structured per-node reports.
getNodeReports() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Structured per-node reports.
getNodeReports() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Structured per-node reports.
getNodeReports() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Structured per-node reports.
getNodes() - Method in class edu.cmu.tetrad.graph.Dag: getNodes.
getNodes() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getNodes.
getNodes() - Method in interface edu.cmu.tetrad.graph.Graph: getNodes.
getNodes() - Method in class edu.cmu.tetrad.graph.LagGraph: getNodes.
getNodes() - Method in class edu.cmu.tetrad.graph.SemGraph: getNodes.
getNodes() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves a list of nodes from the graph.
getNodes() - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the ordered list of nodes in this probabilistic model.
getNodes() - Method in class edu.cmu.tetrad.search.Fas: Retrieves a list of nodes associated with the initialized independence test.
getNodes() - Method in class edu.cmu.tetrad.search.FciOrientDijkstra.Graph: Returns the nodes in the graph.
getNodes() - Method in class edu.cmu.tetrad.search.Ida.NodeEffects: Returns the nodes.
getNodes() - Method in class edu.cmu.tetrad.search.IdaCheck: Returns a list of nodes.
getNodes() - Method in class edu.cmu.tetrad.search.Pcd: Retrieves the list of nodes in the graph.
getNodes() - Method in class edu.cmu.tetrad.search.PdagPagIda.NodeEffects: Retrieves the list of nodes contained in this object.
getNodes() - Method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Graph: Retrieves the nodes in the graph.
getNodes() - Method in class edu.cmu.tetrad.search.utils.Sextad: Returns the list of nodes.
getNodes() - Method in class edu.cmu.tetrad.search.utils.Tetrad: Returns the list of nodes.
getNodes() - Method in class edu.cmu.tetrad.search.utils.TetradNode: getNodes.
getNodes() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: getNodes.
getNodes() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: getNodes.
getNodes() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves a list of nodes.
getNodesHash() - Method in class edu.cmu.tetrad.search.utils.AdTree: Returns the nodes hash, for sharing across bootstrap instances.
getNodesHash() - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Returns the nodes hash, for sharing across bootstrap instances.
getNodesInEvidence() - Method in class edu.cmu.tetrad.sem.SemEvidence: getNodesInEvidence.
getNodesInTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves a list of nodes in the given graph that have edges pointing into the specified node and endpoint.
getNodesInTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Nodes adjacent to the given node with the given proximal endpoint.
getNodesInTo(Node, Endpoint) - Method in interface edu.cmu.tetrad.graph.Graph: Nodes adjacent to the given node with the given proximal endpoint.
getNodesInTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.LagGraph: Nodes adjacent to the given node with the given proximal endpoint.
getNodesInTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.SemGraph: Nodes adjacent to the given node with the given proximal endpoint.
getNodesInTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves a list of nodes that have an incoming edge from a specific node and endpoint.
getNodesOutTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves a list of nodes that have outgoing edges to a specified node and endpoint.
getNodesOutTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Nodes adjacent to the given node with the given distal endpoint.
getNodesOutTo(Node, Endpoint) - Method in interface edu.cmu.tetrad.graph.Graph: Nodes adjacent to the given node with the given distal endpoint.
getNodesOutTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.LagGraph: Nodes adjacent to the given node with the given distal endpoint.
getNodesOutTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.SemGraph: Nodes adjacent to the given node with the given distal endpoint.
getNodesOutTo(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the list of nodes in a graph that have an outgoing edge to the given node and endpoint.
getNodeSubstitutedString(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Retrieves the substituted string representation of a given Node.
getNodeSubstitutedString(Node, Map) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Retrieves the substituted string representation of a given Node.
getNodeType() - Method in class edu.cmu.tetrad.data.ContinuousVariable: Getter for the field nodeType.
getNodeType() - Method in class edu.cmu.tetrad.data.DiscreteVariable: Getter for the field nodeType.
getNodeType() - Method in class edu.cmu.tetrad.graph.GraphNode: Getter for the field nodeType.
getNodeType() - Method in interface edu.cmu.tetrad.graph.Node: Returns the node type for this node.
getNodeVariableType() - Method in class edu.cmu.tetrad.data.ContinuousVariable: Returns the node shape for this node.
getNodeVariableType() - Method in class edu.cmu.tetrad.data.DiscreteVariable: Returns the node shape for this node.
getNodeVariableType() - Method in class edu.cmu.tetrad.graph.GraphNode: Returns the node shape for this node.
getNodeVariableType() - Method in interface edu.cmu.tetrad.graph.Node: Returns the node shape for this node.
getNoiseModel() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the noise model used in the score calculation.
getNoncolliders() - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Return noncolliders
getNoncolliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field noncolliderTriples.
getNoncolliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field noncolliderTriples.
getNoncolliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Getter for the field noncolliderTriples.
getNoncolliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Getter for the field noncolliderTriples.
getNoncolliderTriples() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Getter for the field noncolliderTriples.
getNonGaussianBonus() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the NonGaussianBonus object associated with this instance.
getNonlinearityAfter() - Method in class edu.cmu.tetrad.data.Linearizer: Returns the post-transform nonlinearity measures, in variable order.
getNonlinearityBefore() - Method in class edu.cmu.tetrad.data.Linearizer: Returns the pre-transform nonlinearity measures, in variable order.
getNonparanormalTransformed(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: Returns a nonparanormal-transformed version of the dataset.
getNonsinks() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups.RankDefSet: Returns the non-sink variables used as conditioning variables.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFn: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFp: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFpr: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTn: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTp: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTpr: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorF1: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFn: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFp: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFpr: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecisionCommonEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecallCommonEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTn: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTp: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiff: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedFP: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentExistsLatentCommonCause: Calculates the normalized value of a given statistic value.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentPrecision: Calculates the normalized value of a given statistic value.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTP: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CirclePrecision: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CircleRecall: Returns the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalseNegativeBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalsePositiveBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorTruePositiveBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonMeasuredAncestorRecallBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.CorrectSkeleton: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ElapsedCpuTime: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Adj: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1All: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Arrow: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Circle: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Tail: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FalseNegativesAdjacencies: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FalsePositiveAdjacencies: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.GraphExactlyRight: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaAverageSquaredDistance: Returns a normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMaxSquaredDiffEstTrue: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMinSquaredDiffEstTrue: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgSquaredDifference: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMaximumSquaredDifference: Returns a normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMinimumSquaredDifference: Returns a normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst2: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedOrientationRatioEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliesLegalMag: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalseNegativeBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalsePositiveBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorRecallBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorTruePositiveBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalCpdag: Returns the normalized value for the given input.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalPag: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphPrecision: This method returns the normalized value of a given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphRecall
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MagCgScore: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MagDgScore: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MagSemScore: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPasses: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPassesBestOf10: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingP: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingPBestOf10: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialP: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialPBestOf10: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFdrCutoffP: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH0: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH1: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffP: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffPBestOf10: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPasses: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPassesBestOf10: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrAdj: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrArrow: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.Maximal: Returns the normalized value of the given statistic value.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.MaximalityCondition: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH0: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH1: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoAlmostCyclicPathsCondition: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoCyclicPathsCondition: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoPotentiallyDirectedF1: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumAmbiguousTriples: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsNotInUnshieldedCollidersEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberCollidersEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInCollidersEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInUnshieldedCollidersEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberTailsEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberUnshieldedCollidersEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedBothNonancestorAncestor: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredDD: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredNL: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPD: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPL: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCommonMeasuredAncestorBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDefiniteDirectedEdgeAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeConfounded: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeNonAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeNonAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeNonAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleVisibleNonancestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectBidirected: Returns the normalized value of the given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectDDAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectPDAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectVisibleEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCoveringAdjacenciesInPag: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyDirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyNotDirectedPaths: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeBnaMeasuredCounfounded: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNoMeasureAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNotAncNotRev: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeReversed: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedShouldBePartiallyDirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumEdgeInEstInTrue: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumGenuineAdjacenciesInPag: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectDDAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectPDAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectVisibleAncestors: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumLatentCommonAncestorBidirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumNondirectedEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumParametersEst: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPartiallyOrientedEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPotentiallyDirected: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumUndirectedEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdgeTrue: Returns the normalized value of a given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationPrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyPrecision: Retrieves the normalized value of a given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyRecall: Retrieves the normalized value of this statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ParameterColumn: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentAmbiguous: Calculates the normalized value of a statistic given the original value.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentBidirectedEdges: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedEst: Retrieves the normalized value of the given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedTrue: Calculates the normalized value of a given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueDistanceToAlpha: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueUniformityUnderNull: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPathF1: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPrecision: Returns the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedRecall: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.StructuralHammingDistance: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TailPrecision: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TailRecall: Returns the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesArrows: Retrieves the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesTails: Retrieves the normalized value of the given statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveArrow: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveTails: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionArrow: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionTails: Calculates the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallArrows: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallTails: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveArrow: Retrieves the normalized value of the statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveDirectedPathNonancestor: Retrieves the normalized value of a statistic.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveTails: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalseNegative: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalsePositive: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCyclePrecision: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleRecall: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNormValue(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleTruePositive: Returns a mapping of the statistic to the interval [0, 1], with higher being better.
getNoveltyStrength() - Method in class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Getter for noveltyStrength.
getNparents() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the number of parents.
getNullAdInd() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The null draws' Anderson-Darling uniformity p-values.
getNullFractionDependent() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The null draws' fractions of independence facts judged dependent.
getNumAllowed(int) - Method in class edu.cmu.tetrad.bayes.Proposition: getNumAllowed.
getNumAllowedCategories(int) - Method in class edu.cmu.tetrad.bayes.Proposition: getNumAllowedCategories.
getNumber() - Method in class edu.cmu.tetrad.sem.ParamConstraint: Getter for the field number.
getNumberFormat() - Method in class edu.cmu.tetrad.data.BoxDataSet: getNumberFormat.
getNumberFormat() - Method in interface edu.cmu.tetrad.data.DataSet: The number format of the dataset.
getNumberFormat() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getNumberFormat.
getNumberFormat() - Method in class edu.cmu.tetrad.util.NumberFormatUtil: getNumberFormat.
getNumberOfArguments() - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionSignature: getNumberOfArguments.
getNumberOfColumns(DataModel) - Static method in class edu.cmu.tetrad.util.MultidataUtils: getNumberOfColumns.
getNumCases() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the number of cases for the data.
getNumCategories() - Method in class edu.cmu.tetrad.data.DiscreteVariable: getNumCategories.
getNumCategories(int) - Method in class edu.cmu.tetrad.bayes.Evidence: getNumCategories.
getNumCategories(int) - Method in class edu.cmu.tetrad.bayes.Proposition: getNumCategories.
getNumCategories(Node) - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the number of values for the given node.
getNumCauses() - Method in class edu.cmu.tetrad.search.Cstar.Record: Retrieves the number of possible causes of the target in a record.
getNumCells() - Method in class edu.cmu.tetrad.search.utils.AdTree: Returns the total number of cells in the table.
getNumCells() - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Returns the total number of cells in the table.
getNumCells() - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Return the number of cells in the table.
getNumCells() - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the number of cells in the table.
getNumCellsPerDish() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the number of cells per dish.
getNumCellsPerDish() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getNumCellsPerDish.
getNumClusters() - Method in class edu.cmu.tetrad.cluster.KMeans: getNumClusters.
getNumColumns() - Method in interface edu.cmu.tetrad.bayes.CptMap: Retrieves the number of columns in the CptMap.
getNumColumns() - Method in class edu.cmu.tetrad.bayes.CptMapCounts: Returns the number of columns in the probability map.
getNumColumns() - Method in class edu.cmu.tetrad.bayes.CptMapProbs: Returns the number of columns in the probability map.
getNumColumns() - Method in class edu.cmu.tetrad.data.BoxDataSet: getNumColumns.
getNumColumns() - Method in interface edu.cmu.tetrad.data.DataSet: getNumColumns.
getNumColumns() - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Returns the number of columns of the underlying matrix.
getNumColumns() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getNumColumns.
getNumColumns() - Method in class edu.cmu.tetrad.util.Matrix: getNumColumns.
getNumColumns() - Method in class edu.cmu.tetrad.util.MView: Returns the number of columns in the current matrix view.
getNumColumns() - Method in class edu.cmu.tetrad.util.TextTable: getNumColumns.
getNumColumns(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the number of columns.
getNumColumns(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the number of columns.
getNumColumns(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns the number of columns in the specified node.
getNumColumns(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the number of columns.
getNumColumns(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the number of columns.
getNumCombinations(int, int) - Static method in class edu.cmu.tetrad.util.ChoiceGenerator: Returns the number of combinations of a choose b.
getNumCombinations(int, int) - Static method in class edu.cmu.tetrad.util.SublistGenerator: getNumCombinations.
getNumCompleteRows() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The number of rows with no missing entries--i.e., the sample size that listwise deletion would retain.
getNumCoveringAdjacenciesInPag(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns the number of covering edges in the given estimated partial ancestral graph (PAG) with respect to the given true PAG.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: getNumDataModels.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves the number of data models currently stored in the class.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the number of data models.
getNumDataModels() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Returns the number of data models (1).
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Returns the number of data models.
getNumDataModels() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Returns the number of data models.
getNumDimensions() - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the number of dimensions in the table.
getNumDishes() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the number of dishes to simulate.
getNumDishes() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getNumDishes.
getNumDraws() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The number of null draws.
getNumEdges() - Method in class edu.cmu.tetrad.graph.Dag: getNumEdges.
getNumEdges() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getNumEdges.
getNumEdges() - Method in interface edu.cmu.tetrad.graph.Graph: getNumEdges.
getNumEdges() - Method in class edu.cmu.tetrad.graph.LagGraph: getNumEdges.
getNumEdges() - Method in class edu.cmu.tetrad.graph.SemGraph: getNumEdges.
getNumEdges() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the number of edges in the graph.
getNumEdges() - Method in class edu.cmu.tetrad.search.Grasp: Returns the number of edges in the DAG implied by the discovered permutation.
getNumEdges() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: getNumEdges.
getNumEdges() - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: getNumEdges.
getNumEdges() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns the Total # of edges in this graph
getNumEdges() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field numEdges.
getNumEdges(Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns the number of edges connected to the specified node.
getNumEdges(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getNumEdges.
getNumEdges(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getNumEdges.
getNumEdges(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getNumEdges.
getNumEdges(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getNumEdges.
getNumEdges(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the number of edges connected to a specific node.
getNumEffects() - Method in class edu.cmu.tetrad.search.Cstar.Record: Retrieves the number of possible effects of the target in a record.
getNumericalMean() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns the numerical mean of the empirical cumulative distribution function (CDF).
getNumericalVariance() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns the numerical variance of the empirical cumulative distribution function (CDF).
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: getNumFactors.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: getNumFactors.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Returns the number of factors represented in the graph.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: getNumFactors.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the number of factors.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the number of factors.
getNumFactors() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Returns the number of factors represented in the graph.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Returns the number of factors in the history.
getNumFactors() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Returns the number of factors in the history.
getNumFactors() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.UpdateFunction: getNumFactors.
getNumFeatZ() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the value of the numFeatZ property.
getNumFeatZ() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the number of features for Z.
getNumFixedParams() - Method in class edu.cmu.tetrad.sem.SemIm: getNumFixedParams.
getNumFolds() - Method in class edu.cmu.tetrad.sem.PredictionPruneReport: The number of CV folds used.
getNumFreeParams() - Method in class edu.cmu.tetrad.sem.DagScorer: getNumFreeParams.
getNumFreeParams() - Method in interface edu.cmu.tetrad.sem.ISemIm: getNumFreeParams.
getNumFreeParams() - Method in interface edu.cmu.tetrad.sem.Scorer: getNumFreeParams.
getNumFreeParams() - Method in class edu.cmu.tetrad.sem.SemIm: getNumFreeParams.
getNumFunctions() - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Retrieves the number of functions used in the ConditionalCorrelationIndependence analysis.
getNumImputations() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The number of imputations, m.
getNumImputations() - Method in class edu.cmu.tetrad.search.rlcd.PooledRankTest: Returns the number of imputed data sets being pooled.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.Fasd: Returns the number of conditional independence tests done in the course of search.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.GrowShrink: getNumIndependenceTests.
getNumIndependenceTests() - Method in interface edu.cmu.tetrad.search.IMbSearch: Number of independent tests.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.Pcd: Retrieves the number of independence tests performed by the graph search.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.PcMb: Returns the number of independence tests performed during the most recent search.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.work_in_progress.FasDci: Getter for the field numIndependenceTests.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.work_in_progress.Iamb: getNumIndependenceTests.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.work_in_progress.IambnPc: getNumIndependenceTests.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.work_in_progress.InterIamb: getNumIndependenceTests.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmmb: getNumIndependenceTests.
getNumIndependenceTests() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Getter for the field numIndependenceTests.
getNumInducedAdjacenciesInPag(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the number of induced adjacencies in the given estiamted Partial Ancestral (PAG) with respect to the given true PAG.
getNumInitialLags() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Getter for the field numInitialLags.
getNumIterations() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the number of EM iterations performed.
getNumIterations() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Getter for the field numIterations.
getNumIterationsRun() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: The number of iterations the last estimate ran: up to the maximum for the stochastic form, fewer if the deterministic form converged.
getNumModels() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Returns the number of top models actually available after a search.
getNumNodes() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the name of the given node.
getNumNodes() - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the number of nodes.
getNumNodes() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.bayes.Evidence: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.graph.Dag: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getNumNodes.
getNumNodes() - Method in interface edu.cmu.tetrad.graph.Graph: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.graph.LagGraph: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.graph.SemGraph: getNumNodes.
getNumNodes() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Gets the number of nodes in the graph.
getNumNodes() - Method in class edu.cmu.tetrad.sem.SemEvidence: getNumNodes.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Beta: Uh, there are 2 parameters...
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.ChiSquare: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Discrete: getNumParameters.
getNumParameters() - Method in interface edu.cmu.tetrad.util.dist.Distribution: Returns the number of parameters in the distribution.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Exponential: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Gamma: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.GaussianPower: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Indicator: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.LogNormal: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Normal: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Poisson: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.SingleValue: Returns the number of parameters in the distribution.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Split: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: getNumParameters.
getNumParameters() - Method in class edu.cmu.tetrad.util.dist.Uniform: getNumParameters.
getNumParents(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the number of parents for the given node.
getNumParents(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the number of parents for the given node.
getNumParents(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns the number of parents for the given node.
getNumParents(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the number of parents for the given node.
getNumParents(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the number of parents for the given node.
getNumParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Returns the number of parents of node i.
getNumParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the number of parents of the given factor.
getNumParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the number of parents of the given factor.
getNumPatterns() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The number of distinct missingness patterns among the rows.
getNumPermutations() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the number of permutations to be used in permutation tests.
getNumRandomCalls() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field numRandomCalls.
getNumRegressors() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The number of regressors.
getNumRegressors() - Method in class edu.cmu.tetrad.regression.RegressionResult: getNumRegressors.
getNumResampleGroups() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns the number of distinct groups if resampling was by block, or -1 if it was by row.
getNumResamples() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the number of resample searches aggregated.
getNumRestarts() - Method in interface edu.cmu.tetrad.sem.SemOptimizer: getNumRestarts.
getNumRestarts() - Method in class edu.cmu.tetrad.sem.SemOptimizerEm: Returns the number of restarts for the optimization process.
getNumRestarts() - Method in class edu.cmu.tetrad.sem.SemOptimizerPowell: getNumRestarts.
getNumRestarts() - Method in class edu.cmu.tetrad.sem.SemOptimizerRegression: getNumRestarts.
getNumRestarts() - Method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Returns the number of restarts used by the optimizer.
getNumRestarts() - Method in class edu.cmu.tetrad.sem.SemOptimizerScattershot: getNumRestarts.
getNumRows() - Method in interface edu.cmu.tetrad.bayes.CptMap: Retrieves the number of rows in the CptMap.
getNumRows() - Method in class edu.cmu.tetrad.bayes.CptMapCounts: Returns the number of rows in the probability map.
getNumRows() - Method in class edu.cmu.tetrad.bayes.CptMapProbs: Returns the number of rows in the probability map.
getNumRows() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getNumRows.
getNumRows() - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: getNumRows.
getNumRows() - Method in class edu.cmu.tetrad.data.BoxDataSet: getNumRows.
getNumRows() - Method in interface edu.cmu.tetrad.data.DataSet: getNumRows.
getNumRows() - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Returns the number of rows of the underlying data set.
getNumRows() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getNumRows.
getNumRows() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: getNumRows.
getNumRows() - Method in class edu.cmu.tetrad.util.Matrix: getNumRows.
getNumRows() - Method in class edu.cmu.tetrad.util.MView: Returns the number of rows in the current matrix view.
getNumRows() - Method in class edu.cmu.tetrad.util.TextTable: getNumRows.
getNumRows(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the number of rows.
getNumRows(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the number of rows.
getNumRows(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Retrieves the number of rows in the specified node.
getNumRows(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the number of rows.
getNumRows(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the number of rows.
getNumRows(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the number of rows in the local table for a given node index.
getNumRowsUsed() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the number of rows used, i.e., the number of rows having at least one observed value.
getNumRuns() - Method in class edu.cmu.tetrad.search.rlcd.RlcdStabilitySummary: The number of resampled runs summarized.
getNumSamplesPerDish() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the number of samples generated per dish in the measurement model.
getNumSamplesPerDish() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getNumSamplesPerDish.
getNumSingularities() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the number of local-score evaluations that failed with a singularity (and so returned NaN) since this score was constructed.
getNumStrata() - Method in class edu.cmu.tetrad.util.StratifiedRankTests: Returns the number of strata.
getNumTerms() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Returns the number of terms.
getNumTests(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the number of tests for the given list of results.
getNumTiers() - Method in class edu.cmu.tetrad.data.Knowledge: getNumTiers.
getNumTiers() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns the number of tiers in this structure.
getNumTierStructures() - Method in class edu.cmu.tetrad.data.Knowledge: Returns the number of additional tier structures.
getNumTimePoints() - Method in class edu.cmu.tetrad.data.TimeSeriesData: getNumTimePoints.
getNumTopK() - Method in class edu.cmu.tetrad.search.BossTopK: Returns the number of top models actually available after a search.
getNumTopK() - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Returns the number of top models actually available after a search.
getNumValues(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the number of values for the parameter.
getNumVariables() - Method in class edu.cmu.tetrad.bayes.Proposition: getNumVariables.
getNumVariables() - Method in class edu.cmu.tetrad.sem.SemProposition: Returns the number of variables in the current object.
getNumVariables() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Returns the number of variables.
getNumVariables() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Returns the number of variables in this term.
getNumVars() - Method in class edu.cmu.tetrad.data.TimeSeriesData: getNumVars.
getNumVars() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field numVars.
getNy0() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The number of data points with target = 0.
getNy1() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The number of data points with target = 1.
getObject(int, int) - Method in class edu.cmu.tetrad.data.BoxDataSet: getObject.
getObject(int, int) - Method in interface edu.cmu.tetrad.data.DataSet: getObject.
getObject(int, int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getObject.
getObservedCounts(Node, BayesPm, BayesIm) - Method in class edu.cmu.tetrad.bayes.BdeMetricCache: This method is used in testing and debugging and not in the BDe metric calculations.
getObservedCoverByName(String) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Looks up the observed cover for a variable name.
getObservedData() - Method in class edu.cmu.tetrad.sem.NNEstimator: Returns the observed (input) dataset.
getObservedDistinctCounts() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the number of distinct observed (non-missing) values per column, by variable name, unmodifiable.
getOhat() - Method in class edu.cmu.tetrad.sem.Ricf.RicfResult: Returns the ohat matrix.
getOmegaHat() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Retrieves the error covariance matrix, which includes the bidirected structure.
getOneMbDag(Graph) - Static method in class edu.cmu.tetrad.search.utils.MbUtils: Returns an example DAG from the given MB CPDAG.
getOrder() - Method in class edu.cmu.tetrad.search.PermutationSearch: Retrieves the order list.
getOrder() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Retrieves the order list.
getOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Returns the order.
getOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Returns the order.
getOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Returns the order.
getOrderedPairs() - Method in class edu.cmu.tetrad.search.IdaCheck: Retrieves a list of OrderedPair objects representing all possible pairs of distinct nodes in the graph.
getOrderShallow() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the current permutation without making a copy.
getOrientationClashes() - Method in class edu.cmu.tetrad.search.PcAR: Returns every bidirected orientation clash found during the last PcAR.search() call -- edges that cannot be consistently oriented given the accepted separating sets.
getOrientPrecision() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: getOrientPrecision.
getOrientRecall() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: getOrientRecall.
getOrients() - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Return orients
getOrigin() - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns where this store's numbers came from.
getOriginalSemIm() - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: getOriginalSemIm.
getOriginalSemIm() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Getter for the field originalSemIm.
getOriginalSemIm() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Getter for the field originalSemIm.
getOut() - Method in class edu.cmu.tetrad.search.Fges: Returns the output stream used for printing.
getOut() - Method in class edu.cmu.tetrad.search.FgesMb: Returns the output stream associated with this object.
getOut() - Method in class edu.cmu.tetrad.search.is.IsGFci: Returns the output stream used by the instance.
getOut() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Getter for the field out.
getOut() - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Getter for the field out.
getOut() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Getter for the field out.
getOutdegree(Node) - Method in class edu.cmu.tetrad.graph.Dag: Returns the outdegree of the given node.
getOutdegree(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getOutdegree.
getOutdegree(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getOutdegree.
getOutdegree(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getOutdegree.
getOutdegree(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getOutdegree.
getOutdegree(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the outdegree of the specified node in the graph.
getOutputChildren(Node) - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns the measured output children of the given latent in the true graph.
getOutputNames() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns the currently specified output variable names.
getOutputNames() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns the currently specified output variable names.
getOutputNames() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns the currently specified output variable names.
getOutputs() - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns a defensive copy of the measured output list.
getOutputs() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Getter for the field outputs.
getOutputs(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: getOutputs.
getOverallMissingRate() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The fraction of all entries that are missing.
getP() - Method in class edu.cmu.tetrad.data.AndersonDarlingTest: Constructs an Anderson-Darling test for the given column of data.
getP() - Method in class edu.cmu.tetrad.data.GeneralAndersonDarlingTest: Getter for the field p.
getP() - Method in class edu.cmu.tetrad.data.MultiGeneralAndersonDarlingTest: Getter for the field p.
getP() - Method in class edu.cmu.tetrad.regression.RegressionResult: Getter for the field p.
getPag() - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Returns the current PAG (the final one, after CdnodPagOrienter.run() has completed).
getPag() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the PAG.
getPag() - Method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath: Getter for the field pag.
getPag(Graph, boolean) - Method in class edu.cmu.tetrad.util.PagCache: Retrieves or computes a Partial Ancestral Graph (PAG) from the given input graph.
getPag(Graph, Knowledge, boolean, int) - Method in class edu.cmu.tetrad.util.PagCache: Retrieves or computes a PAG (Partial Ancestral Graph) from the given input graph.
getPagEffects(Node, Node) - Method in class edu.cmu.tetrad.search.PdagPagIda: Returns the possible total effects of x on y together with the count of refinements that could not be estimated; see PdagPagIda.PagEffects.
getPairwiseCompleteCounts() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The matrix of pairwise complete counts: entry (i, j) is the number of rows on which columns i and j are both observed.
getPairwiseCounts() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the matrix of pairwise observed counts, where entry (j, k) is the number of rows on which variables j and k are both observed.
getPairwiseCounts() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: For each pair of columns, the number of rows on which both are observed and uncensored; the diagonal holds the per-column count.
getParam2() - Method in class edu.cmu.tetrad.sem.ParamConstraint: Getter for the field param2.
getParamComparison(Parameter, Parameter) - Method in class edu.cmu.tetrad.sem.SemPm: getParamComparison.
getParameter() - Method in class edu.cmu.tetrad.sem.Mapping: Getter for the field parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Beta: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.ChiSquare: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Discrete: Returns the index'th parameter.
getParameter(int) - Method in interface edu.cmu.tetrad.util.dist.Distribution: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Exponential: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Gamma: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.GaussianPower: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Indicator: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.LogNormal: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Normal: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Poisson: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.SingleValue: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Split: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: Returns the index'th parameter.
getParameter(int) - Method in class edu.cmu.tetrad.util.dist.Uniform: Returns the index'th parameter.
getParameter(Node, Node) - Method in class edu.cmu.tetrad.sem.SemPm: getParameter.
getParameter(String) - Method in class edu.cmu.tetrad.sem.SemPm: getParameter.
getParameterEstimationInitializationExpression(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the initialization expression for a given parameter used in parameter estimation.
getParameterEstimationInitializationExpressionString(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the initialization expression string for the given parameter.
getParameterExpression(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the expression associated with the given parameter.
getParameterExpressionString(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the expression string associated with the given parameter.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Beta: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.ChiSquare: Returns the name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Discrete: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in interface edu.cmu.tetrad.util.dist.Distribution: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Exponential: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Gamma: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.GaussianPower: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Indicator: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.LogNormal: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Normal: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Poisson: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.SingleValue: Returns the name of the index'th parameter.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Split: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: The name of the index'th parameter, for display purposes.
getParameterName(int) - Method in class edu.cmu.tetrad.util.dist.Uniform: The name of the index'th parameter, for display purposes.
getParameterNames() - Static method in class edu.cmu.tetrad.bayes.BayesPm: Returns the parameter names.
getParameterNames() - Static method in class edu.cmu.tetrad.bayes.MlBayesIm: getParameterNames.
getParameterNames() - Static method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the names of the parameters required for a certain operation.
getParameterNames() - Static method in class edu.cmu.tetrad.sem.SemIm: getParameterNames.
getParameterRange(Edge) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getParameterRange.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Dagma: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Returns a list of parameters for the DirectLingam algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Returns the list of parameter names that are used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Returns the list of parameter names that are used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Returns a list of parameters used by the getParameters method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the list of parameters used by this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: getParameters.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.FirstInflection: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Retrieves the list of parameters required for the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam: Retrieves a list of parameter names used in the configuration and execution of the CAM algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Returns the parameters of the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Retrieves a list of parameter names associated with the CD-NOD algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Retrieves the list of parameters used in this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Flop: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Retrieves a list of parameter names used to configure the PCMCI algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Retrieves the list of parameters associated with the algorithm's configuration.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Retrieves the list of parameters used for the BOSS-FCI algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Retrieves the list of parameters used for the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Retrieves the list of parameters used for the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Retrieves the list of parameters used for the BOSS-FCI algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Retrieves the list of parameters used for the BOSS-FCI algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Retrieves the list of parameters used for the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Retrieves the parameters for the search algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Retrieves the parameters for the search algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Retrieves the list of parameters used by this algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Retrieves a list of parameter names required for the configuration of the CPW algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Returns the list of parameters used by this algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Retrieves the list of parameters used by this algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Retrieves a list of parameter names required for the configuration of the FCI-Cyclic-PW algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Retrieves the list of parameters used by this algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Returns a list of parameters used to configure the search algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Returns a list of parameters used to configure the search algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Returns the parameters used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Returns the list of parameter keys used by this algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvBossExperimental: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Retrieves the list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Retrieves the list of parameters for the method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Returns the list of parameters used by the method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis: Retrieves the parameters for the current instance of the FactorAnalysis class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Glasso: Retrieves a list of parameters used by the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Retrieves the parameters for the current instance of the FactorAnalysis class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Retrieves the parameters for the current instance of the FactorAnalysis class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

Returns the names of all Parameters consumed by this algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Retrieves the list of parameters required for the algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Retrieves the list of parameters for the current instance.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Retrieves the list of parameters for the current instance of the class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Retrieves the list of parameters for the method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Retrieves a list of parameters required for the current instance of the class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Retrieves the list of parameters required for the current instance of the class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Retrieves the list of parameter names that are used by this method and its associated algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Returns the list of parameters for the current instance of the class.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Returns the parameters required by the current algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.StabilitySelection: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.algorithm.StARS: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.Cyclic: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.ErdosRenyi: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomForward: Returns the parameters that this graph uses.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.graph.RandomGraph: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomMim: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomMimic: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomSingleFactorMim: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.RandomTwoFactorMim: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.ScaleFree: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.graph.SingleGraph: Returns the parameters that this graph uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisExpandedGcm: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionBlocksIndTest: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionLrt: Returns the parameters that this search uses.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.independence.BlockIndependenceWrapper: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTest: Deprecated.

Retrieves the parameters used by the test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestLemma10: Deprecated.

Retrieves a list of parameter names required for the "Blocks Test Lemma 10" independence test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestTs: Retrieves a list of parameter names required for the "Blocks Test Lemma 10" independence test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.CciTest: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.ChiSquare: Retrieves the parameters required by the Chi Square Test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython: Retrieves a list of parameter names required for the Python-based independence test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.ClKciPython: Retrieves the list of parameters required for the configuration of the ClKciPython test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython: Retrieves the list of parameter names used by this test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.ConditionalGaussianLrt: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.Cord: Retrieves the parameters of the CORD test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.DegenerateGaussianLrt: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.DgBicTest: Retrieves the parameters required for the SEM BIC test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.EquivalenceTest: Returns the parameters this test uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.FfCi: Provides a list of parameters required for configuring the test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.FfCiContinuous: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.FisherZ: Retrieves the parameters of the Fisher Z test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.Gcm: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.Gin: Retrieves the list of parameters required for this test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.GSquare: Returns the parameters that this search uses.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.independence.IndependenceWrapper: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.Kci: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.MinimaxCITest: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.MultinomialLogisticRegressionWald: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.Mvplrt: Deprecated.

Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.PoissonBicTest: Retrieves the parameters required for the SEM BIC test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.ProbabilisticTest: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestLemma10Singletons: Deprecated.

Retrieves the parameters of the Fisher Z test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTs: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTsSingletons: Deprecated.

Retrieves the parameters of the Fisher Z test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestWilkesSingletons: Deprecated.

Retrieves the parameters of the Fisher Z test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.Rcit: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.SemBicTest: Retrieves the parameters required for the SEM BIC test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.StratifiedRank: Retrieves the parameters of the test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.TrekSeparationBlocksIndependence: Retrieves the parameters of the Fisher Z test.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.independence.TRffLrTest: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBgeScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScoreTabular: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BdeuScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BfBlocksBicScore: Retrieves a list of parameter keys used in the computation or configuration of the score.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BgeScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Retrieves a list of parameter keys used in the computation or configuration of the score.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.ConditionalGaussianBicScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBgeScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBicScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.DiscreteBicScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.EbicScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.FfMl: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.FfMlContinuous: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.GicScores: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.HeavyTailSemBicScore: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.HuangMarginalLikelihoodScore: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Retrieves a list of parameter names required by the scoring method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Retrieves a list of parameter names required by the Instance-Specific BDeu (ISBDeu) scoring algorithm.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.KcvBicScore: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.KernelMarginalLikelihoodScore: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.MagDgBicScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.MSepScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.MVPBicScore: Deprecated.

Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.PoissonPriorScore: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.RankBicScore: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.RffBicScore: Returns a list of parameters applicable to this method.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.score.ScoreWrapper: Returns the parameters that this search uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.SemBicScore: Returns a list of parameters applicable to this method.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.TRffBicScore: Retrieves a list of parameter names associated with the Student-T Random Fourier Feature Bayes Information Criterion (BIC) scoring mechanism.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.score.ZhangShenBoundScore: Returns the list of parameters required for the Zhang-Shen Bound Score.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves a list of configuration parameters used by the model, including both parameters from the associated random graph (if applicable) and those specific to this implementation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonGaussianSemSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Retrieves the parameters required for the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the list of parameters used in the simulation.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the list of parameters used in the simulation.
getParameters() - Method in interface edu.cmu.tetrad.algcomparison.utils.HasParameters: Returns the list of parameter names that are used.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Returns the names of the parameters the inner independence wrapper uses.
getParameters() - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Returns the names of the parameters the inner score wrapper uses.
getParameters() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionParser: Getter for the field parameters.
getParameters() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Returns the list of parameters used in the simulation.
getParameters() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the set of parameters.
getParameters() - Method in class edu.cmu.tetrad.sem.SemPm: Getter for the field parameters.
getParametersEstimationInitializationTemplate() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the parameter estimation initialization template.
getParametersNames() - Method in class edu.cmu.tetrad.util.Parameters: getParametersNames.
getParametersTemplate() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the template for the parameters.
getParameterValue(Edge) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getParameterValue.
getParameterValue(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Retrieves the value of a parameter in the model.
getParameterValues() - Method in interface edu.cmu.tetrad.algcomparison.utils.HasParameterValues: getParameterValues.
getParameterValues() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: getParameterValues.
getParameterValues() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: getParameterValues.
getParamName() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field paramName.
getParams() - Method in class edu.cmu.tetrad.sem.NNEstimator: Returns the DAG as a string.
getParams() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field params.
getParams() - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Getter for the field params.
getParamsWithUnsupportedValueType() - Method in class edu.cmu.tetrad.util.ParamDescriptions: Getter for the field paramsWithUnsupportedValueType.
getParamValue(Node, Node) - Method in interface edu.cmu.tetrad.sem.ISemIm: getParamValue.
getParamValue(Node, Node) - Method in class edu.cmu.tetrad.sem.SemIm: getParamValue.
getParamValue(Parameter) - Method in interface edu.cmu.tetrad.sem.ISemIm: getParamValue.
getParamValue(Parameter) - Method in class edu.cmu.tetrad.sem.SemIm: Retrieves the value associated with the given parameter.
getParent(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the ith parent of the givne node.
getParent(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the ith parent of the givne node.
getParent(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Retrieves the parent of a node at the specified index.
getParent(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the ith parent of the givne node.
getParent(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the ith parent of the givne node.
getParent(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns the given parent as an IndexedParent.
getParent(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns the given parent as an IndexedParent.
getParentDim(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the dimension of the given parent for the given node.
getParentDim(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the dimension of the given parent for the given node.
getParentDim(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Retrieves the value of the parent dimension for a given node and parent index.
getParentDim(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the dimension of the given parent for the given node.
getParentDim(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the dimension of the given parent for the given node.
getParentDims(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the dimensions of the pararents of the given node.
getParentDims(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the dimensions of the pararents of the given node.
getParentDims(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns a copy of the dimensions of the parent node at the specified index.
getParentDims(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the dimensions of the pararents of the given node.
getParentDims(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the dimensions of the pararents of the given node.
getParentName() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: Name of the parent variable (tail of the deleted edge).
getParents() - Method in class edu.cmu.tetrad.search.Boss: Returns the map from nodes to the sets of their parents.
getParents() - Method in class edu.cmu.tetrad.search.BossTopK: Returns the map from nodes to the sets of their parents (for the top model after a search).
getParents() - Method in class edu.cmu.tetrad.search.Sp: Retrieves a mapping of nodes to their parent nodes.
getParents() - Method in interface edu.cmu.tetrad.search.SuborderSearch: The map from nodes to parents resulting from the search.
getParents() - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: The map from nodes to parents resulting from the search.
getParents() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the set of nodes that are in TNeighbors.
getParents() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Returns the parents.
getParents() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Returns the parents of this function.
getParents(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the parents of the given node.
getParents(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the parents of the given node.
getParents(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns an array containing the parents of the specified node.
getParents(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the parents of the given node.
getParents(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the parents of the given node.
getParents(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the parents of a node in the hybrid causal graph model.
getParents(int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the parents of the node at index p.
getParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.RevealOutputGraph: Returns the indices of the parent variables for the given variable.
getParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Returns an array with the indexes of the parents of node i.
getParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealOutputGraph: Returns the indices of the parent variables for the given variable.
getParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealOutputGraph: Returns the indices of the parent variables for the given variable.
getParents(int) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.OutputGraph: Returns the indices of the parent variables for the given variable.
getParents(Node) - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the list of parent nodes for a given node in the graph.
getParents(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getParents.
getParents(Node) - Method in interface edu.cmu.tetrad.graph.Graph: getParents.
getParents(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: getParents.
getParents(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getParents.
getParents(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the list of parent nodes for the given node.
getParents(Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns the parents of a node v.
getParents(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the list of parent nodes for the given node.
getParents(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Returns the lagged factors which are into the given factor.
getParents(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Returns the lagged factors which are into the given factor.
getParents(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Returns the lagged factors which are into the given factor.
getParents(String) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Returns the lagged factors which are into the given factor.
getParents(List, int, Graph, boolean, boolean) - Static method in class edu.cmu.tetrad.graph.Paths: Returns the parents of the node at index p, calculated using Pearl's method.
getParentsSubgraphWithTargetNode(Graph, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the subgraph over the parents of a target node for a DAG, CPDAG, MAG, or PAG.
getParentValue(int, int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the given parent value.
getParentValue(int, int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the given parent value.
getParentValue(int, int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Retrieves the value of the parent node at the specified row and column index.
getParentValue(int, int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the given parent value.
getParentValue(int, int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the given parent value.
getParentValues(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: getParentValues.
getParentValues(int[], int) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Returns an integer array containing the parent values for a given node index and row index.
getParentValues(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the parents values of the given node.
getParentValues(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the parents values of the given node.
getParentValues(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns an integer array containing the parent values for a given node index and row index.
getParentValues(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the parents values of the given node.
getParentValues(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the parents values of the given node.
getParentValues(int, int, int[]) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the parent values for a given node index, row index, and dimensions.
getParentValuesForCombination(int, int[]) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the parent values for a given row index and dimensions.
getParentValuesForCombination(int, int[]) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Computes the parent values for a given row index based on the provided dimensions.
getPart(int, int) - Method in class edu.cmu.tetrad.util.Vector: Extracts a subvector from the current vector from specified indices.
getPart(int, int, int, int) - Method in class edu.cmu.tetrad.util.Matrix: Extracts a submatrix from the current matrix based on the specified row and column ranges.
getPartition() - Method in class edu.cmu.tetrad.search.Rlcd: Returns the stage-2 partition.
getPath() - Method in interface edu.cmu.tetrad.algcomparison.statistic.utils.SimulationPath: getPath.
getPath() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: getPath.
getPath() - Method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath: Getter for the field path.
getPath(Map, Node, Node) - Static method in class edu.cmu.tetrad.search.FciOrientDijkstra: Returns the shortest path from the start node to the end node.
getPattern() - Method in class edu.cmu.tetrad.data.DelimiterType: Getter for the field pattern.
getPatternCounts() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: The distinct missingness patterns and their row counts.
getPc(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.Mmmb: Getter for the field pc.
getPcAlpha() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the alpha value used in the PC algorithm for statistical testing.
getPcColliderStyle() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the collider style used in the PC algorithm for handling colliders during causal discovery.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the penalty discount value, which is used in scoring mechanisms within the class to adjust the influence of complexity penalties.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: The penalty discount in use, which may have been set automatically by the algcomparison wrapper.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Retrieves the penalty discount value used in the scoring calculations.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Returns the penalty discount.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns the penalty discount.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the penalty discount used in the penalization of model complexity.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Retrieves the multiplier applied to the penalty term for this score.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the penalty discount factor used in the score calculation.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Returns the multiplier on the BIC penalty term.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The recommended penalty discount: the larger of the two criteria.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the penalty discount factor.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the multiplier on the penalty term for this score.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: For BIC score, a multiplier on the penalty term.
getPenaltyDiscount() - Method in interface edu.cmu.tetrad.search.utils.HasPenaltyDiscount: getPenaltyDiscount.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: getPenaltyDiscount.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: getPenaltyDiscount.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: getPenaltyDiscount.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Specialized scoring method for a single parent.
getPenaltyDiscount() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field penaltyDiscount.
getPenaltyDiscountFromFdr() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The penalty discount from the false-discovery criterion alone.
getPenaltyDiscountFromMinEffect() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: The penalty discount from the effect-size criterion alone, or 0 when minEffect is 0.
getPercent() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Returns the percentage value.
getPercentCorrect() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Getter for the field percentCorrect.
getPercentCorrect() - Method in interface edu.cmu.tetrad.classify.ClassifierDiscrete: getPercentCorrect.
getPercentCorrect() - Method in class edu.cmu.tetrad.classify.ClassifierMbDiscrete: Getter for the field percentCorrect.
getPercentUnregulated() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Getter for the field percentUnregulated.
getPerms() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Gets the getModel number of bootstrap samples used
getPermutedMatrix() - Method in class edu.cmu.tetrad.search.utils.PermutationMatrixPair: Returns W, permuted rowwise by the permutation passed in through the constructor.
getPi() - Method in class edu.cmu.tetrad.search.Cstar.Record: Retrieves the value of pi.
getPi() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Getter for the field pi.
getPinvTolerance() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Gets the tolerance for the pseudoinverse computation.
getPixelDigitalization() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the pixel digitalization error.
getPixelDigitalization() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getPixelDigitalization.
getPlateauEscapes() - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: The number of successful plateau escapes in the last projection.
getPm() - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Retrieves the probabilistic model associated with this hybrid causal graph model.
getPointGraph() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the point-estimate graph from searching the full dataset at this setting.
getPolicy() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The policy.
getPolyCoef0() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the coefficient of the polynomial for the term of degree 0.
getPolyDegree() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the degree of the polynomial.
getPolyGamma() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the gamma parameter for the polynomial kernel.
getPolynomial(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Returns the polynomial for the given factor.
getPooledLatentEdgeFalseNegatives() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns the pooled number of false negative latent-latent edges by algorithm.
getPooledLatentEdgeFalsePositives() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns the pooled number of false positive latent-latent edges by algorithm.
getPooledLatentEdgePrecision() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns pooled latent-latent edge precision by algorithm.
getPooledLatentEdgeRecall() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns pooled latent-latent edge recall by algorithm.
getPooledLatentEdgeTruePositives() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns the pooled number of true positive latent-latent edges by algorithm.
getPopulationTripleType(Node, Node, Node, IndependenceTest, int, Graph, boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: getPopulationTripleType.
getPopulationTripleType(Node, Node, Node, IndependenceTest, int, Graph, boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: getPopulationTripleType.
getPosition() - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: getPosition.
getPosition() - Method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: getPosition.
getPosition() - Method in interface edu.cmu.tetrad.calculator.expression.Expression: getPosition.
getPosition() - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionDescriptor: getPosition.
getPosition() - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: getPosition.
getPosterior() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the posterior probability of the independence test.
getPosterior() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the posterior probability of the model.
getPosterior() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns the posterior probability of the last independence test.
getPosterior() - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Getter for the field posterior.
getPower() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Returns the power.
getPreamble() - Method in class edu.cmu.tetrad.regression.RegressionResult: getPreamble.
getPrecision() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the precision.
getPrecision() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Gets the getModel precision for the Incomplete Cholesky
getPrecisionAndRecallGraphPlotData(Node, Graph, Graph, ConditioningSetType, String) - Method in class edu.cmu.tetrad.search.MarkovCheck: Computes the precision and recall values for a specified subgraph structure between an estimated graph and a true graph.
getPrecisionAndRecallOnMarkovBlanketGraph(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the precision and recall on the Markov Blanket graph for a given node.
getPrecisionAndRecallOnMarkovBlanketGraph2(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the precision and recall using LocalGraphConfusion (which calculates the combination of Adjacency and ArrowHead) on the Markov Blanket graph for a given node.
getPrecisionAndRecallOnMarkovBlanketGraphPlotData(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates the precision and recall on the markov blanket graph plot data.
getPrecisionAndRecallOnMarkovBlanketGraphPlotData2(Node, Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: This method calculates the precision and recall of a target node's Markov Blanket in the given estimated graph.
getPrecisionAndRecallWholeGraph(Graph, Graph) - Method in class edu.cmu.tetrad.search.MarkovCheck: Calculates and prints precision and recall metrics for the whole graph, comparing an estimated graph to the true graph.
getPredictedValue(double[]) - Method in class edu.cmu.tetrad.regression.RegressionResult: getPredictedValue.
getPrefix(int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Retrieves a prefix of the size specified by the parameter.
getPrefix(String) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: getPrefix.
getPrefix(String) - Static method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getPrefix.
getPrefix(String) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: getPrefix.
getPriorCount() - Method in class edu.cmu.tetrad.bayes.CptMapCounts: Retrieves the prior count for all cells in the CptMapCounts.
getPriorEquivalentSampleSize() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Retrieves the prior equivalent sample size, which is a parameter used in Bayesian scoring to incorporate prior knowledge or assumptions about the data into the scoring function.
getPriorEquivalentSampleSize() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Returns the sample prior.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.BayesImProbs: Calculates the probability of a given proposition.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.CellTableProbs: This method calculates the probability of a given proposition.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.DataSetProbs: Calculates the probability of the given assertion in the data set.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: Retrieves the probability of the given assertion in the DirichletDataSetProbs.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.IntAveDataSetProbs: Calculates the probability of a given assertion.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.StoredCellProbs: Calculates the probability for the given proposition assertion.
getProb(Proposition) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: getProb.
getProbability() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field probability.
getProbability() - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: Getter for the field probability.
getProbability(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getProbability.
getProbability(int, int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the probability for the given cell in the given CPT.
getProbability(int, int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns the probability for the given cell in the given CPT.
getProbability(int, int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns the probability for a given node in the table.
getProbability(int, int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns the probability for the given cell in the given CPT.
getProbability(int, int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns the probability for the given cell in the given CPT.
getProbability(int, int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Retrieves the probability of a specific category for a discrete child node.
getProbs() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The array of coefP-values for the regression coefficients.
getProbTail(double, double) - Method in class edu.cmu.tetrad.data.GeneralAndersonDarlingTest: Computes the probability of the tail for a given value of sample size and AD test statistic values as given in Marsaglia, G., & Marsaglia, J.
getProjection() - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: The projection used by the search, for configuring its caps.
getProperties() - Method in class edu.cmu.tetrad.graph.Edge: Getter for the field properties.
getProperties() - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: Getter for the field properties.
getProperties() - Method in class edu.cmu.tetrad.util.TetradProperties: getProperties.
getProposition() - Method in class edu.cmu.tetrad.bayes.Evidence: Getter for the field proposition.
getProposition() - Method in class edu.cmu.tetrad.sem.SemEvidence: Getter for the field proposition.
getProtocolDescription() - Static method in class edu.cmu.tetrad.util.NamingProtocol: Provides a description of the naming protocol used for variable names.
getProximalEndpoint(Node) - Method in class edu.cmu.tetrad.graph.Edge: getProximalEndpoint.
getPrunedGraph() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport: The proposed graph: the input graph minus the deleted edges.
getPrunedGraph() - Method in class edu.cmu.tetrad.sem.PredictionPruneReport: A copy of the input DAG with the proposed deletions removed.
getPseudocount(int, int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getPseudocount.
getPurifyTestDescriptions() - Static method in enum class edu.cmu.tetrad.search.utils.BpcTestType: Returns the test type descriptions for the Purify algorithm.
getpValue() - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

The p-value of the model.
getPValue() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: The LRT p-value at the time of removal; not a valid p-value for the final graph (see class Javadoc).
getPValue() - Method in class edu.cmu.tetrad.search.MimbuildBollen: Retrieves the p-value associated with the MimbuildBollen class.
getPValue() - Method in class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Retrieves the p-value from the statistical test associated with the NTAD analysis result.
getPValue() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Returns the pValue of the result, or NaN if the p-value cannot be determined.
getPValue() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Retrieves the previously calculated p-value.
getPValue() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns the p-value of the fact under the null hypothesis of independence.
getPValue() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns the probability associated with the most recently executed independence test, or Double.NaN if p value is not meaningful for this test.
getPValue() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the p-value of the independence test.
getPValue() - Method in class edu.cmu.tetrad.search.test.IndTestGin: Returns the p-value from the most recent independence test.
getPValue() - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: No p-values are available.
getPValue() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Retrieves the most recently computed p-value from the independence test.
getPValue() - Method in class edu.cmu.tetrad.search.test.Rcit: Retrieves the most recently computed p-value.
getPValue() - Method in class edu.cmu.tetrad.search.utils.TetradNode: Getter for the field pValue.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getPValue.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.GraphWithPValue: getPValue.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the P value.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Calculates the p-value for the independence test.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

getPValue.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Returns the p-value from the last independence test.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: getPValue.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Calculates the p-value for the independence test.
getPValue() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Needed for IndependenceTest interface.
getPValue() - Method in class edu.cmu.tetrad.sem.DagScorer: getPValue.
getPValue() - Method in interface edu.cmu.tetrad.sem.ISemIm: getPValue.
getPValue() - Method in interface edu.cmu.tetrad.sem.Scorer: getPValue.
getPValue() - Method in class edu.cmu.tetrad.sem.SemIm: getPValue.
getPValue() - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: getPValue.
getPValue(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the p-value using Fisher's exact test.
getPValue(double) - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Calculates the p-value for a given score using the cumulative distribution function (CDF) of a standard normal distribution.
getPValue(double) - Method in class edu.cmu.tetrad.search.work_in_progress.InverseCorrelation: getPValue.
getPValue(Node, Node) - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Looks up the p-value for {x, y}
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.Gcm: Calculates the p-value for testing conditional independence between two variables given a set of conditioning variables in the context of a statistical model.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the pvalue if the fact of X _||_ Y | Z is within the cache of results for independence fact.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Calculates the p-value for the partial correlation between two nodes conditioned on a set of other nodes.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Computes the p-value for testing the conditional independence between two variables (x and y) given a conditioning set of variables (z).
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.DagSepsets
getPValue(Node, Node, Set) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Calculates the p-value for a statistical test a _||_ b | sepset.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Returns the p-value for the independence test between two nodes, given a set of separator nodes.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Returns the p-value for the independence test between two nodes, given a set of separator nodes.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Retrieves the p-value from the result of an independence test between two nodes, given a set of separating nodes.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Returns the p-value for the independence test between two nodes, given a set of separator nodes.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Returns the p-value for the independence test between two nodes, given a set of separator nodes.
getPValue(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet
getPValue(Sextad...) - Method in class edu.cmu.tetrad.search.utils.DeltaSextadTest: Takes a list of tetrads for the given data set and returns the chi square value for the test.
getPValue(TetradInt...) - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest2: Takes a list of tetrads for the given data set and returns the chi square value for the test.
getPValue(Parameter, int) - Method in interface edu.cmu.tetrad.sem.ISemIm: getPValue.
getPValue(Parameter, int) - Method in class edu.cmu.tetrad.sem.SemIm: getPValue.
getPValueChisq() - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: getPValueChisq.
getPValueDf() - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Getter for the field pValueDf.
getPValuePooled(ResolveSepsets.Method, List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Tests for independence using one of the pooled methods and returns the combined p-value, on the scale where "independent iff p > alpha" is the correct decision for every method.
getPValuePooledFisher(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Returns the pooled p-value using Fisher's method.
getPValuePooledFisher2(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Returns the pooled p-value using Fisher's method 2.
getPValuePooledTippett(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Returns the pooled p-value using Tippett's method.
getPValues(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the list of p-values for the given list of results.
getQueries() - Method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: The number of rank queries made.
getR2OnOtherContinuous() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the multiple R-squared of each continuous variable regressed on the other continuous variables, by name, for those variables where this was computable, unmodifiable.
getR2OnOthers() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the multiple R-squared of each positive-variance variable regressed on the others, by name, where this was computable from the implied correlation matrix, unmodifiable.
getR4AbstentionCount() - Method in class edu.cmu.tetrad.search.utils.FciOrient: Retrieves the count of R4 abstentions.
getRandomGenerator() - Method in class edu.cmu.tetrad.util.RandomUtil: Retrieves the RandomGenerator instance being used.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the random graph class used in the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the random graph class used in the simulation (ignored for structure).
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves the class type of the random graph associated with the model.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the random graph class used in the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the random graph class used in the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the random graph class used in the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the random graph class used in the simulation (ignored for structure).
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the random graph class used in the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize
getRandomGraphClass() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Retrieves the class of a random graph for the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Returns null, as there is not random graph for this simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Retrieves the class of a random graph for the simulation.
getRandomGraphClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel
getRandomGraphClass() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs
getRandomGraphClass() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph
getRandomGraphClass() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim
getRandomGraphClass() - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs
getRandomGraphClass() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph
getRank() - Method in class edu.cmu.tetrad.data.ContinuousVariable
getRank() - Method in interface edu.cmu.tetrad.graph.Node: Returns the rank, or -1 if no rank is set.
getRank() - Method in class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Retrieves the rank of the canonical correlation analysis (CCA) result.
getRanks(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: getRanks.
getRankTester() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Returns the rank engine supplied to this test, if any.
getRawData() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the raw data that is the result of a simulation, in the form of a three-dimensional double array.
getRawData() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getRawData.
getRccaEntry(SimpleMatrix, int[], int[], double) - Static method in class edu.cmu.tetrad.util.RankTests: Retrieves or computes an RCCA (Regularized Canonical Correlation Analysis) entry for the given parameters.
getRccaEntryConditioned(SimpleMatrix, int[], int[], int[], double) - Static method in class edu.cmu.tetrad.util.RankTests: RCCA entry for (C, D) after partialing out Z: S_|Z = S - S_{.,Z} * inv(S_{Z,Z} + ridge*I) * S_{Z,.} Then run RCCA on (C, D) blocks of S_|Z with the same ridge regularization on R_cc and R_dd that getRccaEntry(...) uses.
getReachableNodes(List, LegalPairs, List, List, Graph, int) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: getReachableNodes.
getRealAdInd() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The real data's Anderson-Darling uniformity p-value over independence facts.
getRealDistribution(Context) - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: getRealDistribution.
getRealDistribution(Context) - Method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: getRealDistribution.
getRealDistribution(Context) - Method in interface edu.cmu.tetrad.calculator.expression.Expression: getRealDistribution.
getRealDistribution(Context) - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: getRealDistribution.
getRealFractionDependent() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The real data's fraction of independence facts judged dependent.
getReason() - Method in class edu.cmu.tetrad.search.utils.PagLegalityCheck.LegalMagRet: Returns the reason why the graph is not a legal MAG, if not.
getReason() - Method in class edu.cmu.tetrad.search.utils.PagLegalityCheck.LegalPagRet: Returns the reason why the graph is not a legal PAG, if not.
getRecall() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the recall.
getRecords() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Getter for the field records.
getRecords(DataSet, List, List, int, String) - Method in class edu.cmu.tetrad.search.Cstar: Returns records for a set of variables with expected number of false positives bounded by q.
getRecoveredPairs() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the recovered latent-latent adjacency pairs.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.Fcit: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.Fcit2: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitMarkov: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitSl: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitSl2: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitZm: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitZm2: Retrieves the current depth of recursion.
getRecursiveDepth() - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Retrieves the current depth of recursion.
getReferencedNodes(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves a set of referenced nodes for the given node.
getReferencedParameters(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the set of referenced parameters from a given node.
getReferencingNodes(Node) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the set of referencing nodes for a given node.
getReferencingNodes(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns a set of nodes that reference the given parameter.
getRegressorNames() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The variables.
getRegressorNames() - Method in class edu.cmu.tetrad.regression.RegressionResult: Getter for the field regressorNames.
getRemap() - Method in class edu.cmu.tetrad.data.DiscreteDiscretizationSpec: Getter for the field remap.
getRemoves() - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Return removes
getReplicatingGraph(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Creates a replicating graph based on the provided graph.
getReplicationPolicy() - Method in class edu.cmu.tetrad.graph.ReplicatingGraph: Retrieves the current edge replication policy for the graph.
getReport() - Method in class edu.cmu.tetrad.sem.GeneralizedSemEstimator: Retrieves the report generated by the GeneralizedSemEstimator.
getRequired() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: Getter for the field required.
getResampleGroups() - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Returns the row groups for block resampling, or null for row resampling.
getResamplingDataset(DataSet, int) - Static method in class edu.cmu.tetrad.data.DataTransforms: getResamplingDataset.
getResamplingDataset(DataSet, int, RandomGenerator) - Static method in class edu.cmu.tetrad.data.DataTransforms: Get dataset sampled without replacement.
getResidual() - Method in class edu.cmu.tetrad.search.FactorAnalysis: Returns the matrix of residuals.
getResiduals() - Method in class edu.cmu.tetrad.regression.RegressionResult: getResiduals.
getResiduals() - Method in class edu.cmu.tetrad.search.utils.TsUtils.VarResult: Getter for the field residuals.
getResidualsWithoutFirstRegressor() - Method in class edu.cmu.tetrad.regression.RegressionDataset: getResidualsWithoutFirstRegressor.
getResidualVariance(int, int[], Matrix, ICovarianceMatrix, boolean, double) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the variance of the residual of the regression of the ith variable on its parents.
getResult(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Computes the full result (partial correlation, p-value, degrees of freedom, BIC, and Ledoit-Wolf delta) for a single conditional-independence test.
getResultGraph() - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Getter for the field resultGraph.
getResults() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns the per-setting results, in evaluation order, unmodifiable.
getResults(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: After the generateResults method has been called, this method returns the results for the Markov or dependency check, depending on the value of the indep parameter.
getResultsTable() - Method in class edu.cmu.tetrad.regression.RegressionResult: getResultsTable.
getResultType() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field resultType.
getReversed(EdgeTypeProbability.EdgeType) - Static method in class edu.cmu.tetrad.util.GraphSampling: Returns the reversed counterpart of the specified edge type.
getRffEnsemble() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the current number of random Fourier feature ensembles (RFF ensembles) being utilized in computations.
getRho() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Deprecated.
This reflects thread-local per-call state. Prefer IndTestFisherZ.getResult(Node, Node, Set) and IndTestFisherZ.Result.r().
getRho() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Getter for the field rho.
getRidge() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the ridge parameter.
getRidge() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the ridge parameter used in the score calculation.
getRidge() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the current value of the ridge parameter associated with this instance.
getRmsea() - Method in interface edu.cmu.tetrad.sem.ISemIm: getRmsea.
getRmsea() - Method in class edu.cmu.tetrad.sem.SemIm: getRmsea.
getRobustStdErrs() - Method in class edu.cmu.tetrad.sem.SemSandwichEstimator: Returns the robust (sandwich) standard errors of the free parameters, in the same order as estSem.getFreeParameters(), matching the convention of SemStdErrorEstimator.getStdErrors().
getRootElement(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: getRootElement.
getRow(boolean[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Returns the row for the given combination of parent values.
getRowCaseCount(int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Returns the number of complete cases behind a local-table row, or -1 if the count is unknown.
getRowCounts(List) - Static method in class edu.cmu.tetrad.util.MultidataUtils: getRowCounts.
getRowDims(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Computes and returns the row dimensions for a specific node in the network based on its discrete and continuous parents.
getRowIndex(int, int[]) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns a row index.
getRowIndex(int, int[]) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getRowIndex.
getRowIndex(int, int[]) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Returns the row index corresponding to the given node index and combination of parent values.
getRowIndex(int, int[]) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getRowIndex.
getRowIndex(int, int[]) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getRowIndex.
getRowIndex(int, int[], int[]) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Computes the row index for a given node in the model based on the state indices of its discrete and continuous parents.
getRowPerm() - Method in class edu.cmu.tetrad.search.utils.PermutationMatrixPair: Getter for the field rowPerm.
getRowPseudocount(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getRowPseudocount.
getRows() - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Retrieves the list of row indices currently set for the analysis.
getRows() - Method in class edu.cmu.tetrad.search.test.FfCi
getRows() - Method in class edu.cmu.tetrad.search.test.FfCi1: Retrieves the list of integers representing the rows used in the mixed independence test.
getRows() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Retrieves the list of row indices currently associated with this instance.
getRows() - Method in class edu.cmu.tetrad.search.test.Gcm: Retrieves the current row restriction, or null if all rows are used.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Returns the current row subset, or null for all rows.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Returns the rows used in the test.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the rows used for the test.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Returns the number of orthogonal functions used to do the calculations.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns the user-specified row restriction (or null if none).
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Returns the row indices currently in use, or null if all rows are used.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Returns the row indices currently in use, or null if all rows are used.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the list of rows used for the independence test.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Returns the rows used in the test.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the list of row indices currently associated with this instance.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Retrieves the list of rows to use for the test.
getRows() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes
getRows() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Retrieves the list of rows.
getRows() - Method in class edu.cmu.tetrad.search.test.Rcit
getRows() - Method in interface edu.cmu.tetrad.search.test.RowsSettable: Gets the rows to use for the test.
getRows() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Retrieves the array of row indices.
getRows(double[], double[], double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: getRows.
getRows(double[], double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: getRows.
getRowValues(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getRowValues.
getRuntime() - Method in class edu.cmu.tetrad.search.Ion: Getter for the field runtime.
getSampleCovar() - Method in class edu.cmu.tetrad.sem.DagScorer: Getter for the field sampleCovar.
getSampleCovar() - Method in interface edu.cmu.tetrad.sem.Scorer: getSampleCovar.
getSampleCovar() - Method in class edu.cmu.tetrad.sem.SemIm: getSampleCovar.
getSamplePrior() - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Retrieves the sample prior value used for scoring in the BDeu scoring metric.
getSamplePrior() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the prior probability assigned to the sample during computations or analysis.
getSamplePrior() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the sample prior value, which is used in the scoring mechanisms of the class to represent prior assumptions or knowledge about the sample distribution.
getSamplePrior() - Method in interface edu.cmu.tetrad.search.is.IsScore: Retrieves the sample prior or equivalent sample size used in the scoring process.
getSamplePrior() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field samplePrior.
getSampleSampleVariability() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the sample to sample variability, which is the standard deviation of a normal distribution with mean 0 from which errors in measured expression levels due to the microarray being used for measurement are drawn.
getSampleSampleVariability() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getSampleSampleVariability.
getSampleSize() - Method in class edu.cmu.tetrad.bayes.BayesProperties: Retrieves the sample size for the current instance.
getSampleSize() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: The size of the sample used to calculated this covariance matrix.
getSampleSize() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: The size of the sample used to calculated this covariance matrix.
getSampleSize() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: The size of the sample used to calculated this covariance matrix.
getSampleSize() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the sample size used in the covariance matrix.
getSampleSize() - Method in class edu.cmu.tetrad.data.SplitCasesSpec: Getter for the field sampleSize.
getSampleSize() - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Retrieves the sample size used in the BDeu scoring metric.
getSampleSize() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the sample size of the dataset used in this independence test.
getSampleSize() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the sample size of the data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Retrieves the sample size of the data being scored.
getSampleSize() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the sample size used in the computation or analysis.
getSampleSize() - Method in class edu.cmu.tetrad.search.is.IsBicScore
getSampleSize() - Method in interface edu.cmu.tetrad.search.is.IsScore: Retrieves the sample size used in the context of the implementation.
getSampleSize() - Method in class edu.cmu.tetrad.search.rlcd.Chi2RankTest: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Retrieves the sample size from the underlying BIC score component.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Retrieves the sample size from the underlying BIC score component.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BdeScore: Returns the sample size of the data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Returns the sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BgeScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Retrieves the sample size of the dataset associated with the current block specification.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Retrieves the sample size by returning the number of rows in the data set associated with the block specification.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.EbicScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Retrieves the sample size, which corresponds to the number of rows in the associated data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Retrieves the sample size, which corresponds to the number of rows in the associated data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.FfMl: Returns the sample size of the dataset by retrieving the number of rows in the dataset.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Retrieves the sample size, which corresponds to the number of rows in the associated data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.GraphScore: getSampleSize.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the sample size from the underlying data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Retrieves the sample size used in the scoring process.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.ImagesScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Retrieves the sample size associated with the base `SemBicScore`.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the sample size from the associated dataset.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
getSampleSize() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the sample size used for scoring.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Retrieves the sample size of the dataset used for scoring.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: The sample size of the covariance matrix.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.MvpScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the sample size used in the score calculation.
getSampleSize() - Method in interface edu.cmu.tetrad.search.score.Score: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Retrieves the sample size of the dataset associated with this instance.
getSampleSize() - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Returns the sample size of the base test.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the sample size of the wrapped test.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.Gcm
getSampleSize() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Returns the sample size (number of rows in use).
getSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Returns the sample size (number of rows in use).
getSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Retrieves the sample size used in the underlying independence test.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the sample size of the data set associated with this instance.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns the total sample size over all domains.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Returns the sample size used in the covariance matrix.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Retrieves the sample size from the dataset associated with this object.
getSampleSize() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Retrieves the sample size of the data set.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Returns the sample size.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Retrieves the sample size of the covariance matrix.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: The sample size of the data.
getSampleSize() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Getter for the field sampleSize.
getSampleSize() - Method in class edu.cmu.tetrad.sem.DagScorer: getSampleSize.
getSampleSize() - Method in interface edu.cmu.tetrad.sem.ISemIm: getSampleSize.
getSampleSize() - Method in interface edu.cmu.tetrad.sem.Scorer: getSampleSize.
getSampleSize() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field sampleSize.
getSampleSize() - Method in class edu.cmu.tetrad.sem.SemSandwichEstimator: Returns the sample size of the data set used.
getSampleSize() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Getter for the field sampleSize.
getSampleSize() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field sampleSize.
getSampleSizeSummary() - Method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: A one-line summary of the complete-row counts of the distinct column unions tested: how many unions, and the least, median, and greatest number of complete rows among them.
getScaledBHat(PermutationMatrixPair) - Static method in class edu.cmu.tetrad.search.LingD: Computes the scaled matrix B̂ from a given permutation matrix pair.
getScaledBHats(Matrix) - Method in class edu.cmu.tetrad.search.LingD: Local LiNG-D from a given W: 1) Threshold W (small entries -> 0).
getScaledChiSquare() - Method in class edu.cmu.tetrad.sem.SemSandwichEstimator: Returns the Satorra-Bentler scaled chi-square, the normal-theory chi-square divided by the scaling factor.
getScaledPValue() - Method in class edu.cmu.tetrad.sem.SemSandwichEstimator: Returns the p-value of the scaled chi-square against the model degrees of freedom.
getScaledRocPlot() - Method in class edu.cmu.tetrad.util.RocCalculator: getScaledRocPlot.
getScalingFactor() - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Retrieves the kernel scaling factor.
getScalingFactor() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the scaling factor for the Gaussian bandwidth heuristic.
getScalingFactor() - Method in class edu.cmu.tetrad.sem.SemSandwichEstimator: Returns the Satorra-Bentler scaling factor c.
getScore() - Method in class edu.cmu.tetrad.search.Boss: Returns the score being used for the search.
getScore() - Method in class edu.cmu.tetrad.search.BossTopK: Returns the score being used for the search.
getScore() - Method in class edu.cmu.tetrad.search.score.ScoredGraph: Returns the score.
getScore() - Method in class edu.cmu.tetrad.search.Sp: Retrieves the score associated with this object.
getScore() - Method in interface edu.cmu.tetrad.search.SuborderSearch: The score being used.
getScore() - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: The score being used.
getScore() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns the score of the test, which is alpha - p if the test returns a p-value or else a bump if the test is based on a score.
getScore() - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Returns the score of the object.
getScore() - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Returns the score of the object.
getScore() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Calculates the score for the given Sepsets object.
getScore() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Calculates the score for the given Sepsets object.
getScore() - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Calculates the score for the given Sepsets object.
getScore() - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Calculates the score for the given Sepsets object.
getScore() - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Returns the score of the object.
getScore() - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: Returns the score of the object.
getScore() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: getScore.
getScore() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Returns the score.
getScore() - Method in interface edu.cmu.tetrad.sem.ISemIm: getScore.
getScore() - Method in class edu.cmu.tetrad.sem.SemIm: The value of the maximum likelihood function for the getModel the model (Bollen 107).
getScore() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field score.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBgeScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScoreTabular: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BdeuScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BfBlocksBicScore: Computes and returns a score based on the provided data model and parameter settings.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BgeScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Computes and returns a score based on the provided data model and parameter settings.
getScore(DataModel, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Computes and returns a score for the given data model and parameters.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.ConditionalGaussianBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBgeScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.DiscreteBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.EbicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.FfMl: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.FfMlContinuous: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.GicScores: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.HeavyTailSemBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.HuangMarginalLikelihoodScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Computes the score for a given data model and set of parameters.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Calculates and returns a score for the given data model and associated parameters.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.KcvBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.KernelMarginalLikelihoodScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.MagDgBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.MSepScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.MVPBicScore: Deprecated.

Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.PoissonPriorScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.RankBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.RffBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.score.ScoreWrapper: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.SemBicScore: Returns true, iff x and y are independent, conditional on z for the given data set.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.TRffBicScore: Calculates and returns a score for the given dataset based on the specified parameters.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.ZhangShenBoundScore: Calculates the score based on the given data set and parameters.
getScore(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Builds one score per pooled data set and combines them into an ImagesScore, which sums the local scores across data sets.
getScore(Graph) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: getScore.
getScoreCount(Node, Set) - Method in class edu.cmu.tetrad.bayes.BdeMetricCache: This is just for testing the operation of the inner class and the map from nodes and parent sets to scores.
getScoreFact(int, int[], List) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: getScoreFact.
getScoreFact(Node, List) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: getScoreFact.
getScoreParameters(Algorithm) - Static method in class edu.cmu.tetrad.util.Params: getScoreParameters.
getScores(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Constructs one score per data model, coordinating data-dependent representation choices across the data models so that all scores share a common parameterization.
getScores(List, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.utils.MultiDataSetScoreWrapper: Constructs one score per data model, coordinating data-dependent representation choices across the data models so that all scores share a common parameterization.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Retrieves the ScoreWrapper object associated with this class.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Retrieves the ScoreWrapper object associated with this class.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Retrieves the score wrapper object.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Retrieves the ScoreWrapper associated with this algorithm.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Retrieves the ScoreWrapper associated with this algorithm.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Retrieves the ScoreWrapper associated with this algorithm.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Retrieves the ScoreWrapper associated with this algorithm.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Retrieves the ScoreWrapper associated with this algorithm.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Retrieves the ScoreWrapper associated with this algorithm.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Returns the ScoreWrapper object associated with this instance.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Retrieves the ScoreWrapper associated with this instance.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Retrieves the ScoreWrapper associated with this instance.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Returns the score wrapper.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Retrieves the ScoreWrapper object associated with this method.
getScoreWrapper() - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Retrieves the ScoreWrapper object associated with this algorithm.
getScoreWrapper() - Method in interface edu.cmu.tetrad.algcomparison.utils.TakesScoreWrapper: Returns the score wrapper.
getSd() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Returns the standard deviation.
getSe() - Method in class edu.cmu.tetrad.regression.RegressionResult: Getter for the field se.
getSecond() - Method in class edu.cmu.tetrad.graph.NodePair: Getter for the field second.
getSecond() - Method in class edu.cmu.tetrad.graph.OrderedPair: Getter for the field second.
getSecondEdge() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move: Getter for the field secondEdge.
getSeed() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns the random seed used to draw the resamples, or -1 if time-seeded.
getSeed() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: The random seed, or -1 if none is fixed.
getSeed() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the random seed used for reproducibility.
getSeed() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the current value of the seed.
getSeed() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the base random seed for the A/B/C split.
getSeed() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the base random seed for the A/B/C split.
getSelectedIndices() - Method in class edu.cmu.tetrad.data.BoxDataSet: getSelectedIndices.
getSelectedIndices() - Method in interface edu.cmu.tetrad.data.DataSet: getSelectedIndices.
getSelectedIndices() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getSelectedIndices.
getSelectedModel() - Method in class edu.cmu.tetrad.data.DataModelList: Getter for the field selectedModel.
getSelectedVariableNames() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getSelectedVariableNames.
getSelectedVariableNames() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getSelectedVariableNames.
getSelectedVariableNames() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSelectedVariableNames.
getSelectedVariableNames() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves a list of names of the currently selected variables.
getSelectedVariables() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getSelectedVariables.
getSelection(int[]) - Method in class edu.cmu.tetrad.util.Vector: viewSelection.
getSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.CorrelationMatrix: Returns a submatrix based on the specified rows and columns.
getSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Returns a submatrix based on the specified rows and columns.
getSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Returns a submatrix based on the specified rows and columns.
getSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Returns a submatrix based on the specified rows and columns.
getSelection(int[], int[]) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Returns a submatrix based on the specified rows and columns.
getSelection(int[], int[], int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSelection.
getSelectionBias() - Method in class edu.cmu.tetrad.data.ContinuousVariable
getSelectionBias() - Method in class edu.cmu.tetrad.data.DiscreteVariable
getSelectionBias() - Method in class edu.cmu.tetrad.graph.GraphNode: Returns true if this node is selected as a bias node.
getSelectionBias() - Method in interface edu.cmu.tetrad.graph.Node: Returns the selection bias status for this node.
getSelfLoopCoef() - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Getter for the field selfLoopCoef.
getSelfLoopCoefs(DataSet) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: getSelfLoopCoefs.
getSemantics() - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns the semantics of the stored values.
getSemIm() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field semIm.
getSemIm() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field semIm.
getSemIm() - Method in class edu.cmu.tetrad.sem.ParamConstraint: Getter for the field semIm.
getSemIm() - Method in class edu.cmu.tetrad.sem.SemEvidence: Getter for the field semIm.
getSemIm() - Method in class edu.cmu.tetrad.sem.SemProposition: Retrieves the SemIm object associated with this SemProposition.
getSemIm() - Method in class edu.cmu.tetrad.sem.SemUpdater: Getter for the field semIm.
getSemIm(Element) - Static method in class edu.cmu.tetrad.sem.SemXmlParser: Takes an xml representation of a SEM IM and reinstantiates the IM
getSemPm() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Getter for the field semPm.
getSemPm() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Getter for the field semPm.
getSemPm() - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: getSemPm.
getSemPm() - Method in interface edu.cmu.tetrad.sem.ISemIm: getSemPm.
getSemPm() - Method in class edu.cmu.tetrad.sem.SemEstimator: Getter for the field semPm.
getSemPm() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbs: Getter for the field semPm.
getSemPm() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field semPm.
getSemPm() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Getter for the field semPm.
getSeparatedPairs() - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: getSeparatedPairs.
getSeparators(Node[], Map) - Static method in class edu.cmu.tetrad.bayes.GraphTools: Calculate separator sets in clique tree.
getSepset() - Method in class edu.cmu.tetrad.search.work_in_progress.Dci: Gets the resulting sepsets
getSepset() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field sepset.
getSepset(Node, Node, boolean, IndependenceTest, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds a sepset for x and y, if there is one; otherwise, returns null.
getSepset(Node, Node, int) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Retrieves the set of nodes that form the sepset between two given nodes.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Retrieves the sepset, which is the set of common neighbors between two given nodes.
getSepset(Node, Node, int, List) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Retrieves the sepset, which is the set of common neighbors between two given nodes.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Retrieves the separating set (sepset) between two nodes in the graph.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Retrieves the sepset (separating set) between two nodes which contains a set of nodes.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Retrieves the separation set (sepset) between two nodes.
getSepset(Node, Node, int, List) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: Retrieves the sepset between two nodes.
getSepset(Node, Node, IndependenceTest) - Method in class edu.cmu.tetrad.graph.Dag: Returns the sepset between two given nodes in the graph.
getSepset(Node, Node, IndependenceTest) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Returns the set of nodes that form the separating set between two given nodes.
getSepset(Node, Node, IndependenceTest) - Method in interface edu.cmu.tetrad.graph.Graph: Returns the set of nodes that form the separating set between two given nodes.
getSepset(Node, Node, IndependenceTest) - Method in class edu.cmu.tetrad.graph.LagGraph: Returns the set of nodes that form the separating set between two given nodes.
getSepset(Node, Node, IndependenceTest) - Method in class edu.cmu.tetrad.graph.SemGraph: Returns the set of nodes that form the separating set between two given nodes.
getSepset(Node, Node, IndependenceTest) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Retrieves the sepset of two nodes in the graph.
getSepset(IndependenceTest, Node, Node) - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Getter for the field sepset.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Retrieves the set of nodes that form the sepset between two given nodes.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.graph.Paths: Retrieves a sepset (a set of nodes) between two given nodes.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Returns the sepset containing nodes 'a' and 'b' that also contains all the nodes in the given set 's'.
getSepsetContaining(Node, Node, Set, int) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Retrieves a sepset containing nodes in s from the given set of nodes.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Retrieves a sepset (separating set) between two nodes containing a set of nodes, containing the nodes in s, or null if no such sepset is found.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Retrieves a sepset (separating set) between two nodes containing a set of nodes, containing the nodes in s, or null if no such sepset is found.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Retrieves a sepset (separating set) between two nodes containing a set of nodes containing the nodes in s, or null if no such sepset is found.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Retrieves a sepset (separating set) between two nodes containing a set of nodes containing the nodes in s, or null if no such sepset is found.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Retrieves the separation set (sepset) between two nodes i and k that contains a given set of nodes s.
getSepsetContaining(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: Retrieves the sepset for a and b, where we are expecting this sepset to contain all the nodes in s.
getSepsetContainingGreedySubsetMb(Graph, Graph, Node, Node, Set, IndependenceTest, int) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Identifies a separating set (sepset) containing a given subset of nodes between two nodes x and y in a graph using a greedy approach and subsets of (adj(x) U adu(y)) \ {x, y}.
getSepsetContainingMaxPHybrid(Graph, Node, Node, Set, IndependenceTest, int) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Returns the set of nodes that act as a separating set between two given nodes (x and y) in a graph.
getSepsetContainingMinPHybrid(Graph, Node, Node, IndependenceTest, int) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Returns the sepset containing the minimum p-value for the given variables x and y.
getSepsetMap() - Method in class edu.cmu.tetrad.search.FcitSl: Returns the map of committed separating sets discovered during the search.
getSepsetMap() - Method in class edu.cmu.tetrad.search.FcitSl2: Returns the map of committed separating sets discovered during the search.
getSepsetMap() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Returns the map of committed separating sets discovered during the search.
getSepsetMap() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Returns the map of committed separating sets discovered during the search.
getSepsets() - Method in class edu.cmu.tetrad.search.Fas: Retrieves the separation set map that is used during the search process to store information about conditional independence tests.
getSepsets() - Method in class edu.cmu.tetrad.search.Fasd: Returns the map of node pairs to sepsets from the search.
getSepsets() - Method in class edu.cmu.tetrad.search.Fci: Returns the sepset map maintained by this instance of the FCI algorithm.
getSepsets() - Method in class edu.cmu.tetrad.search.Icd: Retrieves the SepsetMap associated with the current instance.
getSepsets() - Method in interface edu.cmu.tetrad.search.IFas: Sepsets discovered during the search.
getSepsets() - Method in class edu.cmu.tetrad.search.Pcd: Getter for the field sepsets.
getSepsets() - Method in class edu.cmu.tetrad.search.Rfci: Returns the map from node pairs to sepsets found in search.
getSerialDependencePValues() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the Ljung-Box p-value per continuous variable name, testing the joint null that the first serialMaxLag autocorrelations in file order are zero, for those variables with enough observed values to compute it, unmodifiable.
getSet(Node, Node) - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: Retrieves the set of all condioning sets for {x, y} or null if no such set was ever set
getSetting() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns the parameter values for this setting, unmodifiable, in insertion order.
getSetType() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns type of conditioning sets to use in the Markov check.
getSeverity() - Method in class edu.cmu.tetrad.data.audit.AuditFinding: Returns the severity of this finding.
getShat() - Method in class edu.cmu.tetrad.sem.Ricf.RicfResult: Retrieves the shat matrix.
getShd() - Method in class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Returns the adjacency precision.
getShortDescription() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field shortDescription.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves the short name associated with the General Additive Model (GAM).
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonGaussianSemSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the short name of the simulation.
getShortName() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the short name of the simulation.
getShortName() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the short name of the simulation.
getShrinkage() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the shrinkage parameter.
getShrinkageMode() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the current shrinkage mode used by this instance.
getShuffledVariables() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: getShuffledVariables.
getSigmaHat() - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns a copy of the fitted Sigma-hat, indexed in the covariance matrix's variable order.
getSigmaHat() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Retrieves the implied covariance matrix associated with the model.
getSignature() - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionDescriptor: getSignature.
getSignature() - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionSignature: getSignature.
getSignificance() - Method in interface edu.cmu.tetrad.search.utils.TetradTest: getSignificance.
getSignificance() - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: getSignificance.
getSignificance() - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: getSignificance.
getSignificance() - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: getSignificance.
getSignificantModels() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Getter for the field significantModels.
getSimpleMatrix() - Method in class edu.cmu.tetrad.util.Matrix: Creates and returns a copy of the internal data matrix.
getSimpleMatrix() - Method in class edu.cmu.tetrad.util.Vector: Retrieves the SimpleMatrix instance associated with this Vector.
getSimReportText(double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Generates a simulation report summarizing the results of a TrainedDagSimulator.
getSimReportText(double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Generates a simulation report as a string, summarizing details of the simulation and support warnings based on input parameters and internal state.
getSimReportText(double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Generates a simulation report as a string, summarizing details of the simulation and support warnings based on input parameters and internal state.
getSimReportText(double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Generates a simulation report as a string, summarizing details of the simulation and support warnings based on input parameters and internal state.
getSimulatedData() - Method in class edu.cmu.tetrad.sem.NNEstimator: Returns the number of nodes in the DAG.
getSimulateMillis() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: Total time spent simulating the null draws, in milliseconds.
getSimulation() - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: Getter for the field simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves the class type of the simulation associated with the General Additive Model.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the class of the current simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize
getSimulationClass() - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the class of the simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Retrieves the class of the simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the class of the simulation.
getSimulationClass() - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel
getSimulationClass() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs
getSimulationClass() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph
getSimulationClass() - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim
getSimulationClass() - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs
getSimulationClass() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph
getSimulations() - Method in class edu.cmu.tetrad.algcomparison.simulation.Simulations: Returns the list of simulations.
getSimulator() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: The simulator used for the null draws.
getSimulator() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: Getter for the field simulator.
getSingleCategory(int) - Method in class edu.cmu.tetrad.bayes.Proposition: getSingleCategory.
getSize() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getSize.
getSize() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getSize.
getSize() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSize.
getSize() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the size of the matrix.
getSize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.RevealOutputGraph: getSize.
getSize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealOutputGraph: getSize.
getSize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealOutputGraph: getSize.
getSize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns the # nodes in this graph
getSize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Returns # rows ( == # columns) of this matrix
getSize() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.OutputGraph: Returns the number of variables over which the graph is defined.
getSize() - Method in class edu.cmu.tetrad.util.Point: getSize.
getSkeleton() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: getSkeleton.
getSkippedQueries() - Method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: The number of queries resolved as rejections by a guard rather than by the test: too few complete rows, or a non-positive Lawley multiplier.
getSmallestSubset(Node, Node, Set, Set, Graph, boolean) - Static method in class edu.cmu.tetrad.search.SepsetFinder: Finds a smallest subset S of blocking that renders two nodes x and y conditionally d-separated conditional on S in the given graph.
getSoCalledPoissonShocks(int) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getSoCalledPoissonShocks.
getSplitNames() - Method in class edu.cmu.tetrad.data.SplitCasesSpec: Getter for the field splitNames.
getSquaredDistance(OrderedPair) - Method in class edu.cmu.tetrad.search.IdaCheck: Calculates the squared distance of the true total effect to the [min, max] IDA effect range of the given (x, y) node pair, for x predicting y.
getSquaredMaxTrueDist(OrderedPair) - Method in class edu.cmu.tetrad.search.IdaCheck: Returns the squared difference between the maximum total effect and the true total effect for the given pair of nodes.
getSquaredMinTrueDistance(OrderedPair) - Method in class edu.cmu.tetrad.search.IdaCheck: Returns the squared difference between the minimum total effect and the true total effect for the given pair of nodes.
getSs() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Getter for the field ss.
getStableBHats() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns the B-Hat matrices of the stable graphs.
getStableBHats() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the list of stable B matrices generated by the algorithm.
getStableGraphs() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns the stable graphs found by the most recent search.
getStableGraphs() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the list of stable graphs generated by the algorithm.
getStage1Graph() - Method in class edu.cmu.tetrad.search.Rlcd: Returns the stage-1 graph.
getStandardError(Parameter, int) - Method in interface edu.cmu.tetrad.sem.ISemIm: getStandardError.
getStandardError(Parameter, int) - Method in class edu.cmu.tetrad.sem.SemIm: getStandardError.
getStartIm() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Getter for the field startIm.
getStartingValue() - Method in class edu.cmu.tetrad.sem.Parameter: Getter for the field startingValue.
getStartsWithParameterEstimationInitializationTemplate(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the initialization template for parameter estimation based on the provided starting string.
getStartsWithParameterTemplate(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: getStartsWithParameterTemplate.
getStatistic() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: The LRT statistic at the time of removal.
getStatistics() - Method in class edu.cmu.tetrad.algcomparison.statistic.Statistics: Return the list of statistics.
getStats() - Method in class edu.cmu.tetrad.search.Ion: Summarizes time and hitting set time and size information for latex
getStatsListTable(Graph, Graph) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns a string representing a table of statistics that can be printed.
getStatsListTable(Graph, Graph, DataModel, long) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns a string representing a table of statistics that can be printed.
getStatsListTable(Graph, Graph, DataModel, long, List) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: As CompareTwoGraphs.getStatsListTable(Graph, Graph, DataModel, long), but with the statistics to show given explicitly, in display order.
getStatsListTable(Graph, Graph, Graph, DataModel, long) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: As CompareTwoGraphs.getStatsListTable(Graph, Graph, DataModel, long), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List).
getStatsListTable(Graph, Graph, Graph, DataModel, long, List) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: As CompareTwoGraphs.getStatsListTable(Graph, Graph, DataModel, long, List), but with the true DAG given separately from the comparison graph.
getStatsListTable(Graph, Graph, List, List, DataModel, List) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List).
getStatsListTable(Graph, Graph, List, List, DataModel, List, List) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List, List), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List) for the division of labor between the two.
getStatsListTable(Graph, List, List, DataModel, List) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: Returns a string representing a table of statistics for several target graphs against a single true graph, one column per target.
getStatsListTable(Graph, List, List, DataModel, List, List) - Static method in class edu.cmu.tetrad.algcomparison.CompareTwoGraphs: As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List), but with the statistics to show given explicitly, in display order.
getStdDev(Node, Matrix) - Method in interface edu.cmu.tetrad.sem.ISemIm: getStdDev.
getStdDev(Node, Matrix) - Method in class edu.cmu.tetrad.sem.SemIm: getStdDev.
getStdErrors() - Method in class edu.cmu.tetrad.sem.SemStdErrorEstimator: getStdErrors.
getStdErrs() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The array of standard errors for the regression coefficients.
getStep() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion: The elimination round within this child at which the edge fell, 0-based.
getStep() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Returns the getModel step.
getStepsGenerated() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the number of steps generated.
getStepsGenerated() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getStepsGenerated.
getStratifiedRankTests() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns the engine computing the pooled statistics, for use by rank-based consumers over the same variable order (FOFC, TSC).
getStretch() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Getter for the field stretch.
getString(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the string value of the given parameter, looking up its default in the ParamDescriptions map.
getString(String, String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the string value of the given parameter, using the given default.
getStructurePrior() - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Retrieves the structure prior value used in the BDeu scoring metric.
getStructurePrior() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the required effect/gain threshold for determining an effect edge.
getStructurePrior() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the structure prior value, which is used in the scoring mechanisms of the class to reflect prior beliefs or assumptions about the structure of the model.
getStructurePrior() - Method in interface edu.cmu.tetrad.search.is.IsScore: Retrieves the structure prior, which is an implementation-specific component used to influence the local score calculation.
getStructurePrior() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Retrieves the structure prior associated with this BdeuScore object.
getStructurePrior() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the structure prior for this score.
getStructurePrior() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field structurePrior.
getStructurePrior(int) - Method in class edu.cmu.tetrad.search.score.MvpLikelihood: Returns the structure prior.
getStudentTNu() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the value of the Student's t-distribution degrees of freedom (ν).
getSubCorrMatrix(String[]) - Method in class edu.cmu.tetrad.data.CorrelationMatrix: getSubCorrMatrix.
getSubcovers() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups.Entry: Returns the strict subcovers of this entry's cover.
getSubFunction(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: getSubFunction.
getSubjectStarts(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the subject start row indices for the given data model.
getSubmatrix(int[]) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getSubmatrix.
getSubmatrix(int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getSubmatrix.
getSubmatrix(int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSubmatrix.
getSubmatrix(int[]) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Returns a submatrix of the covariance matrix, including only the specified variables.
getSubmatrix(int[], int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSubmatrix.
getSubmatrix(String[]) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getSubmatrix.
getSubmatrix(String[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getSubmatrix.
getSubmatrix(String[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSubmatrix.
getSubmatrix(String[]) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Returns a submatrix of the covariance matrix, including only the specified variables.
getSubmatrix(List) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getSubmatrix.
getSubmatrix(List) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getSubmatrix.
getSubmatrix(List) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getSubmatrix.
getSubmatrix(List) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Returns a submatrix of the covariance matrix, including only the specified variables.
getSubMatrix(int[], int[]) - Method in interface edu.cmu.tetrad.data.Covariances: Returns a submatrix of the covariance matrix for the given rows and columns.
getSubMatrix(int[], int[]) - Method in class edu.cmu.tetrad.data.CovariancesDoubleForkJoin: getSubMatrix.
getSupersetScore() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the score used for parent superset feature.
getSupersetTopM() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the top M value for the parent superset feature.
getSupportedEvents() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: getSupportedEvents.
getSupportedEvents() - Method in class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: getSupportedEvents.
getSupportedEvents() - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig: Retrieves the list of supported events.
getSupportLowerBound() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns the lower bound of the support for the distribution.
getSupportUpperBound() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns the upper bound of the support for the distribution.
getT() - Method in class edu.cmu.tetrad.regression.RegressionResult: Getter for the field t.
getTarget() - Method in class edu.cmu.tetrad.data.Histogram: Retrieves the name of the target associated with this instance.
getTarget() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The target.
getTargetNode() - Method in class edu.cmu.tetrad.data.Histogram: Retrieves the target node.
getTargets() - Method in class edu.cmu.tetrad.search.PcMb: Return the targets of the most recent search.
getTargetVariable() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Getter for the field targetVariable.
getTerm(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Returns the coefficient.
getTest() - Method in class edu.cmu.tetrad.search.BossCcd: Retrieves the independence test used by the CCD phase.
getTest() - Method in class edu.cmu.tetrad.search.Ccd: Retrieves the current independence test being used.
getTest() - Method in class edu.cmu.tetrad.search.Cdnod
getTest() - Method in class edu.cmu.tetrad.search.CdnodBoss
getTest() - Method in class edu.cmu.tetrad.search.Fas
getTest() - Method in class edu.cmu.tetrad.search.Fci: Retrieves the IndependenceTest instance.
getTest() - Method in class edu.cmu.tetrad.search.Fcit
getTest() - Method in class edu.cmu.tetrad.search.Fcit2
getTest() - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations
getTest() - Method in class edu.cmu.tetrad.search.FcitMagMarkov
getTest() - Method in class edu.cmu.tetrad.search.FcitMarkov
getTest() - Method in class edu.cmu.tetrad.search.FcitSl: Returns the independence test used in this search.
getTest() - Method in class edu.cmu.tetrad.search.FcitSl2: Returns the independence test used in this search.
getTest() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Returns the independence test used in this search.
getTest() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Returns the independence test used in this search.
getTest() - Method in class edu.cmu.tetrad.search.FcitZm
getTest() - Method in class edu.cmu.tetrad.search.FcitZm2
getTest() - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations
getTest() - Method in interface edu.cmu.tetrad.search.IGraphSearch: Gets the test used by the search.
getTest() - Method in class edu.cmu.tetrad.search.mimic.Dm: Returns the independence test in use.
getTest() - Method in class edu.cmu.tetrad.search.Pc
getTest() - Method in class edu.cmu.tetrad.search.PcAR: Returns the independence test this search uses.
getTest() - Method in class edu.cmu.tetrad.search.PcMb: Returns the test used in search.
getTest() - Method in class edu.cmu.tetrad.search.Pcmci: Retrieves the current independence test used by the PCMCI algorithm.
getTest() - Method in class edu.cmu.tetrad.search.Rfci
getTest() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Retrieves the current instance of the IndependenceTest being used.
getTest() - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Retrieves the IndependenceTest object used by the strategy.
getTest() - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Returns the independence test backing this search (delegates to the model).
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.BasisExpandedGcm: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionBlocksIndTest: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.BasisFunctionLrt: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTest: Deprecated.

Creates and returns an instance of the IndTestBlocksWilkes initialized with a specific block specification and a significance level extracted from the provided parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestLemma10: Deprecated.

Creates and returns an instance of an independence test based on the specified block structure and parameters provided.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestTs: Creates and returns an instance of an independence test based on the specified block structure and parameters provided.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.CciTest: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.ChiSquare: Retrieves an instance of the IndependenceTest interface that performs a Chi Square Test for independence.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython: Retrieves an independence test based on the provided data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.ClKciPython: Creates and returns an IndependenceTest instance using the provided DataModel and Parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython: Retrieves an independence test based on the provided data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.ConditionalGaussianLrt: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.Cord: Gets a CORD (Eric's engine) independence test for the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.DegenerateGaussianLrt: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.DgBicTest: Returns an instance of IndependenceTest for the SEM BIC test.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.EquivalenceTest: Builds the equivalence test for the given data and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.FfCi: Creates and configures an instance of the FfCi independence test with specified parameters derived from the provided dataset and parameter set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.FfCiContinuous: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.FisherZ: Gets an independence test based on the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.Gcm: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.Gin: Configures and returns an IndependenceTest object based on the provided data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.GSquare: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.independence.IndependenceWrapper: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.Kci: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.MinimaxCITest: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.MultinomialLogisticRegressionWald: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.Mvplrt: Deprecated.

Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.PoissonBicTest: Returns an instance of IndependenceTest for the SEM BIC test.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.ProbabilisticTest: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestLemma10Singletons: Deprecated.

Gets an independence test based on the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTs: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTsSingletons: Deprecated.

Gets an independence test based on the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestWilkesSingletons: Deprecated.

Gets an independence test based on the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.Rcit: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.SemBicTest: Returns an instance of IndependenceTest for the SEM BIC test.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.StratifiedRank: Gets an independence test based on the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.TrekSeparationBlocksIndependence: Gets an independence test based on the given data model and parameters.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.independence.TRffLrTest: Returns true iff x and y are independent conditional on z for the given data set.
getTest(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Builds one test per pooled data set and combines them into an IndTestMulti, which pools the per-data-set p-values by the method selected with PooledIndependenceWrapper.method(Parameters) (Fisher's method by default, or Tippett's).
getTestData() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the test data being used.
getTestDescriptions() - Static method in enum class edu.cmu.tetrad.search.utils.BpcTestType: Returns the test type descriptions for the BuildPureClusters algorithm.
getTestFailureCount() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the number of implied facts whose independence test threw an exception during the last result generation.
getTestFailureExample() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the message of the first such test failure, as an example for user-facing reporting, or null if no failure has occurred since the last reset.
getTestingData() - Method in class edu.cmu.tetrad.data.Knowledge: Retrieves the testing dataset.
getTestParameters(Algorithm) - Static method in class edu.cmu.tetrad.util.Params: getTestParameters.
getTestsPerPair() - Method in class edu.cmu.tetrad.search.test.AlphaReport: The number of conditioning sets tried per adjacent pair at the stated depth.
getTestType() - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: getTestType.
getTetradLoggerConfig() - Method in class edu.cmu.tetrad.util.TetradLoggerEvent: getTetradLoggerConfig.
getThr() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Getter for the field thr.
getThreshold() - Method in class edu.cmu.tetrad.sem.PredictionPruneReport: The keep threshold in standard errors.
getTier(int) - Method in class edu.cmu.tetrad.data.Knowledge: getTier.
getTier(int) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns a copy of the given tier, in natural sort order.
getTierKnowledge() - Method in class edu.cmu.tetrad.search.mimic.MimicModel: Returns knowledge placing all inputs in tier 0 and all outputs in tier 1.
getTierStructures() - Method in class edu.cmu.tetrad.data.Knowledge: Returns the additional tier structures, in order, as a shallow copy of the list.
getTimeLagGraph() - Method in class edu.cmu.tetrad.graph.Dag: getTimeLagGraph.
getTimeLagGraph() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getTimeLagGraph.
getTimeLagGraph() - Method in interface edu.cmu.tetrad.graph.Graph: getTimeLagGraph.
getTimeLagGraph() - Method in class edu.cmu.tetrad.graph.LagGraph: getTimeLagGraph.
getTimeLagGraph() - Method in class edu.cmu.tetrad.graph.SemGraph: getTimeLagGraph.
getTimeLagGraph() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the TimeLagGraph object.
getTimes() - Method in class edu.cmu.tetrad.search.Boss: Returns the times.
getTimes() - Method in class edu.cmu.tetrad.search.BossTopK: Returns the times.
getTimeSteps() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the time steps that will be stored in the simulation.
getTimeSteps() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: getTimeSteps.
getTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.AdjacencyConfusion: Returns the true negative count.
getTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: True negatives.
getTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.BidirectedConfusion: Returns the number of true negatives.
getTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: True negatives.
getTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.LocalGraphConfusion: Retrieves the value of true negatives (TN) from the LocalGraphConfusion object.
getTn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.OrientationConfusion: Getter for the field tn.
getTnc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: True Negatives for common edges.
getTnc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: True Negatives for common edges.
getTNeighbors() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the set of nodes that are in TNeighbors.
getTo() - Method in class edu.cmu.tetrad.data.KnowledgeEdge: Getter for the field to.
getToken() - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: getToken.
getToken() - Method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: getToken.
getToken() - Method in interface edu.cmu.tetrad.calculator.expression.Expression: getToken.
getToken() - Method in interface edu.cmu.tetrad.calculator.expression.ExpressionDescriptor: getToken.
getToken() - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: getToken.
getTokenAt(int, int) - Method in class edu.cmu.tetrad.util.TextTable: getTokenAt.
getTokenString() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionLexer: getTokenString.
getTolerance() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Getter for the field tolerance.
getToNode() - Method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra.DijkstraEdge: Retrieves to-node represented by this DijkstraEdge.
getTopKParents(int) - Method in class edu.cmu.tetrad.search.BossTopK: Returns the parent map of the i-th top permutation (0 = highest scoring), suitable for building the corresponding graph via PermutationSearchTopK.getGraph(...).
getTopKParents(int) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Returns the parent map of the i-th top permutation (0 = highest scoring), suitable for building the corresponding graph via PermutationSearchTopK.getGraph(...).
getTopKPermutation(int) - Method in class edu.cmu.tetrad.search.BossTopK: Returns the i-th top permutation (0 = highest scoring) as an array of indices into SuborderSearchTopK.getVariables().
getTopKPermutation(int) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Returns the i-th top permutation (0 = highest scoring) as an array of indices into SuborderSearchTopK.getVariables().
getTopKScore(int) - Method in class edu.cmu.tetrad.search.BossTopK: Returns the score of the i-th top permutation (0 = highest scoring).
getTopKScore(int) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Returns the score of the i-th top permutation (0 = highest scoring).
getTotalEffect(Node, Node) - Method in class edu.cmu.tetrad.sem.SemIm: Calculates the total effect between two nodes.
getTotalEffects(Node, Node) - Method in class edu.cmu.tetrad.search.Ida: Calculates the total effects of node x on node y.
getTotalEffects(Node, Node) - Method in class edu.cmu.tetrad.search.PdagPagIda: Calculates the total effects of one variable on another.
getTotalSampleSize() - Method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: Returns the total sample size over all domains.
getTotalSampleSize() - Method in class edu.cmu.tetrad.util.StratifiedRankTests: Returns the total sample size over all strata.
getTotalUsableCases() - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Returns the number of usable cases for the data.
getToVariables() - Method in class edu.cmu.tetrad.data.KnowledgeGroup: getToVariables.
getTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.AdjacencyConfusion: Returns the true positive count.
getTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: True positives.
getTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.BidirectedConfusion: Returns the number of true positives.
getTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: True positives.
getTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.LocalGraphConfusion: Returns the true positives (TP) value of the LocalGraphConfusion object.
getTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.OrientationConfusion: Getter for the field tp.
getTpc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: True positives for common edges.
getTpc() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: True positives for common edges.
getTrekSource(Graph, List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: This method returns the source node of a given trek in a graph.
getTrial() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Getter for the field trial.
getTrials() - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Returns the individual trial results.
getTriple(Node) - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Returns the set of triples for the given node.
getTriplesClassificationTypes() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getTriplesClassificationTypes.
getTriplesClassificationTypes() - Method in interface edu.cmu.tetrad.graph.TripleClassifier: getTriplesClassificationTypes.
getTriplesClassificationTypes() - Method in class edu.cmu.tetrad.graph.Underlines: getTriplesClassificationTypes.
getTriplesLists(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getTriplesLists.
getTriplesLists(Node) - Method in interface edu.cmu.tetrad.graph.TripleClassifier: getTriplesLists.
getTriplesLists(Node) - Method in class edu.cmu.tetrad.graph.Underlines: getTriplesLists.
getTrueDag() - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Getter for the field trueDag.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.AdditiveNoiseSimulation: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.BayesNetSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.DesignedExperimentSimulation: Returns the true (pre-selection) graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralAdditiveModel: Retrieves the true graph specified by the given index from the list of graphs.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralNoiseSimulation: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulation: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GeneralSemSimulationSpecial1: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Returns the true graph at the specified index: a DAG, or a TimeLagGraph with maximum lag 1 in serial mode.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Returns the true graph at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Returns the true graph at the given index.
getTrueGraph(int) - Method in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: Gets the true graph for the simulation at the specified index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Returns the true graph at the given index.
getTrueGraph(int) - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Returns the true graph at the given index.
getTrueInputs() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Getter for the field trueInputs.
getTrueLatentCount() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Retrieves the true latent count value.
getTrueModel() - Method in class edu.cmu.tetrad.search.mimic.MimicTrialResult: Returns the true model.
getTruePag() - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Getter for the field truePag.
getTruePositives() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns the number of true positives.
getTrueTotalEffect(OrderedPair) - Method in class edu.cmu.tetrad.search.IdaCheck: Calculates the true total effect between two nodes in the graph.
getTruncationLimit() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the truncation limit of the basis expansion.
getTruncLL() - Method in class edu.cmu.tetrad.sem.SemIm: The negative of the log likelihood function for the getModel model, with the constant chopped off.
getTValue(Parameter, int) - Method in interface edu.cmu.tetrad.sem.ISemIm: getTValue.
getTValue(Parameter, int) - Method in class edu.cmu.tetrad.sem.SemIm: getTValue.
getTwoCycleErrors(Graph, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns the TwoCycleErrors object that represents errors for direct feedback loops.
getTwoCycleFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: False negatives for two-cycles.
getTwoCycleFn() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getTwoCycleFn.
getTwoCycleFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: False positives for two-cycles.
getTwoCycleFp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getTwoCycleFp.
getTwoCycleTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: Two positives for two-cycles.
getTwoCycleTp() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: getTwoCycleTp.
getType() - Method in class edu.cmu.tetrad.data.KnowledgeGroup: Getter for the field type.
getType() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move: getType.
getType() - Method in class edu.cmu.tetrad.sem.ParamConstraint: Getter for the field type.
getType() - Method in class edu.cmu.tetrad.sem.Parameter: Getter for the field type.
getUncorrelatedShocks(int) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: getUncorrelatedShocks.
getUnderlinedTriplesFromGraph(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Retrieves the underlined triples from the given graph that involve the specified node.
getUnderLines() - Method in class edu.cmu.tetrad.graph.Dag: Retrieves the set of underlined triples.
getUnderLines() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: getUnderLines.
getUnderLines() - Method in interface edu.cmu.tetrad.graph.Graph: getUnderLines.
getUnderLines() - Method in class edu.cmu.tetrad.graph.LagGraph: getUnderLines.
getUnderLines() - Method in class edu.cmu.tetrad.graph.SemGraph: getUnderLines.
getUnderLines() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns a set of Triple objects representing the underlines.
getUnderLines() - Method in class edu.cmu.tetrad.graph.Underlines: getUnderLines.
getUnderlyingTest() - Method in class edu.cmu.tetrad.search.rlcd.EmCovarianceRankTest: The underlying test over the estimated matrix, for inspection of canonical correlations.
getUniformityP(List) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Calculates the uniformity p-value of a given list of p-values using either the Anderson-Darling test or the Kolmogorov-Smirnov test based on the configured method.
getUnmixedResult(DataSet) - Static method in class edu.cmu.tetrad.search.unmix.CausalUnmixer: Retrieves the unmixing result by processing the provided dataset using default configuration settings.
getUnmixedResult(DataSet, CausalUnmixer.Config) - Static method in class edu.cmu.tetrad.search.unmix.CausalUnmixer: Retrieves the unmixing results from the provided dataset and configuration.
getUnorientedLinks() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the links to be written as unoriented edges.
getUnparsedExpression() - Method in class edu.cmu.tetrad.calculator.expression.Equation: Getter for the field unparsedExpression.
getUnscorableMoves() - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: The number of candidate moves in the last projection whose scoring threw (singular subproblem) and were treated as non-improving.
getUnshieldedColliders() - Method in class edu.cmu.tetrad.search.Pcd: Retrieves the set of unshielded colliders in the graph returned by the method search().
getUnshieldedColliders() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field unshieldedColliders.
getUnshieldedNoncolliders() - Method in class edu.cmu.tetrad.search.Pcd: Retrieves the set of unshielded noncolliders in the graph returned by the method search().
getUnshieldedNoncolliders() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Getter for the field unshieldedNoncolliders.
getUnstableBHats() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns the B-Hat matrices of the unstable graphs.
getUnstableBHats() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the list of unstable B matrices generated by the algorithm.
getUnstableGraphs() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Returns the unstable graphs found by the most recent search.
getUnstableGraphs() - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Retrieves the list of unstable graphs generated by the algorithm.
getUntested() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport: Descriptions of edges that were kept because they were never testable on this data.
getUpdatedBayesIm() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: getUpdatedBayesIm.
getUpdatedBayesIm() - Method in class edu.cmu.tetrad.bayes.Identifiability: The updated BayesIm.
getUpdatedBayesIm() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Retrieves the updated Bayesian network representation associated with this updater.
getUpdatedBayesIm() - Method in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Returns the updated Bayes IM.
getUpdatedBayesIm() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: The updated BayesIm.
getUpdatedBayesIm() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Retrieves the updated Bayesian network representation associated with this updater.
getUpdatedSemIm() - Method in class edu.cmu.tetrad.sem.SemUpdater: See http://en.wikipedia.org/wiki/Multivariate_normal_distribution.
getUpdateFunction() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Returns the update function.
getUpperBoundDouble() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field upperBoundDouble.
getUpperBoundInt() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field upperBoundInt.
getUpperBoundLong() - Method in class edu.cmu.tetrad.util.ParamDescription: Getter for the field upperBoundLong.
getUpperBounds() - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: The bounds in force, after NaN has been read as "no bound".
getV() - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Retrieves the middle node that forms a collider in the discriminating path.
getValidOrder(List, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Returns a valid causal order for either a DAG or a CPDAG.
getValidOrderMag(List, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Generates a valid topological ordering of nodes in a directed graph without cycles.
getValue() - Method in class edu.cmu.tetrad.sem.Mapping: getValue.
getValue() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Getter for the field value.
getValue() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbComponent: Returns the parents of this component.
getValue(int) - Method in class edu.cmu.tetrad.sem.SemProposition: Retrieves the value at the specified index.
getValue(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Returns the boolean value in a given row of the table.
getValue(int) - Method in class edu.cmu.tetrad.util.Point: getValue.
getValue(int[]) - Method in interface edu.cmu.tetrad.data.CellTable: Returns the value of the cell specified by the given coordinates.
getValue(int[]) - Method in class edu.cmu.tetrad.data.CellTableAdTree: Returns the value of the cell specified by the given coordinates.
getValue(int[]) - Method in class edu.cmu.tetrad.data.CellTableCountSample: Returns the value of the cell specified by the given coordinates.
getValue(int[]) - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Returns the value of the cell at the given coordinates.
getValue(int, double[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Returns the value of the function.
getValue(int, double[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Returns the value of the function.
getValue(int, double[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Returns the value of the function.
getValue(int, double[][]) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.UpdateFunction: Returns the value of the glass function for a given factor.
getValue(int, int) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Retrieves the value from the covariance matrix at the specified row and column indices.
getValue(int, int) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Retrieves the value from the covariance matrix at the specified row and column indices.
getValue(int, int) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Retrieves the value from the covariance matrix at the specified row and column indices.
getValue(int, int) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the value from the covariance matrix at the specified row and column indices.
getValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Returns element (r,c)
getValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Returns element (r,c)
getValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Returns the value stored at element (r,c)
getValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Returns the value stored at element (r,c)
getValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrix: Returns element (r,c)
getValue(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrixF: Returns element (r,c)
getValue(int, int) - Method in class edu.cmu.tetrad.util.IndexedMatrix: getValue.
getValue(int, int, int[]) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getValue.
getValue(Graph, Graph) - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns the value of this statistic, given the true graph and the estimated graph.
getValue(Graph, Graph, DataModel) - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns the value of this statistic, given the true graph and the estimated graph.
getValue(Graph, Graph, DataModel, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns the value of this statistic, given the true graph and the estimated graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFn: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFp: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyFpr: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTn: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTp: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AdjacencyTpr: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorF1: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestorRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AncestralRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFn: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFp: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadFpr: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadPrecisionCommonEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadRecallCommonEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTn: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ArrowheadTp: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiff: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedFP: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentExistsLatentCommonCause: Calculates the percentage of correctly identified bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedLatentPrecision: Calculates the percentage of correctly identified bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTP: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CirclePrecision: Calculates the circle precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CircleRecall: Calculates the circle recall value for a given true graph, estimated graph, and data model.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalseNegativeBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorFalsePositiveBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonAncestorTruePositiveBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CommonMeasuredAncestorRecallBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.CorrectSkeleton: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.DefiniteDirectedPathRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ElapsedCpuTime: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Adj: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1All: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Arrow: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Circle: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.F1Tail: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.FalseNegativesAdjacencies: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.FalsePositiveAdjacencies: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.GraphExactlyRight: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaAverageSquaredDistance: Calculates the value of the IDA Average Squared Distance statistic.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMaxSquaredDiffEstTrue: Calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMinSquaredDiffEstTrue: Calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgSquaredDifference: Retrieves the value of the statistic, which is the average squared difference between the estimated and true values for a given data model and graphs.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMaximumSquaredDifference: Calculates the value of the statistic "IDA Average Maximum Squared Difference".
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.IdaMinimumSquaredDifference: Calculates the value of the statistic "IDA Average Minimum Squared Difference".
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst2: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliedOrientationRatioEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliesLegalMag: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalseNegativeBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalsePositiveBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorRecallBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorTruePositiveBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalCpdag: Calculates the value indicating whether the estimated graph is a Legal CPDAG (1.0) or not (0.0).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalPag: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphPrecision: This method calculates the Local Graph Precision.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.LocalGraphRecall
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MagCgScore: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MagDgScore: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MagSemScore: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPasses: Calculates the Anderson Darling p-value > 0.05.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPassesBestOf10: Calculates the Anderson Darling p-value > 0.05.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingP: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingPBestOf10: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialP: Calculates the Binomial P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialPBestOf10: Calculates the Binomial P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFdrCutoffP: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH0: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH1: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffP: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffPBestOf10: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPasses: Calculates whether Kolmogorov-Smirnoff P > 0.05.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPassesBestOf10: Calculates whether Kolmogorov-Smirnoff P > 0.05.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrAdj: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrArrow: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.Maximal: Checks whether a PAG is maximal.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.MaximalityCondition: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH0: Calculates the number of tests done under the null hypothesis of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH1: Calculates the number of tests done under the null hypothesis of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoAlmostCyclicPathsCondition: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoCyclicPathsCondition: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NonancestorRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoPotentiallyDirectedF1: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumAmbiguousTriples: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsNotInUnshieldedCollidersEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberCollidersEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInCollidersEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInUnshieldedCollidersEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberTailsEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberUnshieldedCollidersEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedBothNonancestorAncestor: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredDD: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredNL: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPD: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPL: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCommonMeasuredAncestorBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDefiniteDirectedEdgeAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeConfounded: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeNonAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeNonAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeNonAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleVisibleNonancestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectBidirected: Returns the number of bidirected edges for which a latent confounder exists.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectDDAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectPDAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCorrectVisibleEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumCoveringAdjacenciesInPag: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyDirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyNotDirectedPaths: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeBnaMeasuredCounfounded: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNoMeasureAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNotAncNotRev: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeReversed: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedShouldBePartiallyDirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumEdgeInEstInTrue: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumGenuineAdjacenciesInPag: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectDDAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectPDAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectVisibleAncestors: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumLatentCommonAncestorBidirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumNondirectedEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumParametersEst: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPartiallyOrientedEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPotentiallyDirected: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumUndirectedEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdgeTrue: Retrieves the number of X-->Y edges for which X-->Y is visible in the true PAG.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationPrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.OrientationRecall: Calculates the Orientation Recall statistic, which measures the accuracy of the estimated orientation of edges in a graph compared to the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyPrecision: Calculates the adjacency precision of the estimated graph compared to the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyRecall: Calculates the adjacency recall compared to the true PAG (Partial Ancestral Graph).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ParameterColumn: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentAmbiguous: Calculates the percentage of ambiguous triples in the estimated graph compared to the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentBidirectedEdges: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedEst: Calculates the proportion of semi(X, Y) in the estimated graph for which there is no semi(Y, X) in the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedTrue: Calculates the proportion of semi(X, Y) paths in the true graph for which there is no semi(Y, Z) path in the estimated graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueDistanceToAlpha: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueUniformityUnderNull: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPathF1: Calculates the F1 statistic for adjacencies.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPrecision: Calculates the potentially directed precision value.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.SemidirectedRecall: Calculates the Semidirected-Rec statistic, which is the proportion of (X, Y) where if there is a potentially directed path in the true graph, then there is also a potentially directed path in the estimated graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.StructuralHammingDistance: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TailPrecision: Calculates the tail precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TailRecall: Calculates the tail recall value for a given true graph, estimated graph, and data model.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesArrows: Computes the number of False Negatives for Arrows in the estimated graph compared to the true DAG.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesTails: Calculates the number of false negatives for tail endpoints compared to the true Directed Acyclic Graph (DAG).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveArrow: Calculates the number of false positive arrows in the estimated graph compared to the true directed acyclic graph (DAG).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveTails: Calculates the number of false positives for tail endpoints in the estimated graph compared to the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionArrow: Calculates the precision of arrow edges (X*->Y) in the estimated graph compared to the true DAG.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionTails: Calculates the precision of directed edges in the estimated graph (estGraph) relative to a true directed acyclic graph (trueDag).
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallArrows: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallTails: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveArrow: Computes the count of bidirected true positives for arrows in the estimated graph compared to the true graph.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveDirectedPathNonancestor: Computes the number of true positives for directed paths in the estimated graph that are consistent with the true graph, excluding cases where the estimated graph incorrectly identifies the reverse relationship.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveTails: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalseNegative: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalsePositive: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCyclePrecision: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleRecall: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Graph, DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.statistic.TwoCycleTruePositive: Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
getValue(Graph, Graph, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns the value of this statistic, given the true graph and the estimated graph.
getValue(Node) - Method in class edu.cmu.tetrad.sem.SemProposition: Retrieves the value associated with the given node.
getValue(String) - Method in interface edu.cmu.tetrad.calculator.expression.Context: getValue.
getValue(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemEstimator.MyContext: Get the value of a variable or parameter.
getValue(String) - Method in class edu.cmu.tetrad.util.TetradProperties: getValue.
getValues() - Method in class edu.cmu.tetrad.data.audit.AuditFinding: Returns the named numeric values supporting this finding, unmodifiable, in insertion order.
getValues(String) - Method in class edu.cmu.tetrad.util.Parameters: Returns the values set for the given parameter.
getVariable() - Method in class edu.cmu.tetrad.calculator.expression.Equation: Getter for the field variable.
getVariable() - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: Getter for the field variable.
getVariable() - Method in class edu.cmu.tetrad.data.Discretizer.Discretization: Retrieves the variable associated with the object.
getVariable(int) - Method in class edu.cmu.tetrad.data.BoxDataSet: getVariable.
getVariable(int) - Method in interface edu.cmu.tetrad.data.DataSet: getVariable.
getVariable(int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getVariable.
getVariable(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Returns the index'th variable.
getVariable(Node) - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the variable for the given node.
getVariable(Node) - Method in class edu.cmu.tetrad.search.IndTestIod: Returns the variable associated with the given node in the graph.
getVariable(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBgeScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BasisFunctionBicScoreTabular: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BdeuScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BfBlocksBicScore: Retrieves a variable by its name from the associated data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BgeScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Retrieves a variable by its name from the associated data set.
getVariable(String) - Method in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.ConditionalGaussianBicScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBgeScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.DegenerateGaussianBicScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.DiscreteBicScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.EbicScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.FfMl: d Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.FfMlContinuous: d Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.GicScores: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.HeavyTailSemBicScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.HuangMarginalLikelihoodScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Retrieves a variable node by its name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Retrieves a variable from the model by its name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.KcvBicScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.KernelMarginalLikelihoodScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.MagDgBicScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.MSepScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.MVPBicScore: Deprecated.

Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.PoissonPriorScore: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.RankBicScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.RffBicScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in interface edu.cmu.tetrad.algcomparison.score.ScoreWrapper: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.SemBicScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.TRffBicScore: Retrieves a variable from the dataset based on the given name.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.score.ZhangShenBoundScore: Retrieves the variable with the given name from the data set.
getVariable(String) - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.bayes.BayesProperties: Retrieves a variable from the list of variables that matches the specified target name.
getVariable(String) - Method in class edu.cmu.tetrad.bayes.Evidence: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.data.BoxDataSet: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.
getVariable(String) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.
getVariable(String) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.
getVariable(String) - Method in interface edu.cmu.tetrad.data.DataModel: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.data.DataModelList: getVariable.
getVariable(String) - Method in interface edu.cmu.tetrad.data.DataSet: getVariable.
getVariable(String) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.
getVariable(String) - Method in class edu.cmu.tetrad.data.IndependenceFacts: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.data.TimeSeriesData: getVariable.
getVariable(String) - Method in class edu.cmu.tetrad.search.IndTestIod: Retrieves a variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Retrieves a Node from the list of variables that matches the specified target name.
getVariable(String) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves a Node variable by its name from the list of nodes.
getVariable(String) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves a node corresponding to the given variable name.
getVariable(String) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the variable node with the specified name.
getVariable(String) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Searches the list of variables for a node with the specified name and returns it.
getVariable(String) - Method in interface edu.cmu.tetrad.search.is.IsScore: Resolve a variable by name.
getVariable(String) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore
getVariable(String) - Method in interface edu.cmu.tetrad.search.score.Score: Returns the variable with the given name.
getVariable(String) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns The variable by the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves a variable by its name from the internal mapping of variable names to nodes.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Returns the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Retrieves a variable node based on its name.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Retrieves the Node object that matches the given name from the list of nodes.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Gets the variable with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.MsepTest: Returns the Node object with the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Retrieves the Node object with the specified name.
getVariable(String) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Returns The variable by the given name.
getVariable(String) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Retrieves the node associated with the given variable name.
getVariable(String) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Retrieves the variable with the specified name.
getVariable(String) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Returns The variable by the given name.
getVariableMeans() - Method in class edu.cmu.tetrad.sem.SemIm: Getter for the field variableMeans.
getVariableName(int) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Retrieves the name of the variable at the specified index from the covariance matrix.
getVariableName(int) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Retrieves the name of the variable at the specified index from the covariance matrix.
getVariableName(int) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Retrieves the name of the variable at the specified index from the covariance matrix.
getVariableName(int) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the name of the variable at the specified index from the covariance matrix.
getVariableNames() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the list of variable names.
getVariableNames() - Method in class edu.cmu.tetrad.bayes.BayesPm: Returns the variable names.
getVariableNames() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns the names of the positive-variance variables, in matrix order (see the class Javadoc), unmodifiable.
getVariableNames() - Method in class edu.cmu.tetrad.data.BoxDataSet: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.DataModelList: getVariableNames.
getVariableNames() - Method in interface edu.cmu.tetrad.data.DataSet: getVariableNames.
getVariableNames() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the list of names of the variables in the covariance matrix.
getVariableNames() - Method in class edu.cmu.tetrad.data.IndependenceFacts: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.data.TimeSeriesData: getVariableNames.
getVariableNames() - Method in interface edu.cmu.tetrad.data.VariableSource: Returns the variable names associated with this getVariableNames.
getVariableNames() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the names of variables associated with the nodes in this object.
getVariableNames() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the names of the variables represented in the model.
getVariableNames() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns the list of names for the variables in getNodesInEvidence.
getVariableNode(String) - Method in class edu.cmu.tetrad.sem.SemIm: getVariableNode.
getVariableNodes() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns the list of variable nodes--that is, node that is not error nodes.
getVariableNodes() - Method in interface edu.cmu.tetrad.sem.ISemIm: getVariableNodes.
getVariableNodes() - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Getter for the field variableNodes.
getVariableNodes() - Method in class edu.cmu.tetrad.sem.SemIm: The list of measured and latent nodes for the semPm.
getVariableNodes() - Method in class edu.cmu.tetrad.sem.SemPm: Getter for the field variableNodes.
getVariableNodes() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: getVariableNodes.
getVariableOrder() - Method in class edu.cmu.tetrad.search.GRegression: The variable order that indexes the rows and columns of GRegression.getLambda(): the covariance matrix's variable order, mapped to graph nodes.
getVariableOrder() - Method in class edu.cmu.tetrad.search.GRegressionPag: The variable order indexing GRegressionPag.getSigmaHat(): the covariance matrix's order, mapped to graph nodes.
getVariables() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns the list of variables.
getVariables() - Method in class edu.cmu.tetrad.bayes.BayesImProbs: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.bayes.BayesPm: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.CellTableProbs: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.DataSetProbs: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.IntAveDataSetProbs: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: getVariables.
getVariables() - Method in class edu.cmu.tetrad.bayes.StoredCellProbs: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: getVariables.
getVariables() - Method in class edu.cmu.tetrad.data.audit.AuditFinding: Returns the names of the variables involved in this finding, unmodifiable.
getVariables() - Method in class edu.cmu.tetrad.data.BoxDataSet: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.data.DataModelList: getVariables.
getVariables() - Method in interface edu.cmu.tetrad.data.DataSet: getVariables.
getVariables() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Retrieves the list of Node variables for the covariance matrix.
getVariables() - Method in class edu.cmu.tetrad.data.IndependenceFacts: getVariables.
getVariables() - Method in class edu.cmu.tetrad.data.Knowledge: Get a list of variables.
getVariables() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns the set of variable names appearing in some tier of this structure.
getVariables() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.data.TimeSeriesData: getVariables.
getVariables() - Method in interface edu.cmu.tetrad.data.VariableSource: Returns the list of variables associated with this object.
getVariables() - Method in class edu.cmu.tetrad.search.Boss: Returns the variables.
getVariables() - Method in class edu.cmu.tetrad.search.BossTopK: Returns the variables.
getVariables() - Method in class edu.cmu.tetrad.search.Grasp: Returns the variables.
getVariables() - Method in class edu.cmu.tetrad.search.IndTestIod: Returns the list of TetradNodes over which this independence checker is capable of determining independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Retrieves the list of variables associated with the scoring metric.
getVariables() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the list of variables associated with this independence test.
getVariables() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Retrieves the list of variables represented as nodes in the model.
getVariables() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Retrieves the list of variables represented by nodes in the score computation.
getVariables() - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2
getVariables() - Method in class edu.cmu.tetrad.search.is.IsBicScore: Retrieves the list of variable nodes.
getVariables() - Method in interface edu.cmu.tetrad.search.is.IsScore: Retrieves the list of variables (nodes) associated with the current implementation.
getVariables() - Method in class edu.cmu.tetrad.search.PermutationSearch: Retrieves the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Retrieves the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Retrieves the list of nodes representing the variables in the basis function score.
getVariables() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Retrieves the list of nodes representing the variables in the basis function score.
getVariables() - Method in class edu.cmu.tetrad.search.score.BdeScore: Returns the variables present in the DataSet associated with this method.
getVariables() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Retrieves the list of variables used in the object.
getVariables() - Method in class edu.cmu.tetrad.search.score.BgeScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Retrieves the list of variables associated with this object.
getVariables() - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Retrieves the list of variables associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Returns the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.EbicScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Retrieves the list of variables in the current instance.
getVariables() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Retrieves the list of variables in the current instance.
getVariables() - Method in class edu.cmu.tetrad.search.score.FfMl: Retrieves a list of variable nodes.
getVariables() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Retrieves the list of variables in the current instance.
getVariables() - Method in class edu.cmu.tetrad.search.score.GicScores: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.GraphScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Retrieves the list of variable nodes associated with the current score calculation.
getVariables() - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Retrieves the list of variables used in the scoring process.
getVariables() - Method in class edu.cmu.tetrad.search.score.ImagesScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.IndTestScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Retrieves the list of variables associated with the current instance score.
getVariables() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves the list of variables used in the computation or associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
getVariables() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the list of variables associated with this score object.
getVariables() - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Retrieves a list of variables as Node objects.
getVariables() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: The variables of the covariance matrix all scoring is done against.
getVariables() - Method in class edu.cmu.tetrad.search.score.MvpScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Retrieves the list of variable nodes.
getVariables() - Method in interface edu.cmu.tetrad.search.score.Score: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the variables of the covariance matrix.
getVariables() - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Retrieves a list of nodes representing the variables used in the context of this scoring method.
getVariables() - Method in class edu.cmu.tetrad.search.score.ZsbScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.Sp: Returns the list of variables associated with this object.
getVariables() - Method in interface edu.cmu.tetrad.search.SuborderSearch: The list of all variables, in order.
getVariables() - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: The list of all variables, in order.
getVariables() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Retrieves a list of variables associated with the current object.
getVariables() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Retrieves the list of variables from the underlying independence test.
getVariables() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the variables of the test.
getVariables() - Method in class edu.cmu.tetrad.search.test.FfCi: Retrieves a list of variable nodes.
getVariables() - Method in class edu.cmu.tetrad.search.test.FfCi1: Retrieves a list of variable nodes.
getVariables() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Retrieves the list of variables (nodes) associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.Gcm
getVariables() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Retrieves the list of variables associated with this independence test.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Returns the variables of this test (the caller's Node instances).
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Retrieves the list of nodes (variables) associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Retrieves the list of nodes (variables) associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Retrieves the list of nodes (variables) associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns the list of variables over which this independence checker is capable of determining independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Returns the list of variables over which this independence checker is capable of determining independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns the list of variables over which this independence checker is capable of determining independence relations.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Retrieves the list of variables associated with this test.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Retrieves the list of variables associated with this test.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the list of variables involved in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Retrieves the list of nodes (variables) associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns the variables over which this test is defined.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Retrieves a list of variables involved in the independence tests.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Returns the list of variables used in this method.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Returns the list of variables over which this independence checker is capable of determinine independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestGin: Returns the variables involved in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Return the list of variables over which this independence checker is capable of determining independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Returns the list of variables over which this independence checker is capable of determinine independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Returns the list of variables for the facts.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestMulti
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Returns the list of searchVariables over which this independence checker is capable of determinining independence relations.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns the list of variables used in this object.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Retrieves the list of variables that are being used in the current context.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Returns the list of variables associated with this object.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns the variables of the test: the common variables of the strata, with the domain column removed.
getVariables() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Returns the list of variables over which this independence checker is capable of determinine independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves the list of variables associated with the current instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Retrieves the list of variables used in the statistical independence test.
getVariables() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Retrieves the list of variables associated with this object.
getVariables() - Method in class edu.cmu.tetrad.search.test.MsepTest: Return the list of TetradNodes over which this independence checker is capable of determinine independence relations-- that is, all the variables in the given graph or the given data set.
getVariables() - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Retrieves the list of variables involved in the analysis.
getVariables() - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Retrieves the list of variables involved in the analysis.
getVariables() - Method in class edu.cmu.tetrad.search.test.Rcit: Retrieves the list of variables represented as Node objects.
getVariables() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns the list of variables over which this independence checker is capable of determinining independence relations.
getVariables() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Retrieves the list of variable nodes associated with this instance.
getVariables() - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Retrieves the list of variables involved in the test.
getVariables() - Method in class edu.cmu.tetrad.search.utils.Bes: Returns the variables being searched over.
getVariables() - Method in class edu.cmu.tetrad.search.utils.BesPermutation: Returns the variables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Retrieves the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.DeltaSextadTest: Returns the variables of the data being used.
getVariables() - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest: Returns the list of variables in the dataset.
getVariables() - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest2: Returns the variables of the data being used.
getVariables() - Method in class edu.cmu.tetrad.search.utils.DeltaTetradTest3: Returns the list of variables in the dataset.
getVariables() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: getVariables.
getVariables() - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Retrieves the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Retrieves the variables used in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Retrieves the variables used in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Retrieves the variables used in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Retrieves the variables used in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Retrieves the list of variables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: Retrieves the list of variables.
getVariables() - Method in interface edu.cmu.tetrad.search.utils.TetradTest: getVariables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: getVariables.
getVariables() - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: getVariables.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Retrieves the list of variables used in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Getter for the field variables.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Retrieves the list of variables used in the original data set.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: getVariables.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Retrieves the list of variables used in the independence test.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: getVariables.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Retrieves the list of variables associated with this independence test.
getVariables() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: The variables of the score.
getVariables() - Method in class edu.cmu.tetrad.sem.DagScorer: Getter for the field variables.
getVariables() - Method in interface edu.cmu.tetrad.sem.Scorer: getVariables.
getVariables() - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
getVariables() - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: getVariables.
getVariables(List, List, List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns the variables of the independence test.
getVariablesInEvidence() - Method in class edu.cmu.tetrad.bayes.Evidence: getVariablesInEvidence.
getVariablesNotInTiers() - Method in class edu.cmu.tetrad.data.Knowledge: getVariablesNotInTiers.
getVariableSource() - Method in class edu.cmu.tetrad.bayes.Evidence: getVariableSource.
getVariableSource() - Method in class edu.cmu.tetrad.bayes.Proposition: Getter for the field variableSource.
getVariablesTemplate() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Retrieves the variable template.
getVariableValues(int) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: getVariableValues.
getVariableVectors() - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: getVariableVectors.
getVariableVectors() - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: getVariableVectors.
getVariance(int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Retrieves the variance value for a continuous child node.
getVariance(Node, Matrix) - Method in interface edu.cmu.tetrad.sem.ISemIm: getVariance.
getVariance(Node, Matrix) - Method in class edu.cmu.tetrad.sem.SemIm: Returns the variance for a given node.
getVarianceParameter(Node) - Method in class edu.cmu.tetrad.sem.SemPm: Return the parameter for the variance of the error term for the given node, which is the variance of the node if the node is an error term, and the variance of the node's error term if not.
getVariances() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: getVariances.
getVarNames() - Method in interface edu.cmu.tetrad.search.utils.TetradTest: getVarNames.
getVarNames() - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: getVarNames.
getVarNames() - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: getVarNames.
getVarNames() - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: getVarNames.
getVarNode(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: getVarNode.
getVars() - Method in class edu.cmu.tetrad.search.rlcd.Cover: Returns the variable names of this cover.
getVarsPerInd() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Getter for the field varsPerInd.
getVarsPerInd() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraphParams: Returns the number of variables per individual.
getVector() - Method in class edu.cmu.tetrad.util.Point: Getter for the field vector.
getVicinity(Edge, int) - Method in class edu.cmu.tetrad.simulation.Vicinity: getVicinity.
getVs() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups.RankDefSet: Returns the covers found.
getW() - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Retrieves the node following the starting node in the discriminating path.
getWeight() - Method in class edu.cmu.tetrad.search.FciOrientDijkstra.DijkstraEdge: Returns the weight of the edge.
getWeight() - Method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra.DijkstraEdge: Retrieves the weight of the edge represented by this DijkstraEdge.
getWeight(Statistic) - Method in class edu.cmu.tetrad.algcomparison.statistic.Statistics: The utility weight for the statistic.
getWeights() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: getWeights.
getWrappedScore() - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the wrapped score.
getWrappedScore() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns the score object that this test wraps.
getWrappedTest() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the wrapped test.
getWThreshold() - Method in class edu.cmu.tetrad.search.Dagma: Retrieves the value of the wThreshold field.
getWwi() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso.Result: Getter for the field wwi.
getX() - Method in class edu.cmu.tetrad.graph.IndependenceFact: Getter for the field x.
getX() - Method in class edu.cmu.tetrad.graph.Triple: Getter for the field x.
getX() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the observed covers of this partition.
getX() - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Retrieves the starting node of the discriminating path.
getX() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Returns the set of nodes X.
getX() - Method in class edu.cmu.tetrad.util.PointXy: Getter for the field x.
getXDict() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the observed-name dictionary.
getxMeans() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: THe array of means.
getXNames() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Returns the observed variable names.
getXSquare() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Returns the chi square value, or NaN if the chi square value cannot be determined.
getxStdDevs() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: The array of standard devs.
gety() - Method in class edu.cmu.tetrad.search.FciOrientDijkstra.DijkstraEdge: Returns the node.
getY() - Method in class edu.cmu.tetrad.graph.IndependenceFact: Getter for the field y.
getY() - Method in class edu.cmu.tetrad.graph.Triple: Getter for the field y.
getY() - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Retrieves the ending node of the discriminating path.
getY() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Returns the set of nodes Y.
getY() - Method in class edu.cmu.tetrad.util.PointXy: Getter for the field y.
getZ() - Method in class edu.cmu.tetrad.graph.IndependenceFact: getZ.
getZ() - Method in class edu.cmu.tetrad.graph.Triple: Getter for the field z.
getZ() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Returns the set of nodes Z.
getZForAlpha(double) - Static method in class edu.cmu.tetrad.util.StatUtils: getZForAlpha.
Gfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: The Fges-FCI class represents the Greedy Fast Causal Inference algorithm, adjusted as in *-FCI.
Gfci - Class in edu.cmu.tetrad.search: Uses FGES for the initial step of *-FCI.
Gfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: The FGES-FCI class represents the Greedy Fast Causal Inference algorithm.
Gfci(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Constructs a new instance of FGES-FCI with the given IndependenceWrapper and ScoreWrapper.
Gfci(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.Gfci: Constructs a new GFci algorithm with the given independence test and score.
GfciOld - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: The GFCI class represents the Greedy Fast Causal Inference algorithm, adjusted as in *-FCI.
GfciOld - Class in edu.cmu.tetrad.search: Implements the GFCI (Greedy Fast Causal Inference) algorithm as described in:
GfciOld() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: The GFCI class represents the Greedy Fast Causal Inference algorithm.
GfciOld(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Constructs a new instance of GFCI with the given IndependenceWrapper and ScoreWrapper.
GfciOld(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.GfciOld: Constructs a new GFci algorithm with the given independence test and score.
gffc(DataSet, double, int, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: Constructs a GffcBlockDiscoverer instance, which implements the GFFC algorithm for discovering clusters or "blocks" of indices in a dataset.
gffc(DataSet, CovarianceMatrix, double, int, int, SingletonClusterPolicy, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: GFFC over a pooled covariance matrix; see GffcBlockDiscoverer.
Gffc - Class in edu.cmu.tetrad.search: Generalized Find Factor Clusters (GFFC).
Gffc(CorrelationMatrix, double, int, int) - Constructor for class edu.cmu.tetrad.search.Gffc: Constructs an instance of the Gffc class from a correlation matrix, for use when the correlations do not come from a single raw data set--for example, the pooled correlations of several imputations of a data set with missing values.
Gffc(DataSet, double, int, int) - Constructor for class edu.cmu.tetrad.search.Gffc: Constructs a new Gffc instance with the specified data set, alpha value, maximum rank (rMax), and effective sample size (ess).
GffcBlockDiscoverer - Class in edu.cmu.tetrad.search.blocks: A concrete implementation of the BlockDiscoverer interface that discovers clusters or "blocks" of indices using the FOFC algorithm.
GffcBlockDiscoverer(DataSet, double, int, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.GffcBlockDiscoverer: Constructs a new instance of FofcBlockDiscoverer, which is used to discover clusters or "blocks" of indices in a dataset based on the FOFC algorithm.
GffcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, int, SingletonClusterPolicy, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.GffcBlockDiscoverer: Constructs a GffcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
GIC2 - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The GIC2 rule.
GIC5 - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The GIC5 rule.
GIC6 - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The GIC6 rule.
GicScores - Class in edu.cmu.tetrad.algcomparison.score: The GicScores class is an implementation of the ScoreWrapper interface that calculates the Generalized Information Criterion (GIC) scores for a given data model.
GicScores - Class in edu.cmu.tetrad.search.score: Implements scores motivated by the Generalized Information Criterion (GIC) approach as given in Kim et al.
GicScores() - Constructor for class edu.cmu.tetrad.algcomparison.score.GicScores: Constructs a new instance of the algorithm.
GicScores(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.score.GicScores: Constructs the score using a covariance matrix.
GicScores(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.GicScores: Constructs the score using a covariance matrix.
GicScores.RuleType - Enum Class in edu.cmu.tetrad.search.score: Gives the options for the rules to use for calculating the scores.
Gin - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for GIN (Generalized Independent Noise) residual-independence test.
Gin() - Constructor for class edu.cmu.tetrad.algcomparison.independence.Gin: Default constructor for the Gin class.
GIN_BACKEND - Static variable in class edu.cmu.tetrad.util.Params: Constant GIN_BACKEND="ginBackend"
GIN_INDEP_TEST - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

Independence test method passed to causal-learn GIN.
GIN_PERMUTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant GIN_PERMUTATIONS="ginPermutations"
GIN_RIDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant GIN_RIDGE="ginRidge"
giveGoodLatentNames(BlockSpec, Map, BlocksUtil.NamingMode) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Assigns meaningful names to latent variables in the provided BlockSpec object based on the given true clusters and the specified naming mode.
giveGoodLatentNames(BlockSpec, Map, BlocksUtil.NamingMode) - Static method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner: Assigns descriptive and consistent names to the latent blocks in the given specification.
giveGoodLatentNames(BlockSpec, Map, BlocksUtil.NamingMode, LatentNameAssigner.Config) - Static method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner: Assigns meaningful and unique names to latent variables in a given block specification according to the provided naming mode, true clusters, and configuration.
GiveGoodLatentNamesTest - Class in edu.cmu.tetrad.search.blocks: Naming convention tests for giveGoodLatentNames(...).
GiveGoodLatentNamesTest() - Constructor for class edu.cmu.tetrad.search.blocks.GiveGoodLatentNamesTest: Default constructor for the GiveGoodLatentNamesTest class.
Glasso - Class in edu.cmu.tetrad.algcomparison.algorithm.other: GLASSO.
Glasso - Class in edu.cmu.tetrad.search.work_in_progress: A translation from Tibshirani's 2008 Fortran implementation of glasso.
Glasso() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.other.Glasso: Constructs a new instance of the algorithm.
Glasso(Matrix) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Glasso: Constructor for Glasso.
Glasso.Result - Class in edu.cmu.tetrad.search.work_in_progress: Return value of the algorithm.
GLOBAL_MARKOV - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Global Markov property, conditioning on all subsets implied by global Markov.
GLOBAL_QUEUE - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.RepairStrategy: Represents the global queue strategy for repairs.
glsRidge - Variable in class edu.cmu.tetrad.search.CordEngine4: Specifies the ridge regularization parameter used in the generalized least squares (GLS) computation.
glsRidge - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the ridge value applied to Sigma_hat in statistical computations.
Gmas - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: GMAS: a latent-variable search that uses no independence tests.
Gmas - Class in edu.cmu.tetrad.search: GMAS: a latent-variable search that uses NO independence tests at all.
Gmas() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Used for reflection; do not delete.
Gmas(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Constructs a GMAS algorithm with the given score.
Gmas(Score) - Constructor for class edu.cmu.tetrad.search.Gmas: Constructor.
GMAS_ALLOW_ADDITIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant GMAS_ALLOW_ADDITIONS="gmasAllowAdditions"
GMAS_LOOKAHEAD_DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant GMAS_LOOKAHEAD_DEPTH="gmasLookaheadDepth"
GMAS_TRIANGLE_ESCAPE_ONLY - Static variable in class edu.cmu.tetrad.util.Params: Constant GMAS_TRIANGLE_ESCAPE_ONLY="gmasTriangleEscapeOnly"
gmmModel - Variable in class edu.cmu.tetrad.search.unmix.UnmixResult: The Gaussian Mixture Model (GMM) representation obtained from the Expectation-Maximization (EM) algorithm.
goToBookmark() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Retries the bookmark with key = Integer.MIN_VALUE and removes the bookmark.
goToBookmark(int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Retrieves the bookmarked state for index 'key' and removes that bookmark.
gpLogMarginalLikelihood(double[], DMatrixRMaj, double) - Static method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Computes the single-output GP log marginal likelihood -0.5*yᵀ C⁻¹ y - 0.5*log|C| using Cholesky with jitter escalation.
gpLogMarginalLikelihoodMulti(double[][], DMatrixRMaj, double) - Static method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Multi-output GP log marginal likelihood for a shared covariance C.
GpSemSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: NL SEM simulation.
GpSemSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.GpSemSimulation: Constructor for NLSemSimulation.
grabLayout(List) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: grabLayout.
graph - Variable in class edu.cmu.tetrad.search.TwoStep.Result: Deprecated.

A directed graph representing the relationships between variables as inferred from the coefficient matrix after pruning.
graph() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the graph record component.
graph() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the graph record component.
Graph - Enum constant in enum class edu.cmu.tetrad.data.DataType: Graph.
Graph - Interface in edu.cmu.tetrad.graph: Implements a graph capable of storing edges of type N1 *-# N2 where * and # are endpoints of type Endpoint.
Graph(Graph, boolean) - Constructor for class edu.cmu.tetrad.search.FciOrientDijkstra.Graph: Represents a graph used in Dijkstra's algorithm.
Graph(Graph, R5R9Dijkstra.Rule) - Constructor for class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Graph: Represents a graph for Dijkstra's algorithm.
graphAttributesToText(Graph, String) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the attributes of a given graph into a text format.
GraphChange - Class in edu.cmu.tetrad.search.work_in_progress: Provides s a data structure created mainly for use in the ION search algorithm.
GraphChange() - Constructor for class edu.cmu.tetrad.search.work_in_progress.GraphChange: Default constructor, holds no changes.
GraphChange(GraphChange) - Constructor for class edu.cmu.tetrad.search.work_in_progress.GraphChange: Copy constructor.
graphComparison(Graph, Graph, PrintStream) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: graphComparison.
GraphComparison(int, int, int, int, int, int, double, double, double, double, int, List, List, int[][]) - Constructor for class edu.cmu.tetrad.graph.GraphUtils.GraphComparison: Constructs a new GraphComparison.
GraphDiff(double, double, int) - Constructor for class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.GraphDiff: Constructs a GraphDiff instance representing the difference between two graphs based on adjacency F1 score, arrow F1 score, and Structural Hamming Distance (SHD).
graphEdgesToText(Graph, String) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the edges of a graph to text representation.
GraphExactlyRight - Class in edu.cmu.tetrad.algcomparison.statistic: Return a 1 if the graph is exactly right, 0 otherwise.
GraphExactlyRight() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.GraphExactlyRight: Constructor for GraphExactlyRight.
GraphFactoryUtil - Class in edu.cmu.tetrad.graph: Utility class providing factory methods for creating instances of different types of Graph objects.
GraphInitializer - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.history: Instantiations of this interface know how to randomize lag graphs in particular ways.
GraphInPag - Class in edu.cmu.tetrad.search.utils: Contains methods which can be used to determine whether a directed graph is in the equivalence class determined by the given PAG.
graphInPagStep0(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphInPag: This method implements step (1) of the definition.
graphInPagStep1(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphInPag: graphInPagStep1.
graphInPagStep2(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphInPag: graphInPagStep2.
graphInPagStep3(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphInPag: graphInPagStep3.
graphInPagStep4(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphInPag: graphInPagStep4.
graphInPagStep5(Graph, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphInPag: graphInPagStep5.
GraphNode - Class in edu.cmu.tetrad.graph: Implements a basic node in a graph--that is, a node that is not itself a variable.
GraphNode(GraphNode) - Constructor for class edu.cmu.tetrad.graph.GraphNode: Copy constructor.
GraphNode(String) - Constructor for class edu.cmu.tetrad.graph.GraphNode: Constructs a new Tetrad node with the given (non-null) string.
graphNodeAttributesToText(Graph, String, char) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the attributes of nodes in a graph to text format.
graphNodesToText(Graph, String, char) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the nodes of a graph to a formatted text representation.
graphoid() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks whether teh IM is a semigraphoid.
GRAPHOID - Enum constant in enum class edu.cmu.tetrad.search.harness.GraphoidClosureHarness.ClosureType: Graphoid closure.
GraphoidAxioms - Class in edu.cmu.tetrad.search.utils: Checks the graphoid axioms for a set of Independence Model statements.
GraphoidAxioms(Set, List) - Constructor for class edu.cmu.tetrad.search.utils.GraphoidAxioms: Constructor.
GraphoidAxioms(Set, List, Map) - Constructor for class edu.cmu.tetrad.search.utils.GraphoidAxioms: Constructor.
GraphoidAxioms.GraphoidIndFact - Class in edu.cmu.tetrad.search.utils: Represents a graphoid independence fact--i.e., a fact in a general independence model (IM) X _||_Y | Z.
GraphoidClosureHarness - Class in edu.cmu.tetrad.search.harness: A harness for exploring graphoid closure of CI facts implied by a Markov Checker.
GraphoidClosureHarness() - Constructor for class edu.cmu.tetrad.search.harness.GraphoidClosureHarness: Constructor.
GraphoidClosureHarness.ClosureType - Enum Class in edu.cmu.tetrad.search.harness: The graphoid closure type to use.
GraphoidIndFact(Set, Set, Set) - Constructor for class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Constructor.
GraphRandomizer - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.history: Instantiations of this interface know how to randomize update graphs in particular ways.
graphRMatrixTxt(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: graphRMatrixTxt.
GraphSampling - Class in edu.cmu.tetrad.util: A utility for computing frequency probabilities.
GraphSaveLoadUtils - Class in edu.cmu.tetrad.graph: Methods to load or save graphs.
GraphScore - Class in edu.cmu.tetrad.search.score: Implements a pscudo-"score" that implmenets implements Chickering and Meek's (2002) locally consistent score criterion.
GraphScore(double, double, double, double, int, int, int, int, int, int) - Constructor for record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Creates an instance of a GraphScore record class.
GraphScore(IndependenceFacts) - Constructor for class edu.cmu.tetrad.search.score.GraphScore: Constructs a GraphScore from a list of independence facts.
GraphScore(Graph) - Constructor for class edu.cmu.tetrad.search.score.GraphScore: Constructs a GraphScore from a DAG.
GraphSearchUtils - Class in edu.cmu.tetrad.search.utils: Provides some graph utilities for search algorithm.
GraphSearchUtils.CpcTripleType - Enum Class in edu.cmu.tetrad.search.utils: Gives the options for triple type for a conservative unshielded collider orientation, which may be "collider" or "noncollider" or "ambiguous".
graphToAmatCpdag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Converts a given graph into an adjacency matrix in CPAG format.
graphToAmatPag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Saves a PAG in the "amat.pag" format of PCALG.
graphToDot(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Converts a graph to a Graphviz .dot file
graphToDot(Graph, File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: graphToDot.
graphToJson(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Converts a graph object to a JSON-formatted string.
graphToLagGraph(Graph, int, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: Converts a directed graph into a time-lagged graph representation.
graphToLavaan(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: graphToLavaan.
GraphTools - Class in edu.cmu.tetrad.bayes: A utility class containing graph function from graph theory.
graphToPcalg(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: graphToPcalg.
graphToXml(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: graphToXml.
GraphTransforms - Class in edu.cmu.tetrad.graph: Transformations that transform one graph into another.
GraphTypes - Class in edu.cmu.tetrad.algcomparison.graph: Jun 4, 2019 3:21:47 PM
GraphUtils - Class in edu.cmu.tetrad.graph: Utility class for working with graphs.
GraphUtils.GraphComparison - Class in edu.cmu.tetrad.graph: Represents a comparison between two graphs.
GraphUtils.GraphType - Enum Class in edu.cmu.tetrad.graph: The GraphType enum represents the types of graphs that can be used in the application.
GraphUtils.TwoCycleErrors - Class in edu.cmu.tetrad.graph: Two-cycle errors.
GraphWithPValue(Graph, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.GraphWithPValue: Constructor for GraphWithPValue.
grasp(TeyssierScorer) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: grasp.
Grasp - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: GRaSP (Greedy Relaxations of Sparsest Permutation)
Grasp - Class in edu.cmu.tetrad.search: Implements the GRaSP algorithms, which uses a certain procedure to search in the space of permutations of variables for ones that imply PDAGs that are especially close to the PDAG of the true model.
Grasp() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Constructor for Grasp.
Grasp(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Constructor for Grasp.
Grasp(Score) - Constructor for class edu.cmu.tetrad.search.Grasp: Constructor for a score.
Grasp(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Grasp: Constructor for a test.
Grasp(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.Grasp: Constructor that takes both a test and a score; only one is used-- the parameter setting will decide which.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

A permutation search with the GRASP algorithm.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Start with GRaSP.
GRASP - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Start with GRaSP.
GRASP_ALG - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_ALG="graspAlg"
GRASP_BREAK_AFTER_IMPROVEMENT - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_BREAK_AFTER_IMPROVEMENT="graspBreakAFterImprovement"
GRASP_CHECK_COVERING - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_CHECK_COVERING="graspCheckCovering"
GRASP_DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_DEPTH="graspDepth"
GRASP_FORWARD_TUCK_ONLY - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_FORWARD_TUCK_ONLY="graspForwardTuckOnly"
GRASP_NONSINGULAR_DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_NONSINGULAR_DEPTH="graspNonSingularDepth"
GRASP_ORDERED_ALG - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_ORDERED_ALG="graspOrderedAlg"
GRASP_SINGULAR_DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_SINGULAR_DEPTH="graspSingularDepth"
GRASP_TOLERANCE_DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_TOLERANCE_DEPTH="graspToleranceDepth"
GRASP_USE_RASKUTTI_UHLER - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_USE_RASKUTTI_UHLER="graspUseRaskuttiUhler"
GRASP_USE_SCORE - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_USE_SCORE="graspUseScore"
GRASP_USE_VP_SCORING - Static variable in class edu.cmu.tetrad.util.Params: Constant GRASP_USE_VP_SCORING="graspUseVpScoring"
GraspFci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: GRaSP-FCI, an implentatation of *-FCI using GRaSP.
GraspFci - Class in edu.cmu.tetrad.search: Uses GRaSP in the *-FCI algorithm.
GraspFci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Constructor for GraspFci.
GraspFci(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Constructor for GraspFci.
GraspFci(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.GraspFci: Constructs a new GraspFci object.
GraspTol - Class in edu.cmu.tetrad.search.work_in_progress: Implements the GRASP algorithms, with various execution flags.
GraspTol(Score) - Constructor for class edu.cmu.tetrad.search.work_in_progress.GraspTol: Constructor for GraspTol.
GraspTol(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.GraspTol: Constructor for GraspTol.
GraspTol(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.work_in_progress.GraspTol: Constructor for GraspTol.
GraspTol(List) - Constructor for class edu.cmu.tetrad.search.work_in_progress.GraspTol: Constructor for GraspTol.
GREEDY - Enum constant in enum class edu.cmu.tetrad.search.Ccd.ColliderRule: One minimal-cardinality-first sepset, as returned by the sepset producer, decides.
GREEDY - Enum constant in enum class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased.BlockingType: Greedy blocking.
GRegression - Class in edu.cmu.tetrad.search: Implements the G-regression estimator of Guo and Perković (2022) for total causal effects under linearity and causal sufficiency, given an MPDAG.
GRegression(Graph, ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.GRegression: Constructs a G-regression estimator for the given MPDAG and covariance matrix.
GRegression.BootstrapResult - Record Class in edu.cmu.tetrad.search: The result of a bootstrapped G-regression estimate.
GRegressionPag - Class in edu.cmu.tetrad.search: An estimator of an identified total effect when the causal structure is known only up to a partial ancestral graph (PAG), so that latent confounding is allowed.
GRegressionPag(Graph, ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.GRegressionPag: Constructs the estimator with default RICF settings.
GRegressionPag(Graph, ICovarianceMatrix, double, int) - Constructor for class edu.cmu.tetrad.search.GRegressionPag: Constructs the estimator, fitting the Gaussian ancestral graph model once.
GRegressionPag.BootstrapResult - Record Class in edu.cmu.tetrad.search: The result of a bootstrapped estimate.
grid - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: The grid points used in the density estimation.
grid1 - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The grid points used in the density estimation in the first dimension.
grid2 - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The grid points used in the density estimation in the second dimension.
gridBlocks(double[][], double) - Static method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Bins coordinates into a square grid and returns one block id per row: rows falling in the same grid cell share a block.
GROUP_CONSTANT_VARIABLE - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A variable is constant within every level of the configured serial grouping variable (e.g., a subject-level attribute in a repeated-measures file).
groupInLatentSet(Cover, Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: Whether the cover shares a variable with any cover in the set (Python groupInLatentSet).
GrowShrink - Class in edu.cmu.tetrad.search: Implements the Grow-Shrink algorithm of Margaritis and Thrun, a simple yet correct and useful Markov blanket search.
GrowShrink(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.GrowShrink: Constructs a new search.
GrowShrinkTree - Class in edu.cmu.tetrad.search.utils: GrowShrinkTree class.
GrowShrinkTree(Score, Map, Node) - Constructor for class edu.cmu.tetrad.search.utils.GrowShrinkTree: Constructor for GrowShrinkTree.
GSquare - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Fisher Z test.
GSquare() - Constructor for class edu.cmu.tetrad.algcomparison.independence.GSquare: GSquare class represents a wrapper for the G Square test, which is a statistical test for independence between two variables conditional on a third variable.
GT - Enum constant in enum class edu.cmu.tetrad.sem.ParamConstraintType: Represents a parameter constraint type GT (greater than).
GUARANTEE_ACYCLIC - Static variable in class edu.cmu.tetrad.util.Params: Constant GUARANTEE_ACYCLIC="guaranteeAcyclic"
GUARANTEE_CPDAG - Static variable in class edu.cmu.tetrad.util.Params: Constant GUARANTEE_CPDAG="guaranteeCpdag"
GUARANTEE_IID - Static variable in class edu.cmu.tetrad.util.Params: Constant GUARANTEE_IID="guaranteeIid"
GUARANTEE_PAG - Static variable in class edu.cmu.tetrad.util.Params: Constant GUARANTEE_PAG="guaranteePag"
guaranteePag(Graph, FciOrient, Knowledge, Set, boolean, Set, boolean, int) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Guarantees the correctness of a Partial Ancestral Graph (PAG) by repairing faulty structures such as cycles, violations of maximality, and incorrectly oriented edges, using FCI orientation rules and knowledge constraints.
GuidedWitnessHarness - Class in edu.cmu.tetrad.search.harness: Oracle head-to-head for FCIT-SL candidate generators: the class-walk default versus the guided witness construction, with and without the legacy fallback.

H

handlesUnmeasuredConfounder(Class) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Checks if the algorithm handles unmeasured confounders.
HandleyConvert - Class in edu.cmu.tetrad.study.gene.tetrad.gene.util: The purpose of this little converter is to convert "effect" files into "cause" (BasicLagGraph) files.
hasContinuousBounds() - Method in class edu.cmu.tetrad.data.Histogram: Checks if both the lower and upper bounds for a continuous interval are set.
hasDimensions(double[][], int, int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: hasDimensions.
hasFinding(FindingCode) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns true if any finding has the given code.
hasFinding(FindingCode) - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns true if any finding has the given code.
hasFoldModels(int) - Method in class edu.cmu.tetrad.sem.NNEstimator: Reports whether fold models for k are currently cached.
hashCode() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.annotation.AnnotatedClass
hashCode() - Method in class edu.cmu.tetrad.bayes.Evidence: hashCode.
hashCode() - Method in class edu.cmu.tetrad.bayes.Manipulation: hashCode.
hashCode() - Method in class edu.cmu.tetrad.bayes.Proposition: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.data.ContinuousVariable: hashCode.
hashCode() - Method in class edu.cmu.tetrad.data.DataModelList: hashCode.
hashCode() - Method in class edu.cmu.tetrad.data.DiscreteVariable: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.data.JoinDatasets.Result: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.data.Knowledge: Computes a hashcode.
hashCode() - Method in class edu.cmu.tetrad.data.KnowledgeEdge: hashCode.
hashCode() - Method in class edu.cmu.tetrad.data.KnowledgeGroup: Computes a hashcode.
hashCode() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Computes a hashcode.
hashCode() - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.LongToWide.Result: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Result: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Spec: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.graph.Edge: hashCode.
hashCode() - Method in class edu.cmu.tetrad.graph.EdgeListGraph
hashCode() - Method in class edu.cmu.tetrad.graph.GraphNode: hashCode.
hashCode() - Method in class edu.cmu.tetrad.graph.IndependenceFact
hashCode() - Method in interface edu.cmu.tetrad.graph.Node: Removes a property change listener.
hashCode() - Method in class edu.cmu.tetrad.graph.NodePair: hashCode.
hashCode() - Method in class edu.cmu.tetrad.graph.OrderedPair: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.graph.Triple: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.search.blocks.BlockSpec: Computes the hash code for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.ParseResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Uniformity: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.StudyResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.ConditionResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.StudyResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentSignature: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.PcAR.OrientationClash: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.Mark: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.search.rlcd.Cover
hashCode() - Method in record class edu.cmu.tetrad.search.rlcd.LatentGroups.AdjacencyResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.search.score.ScoredGraph: Return s the hashcode of the score.
hashCode() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.CovAndCoefs: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.NestedFits: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Eval: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Returns the hash code.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.MinimaxBinningConfig: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.search.utils.Sextad: hashCode.
hashCode() - Method in class edu.cmu.tetrad.search.utils.Tetrad: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Computes the hash code for this TetradInt instance.
hashCode() - Method in class edu.cmu.tetrad.search.utils.TetradNode: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: hashCode.
hashCode() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.GraphWithPValue: hashCode.
hashCode() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.sem.ParameterPair: hashCode.
hashCode() - Method in class edu.cmu.tetrad.sem.SemEvidence: hashCode.
hashCode() - Method in record class edu.cmu.tetrad.sem.SemIm.CyclicSimResult: Returns a hash code value for this object.
hashCode() - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Returns a hash code value for this object.
hashCode() - Method in class edu.cmu.tetrad.sem.SemManipulation: hashCode.
hashCode() - Method in class edu.cmu.tetrad.sem.SemProposition: Calculates the hash code for this object.
hashCode() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Probably should recheck this later.
hashCode() - Method in class edu.cmu.tetrad.util.Version: hashCode.
hasMoreTokens() - Method in class edu.cmu.tetrad.data.RegexTokenizer: hasMoreTokens.
hasNext() - Method in class edu.cmu.tetrad.graph.ConsistentDagIterator
hasNext() - Method in class edu.cmu.tetrad.graph.LegEnumerator
hasNext() - Method in class edu.cmu.tetrad.graph.ValidOrderIterator
hasNext() - Method in class edu.cmu.tetrad.search.utils.DagInCpcagIterator: Returns true just in case there is still a DAG remaining in the enumeration of DAGs for this CPDAG.
hasNext() - Method in class edu.cmu.tetrad.search.utils.DagIterator: Returns true just in case there is still a DAG remaining in the enumeration of DAGs for this pattern.
hasNext() - Method in class edu.cmu.tetrad.util.CombinationIterator: hasNext.
hasNoEvidence(int) - Method in class edu.cmu.tetrad.bayes.Evidence: hasNoEvidence.
HasParameters - Interface in edu.cmu.tetrad.algcomparison.utils: Tags a gadget as having parameters
HasParameterValues - Interface in edu.cmu.tetrad.algcomparison.utils: Tags a gadget as having parameters values.
HasPenaltyDiscount - Interface in edu.cmu.tetrad.search.utils: Provides an interface for an algorithm can can get/set a value for penalty disoucnt.
hasRestrictiveProperty(Graph, Set) - Static method in class edu.cmu.tetrad.search.GRegression: Checks the restrictive property (Corollary 4 of Guo and Perković, 2022) for a bucket: every node in the bucket has the same set of external parents.
Hbsms - Interface in edu.cmu.tetrad.search.work_in_progress: Interface for Bff (Heuristic Best Significant Model Search) algorithm.
HbsmsBeam - Class in edu.cmu.tetrad.search.work_in_progress: Heuristic Best Significant Model Search using a beam search.
HbsmsBeam(Graph, CovarianceMatrix, Knowledge) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Constructor for HbsmsBeam.
HbsmsBeam(Graph, DataSet, Knowledge) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: Constructor for HbsmsBeam.
HbsmsBeam.Move - Class in edu.cmu.tetrad.search.work_in_progress: A move.
HbsmsBeam.Move.Type - Enum Class in edu.cmu.tetrad.search.work_in_progress: Types of moves the algorithm can make.
HbsmsBeam.Score - Class in edu.cmu.tetrad.search.work_in_progress: The score.
HbsmsGes - Class in edu.cmu.tetrad.search.work_in_progress: Heuristic Best Significant Model Search using the GES algorithm.
HbsmsGes(Graph, DataSet) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: Constructor for HbsmsGes.
HbsmsGes.GraphWithPValue - Class in edu.cmu.tetrad.search.work_in_progress: A graph with a P value.
HbsmsGes.Score - Class in edu.cmu.tetrad.search.work_in_progress: The score of a model.
header(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a section header with optional key-value pairs.
HeavyTailSemBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for linear, Gaussian SEM BIC score.
HeavyTailSemBicScore - Class in edu.cmu.tetrad.search.score: A BIC-style local score for structural equation models (SEMs) that replaces the standard Gaussian log-likelihood with a heavier-tailed or more robust noise model, making it better suited to data with outliers or non-Gaussian residuals.
HeavyTailSemBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.HeavyTailSemBicScore: Constructs a new instance of the SemBicScore.
HeavyTailSemBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Constructs an instance of the HeavyTailSemBicScore class with the specified data set.
HeavyTailSemBicScore.NoiseModel - Enum Class in edu.cmu.tetrad.search.score: Represents different types of statistical noise models used in data processing, signal processing, or optimization problems.
HeavyTailSemBicScore.NonGaussianBonus - Enum Class in edu.cmu.tetrad.search.score: An enumeration representing the types of non-Gaussian bonuses.
height(Node) - Method in interface edu.cmu.tetrad.graph.LayoutUtil.NodeSize: Returns the height of the rendered box of the given node in pixels.
heldoutPerSampleLoglikGain(double[][], double[][], GaussianMixtureEM.Config) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Computes the difference in per-sample log-likelihoods on a held-out test set between Gaussian Mixture Models (GMMs) trained with K=2 and K=1 clusters.
hermite1(int, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the probabilist's Hermite polynomial of the given index at the specified point.
hermite2(int, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the (physicis's) Hermite polynomial of a given index and value.
hidden - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Hidden units in each node's network (single hidden layer).
hidden - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Represents the number of hidden units or nodes in a layer or model.
hidden - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Specifies the default number of hidden units in a single layer of the model.
hidden - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Configuration parameters for the TrainedDagSimulatorGNM class.
hidden - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Configuration parameters for the TrainedDagSimulatorGNM class.
HIDDEN_DIMENSION - Static variable in class edu.cmu.tetrad.util.Params: Constant HIDDEN_DIMENSION="hiddenDimension"
HIDDEN_DIMENSIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant HIDDEN_DIMENSIONS="hiddenDimensions"
hiddenLayers - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Specifies the layer configuration of a neural network by defining the number of hidden units for each layer.
HIGH_CORRELATION - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A pair of continuous variables is very highly correlated in absolute value.
HIGH_MISSING_RATE - Static variable in class edu.cmu.tetrad.data.missing.MissingDataAudit: The per-variable missing rate at or above which advice() flags a variable as having high missingness.
HIGHER - Enum constant in enum class edu.cmu.tetrad.sem.DagMetricResult.Better: Indicates that a higher value is considered preferable in the context of metric evaluation.
Highest - Enum constant in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Choose an edge iff its prob.
histogram(List, double[]) - Static method in class edu.cmu.tetrad.simulation.GdistanceUtils: Calculates the histogram of a given list of input values based on thresholds.
Histogram - Class in edu.cmu.tetrad.data: Model for a conditional histogram for mixed continuous and discrete variables.
Histogram(DataSet, String, boolean) - Constructor for class edu.cmu.tetrad.data.Histogram: Constructs a Histogram object for the given data set.
holdoutFrac - Variable in class edu.cmu.tetrad.sem.AdequacyParams: The fraction of the dataset to be held out for testing or validation purposes.
holdoutLoss - Variable in class edu.cmu.tetrad.sem.NodeAdequacySummary: The loss value obtained from the holdout set.
How it works - Section in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython
How it works - Section in class edu.cmu.tetrad.algcomparison.independence.ClKciPython
How it works - Section in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython
How the real pattern is applied - Section in class edu.cmu.tetrad.search.harness.ChemMaskHarness
How to read the output - Section in class edu.cmu.tetrad.search.harness.MissingDataScoreDiagnostics
Hsim - Class in edu.cmu.tetrad.simulation: Hsim class.
Hsim(Dag, Set, DataSet) - Constructor for class edu.cmu.tetrad.simulation.Hsim: Constructor for Hsim.
HsimAutoC - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/28/2016.
HsimAutoC(DataSet) - Constructor for class edu.cmu.tetrad.simulation.HsimAutoC: Constructor for HsimAutoC.
HsimAutoC(String, char) - Constructor for class edu.cmu.tetrad.simulation.HsimAutoC: Constructor for HsimAutoC.
HsimAutoRun - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/28/2016.
HsimAutoRun(DataSet) - Constructor for class edu.cmu.tetrad.simulation.HsimAutoRun: Constructor for HsimAutoRun.
HsimAutoRun(String, char) - Constructor for class edu.cmu.tetrad.simulation.HsimAutoRun: Constructor for HsimAutoRun.
HsimCompareRepeat - Class in edu.cmu.tetrad.simulation: Created by Erich on 7/15/2016.
HsimContinuous - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/27/2016.
HsimContinuous(Dag, Set, DataSet) - Constructor for class edu.cmu.tetrad.simulation.HsimContinuous: Constructor for HsimContinuous.
HsimEvalFromData - Class in edu.cmu.tetrad.simulation: Created by ekummerfeld on 1/26/2017.
HsimRepeatAC - Class in edu.cmu.tetrad.simulation: Created by Erich on 4/29/2016.
HsimRepeatAC(DataSet) - Constructor for class edu.cmu.tetrad.simulation.HsimRepeatAC: Constructor for HsimRepeatAC.
HsimRepeatAC(String, char) - Constructor for class edu.cmu.tetrad.simulation.HsimRepeatAC: Constructor for HsimRepeatAC.
HsimRepeatAuto - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/28/2016.
HsimRepeatAutoRun - Class in edu.cmu.tetrad.simulation: Created by Erich on 4/29/2016.
HsimRepeatAutoRun(DataSet) - Constructor for class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: Constructor for HsimRepeatAutoRun.
HsimRepeatAutoRun(String, char) - Constructor for class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: Constructor for HsimRepeatAutoRun.
HsimRobustCompare - Class in edu.cmu.tetrad.simulation: generate data from random graph, generated from parameters.
HsimRun - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/28/2016.
HsimSchedule - Class in edu.cmu.tetrad.simulation: Created by Erich on 4/29/2016.
HsimStudy - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/28/2016.
HsimStudyAuto - Class in edu.cmu.tetrad.simulation: Created by Erich on 3/28/2016.
HsimUtils - Class in edu.cmu.tetrad.simulation: Created by Erich on 4/22/2016.
HsimUtils() - Constructor for class edu.cmu.tetrad.simulation.HsimUtils: Constructor for HsimUtils.
HuangMarginalLikelihoodScore - Class in edu.cmu.tetrad.algcomparison.score: // * Wrapper for Huang Marginal Likelihood Score.
HuangMarginalLikelihoodScore - Class in edu.cmu.tetrad.search.score: Kernel-based marginal likelihood score of Huang et al.
HuangMarginalLikelihoodScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.HuangMarginalLikelihoodScore: Constructs a new instance of the SemBicScore.
HuangMarginalLikelihoodScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Constructs a HuangMarginalScore object by initializing it with the given dataset and precomputing internal structures required for scoring.
HungarianAlgorithm - Class in edu.cmu.tetrad.search.utils: Provides an implemetation of the KuhnâMunkres assignment algorithm of the year 1957.
HungarianAlgorithm(double[][]) - Constructor for class edu.cmu.tetrad.search.utils.HungarianAlgorithm: Trying to find lowest-cost assignment.
HybridCgCutpoints - Class in edu.cmu.tetrad.hybridcg: Utilities to populate HybridCgPm cutpoints for discrete children that have continuous parents.
HybridCgCutpoints.Method - Enum Class in edu.cmu.tetrad.hybridcg: Represents the method used to compute and install cutpoints for continuous parents within a hybrid continuous-discrete graphical model.
HybridCgEdgeSignificance - Class in edu.cmu.tetrad.hybridcg: Computes per-edge significance for a HybridCgModel.HybridCgIm against a dataset, and per-stratum t-tests for the linear coefficients of continuous children.
HybridCgEdgeSignificance.ModelFit - Record Class in edu.cmu.tetrad.hybridcg: Model-level fit summary for a Hybrid CG structure against a dataset.
HybridCgEdgeSignificance.Result - Record Class in edu.cmu.tetrad.hybridcg: Significance summary for one edge.
HybridCgEdgeStrengths - Class in edu.cmu.tetrad.hybridcg: Computes a per-edge strength summary for a HybridCgModel.HybridCgIm and produces a display copy of its graph with edges colored by that strength.
HybridCgEdgeStrengths.Kind - Enum Class in edu.cmu.tetrad.hybridcg: The family an edge's strength belongs to.
HybridCgEdgeStrengths.Strength - Record Class in edu.cmu.tetrad.hybridcg: Strength summary for one edge.
HybridCgEstimator - Class in edu.cmu.tetrad.hybridcg: Estimator that prepares cutpoints (binning policy) and then runs MLE via HybridCgIm.HybridEstimator.
HybridCgIm(HybridCgModel.HybridCgPm) - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Constructs a HybridCgIm instance from a HybridCgPm.
HybridCgIo - Class in edu.cmu.tetrad.hybridcg: Saves and loads HybridCgModel.HybridCgIm models (parameters plus the full parametric model they sit on) as JSON text.
HybridCgModel - Class in edu.cmu.tetrad.hybridcg: Mixed Continuous/Discrete model with discretization for discrete children that have continuous parents.
HybridCgModel() - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgModel: Constructs a new instance of the HybridCgModel class.
HybridCgModel.HybridCgIm - Class in edu.cmu.tetrad.hybridcg: Represents a hybrid causal graph instantiated model (IM) derived from a hybrid causal graph probabilistic model (PM).
HybridCgModel.HybridCgIm.HybridEstimator - Class in edu.cmu.tetrad.hybridcg: The HybridEstimator class estimates Hybrid Causal Graphical Models using Maximum Likelihood Estimation (MLE).
HybridCgModel.HybridCgIm.Sample - Record Class in edu.cmu.tetrad.hybridcg: Represents a sample containing continuous and discrete data arrays along with a specified number of rows.
HybridCgModel.HybridCgPm - Class in edu.cmu.tetrad.hybridcg: The HybridCgPm class represents a structural model for hybrid Bayesian networks, which may include both discrete and continuous variables.
HybridCgPm(Graph, List, Map, Map) - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Constructs a HybridCgPm instance based on the provided directed acyclic graph (DAG), node ordering, discrete flags for nodes, and a mapping of node categories.
HybridCgPruneReport - Class in edu.cmu.tetrad.hybridcg: Result of HybridCgEdgeSignificance.backwardPrune(HybridCgModel.HybridCgPm, DataSet, double, boolean): a per-child backward elimination of edges whose per-edge likelihood-ratio test could not distinguish the reduced local model from the full one at the given level.
HybridCgPruneReport(List, List, Graph, double, boolean) - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgPruneReport: Constructs a prune report.
HybridCgPruneReport.Deletion - Class in edu.cmu.tetrad.hybridcg: One proposed edge deletion.
HybridCgRowShapeTest - Class in edu.cmu.tetrad.hybridcg: This class contains test cases for validating the behavior of row shapes in the Hybrid Conditional Gaussian Parameter Model (HybridCgPm) when the model includes a discrete child node with continuous parent nodes.
HybridCgRowShapeTest() - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgRowShapeTest: Constructor for HybridCgRowShapeTest.
HybridCgSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a Simulation using Structural Equation Modeling (SEM).
HybridCgSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Constructs a SemSimulation object with the given RandomGraph object.
HybridCgSimulation(HybridCgModel.HybridCgIm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Constructs a GeneralSemSimulation object with the given SemIm object.
HybridCgSimulation(HybridCgModel.HybridCgPm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.HybridCgSimulation: Initializes a SemSimulation with the given SemPm object.
HybridCgVars - Class in edu.cmu.tetrad.hybridcg: The `HybridCgVars` class provides utility methods for constructing and managing hybrid continuous and discrete variables.
HybridEstimator() - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.HybridEstimator: Constructs a new instance of the HybridEstimator class using default values.
HybridEstimator(double, boolean) - Constructor for class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.HybridEstimator: Constructs a new instance of the HybridEstimator class with the specified parameters.
hybridsimulate() - Method in class edu.cmu.tetrad.simulation.Hsim: hybridsimulate.
hybridsimulate() - Method in class edu.cmu.tetrad.simulation.HsimContinuous: hybridsimulate.
hypot(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Calculates the square root of the sum of the squares of two arguments without intermediate overflow or underflow.
Hypotheses - Section in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest
Hypotheses - Section in class edu.cmu.tetrad.search.test.Rcit

I

i() - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Returns the value of the i record component.
IA - Static variable in class edu.cmu.tetrad.util.Params: Constant IA="ia"
Iamb - Class in edu.cmu.tetrad.search.work_in_progress: Implements IAMB.
Iamb(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Iamb: Constructs a new search.
IambnPc - Class in edu.cmu.tetrad.search.work_in_progress: Created by IntelliJ IDEA.
IambnPc(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IambnPc: Constructs a new search.
IcaLingam - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: ICA-LiNGAM, the former name of LiNGAM.
IcaLingam() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: No-arg constructor.
IcaLingD - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: ICA-LiNG-D, the former name of LiNG-D.
IcaLingD() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: No-arg constructor.
IcaResult(Matrix, Matrix, Matrix, Matrix) - Constructor for record class edu.cmu.tetrad.search.FastIca.IcaResult: Creates an instance of a IcaResult record class.
Icd - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: The Iterative Causal Discovery (ICD) algorithm.
Icd - Class in edu.cmu.tetrad.search: Implements the Iterative Causal Discovery (ICD) algorithm for learning a Partial Ancestral Graph (PAG) from observational data.
Icd() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: No-arg constructor required by the framework.
Icd(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Constructor.
Icd(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Icd: Constructs an ICD search using the supplied independence test.
ICovarianceMatrix - Interface in edu.cmu.tetrad.data: Interface for covariance matrices.
Ida - Class in edu.cmu.tetrad.search: Implements the IDA algorithm.
Ida(DataSet, Graph, List) - Constructor for class edu.cmu.tetrad.search.Ida: Constructor.
Ida.IDA_TYPE - Enum Class in edu.cmu.tetrad.search: Enumeration representing the types of IDA (Intervention-based Directed Acyclic analysis).
Ida.NodeEffects - Class in edu.cmu.tetrad.search: Gives a list of nodes (parents or children) and corresponding minimum effects for the IDA algorithm.
IdaAverageSquaredDistance - Class in edu.cmu.tetrad.algcomparison.statistic: The IDA average squared distance.
IdaAverageSquaredDistance(SemIm) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.IdaAverageSquaredDistance: The IDA Average Squared Distance.
IdaCheck - Class in edu.cmu.tetrad.search: This calculates total effects and absolute total effects for an PDAG G for all pairs distinct (x, y) of variables, where the total effect is obtained by regressing y on x ∪ S and reporting the regression coefficient.
IdaCheck(Graph, DataSet, SemIm) - Constructor for class edu.cmu.tetrad.search.IdaCheck: Constructs a new IDA check for the given PDAG and data set.
IdaCheckAvgMaxSquaredDiffEstTrue - Class in edu.cmu.tetrad.algcomparison.statistic: IdaCheckAvgMaxSquaredDiffEstTrue calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.
IdaCheckAvgMaxSquaredDiffEstTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMaxSquaredDiffEstTrue: Calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.
IdaCheckAvgMinSquaredDiffEstTrue - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a statistic that calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.
IdaCheckAvgMinSquaredDiffEstTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgMinSquaredDiffEstTrue: Calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.
IdaCheckAvgSquaredDifference - Class in edu.cmu.tetrad.algcomparison.statistic: IdaCheckAvgSquaredDifference is a class that implements the Statistic interface.
IdaCheckAvgSquaredDifference() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.IdaCheckAvgSquaredDifference: IdaCheckAvgSquaredDifference is a class that implements the Statistic interface.
IdaMaximumSquaredDifference - Class in edu.cmu.tetrad.algcomparison.statistic: IdaMaximumSquaredDifference is a statistic that calculates the "IDA Average Maximum Squared Difference" between a true graph and an estimated graph.
IdaMaximumSquaredDifference(SemIm) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.IdaMaximumSquaredDifference: Initializes a new instance of the IdaMaximumSquaredDifference class.
IdaMinimumSquaredDifference - Class in edu.cmu.tetrad.algcomparison.statistic: IdaMinimumSquaredDifference is a statistic that calculates the "IDA Average Minimum Squared Difference" between a true graph and an estimated graph.
IdaMinimumSquaredDifference(SemIm) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.IdaMinimumSquaredDifference: Constructs an instance of the IdaMinimumSquaredDifference class with the specified SEM IM.
Identifiability - Class in edu.cmu.tetrad.bayes: Identifiability, based on RowSummingExactUpdater
Identifiability(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.Identifiability: Constructs a new updater for the given Bayes net.
Identifiability(BayesIm, Evidence) - Constructor for class edu.cmu.tetrad.bayes.Identifiability: Constructor for Identifiability.
identity(int) - Static method in class edu.cmu.tetrad.util.Matrix: identity.
identity(int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: identity.
identityPreservedOnRepeatedCalls() - Method in class edu.cmu.tetrad.util.PagCacheTest: Verifies that the PagCache consistently returns the same PAG object for repeated calls with the same source graph as input.
IFas - Interface in edu.cmu.tetrad.search: The IFas interface represents a framework for performing fast adjacency search in graph structures.
igamma(double, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the incomplete gamma function for two doubles
IGCI - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The IGCI rule.
IGraphSearch - Interface in edu.cmu.tetrad.search: Gives an interface for a search method that searches and returns a graph.
illegal(Paths.CpdagViolation, String) - Static method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Creates an CpdagCheckResult instance indicating an illegal CPDAG.
ILLEGAL_EDGE_TYPE - Enum constant in enum class edu.cmu.tetrad.graph.Paths.CpdagViolation: Indicates a violation where the graph contains edges that are neither directed nor undirected.
im() - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Returns the value of the im record component.
Im - Interface in edu.cmu.tetrad.util: Tagging interface for instantiated models.
Images - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Wraps the IMaGES algorithm for continuous variables.
Images() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Constructor for Images.
Images(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Constructor for ImagesBoss.
IMAGES - Enum constant in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: IMaGES: BOSS on the score averaged across datasets.
IMAGES_META_ALG - Static variable in class edu.cmu.tetrad.util.Params: Constant IMAGES_META_ALG="imagesMetaAlg"
IMAGES_RESTRICT_MG_FAS - Enum constant in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: IMaGES restricted to the MG_FAS skeleton: the MG-FAS adjacencies are computed first and supplied to the IMaGES (BOSS) search as a superstructure, by forbidding both directions of every pair outside it, so the score search can only remove adjacencies from that skeleton, never add them.
ImagesBoss - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Wraps the IMaGES algorithm for continuous variables.
ImagesBoss() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Constructor for ImagesBoss.
ImagesBoss(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Constructor for ImagesBoss.
ImagesFges - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Wraps the IMaGES algorithm for continuous variables.
ImagesFges() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Constructor for Images.
ImagesFges(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Constructor for Images.
ImagesScore - Class in edu.cmu.tetrad.search.score: Implements a score that aggregates results over multiple component scores, one per dataset, for use with the IMaGES algorithm.
ImagesScore(List) - Constructor for class edu.cmu.tetrad.search.score.ImagesScore: Constructs an IMaGES score using the given list of individual scores, using the SAMPLE_UNIFORM weighting scheme (a plain average of the component scores, which reproduces the historical behavior of this class).
ImagesScore(List, ImagesScore.WeightingScheme) - Constructor for class edu.cmu.tetrad.search.score.ImagesScore: Constructs an IMaGES score using the given list of individual scores and the given weighting scheme.
ImagesScore.WeightingScheme - Enum Class in edu.cmu.tetrad.search.score: The weighting scheme used to aggregate component scores when sample sizes differ.
IMAP_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant IMAP_ALPHA="imapAlpha".
imapAlpha() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Returns the significance level of the I-map gate's tests.
IMbSearch - Interface in edu.cmu.tetrad.search: Gives an interface for Markov blanket searches.
implicatedVertices() - Method in class edu.cmu.tetrad.search.PcAR: The union of all vertices this search's diagnostics implicate: endpoints of contested deletions, endpoints of orientation clashes, endpoints of blocked deletions, and the tested pair of every Markov-audit failure.
implicatedVertices(Graph, IndependenceTest, ConditioningSetType, double, boolean) - Static method in class edu.cmu.tetrad.search.MarkovAuditUtils: Identifies and returns the set of vertices implicated by audit failures of a graph based on the provided independence test, conditioning set type, false discovery rate (FDR) level, and correlation constraints.
implicatedVertices(Collection) - Static method in class edu.cmu.tetrad.search.MarkovAuditUtils: The endpoints of the given audit failures: the x and y of each rejected implied fact, deduplicated, in first-appearance order.
impliedAlpha() - Method in interface edu.cmu.tetrad.search.score.ProvidesCalibratedPValue: Returns the significance level at which this score's accept/reject rule operates--that is, the level alpha for which rejecting when calibratedPValue(...) <= alpha reproduces the sign test on the local score difference.
impliedAlpha() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the significance level at which this score's sign rule operates, P(chi-square(1) > c * ln(N)).
ImpliedArrowOrientationRatioEst - Class in edu.cmu.tetrad.algcomparison.statistic: The Implied Arrow Orientation Ratio Est statistic calculates the ratio of the number of implied arrows to the number of arrows in unshielded colliders in the estimated graph.
ImpliedArrowOrientationRatioEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst: Constructs the statistic.
ImpliedArrowOrientationRatioEst2 - Class in edu.cmu.tetrad.algcomparison.statistic: The Implied Arrow Orientation Ratio Est statistic calculates the ratio of the number of implied arrows to the number of arrows in unshielded colliders in the estimated graph.
ImpliedArrowOrientationRatioEst2() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ImpliedArrowOrientationRatioEst2: Constructs the statistic.
impliedCovar(Matrix, Matrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Calculates the implied covariance matrix from the given edge coefficient matrix and error covariance matrix.
impliedFactsForVertex(Node) - Method in interface edu.cmu.tetrad.search.test.CachedIndependenceQueries.ImpliedFactProvider: Retrieves the list of implied IndependenceFact objects for the given vertex.
ImpliedOrientationRatioEst - Class in edu.cmu.tetrad.algcomparison.statistic: The Implied Arrow Orientation Ratio Est statistic calculates the ratio of the number of implied arrows to the number of arrows in unshielded colliders in the estimated graph.
ImpliedOrientationRatioEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ImpliedOrientationRatioEst: Constructs the statistic.
ImpliesLegalMag - Class in edu.cmu.tetrad.algcomparison.statistic: Implies Legal MAG
ImpliesLegalMag() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ImpliesLegalMag: Constructor for LegalPag.
improvement - Variable in class edu.cmu.tetrad.sem.NodeAdequacySummary: The improvement in loss value from the baseline to the holdout set.
imputationGraphs - Variable in class edu.cmu.tetrad.data.missing.ImputationSearch.Result: The per-imputation graphs, in imputation order.
ImputationRegimeStudy - Class in edu.cmu.tetrad.study.missingness: Does MICE imputation with IMaGES-style pooling generalize beyond the linear Gaussian regime? Two phases, both BOSS at 20% MCAR missingness: MIXED: Lee-Hastie simulation, half the variables discrete, scored by degenerate-Gaussian BIC.
ImputationRegimeStudy() - Constructor for class edu.cmu.tetrad.study.missingness.ImputationRegimeStudy: Default constructor for the ImputationRegimeStudy class.
ImputationSearch - Class in edu.cmu.tetrad.data.missing: The search wrapper implementing MissingDataPolicy.MULTIPLE_IMPUTATION: impute m completed datasets, run the given algorithm on each, and pool the resulting graphs by edge frequency using the existing GraphSampling machinery (so pooled graphs carry per-edge probabilities and render with the same GUI visualization as bootstrap results).
ImputationSearch.Result - Class in edu.cmu.tetrad.data.missing: The result of a multiple-imputation search: the pooled graph (with per-edge frequencies attached by GraphSampling) and the individual per-imputation graphs.
impute(DataSet, int, long) - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: Produces m completed copies of the given dataset.
impute(DataSet, int, long) - Method in interface edu.cmu.tetrad.data.missing.MultipleImputer: Produces m completed copies of the given dataset.
impute(DataSet, int, long) - Method in class edu.cmu.tetrad.data.missing.MvnImputer: Produces m completed copies of the given dataset.
INCLUDE - Enum constant in enum class edu.cmu.tetrad.search.blocks.SingletonClusterPolicy: Represents the policy to include single variables as singleton clusters.
INCLUDE_ALL_NODES - Static variable in class edu.cmu.tetrad.util.Params: Constant INCLUDE_ALL_NODES="includeAllNodes"
INCLUDE_NEGATIVE_COEFS - Static variable in class edu.cmu.tetrad.util.Params: Constant INCLUDE_NEGATIVE_COEFS="includeNegativeCoefs"
INCLUDE_NEGATIVE_SKEWS_FOR_BETA - Static variable in class edu.cmu.tetrad.util.Params: Constant INCLUDE_NEGATIVE_SKEWS_FOR_BETA="includeNegativeSkewsForBeta"
INCLUDE_POSITIVE_COEFS - Static variable in class edu.cmu.tetrad.util.Params: Constant INCLUDE_POSITIVE_COEFS="includePositiveCoefs"
INCLUDE_POSITIVE_SKEWS_FOR_BETA - Static variable in class edu.cmu.tetrad.util.Params: Constant INCLUDE_POSITIVE_SKEWS_FOR_BETA="includePositiveSkewsForBeta"
INCLUDE_STRUCTURE_MODEL - Static variable in class edu.cmu.tetrad.util.Params: Constant INCLUDE_STRUCTURE_MODEL="include_structure_model"
includeTreatmentInteractions - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Indicates whether treatment interactions (e.g., X * φ(Z)) should be included in the outcome regression model.
incompleteCholeskyGramMatrix(List, DataSet, List, double) - Static method in class edu.cmu.tetrad.search.utils.KernelUtils: Approximates Gram matrix using incomplete Cholesky factorization
increment(int[], int) - Method in class edu.cmu.tetrad.util.MultiDimIntTable: Increments the value at the given coordinates by the specified amount, returning the new value.
increment(String) - Method in class edu.cmu.tetrad.search.utils.IndependenceCheckCounter: Increment the count for a given call-site label.
indepDecisions - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: A 3D array representing the decisions or classifications derived from independent facts.
IndependenceCheckCounter - Class in edu.cmu.tetrad.search.utils: Counts conditional independence checks broken down by the call site that issued them.
IndependenceCheckCounter() - Constructor for class edu.cmu.tetrad.search.utils.IndependenceCheckCounter: Constructs an instance of IndependenceCheckCounter with an empty internal map.
independenceFact(Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: independenceFact.
IndependenceFact - Class in edu.cmu.tetrad.graph: Stores a triple (x, y, z) of nodes.
IndependenceFact(Node, Node, Node...) - Constructor for class edu.cmu.tetrad.graph.IndependenceFact: Constructor for IndependenceFact.
IndependenceFact(Node, Node, Set) - Constructor for class edu.cmu.tetrad.graph.IndependenceFact: Constructor for IndependenceFact.
independenceFactMsg(Node, Node, Set, double) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: independenceFactMsg.
IndependenceFacts - Class in edu.cmu.tetrad.data: Stores a list of independence facts.
IndependenceFacts() - Constructor for class edu.cmu.tetrad.data.IndependenceFacts: Constructor for IndependenceFacts.
IndependenceFacts(IndependenceFacts) - Constructor for class edu.cmu.tetrad.data.IndependenceFacts: Constructor for IndependenceFacts.
IndependenceFacts(Graph) - Constructor for class edu.cmu.tetrad.data.IndependenceFacts: Constructor for IndependenceFacts.
IndependenceResult - Class in edu.cmu.tetrad.search.test: Stores a single conditional independence result, e.g., whether X _||_ Y | Z1,...,Zn holds or does not, and the p-value of the test.
IndependenceResult(IndependenceFact, boolean, double, double) - Constructor for class edu.cmu.tetrad.search.test.IndependenceResult: Constructor.
IndependenceResult(IndependenceFact, boolean, double, double, boolean) - Constructor for class edu.cmu.tetrad.search.test.IndependenceResult: Constructor.
IndependenceTest - Interface in edu.cmu.tetrad.search.test: Gives an interface that can be implemented by classes that do conditional independence testing.
IndependenceTestDescriptions - Class in edu.cmu.tetrad.util: May 14, 2019 11:25:02 AM
IndependenceWrapper - Interface in edu.cmu.tetrad.algcomparison.independence: Interface that algorithm must implement.
independent - Enum constant in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference.OP: The operation is independent.
independent() - Method in record class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Eval: Returns the value of the independent record component.
INDEPENDENT - Enum constant in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCCausalInference.OP: The operation is independent.
indepFacts - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: A list of independent (Type I) facts generated or analyzed during a computational process.
indepPvals - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: A 2D array representing the p-values corresponding to independent (Type I) facts.
indeterminate() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: Returns the value of the indeterminate record component.
indeterminate() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: Returns the value of the indeterminate record component.
INDETERMINATE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking.Blockable: Analysis was inconclusive (interrupted, recursion depth cap, or radius limit hit).
INDETERMINATE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking1.Blockable: Indicates that the blockable state of the entity is unclear or undefined.
INDETERMINATE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Blockable: Represents a state where it is not explicitly determined whether an entity is blocked or unblockable.
INDETERMINATE - Static variable in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: The search was cut short; no verdict.
index(Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Return the index of v in the current permutation.
IndexedConnectivity - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Stores a "shapshot" of the indexedConnectivity of a lag graph, using indices rather than Strings to refer to factors.
IndexedConnectivity(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Constructs an indexed connectivity for the getModel state of the given lag graph, including all edges.
IndexedConnectivity(LagGraph, boolean) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Constructs an indexed connectivity for the getModel state of the given lag graph.
IndexedLagGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Stores a "shapshot" of the indexedLagGraph of a lag graph, using indices rather than Strings to refer to factors.
IndexedLagGraph(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Constructs an indexed lag graph for the getModel state of the given lag graph, including all edges.
IndexedLagGraph(LagGraph, boolean) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Constructs an indexed lag graph for the getModel state of the given lag graph.
IndexedMatrix - Class in edu.cmu.tetrad.util: Indexed matrix.
IndexedMatrix(double[][]) - Constructor for class edu.cmu.tetrad.util.IndexedMatrix: Constructs a new IndexedMatrix for the given matrix.
IndexedParent - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Holds an ordered pair (index, lag) to represent a causal parent of a factor, where the factor at the given index is independently known.
IndexedParent(int, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedParent: Constructs a new index parent.
indexOf(Node) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Retrieves the index of a given node within the ordered list of nodes.
Indicator - Class in edu.cmu.tetrad.util.dist: Created by IntelliJ IDEA.
indicatorEffects() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Returns the value of the indicatorEffects record component.
indicatorNames() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns the value of the indicatorNames record component.
IndTestBasisExpandedGcm - Class in edu.cmu.tetrad.search.test: Basis-expanded Generalised Covariance Measure (GCM) conditional independence test.
IndTestBasisExpandedGcm(DataSet, int, int, double, boolean) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Constructs the test.
IndTestBasisExpandedGcm(DataSet, int, int, int, double, boolean) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Constructs the test with a separate truncation limit for the Z-side (regression) embedding.
IndTestBasisFunctionBlocks - Class in edu.cmu.tetrad.search.test: Basis–function independence test using block–structured Wilks statistics.
IndTestBasisFunctionBlocks(DataSet, int, int) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Constructs an instance of IndTestBasisFunctionBlocks.
IndTestBasisFunctionBlocks(DataSet, int, int, boolean) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Constructs an instance of IndTestBasisFunctionBlocks, optionally with adaptive basis selection.
IndTestBasisFunctionBlocks(DataSet, int, int, boolean, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: As the four-argument constructor, with an explicit missing-data specification.
IndTestBasisFunctionLrt - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestBasisFunctionLrt(DataSet, int, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Constructs an instance of IndTestBasisFunctionLrtCovariance.
IndTestBasisFunctionLrt(DataSet, int, double, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Constructs the test with an explicit missing-data specification.
IndTestBasisFunctionLrtFullSample - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestBasisFunctionLrtFullSample(DataSet, int, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Constructs an instance of the IndTestBasisFunctionLrt class.
IndTestBlocksLemma10 - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestBlocksLemma10(BlockSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.

Constructs an instance of IndTestBlocksLemma10 using the provided block specification.
IndTestBlocksLemma10.EqualityMode - Enum Class in edu.cmu.tetrad.search.test: Deprecated.

Equality mode for the Lemma-10 criterion.
IndTestBlocksWilkes - Class in edu.cmu.tetrad.search.test: Block-level CI test using Wilks-rank.
IndTestBlocksWilkes(BlockSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestBlocksWilkes: Constructs an instance of IndTestBlocks using the provided block specification.
IndTestBlocksWilkes(BlockSpec, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestBlocksWilkes: Constructs the test with an explicit missing-data specification for the block data set.
IndTestChiSquare - Class in edu.cmu.tetrad.search.test: Checks the conditional independence X _||_ Y | S, where S is a set of discrete variable, and X and Y are discrete variable not in S, by applying a conditional Chi Square test.
IndTestChiSquare(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestChiSquare: Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.
IndTestConditionalCorrelation - Class in edu.cmu.tetrad.search.test: Checks conditional independence of variable in a continuous data set using a conditional correlation test for the nonlinear nonGaussian with the additive error case.
IndTestConditionalCorrelation(DataSet, double, double, int, double, int) - Constructor for class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Constructs a new IndTestConditionalCorrelation to check independence based on conditional correlations derived from the given continuous data set.
IndTestConditionalGaussianLrt - Class in edu.cmu.tetrad.search.test: Performs a test of conditional independence X _||_ Y | Z1...Zn where all variables are either continuous or discrete.
IndTestConditionalGaussianLrt(DataSet, double, boolean) - Constructor for class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Constructor.
IndTestCord - Class in edu.cmu.tetrad.search.test: Tetrad IndependenceTest adapter for the score-pooled CORD engine (CordEngine3), the omnibus, orthogonal rank-score test of the full conditional law that keeps power against variance, tail, and co-volatility dependence that covariance/mean tests (Fisher Z, GCM) miss.
IndTestCord(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestCord: Constructs a CORD independence test over the given (continuous or numeric) data set.
IndTestCordEric - Class in edu.cmu.tetrad.search.test: Tetrad IndependenceTest adapter for Eric's CORD engine (CordEngine2), the omnibus, orthogonal rank-score test of the full conditional law that keeps power against variance, tail, and co-volatility dependence that covariance/mean tests (Fisher Z, GCM) miss.
IndTestCordEric(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestCordEric: Constructs a CORD independence test over the given (continuous or numeric) data set.
IndTestCramerT - Class in edu.cmu.tetrad.search.work_in_progress: Deprecated.
IndTestCramerT(CorrelationMatrix, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.
IndTestCramerT(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
IndTestCramerT(ICovarianceMatrix, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.
IndTestDegenerateGaussianLrt - Class in edu.cmu.tetrad.search.test: Implements degenerate Gaussian test as a likelihood ratio test.
IndTestDegenerateGaussianLrt(DataSet) - Constructor for class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Constructs the test using the given (mixed) data set.
IndTestDegenerateGaussianLrt(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Constructs the test with an explicit missing-data specification.
IndTestDegenerateGaussianLrtFullSample - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestDegenerateGaussianLrtFullSample(DataSet) - Constructor for class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Constructs an instance of the IndTestBasisFunctionLrt class.
IndTestEquivalence - Class in edu.cmu.tetrad.search.test: Reports independence only on positive evidence that a partial correlation is negligible, by a two-one-sided-test (TOST) equivalence test on the Fisher z scale.
IndTestEquivalence(IndTestFisherZ, double, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestEquivalence: Constructs an equivalence test over the given Fisher Z test.
IndTestFdrWrapper - Class in edu.cmu.tetrad.search.test: Wrapper class for IndependenceTest that enforces False Discovery Rate (FDR) control on independence decisions.
IndTestFdrWrapper(IndependenceTest, boolean, double, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Constructs an instance of IndTestFdrWrapper, which wraps around an existing IndependenceTest to apply False Discovery Rate (FDR) control during independence testing.
IndTestFisherZ - Class in edu.cmu.tetrad.search.test: Fisher's Z CI test with shrinkage (RIDGE/Ledoit-Wolf) and optional pseudoinverse fallback.
IndTestFisherZ(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructs an independence test using the Fisher Z test statistic.
IndTestFisherZ(DataSet, double, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructor for IndTestFisherZ which initializes the independence test using the Fisher Z test with specified parameters.
IndTestFisherZ(DataSet, double, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructs the test from a dataset with an explicit missing-data specification.
IndTestFisherZ(ICovarianceMatrix, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructs an instance of IndTestFisherZ using the given covariance matrix and significance level.
IndTestFisherZ(ICovarianceMatrix, double, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructor for the IndTestFisherZ class.
IndTestFisherZ(Matrix, List, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructs an instance of the IndTestFisherZ class, which is a statistical test for conditional independence based on the Fisher Z-test.
IndTestFisherZ(Matrix, List, double, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZ: Constructor for the IndTestFisherZ class, which performs a Fisher Z independence test with ridge regularization applied to handle issues with covariance matrix inversion.
IndTestFisherZ.Result - Record Class in edu.cmu.tetrad.search.test: Immutable, self-contained result of a single conditional-independence test.
IndTestFisherZ.ShrinkageMode - Enum Class in edu.cmu.tetrad.search.test: Shrinkage mode.
IndTestFisherZConcatenateResiduals - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestFisherZConcatenateResiduals(List, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Constructor.
IndTestFisherZFisherPValue - Class in edu.cmu.tetrad.search.test: Calculates independence from multiple datasets from using the Fisher method of pooling independence results.
IndTestFisherZFisherPValue(List, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Constructor.
IndTestFisherZPercentIndependent - Class in edu.cmu.tetrad.search.work_in_progress: Deprecated.
IndTestFisherZPercentIndependent(List, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Initializes an object of the class IndTestFisherZPercentIndependent.
IndTestFisherZRecursive - Class in edu.cmu.tetrad.search.work_in_progress: Deprecated.
IndTestFisherZRecursive(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
IndTestFisherZRecursive(ICovarianceMatrix, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.
IndTestFisherZRecursive(Matrix, List, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Constructs a new Fisher Z independence test with the listed arguments.
IndTestGin - Class in edu.cmu.tetrad.search.test: GIN (Generalized Independent Noise) conditional independence test.
IndTestGin(DataSet) - Constructor for class edu.cmu.tetrad.search.test.IndTestGin: Constructs an instance of IndTestGin with the specified dataset.
IndTestGin(DataSet, IndTestGin.Regressor, IndTestGin.UncondIndTest) - Constructor for class edu.cmu.tetrad.search.test.IndTestGin: Constructs an instance of IndTestGin with the specified dataset, regressor, and backend components.
IndTestGin.DistanceCorrTest - Class in edu.cmu.tetrad.search.test: Distance correlation (biased) with optional permutations.
IndTestGin.OlsRidge - Class in edu.cmu.tetrad.search.test: Represents a ridge-regularized ordinary least squares (OLS) regressor.
IndTestGin.PearsonCorrTest - Class in edu.cmu.tetrad.search.test: Fast Pearson correlation t-test (linear).
IndTestGin.Regressor - Interface in edu.cmu.tetrad.search.test: How we compute residuals.
IndTestGin.UncondIndTest - Interface in edu.cmu.tetrad.search.test: Unconditional independence backend rX ⟂ rY → p-value
IndTestGSquare - Class in edu.cmu.tetrad.search.test: Checks the conditional independence X _||_ Y | S, where S is a set of discrete variable, and X and Y are discrete variable not in S, by applying a conditional G Square test.
IndTestGSquare(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestGSquare: Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.
IndTestHsic - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestHsic(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Constructs a new HSIC Independence test.
IndTestHsic(Matrix, List, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Constructs a new HSIC Independence test.
IndTestIndependenceFacts - Class in edu.cmu.tetrad.search.test: Checks conditional independence against a list of conditional independence facts, manually entered.
IndTestIndependenceFacts(IndependenceFacts) - Constructor for class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Constructor.
IndTestIod - Class in edu.cmu.tetrad.search: Checks independence results by listing all tests with those variables, testing each one, and returning the resolution of these test results.
IndTestIod(List) - Constructor for class edu.cmu.tetrad.search.IndTestIod: Constructs a new pooled independence test from the given list of independence tests.
IndTestMixedMultipleTTest - Class in edu.cmu.tetrad.search.work_in_progress: Deprecated.
IndTestMixedMultipleTTest(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Constructor for IndTestMixedMultipleTTest.
IndTestMulti - Class in edu.cmu.tetrad.search.test: A class that represents a pooled independence test for multiple data sets.
IndTestMulti(List, ResolveSepsets.Method) - Constructor for class edu.cmu.tetrad.search.test.IndTestMulti: Constructs a new pooled independence test for the given data sets.
IndTestMultinomialLogisticRegression - Class in edu.cmu.tetrad.search.work_in_progress: Performs a test of conditional independence X _||_ Y | Z1...Zn where all searchVariables are either continuous or discrete.
IndTestMultinomialLogisticRegression(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Constructor for IndTestMultinomialLogisticRegression.
IndTestMultinomialLogisticRegressionWald - Class in edu.pitt.csb.mgm: Performs a test of conditional independence X _||_ Y | Z1...Zn where all searchVariables are either continuous or discrete.
IndTestMultinomialLogisticRegressionWald(DataSet, double, boolean) - Constructor for class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Constructs a new instance of IndTestMultinomialLogisticRegressionWald with the specified parameters.
IndTestMvpLrt - Class in edu.cmu.tetrad.search.test: Performs a test of conditional independence X _||_ Y | Z1...Zn where all variables are either continuous or discrete.
IndTestMvpLrt(DataSet, double, int, boolean) - Constructor for class edu.cmu.tetrad.search.test.IndTestMvpLrt: Constructor.
IndTestPositiveCorr - Class in edu.cmu.tetrad.search.work_in_progress: Deprecated.
IndTestPositiveCorr(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
IndTestProbabilistic - Class in edu.cmu.tetrad.search.test: Uses BCInference by Cooper and Bui to calculate probabilistic conditional independence judgments.
IndTestProbabilistic(DataSet) - Constructor for class edu.cmu.tetrad.search.test.IndTestProbabilistic: Initializes the test using a discrete data sets.
IndTestProbabilisticBDeu - Class in edu.cmu.tetrad.search.is: Probabilistic conditional independence test using BDeu (population only).
IndTestProbabilisticBDeu(DataSet, double) - Constructor for class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Constructs an independence test object using a probabilistic BDeu (Bayesian Dirichlet equivalent uniform) score.
IndTestProbabilisticISBDeu - Class in edu.cmu.tetrad.search.is: Probabilistic conditional‐independence test using BDeu (instance‑specific split on Z).
IndTestProbabilisticISBDeu(DataSet, DataSet, double) - Constructor for class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Constructs an IndTestProbabilisticISBDeu object for probabilistic independence testing based on BDeu scoring, using a specified prior probability.
IndTestRcotCcaWilkes - Class in edu.cmu.tetrad.search.test: RCoT (Randomized Conditional Correlation Test) using Random Fourier Features + residualization + CCA
IndTestRcotCcaWilkes(DataSet) - Constructor for class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Constructs an instance of IndTestRcot with default parameters.
IndTestRcotCcaWilkes(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Constructs an instance of IndTestRcot with Parameters (legacy keys match IndTestRcit style).
IndTestRegression - Class in edu.cmu.tetrad.search.test: Deprecated.
IndTestRegression(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
IndTestScore - Class in edu.cmu.tetrad.search.score: Gives a method of interpreting a test as a score.
IndTestScore(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.score.IndTestScore: Constructs the score using a covariance matrix.
IndTestSepsetDci - Class in edu.cmu.tetrad.search.work_in_progress: Checks independence facts for variables associated with a sepset by simply querying the sepset
IndTestSepsetDci(SepsetMapDci, List) - Constructor for class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Constructs a new independence test that returns d-separation facts for the given graph as independence results.
IndTestStratifiedRank - Class in edu.cmu.tetrad.search.test: A conditional independence test for data gathered in several domains (batches, sites, cohorts, countries) whose causal structure is shared but whose linear coefficients, means, and noise variances may differ by domain -- the Phase 1 test of LCD-NOD (Dong, Dai, Ng, Spirtes, and Zhang, ICML 2026).
IndTestStratifiedRank(DataSet, String, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Constructs the test from a single data set containing a discrete domain column.
IndTestStratifiedRank(List, double) - Constructor for class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Constructs the test from one data set per domain.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.IndTestIod: Calculates the independence test for a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Returns a new independence test object for the subset of variables provided.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Returns an independence test for a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.Gcm: Generates a subset independence test that is restricted to the provided variables.
indTestSubset(List) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Checks conditional independence between variables in a subset.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Constructs a new Independence test which checks independence facts based on the correlation data implied by the given data set (must be continuous).
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: This method returns an instance of the IndependenceTest interface that can test the independence of a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Returns a new IndependenceTest instance for a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Performs an independence test on a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Subset of variables for independence testing.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Returns an IndependenceTest object for a sublist of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Performs an independence test for a sublist of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Determines independence between variables in a given subset.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Performs an independence test on a subset of variables and returns the result.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.MsepTest: Conducts an independence test on a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Tests the independence between variables in a given sublist.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

This method performs an independence test based on a given sublist of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Performs an independence test on a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Performs an independence test for a sublist of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Performs an independence test on a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Determines independence between a subset of variables.
indTestSubset(List) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Returns an Independence test for a sublist of the variables.
indTestSubset(List) - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Tests the conditional independence between two variables given a sublist of variables.
IndTestTrekSep - Class in edu.cmu.tetrad.search.test: Deprecated.
Use IndTestBlocksTs instead.
IndTestTrekSep(ICovarianceMatrix, double, List, List) - Constructor for class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.
inferFunction(int, SortedSet) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: inferFunction.
INFIX - Enum constant in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: The expression can occur in the infix position.
info(String) - Method in class edu.cmu.tetrad.util.LogUtils: info.
INFO - Enum constant in enum class edu.cmu.tetrad.data.audit.AuditFinding.Severity: A property worth knowing that is not necessarily a defect.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
INITIAL_GRAPH - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Starts with an initial CPDAG over the variables of the independence test that is given in the constructor.
initialize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Initializes the history array.
initialize(double[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasalInitializer: Sets the expression of each unregulated gene in the given history at time step 0 to a random value drawn from N(basalExpression, initStDev), sets the expression level at time step 0 of each regulated gene to zero, and then copies the values at history[0][j] to history[i][j] for all i > 0 less than the maximum time lag.
initialize(double[][]) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.Initializer: Initializes a history array.
initialize(LagGraph) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.GraphInitializer: Randomizes the given lag graph--in other words, chooses random edges for the graph according to a particlar scheme (see instantiations).
initialize(LagGraph) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PreviousStepOnly: Randomizes the given lag graph--in other words, chooses random edges for the graph according to a particlar scheme (see instantiations).
initialize(LagGraph) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.SimpleRandomizer: Randomizes the given lag graph--in other words, chooses random edges for the graph according to a particlar scheme (see instantiations).
INITIALIZE_FROM_DATA - Enum constant in enum class edu.cmu.tetrad.sem.StandardizedSemIm.Initialization: Initialize the SEM from data
initializeIfNeeded(DataSet, List, Map) - Method in class edu.cmu.tetrad.search.test.ProcessPythonCiService
initializeIfNeeded(DataSet, List, Map) - Method in interface edu.cmu.tetrad.search.test.PythonCiService: Prepares the service for operation if it has not been initialized yet.
initializeInternalEdgesRandom(SemIm, Graph, List, double, double) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Randomize existing internal edges (that already exist in the graph) within [low, high], positive.
Initializer - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.history: Initializes a history array.
initializeValues() - Method in class edu.cmu.tetrad.sem.SemIm: Iterates through all freeParameters, picking values for them from the distributions that have been set for them.
initialOrder(DataSet) - Static method in class edu.cmu.tetrad.search.utils.FlopInitialOrder: Computes the FLOP initial order for the variables in the given data set.
initialOrder(ICovarianceMatrix) - Static method in class edu.cmu.tetrad.search.utils.FlopInitialOrder: Computes the FLOP initial order for the variables of the given covariance matrix.
inject(DataSet, MissingnessInjector.Spec) - Static method in class edu.cmu.tetrad.data.MissingnessInjector: Injects missing values into a copy of the data, MCAR.
inject(DataSet, MissingnessInjector.Spec, MissingnessInjector.Mechanism) - Static method in class edu.cmu.tetrad.data.MissingnessInjector: Injects missing values into a copy of the data under the given mechanism.
INNER - Enum constant in enum class edu.cmu.tetrad.data.JoinDatasets.JoinType: Keep only rows with a match on both sides.
innerProduct(double[], double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: innerProduct.
INPUT_SCALE - Static variable in class edu.cmu.tetrad.util.Params: Constant INPUT_SCALE="inputScale"
INSTANCE_ROW - Static variable in class edu.cmu.tetrad.util.Params: Constant INSTANCE_ROW="instanceRow"
INSTANCE_SPECIFIC_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant INSTANCE_SPECIFIC_ALPHA="instanceSpecificAlpha"
InstanceAugmentedSemBicScore - Class in edu.cmu.tetrad.search.score: Instance-augmented SEM-BIC score for continuous data.
InstanceAugmentedSemBicScore(ICovarianceMatrix, double[]) - Constructor for class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Constructs an `InstanceAugmentedSemBicScore` object using a covariance matrix and a fully centered row of instance data.
InstanceAugmentedSemBicScore(ICovarianceMatrix, double[], double[]) - Constructor for class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Constructs an `InstanceAugmentedSemBicScore` object using a covariance matrix, a raw row of instance data, and the population mean values.
InstanceAugmentedSemBicScore(SemBicScore, double[]) - Constructor for class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Constructs an `InstanceAugmentedSemBicScore` object using a base `SemBicScore` and a centered row of instance data.
InstanceAugmentedSemBicScore(SemBicScore, double[], double[]) - Constructor for class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Constructs an `InstanceAugmentedSemBicScore` object using a base `SemBicScore`, a raw row of instance data, and the population mean values.
InstanceAugmentedSemBicScoreWrapper - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for InstanceAugmentedSemBicScore that: - pulls the testing DataSet from Params.TESTING_DATA (injected by the algorithm wrapper), - takes the instance row from Params.INSTANCE_ROW, - aligns columns by training variable names, - uses alpha from Params.IS_ALPHA (default 1.0).
InstanceAugmentedSemBicScoreWrapper() - Constructor for class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Constructs a new instance of the `InstanceAugmentedSemBicScoreWrapper` class.
InstanceSpecificScore - Interface in edu.cmu.tetrad.search.score: Marker interface for scores that include an instance-specific component.
IntAveDataSetProbs - Class in edu.cmu.tetrad.bayes: Estimates probabilities directly from data on the fly using maximum likelihood method, with the exception that if rows do not exist in the data satisfying a required condition because certain values are unattested, an attempt is made to remove each relevant column in turn, record the estimated probability with column removed from the condition (if it is defined), and then return the average over the estimated probabilities calculated this way.
IntAveDataSetProbs(DataSet) - Constructor for class edu.cmu.tetrad.bayes.IntAveDataSetProbs: Creates a cell count table for the given data set.
IntDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of integer data.
IntDataBox(int[][]) - Constructor for class edu.cmu.tetrad.data.IntDataBox: Constructs a new data box using the given 2D short data array as data.
IntDataBox(int, int) - Constructor for class edu.cmu.tetrad.data.IntDataBox: Constructs an 2D short array consisting entirely of missing values (-99).
integrate() - Method in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: Integrates the density function over the grid using the trapezoidal rule.
Integrator - Class in edu.cmu.tetrad.util: Integrates under a function from one endpoint to another.
intercept() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns the value of the intercept record component.
INTERCEPT_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant INTERCEPT_HIGH="interceptHigh"
INTERCEPT_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant INTERCEPT_LOW="interceptLow"
InterIamb - Class in edu.cmu.tetrad.search.work_in_progress: Implements the Inter-IAMB algorithm.
InterIamb(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.InterIamb: Constructs a new search.
intersection() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if intersection holds--e.g., (X _||_ Y | (Z ∪ W)) ∧ (X _||_ W | (Z ∪ Y)) ==> X _||_ (Y ∪ W) | Z
intersects(Cover) - Method in class edu.cmu.tetrad.search.rlcd.Cover: Tests whether this cover intersects another.
INTERVAL_BETWEEN_RECORDINGS - Static variable in class edu.cmu.tetrad.util.Params: Constant INTERVAL_BETWEEN_RECORDINGS="intervalBetweenRecordings"
INTERVAL_BETWEEN_SHOCKS - Static variable in class edu.cmu.tetrad.util.Params: Constant INTERVAL_BETWEEN_SHOCKS="intervalBetweenShocks"
INTERVENTION_STATUS - Enum constant in enum class edu.cmu.tetrad.graph.NodeVariableType: The node variable type is intervened on with a specific status, such as treatment or control.
INTERVENTION_VALUE - Enum constant in enum class edu.cmu.tetrad.graph.NodeVariableType: The node variable type is intervened on with a specific value.
INVALID_INPUT - Enum constant in enum class edu.cmu.tetrad.search.GacTotalEffectElibility.Status: Invalid or degenerate input (e.g., nulls, x==y, empty sets).
invalidate(DataSet) - Static method in class edu.cmu.tetrad.data.missing.TestwiseRows: Discards any cached instance for the given dataset.
invalidate(Matrix) - Static method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Discards any shared instance for the given matrix.
invalidateFeatureCache() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Clears the feature cache by removing all stored entries.
inverse() - Method in class edu.cmu.tetrad.util.Matrix: Returns the inverse of the matrix.
inverse(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Calculates the inverse of a given matrix.
INVERSE_VARIANCE - Enum constant in enum class edu.cmu.tetrad.search.FaskPool.Weighting: Inverse bootstrap-variance weights, w_k = 1 / Var(lr_k).
InverseCorrelation - Class in edu.cmu.tetrad.search.work_in_progress: Returns edges whose entries in the precision matrix exceed a certain threshold.
InverseCorrelation(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.InverseCorrelation: Constructor for InverseCorrelation.
inverseCumulativeProbability(double) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Calculates the inverse cumulative probability of a given point.
invVech(double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: invVech.
Ion - Class in edu.cmu.tetrad.algcomparison.algorithm.multi: Runs the ION (Integration of Overlapping Networks) algorithm on multiple datasets whose variable sets may overlap only partially.
Ion - Class in edu.cmu.tetrad.search: Implements the ION (Integration of Overlapping Networks) algorithm for distributed causal inference.
Ion() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Constructor for Ion.
Ion(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Constructor for Ion.
Ion(List) - Constructor for class edu.cmu.tetrad.search.Ion: Constructs a new instance of the ION search from the input PAGs
ION_ADJACENCY_SEARCH - Static variable in class edu.cmu.tetrad.util.Params: Constant ION_ADJACENCY_SEARCH="ionAdjacencySearch"
ION_PATH_LENGTH_SEARCH - Static variable in class edu.cmu.tetrad.util.Params: Constant ION_PATH_LENGTH_SEARCH="ionPathLengthSearch"
IonHittingSet - Class in edu.cmu.tetrad.search.work_in_progress: Provides a static method which implements a correct version of Reiter's hitting set algorithm as described by Greiner, Smith, and Wilkerson in "A Correction to the Algorithm in Reiter's Theory of Diagnosis" Artificial Intellegence 41 (1989) (see for detailed specification).
IPEN - Static variable in class edu.cmu.tetrad.util.Params: Constant IPEN="ipen"
IPurify - Interface in edu.cmu.tetrad.search.utils: Provides an interface for Purify algorithm.
irlsTol - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Tolerance for convergence in IRLS algorithm.
IS - Static variable in class edu.cmu.tetrad.util.Params: Constant IS="is"
isAboveNoise() - Method in class edu.cmu.tetrad.sem.EdgeStrengthResult: Whether the edge's MMD² clears the refit-noise band: the null mean plus two null standard deviations (or just the null mean if only one refit was run).
isAccommodateNewCategories() - Method in class edu.cmu.tetrad.data.DiscreteVariable: isAccommodateNewCategories.
isAcyclic() - Method in class edu.cmu.tetrad.graph.Paths: Determines whether the graph is acyclic, meaning it does not contain any directed cycles.
isAcyclic(Matrix) - Method in class edu.cmu.tetrad.search.Lingam: Checks whether the graph induced by the scaled coefficient matrix represents an acyclic directed graph (DAG).
isAdjacentTo(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Determines whether two nodes are adjacent in the graph.
isAdjacentTo(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: isAdjacentTo.
isAdjacentTo(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: isAdjacentTo.
isAdjacentTo(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: isAdjacentTo.
isAdjacentTo(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: isAdjacentTo.
isAdjacentTo(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Determines whether two nodes are adjacent in the graph.
isAllDiscrete(Node, Node, Set) - Static method in class edu.cmu.tetrad.search.test.DiscreteIndependenceUtils: Returns true if x, y, and all nodes in z are discrete variables.
isAllowed(int, int) - Method in class edu.cmu.tetrad.bayes.Proposition: isAllowed.
isAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Determines if a triple of nodes is ambiguous.
isAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: States whether r-s-r is an underline triple or not.
isAmbiguousTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: States whether r-s-r is an underline triple or not.
isAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: States whether r-s-r is an underline triple or not.
isAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: States whether r-s-r is an underline triple or not.
isAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks whether a triple of nodes is ambiguous.
isAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: States whether r-s-r is an underline triple or not.
isAmenable(Node, Node) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns true if the PAG is amenable with respect to (x, y), i.e.
isAncestorOf(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether one node is an ancestor of another.
isAncestorOf(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines whether one node is an ancestor of another.
isAncestorOf(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: isAncestorOf.
isAncestorOf(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether one node is an ancestor of another.
isAncestorOfAnyZ(Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: Return true if b is an ancestor of any node in z
isAntilogCalculated() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns true iff the antilog of each expression level should be calculated.
isAntilogCalculated() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: isAntilogCalculated.
isApplyR1() - Method in class edu.cmu.tetrad.search.Ccd: Determines whether the R1 orientation rule is set to be applied during the search process.
isApproximate() - Method in class edu.cmu.tetrad.search.test.Kci: Retrieves whether the method should use an approximate approach or a permutation test.
isArrowheadAllowed(Node, Node, Graph, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.FciOrient: Determines if an arrowhead can be placed at node Y in the given graph, based on the adjacency relationships, endpoint types, and any provided prior knowledge constraints.
isArrowheadAllowed(Object, Object, Knowledge) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Checks if an arrowhead is allowed by background knowledge.
isArrowheadAllowed1(Node, Node, Knowledge) - Static method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: isArrowheadAllowed1.
isArrowheadAllowed1(Node, Node, Knowledge) - Static method in class edu.cmu.tetrad.search.work_in_progress.VcPc: isArrowheadAllowed1.
isAtomic() - Method in class edu.cmu.tetrad.search.rlcd.Cover: Returns the atomic flag.
isAutoSelectK() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves the current value of K.
IsBDeuScore - Class in edu.cmu.tetrad.search.is: Instance‑Specific BDeu score (discrete), using the Dirichlet–multinomial posterior predictive term for the instance row (Jabbari).
IsBDeuScore(DataSet, DataSet) - Constructor for class edu.cmu.tetrad.search.is.IsBDeuScore: Constructs an instance of the IsBDeuScore class, which validates and compares the schema and data consistency between a training dataset and a single-row instance dataset, ensuring they meet the criteria for Bayesian scoring.
IsBDeuScore2 - Class in edu.cmu.tetrad.search.is: Instance‑Specific BDeu (IS‑BDeu) score.
IsBDeuScore2(DataSet, DataSet) - Constructor for class edu.cmu.tetrad.search.is.IsBDeuScore2: Constructs an instance of the IsBDeuScore2 class with the specified training and test datasets.
IsBDeuScoreWrapper - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for ISBDeuScore (discrete).
IsBDeuScoreWrapper() - Constructor for class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Constructor for the `IsBDeuScoreWrapper` class.
IsBicScore - Class in edu.cmu.tetrad.search.is: Instance-Specific BIC (IS-BIC) score for discrete data.
IsBicScore(DataSet, DataSet) - Constructor for class edu.cmu.tetrad.search.is.IsBicScore: Constructs an instance of the IsBicScore class.
isBidirected(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines if the edge between two nodes is bidirected.
isBidirectedEdge(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: isBidirectedEdge.
isBinary(DataSet, int) - Static method in class edu.cmu.tetrad.data.DataUtils: States whether the given column of the given data set is binary.
isBoundsEnforced() - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: True iff bounds checking is performed on variable values indices.
isCanalyzing() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Determines whether the getModel function is canalyzing, according to Kaufmann's definition in Oridins of Order: "I define as a canalyzing Boolean function any Boolean function having the property that it has at least one input having at least one value (1 or 0) which suffices to guarantee that the regulated element assumes a specific value (1 or 0)" (page 203-4).
isCanceled() - Method in interface edu.cmu.tetrad.graph.LayoutUtil.LayoutProgressListener: Determines if the layout operation has been canceled.
isCanceled() - Method in class edu.cmu.tetrad.graph.LayoutUtil.NoOpProgressListener
isCanceled() - Method in class edu.cmu.tetrad.graph.LayoutUtil.SwingProgressListener
isCanceled() - Method in class edu.cmu.tetrad.util.TaskManager: isCanceled.
isCategoryNamesDisplayed() - Method in class edu.cmu.tetrad.data.DiscreteVariable: isCategoryNamesDisplayed.
isCenterData() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Checks whether the data is configured to be centered.
isCenterFeatures() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Retrieves whether feature centering is enabled.
isCenterFeatures() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Determines whether the features are centered.
isCheckingCycles() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: isCheckingCycles.
isChildOf(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Checks if the given node1 is a child of node2 in the graph.
isChildOf(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: isChildOf.
isChildOf(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: isChildOf.
isChildOf(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: isChildOf.
isChildOf(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: isChildOf.
isChildOf(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks if a given node is a child of another node in the graph.
isClassDeprecated(Class) - Static method in class edu.cmu.tetrad.util.DeprecationUtils: Determines if the specified class is marked with the @Deprecated annotation.
isClique(Collection, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Checks if the given set of nodes forms a clique in the specified graph.
isCompatible() - Method in class edu.cmu.tetrad.search.test.AlphaReport: Whether the budget's level is large enough to detect the requested effect at the requested power in every df class.
isCompleteRuleSetUsed() - Method in class edu.cmu.tetrad.search.is.IsGFci: Indicates whether the complete rule set is used in the instance.
isCompleteRuleSetUsed() - Method in class edu.cmu.tetrad.search.SpFci: Returns whether the complete rule set is used.
isCompleteRuleSetUsed() - Method in class edu.cmu.tetrad.search.utils.FciOrient: Checks if the complete rule set is being used.
isCompleteRuleSetUsed() - Method in class edu.cmu.tetrad.search.utils.MagToPag: isCompleteRuleSetUsed.
isCompleteRuleSetUsed() - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: isCompleteRuleSetUsed.
isConditioned(int) - Method in class edu.cmu.tetrad.bayes.Proposition: isConditioned.
isConfoundingRisk(MissingnessThreshold.Candidate) - Static method in class edu.cmu.tetrad.data.missing.MissingnessThreshold: Whether a dropped variable looks risky to drop: strongly associated with something retained, on enough pairwise cases to believe it.
isConfoundingTrek(Graph, List, Node, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Checks if the given trek in a graph is a confounding trek.
isConsistent(GraphChange) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Consistency check, nonexhaustive, but catches the most blatant inconsistencies.
isContinuous() - Method in class edu.cmu.tetrad.data.BoxDataSet: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: isContinuous.
isContinuous() - Method in interface edu.cmu.tetrad.data.DataModel: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.DataModelList: isContinuous.
isContinuous() - Method in interface edu.cmu.tetrad.data.DataSet: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.IndependenceFacts: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: isContinuous.
isContinuous() - Method in class edu.cmu.tetrad.data.TimeSeriesData: isContinuous.
isConverged() - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns true if EM met the convergence tolerance before the iteration cap was reached.
isCopyData() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isCopyData.
isCorrelationInput() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns true if every diagonal entry of the supplied matrix is 1 within tolerance, i.e., the input is a correlation matrix rather than a covariance matrix on the original scales.
isCoupleFeaturesByTarget() - Method in class edu.cmu.tetrad.search.score.FfMl: Indicates whether features are coupled by target.
isCoveringAdjacency(Graph, Graph, Node, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines whether an edge between two nodes in the estimated graph is covering a collider or noncollider in the true graph.
isCyclic() - Method in interface edu.cmu.tetrad.sem.ISemIm: isCyclic.
isCyclic() - Method in class edu.cmu.tetrad.sem.SemIm: isCyclic.
isDag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Determines if the given graph is a directed acyclic graph (DAG).
isDataFromFile() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: isDataFromFile.
isDefaultToKnowledgeLayout() - Method in class edu.cmu.tetrad.data.Knowledge: isDefaultToKnowledgeLayout.
isDefCollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Checks if there is a definite collider between three nodes in the graph.
isDefCollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Added by ekorber, 2004/6/9.
isDefCollider(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Added by ekorber, 2004/6/9.
isDefCollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Added by ekorber, 2004/6/9.
isDefCollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Added by ekorber, 2004/6/9.
isDefCollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Determines if there is a definite collider relationship between the given nodes.
isDefNoncollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Checks if three given nodes form a definite non-collider in a graph.
isDefNoncollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Added by ekorber, 2004/6/9.
isDefNoncollider(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Added by ekorber, 2004/6/9.
isDefNoncollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Added by ekorber, 2004/6/9.
isDefNoncollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Added by ekorber, 2004/6/9.
isDefNoncollider(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Determines if the given nodes form a definite noncollider in the graph.
isDemixed() - Method in class edu.cmu.tetrad.search.work_in_progress.Demixer: isDemixed.
isDependent() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns whether the fact fails to hold--i.e.
isDescendentOf(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines if the specified node x is a descendant of the node n in the current graph structure.
isDescendentOf(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether one node is a descendent of another.
isDetermined(int[], double) - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: Returns a judgment of whether a set of parent variables determines a child variables.
isDirected() - Method in class edu.cmu.tetrad.graph.Edge: isDirected.
isDirected(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Checks if there is a directed edge from node1 to node2 in the graph.
isDirectedEdge(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: isDirectedEdge.
isDirectedFromTo(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph
isDirectedFromTo(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines if there is a directed edge from node x to node y in the graph.
isDiscrete() - Method in class edu.cmu.tetrad.data.BoxDataSet: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: isDiscrete.
isDiscrete() - Method in interface edu.cmu.tetrad.data.DataModel: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.DataModelList: isDiscrete.
isDiscrete() - Method in interface edu.cmu.tetrad.data.DataSet: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.IndependenceFacts: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: isDiscrete.
isDiscrete() - Method in class edu.cmu.tetrad.data.TimeSeriesData: isDiscrete.
isDiscrete(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Checks if a node at a given index is discrete.
isDiscreteVariable(int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Reports whether a variable is discrete.
isDisplayLogEnabled() - Method in class edu.cmu.tetrad.util.TetradLogger: States whether to display the log display.
isDoOrientation() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: isDoOrientation.
isDoOrientation() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: isDoOrientation.
isDoOrientation() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: isDoOrientation.
isDoRcit() - Method in class edu.cmu.tetrad.search.test.Rcit: Returns the current state of the RCIT (Randomized Conditional Independence Test) functionality.
isDoubleMissingValue(double) - Static method in class edu.cmu.tetrad.data.ContinuousVariable: Determines whether the argument is equal to the missing value marker.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Retrieves the required effect/gain threshold for determining an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore
isEffectEdge(double) - Method in interface edu.cmu.tetrad.search.is.IsScore: Heuristic indicating whether the net effect on a candidate edge exceeds a threshold.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Determines if the given bump value represents an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Determines if the given bump value represents an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.BdeScore: Determines if an edge has an effect.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.BDeuScore: Determines whether the bump exceeds zero.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: FGES “effect edge” convention for this score bump.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.EbicScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Determines if the given bump value represents an "effect edge."
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Determines if the given bump value represents an "effect edge."
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.FfMl: Determines whether the given bump value indicates an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Determines if the given bump value represents an "effect edge."
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.GicScores: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.GraphScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Determines whether the given bump value indicates the presence of an "effect edge".
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Determines whether a given edge has an effect based on the specified bump value.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Determines whether a given bump value corresponds to an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Determines if an edge is an effect edge based on a given bump value.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.MvpScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Determines if an edge has an effect based on the given bump value.
isEffectEdge(double) - Method in interface edu.cmu.tetrad.search.score.Score: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Returns true iff the edge between x and y is an effect edge.
isEffectEdge(double) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Returns true iff the edge between x and y is an effect edge.
isEffective() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Determines whether each input parent has an influence on the outcome--that is, whether for each parent there is a combination of states for the other parents for which the function would be different if the given parent were true as opposed to false.
ISemIm - Interface in edu.cmu.tetrad.sem: An interface for SemIM's; see implementations.
isEmpty() - Method in class edu.cmu.tetrad.data.DataModelList
isEmpty() - Method in class edu.cmu.tetrad.data.Knowledge: true if there is no background knowledge recorded.
isEmpty() - Method in class edu.cmu.tetrad.data.KnowledgeGroup: States whether this group is empty, that is there is no edges in it (Note there may be some partial information though).
isEmpty() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns whether no tier of this structure contains a variable.
isEmpty() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns true if the algorithm returned no clusters at all.
isErrorEdge(Edge) - Static method in class edu.cmu.tetrad.graph.SemGraph: isErrorEdge.
isEstimated() - Method in interface edu.cmu.tetrad.sem.ISemIm: isEstimated.
isEstimated() - Method in class edu.cmu.tetrad.sem.SemIm: isEstimated.
isEventActive(String) - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: States whether the event associated with the given id is active, that is whether it should be logged or not.
isEventActive(String) - Method in class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: isEventActive.
isEventActive(String) - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig: States whether the event associated with the given id is active, that is whether it should be logged or not.
isExactFfmlBaseline() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Returns whether the exact FF-ML (fixed-sigma^2) no-parent baseline is in effect.
isExactFfmlBaseline() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Returns whether the exact FF-ML (fixed-sigma^2) no-parent baseline is in effect.
isExogenous(Node) - Method in class edu.cmu.tetrad.graph.Dag: Checks whether a given node is exogenous.
isExogenous(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: isExogenous.
isExogenous(Node) - Method in interface edu.cmu.tetrad.graph.Graph: isExogenous.
isExogenous(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: isExogenous.
isExogenous(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: isExogenous.
isExogenous(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks if a given node is exogenous.
isFaithfulnessAssumed() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: isFaithfulnessAssumed.
isFdr() - Method in class edu.cmu.tetrad.search.Pcd: True iff the algorithm should be run with False Discovery Rate tests.
IsFges - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: IS-FGES (Instance-Specific FGES) wrapper for the algcomparison interface.
IsFges - Class in edu.cmu.tetrad.search.is: Instance-Specific FGES: reuses the FGES search machinery but overrides the bump computations to use IsScore (instance-specific) rather than the pure population score.
IsFges() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges: Default constructor for the IsFges class.
IsFges(IsScore, Score) - Constructor for class edu.cmu.tetrad.search.is.IsFges: Constructs an instance of the IsFges class using the provided score objects.
isFileLoggingEnabled() - Method in class edu.cmu.tetrad.util.TetradLogger: States whether file logging is enabled or not.
isFitted() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: Whether the report used permutation-fitted nulls rather than the exact chi-square null.
isFitted() - Method in class edu.cmu.tetrad.sem.NNEstimator: Computes the baseline MSE for each variable in the DAG.
isFixed() - Method in class edu.cmu.tetrad.sem.Parameter: isFixed.
isFlatPrior() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: isFlatPrior.
isForbidden(Node) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: isForbidden.
isForbidden(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Determines whether the edge var1 --> var2 is forbidden.
isForbidden(String, String) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns whether the edge var1 --> var2 is forbidden by this structure: var1 lies in a later tier than var2, or they lie in the same tier and that tier forbids edges within itself.
isForbiddenByGroups(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Determines whether the edge var1 --> var2 is forbidden by some forbidden knowledge group.
isForbiddenByTiers(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Determines whether the edge var1 --> var2 is forbidden by the temporal tiers.
IsGfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: IS-GFCI (Instance-Specific GFCI) wrapper for the algcomparison interface.
IsGfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Constructor for Fges.
IsGfci(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Constructor for Fges.
IsGFci - Class in edu.cmu.tetrad.search.is: Instance-specific FGES→FCI wrapper ("IS-GFCI").
IsGFci(IndependenceTest, IsScore, Score) - Constructor for class edu.cmu.tetrad.search.is.IsGFci: Constructs an instance of IsGFci with a specified independence test, scoring function, and population score.
isGraphAmenable(Node, Node, String, int, Set) - Method in class edu.cmu.tetrad.graph.Paths: Determines if the graph is amenable based on the provided nodes, graph type, and maximum allowable length adjustment.
isGraphAmenable(Node, Node, String, int, Set) - Method in class edu.cmu.tetrad.search.RecursiveAdjustment: Checks whether the graph is adjustment-amenable relative to (X, Y) in the sense of Perković et al.: every proper potentially directed path from X to Y starts with a visible / directed edge out of X.
isGuaranteeCpdag() - Method in class edu.cmu.tetrad.search.Pcd: Returns whether the algorithm should prevent cycles during the search.
isGuaranteeCpdag() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Checks if guaranteeCpdag is set to true.
isGuaranteeCpdag() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: isMeekPreventCycles.
isHighlighted() - Method in class edu.cmu.tetrad.graph.Edge: isHighlighted.
isIa() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: isIa.
isIdentified(Graph, Set, Node) - Static method in class edu.cmu.tetrad.search.GRegression: Determines whether the total effect of the treatments on the outcome is identified given the MPDAG, according to Theorem 2 of Guo and Perković (2022) (Perković, 2020, Proposition 3.2): the effect is identified if and only if there is no proper possibly causal path from the treatment set to the outcome that starts with an undirected edge.
isIdentified(Collection, Node) - Method in class edu.cmu.tetrad.search.GRegression: Returns true if the total effect of the treatments on the outcome is identified from the MPDAG.
isIncludeDishAndChipColumns() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: isIncludeDishAndChipColumns.
isIncludeDishAndChipVariables() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: isIncludeDishAndChipVariables.
isIncludeInterceptWhenNotCentered() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Determines whether the intercept should be included when the data is not centered.
isIncompatibleWith(VariableSource) - Method in class edu.cmu.tetrad.bayes.Evidence: Returna true just in case this evidence has a list of variables equal to those of the given variable source.
isIncomplete(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns true iff the given node has a Double.NaN value in it.
isIncomplete(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns true iff the given node has a Double.NaN value in it.
isIncomplete(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Checks if the specified table has any incomplete rows.
isIncomplete(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns true iff the given node has a Double.NaN value in it.
isIncomplete(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns true iff the given node has a Double.NaN value in it.
isIncomplete(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns true iff the given row in the given node has a Double.NaN value in it.
isIncomplete(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Returns true iff the given row in the given node has a Double.NaN value in it.
isIncomplete(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Checks if the specified row of a table is incomplete, i.e., if any of the columns have a NaN value.
isIncomplete(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Returns true iff the given row in the given node has a Double.NaN value in it.
isIncomplete(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Returns true iff the given row in the given node has a Double.NaN value in it.
isIndep() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Returns whether the conditional independence holds or not.
isIndependenceConditional(Node, Node, List) - Method in class edu.cmu.tetrad.search.test.Kci: Tests for conditional independence between two variables given a set of conditioning variables.
isIndependent() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns true if the judgment is for independence, false if for dependence.
isIndependent(IndependenceFact) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Determines whether the given IndependenceFact represents an independent state.
isIndependent(Node, Node, Node...) - Method in class edu.cmu.tetrad.data.IndependenceFacts: isIndependent.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.data.IndependenceFacts: isIndependent.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Determines whether two given nodes are independent given a set of conditioning nodes, and calculates a score.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Checks if node d is independent of node c given the set of nodes in sepset.
isIndependent(Node, Node, Set) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Checks if node d is independent of node c given the set of nodes in sepset.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Checks if node d is independent of node c given the set of nodes in sepset.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Checks if node d is independent of node c given the set of nodes in sepset.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Determines if two nodes are independent given a set of separating nodes.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Determines if two nodes are independent given a set of separating nodes.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Checks if node d is independent of node c given the set of nodes in sepset.
isIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: Checks if node d is independent of node c given the set of nodes in sepset.
isIndependentPooled(ResolveSepsets.Method, List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Tests for independence using one of the pooled methods
isIndependentPooled(ResolveSepsetsDci.Method, List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Tests for independence using one of the pooled methods
isIndependentPooledAverage(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples by taking the average p value
isIndependentPooledAverage(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples by taking the average p value
isIndependentPooledAverageTest(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples by taking the average test statistic CURRENTLY ONLY WORKS FOR CHISQUARE TEST
isIndependentPooledAverageTest(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples by taking the average test statistic CURRENTLY ONLY WORKS FOR CHISQUARE TEST
isIndependentPooledFisher(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples using Fisher's method.
isIndependentPooledFisher(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples using Fisher's method.
isIndependentPooledFisher2(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Eliminates from considerations independence tests that cannot be evaluated (due to missing variables mainly).
isIndependentPooledFisher2(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Eliminates from considerations independence tests that cannot be evaluated (due to missing variables mainly).
isIndependentPooledMudholkerGeorge(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples using Mudholker and George's method
isIndependentPooledMudholkerGeorge(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples using Mudholker and George's method
isIndependentPooledMudholkerGeorge2(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.
isIndependentPooledMudholkerGeorge2(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.
isIndependentPooledRandom(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples by randomly selecting a p value
isIndependentPooledRandom(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples by randomly selecting a p value
isIndependentPooledStouffer(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples using Stouffer et al.'s method
isIndependentPooledStouffer(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples using Stouffer et al.'s method
isIndependentPooledTippett(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples using Tippett's method
isIndependentPooledTippett(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples using Tippett's method
isIndependentPooledWilkinson(List, Node, Node, Set, int) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples using Wilkinson's method
isIndependentPooledWilkinson(List, Node, Node, Set, int) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples using Wilkinson's method
isIndependentPooledWorsleyFriston(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.utils.ResolveSepsets: Checks independence from pooled samples using Worsley and Friston's method
isIndependentPooledWorsleyFriston(List, Node, Node, Set) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Checks independence from pooled samples using Worsley and Friston's method
isInitializedRandomly() - Method in class edu.cmu.tetrad.sem.Parameter: isInitializedRandomly.
isInitSync() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns 'true' if cells in the simulation will be synchronized, 'false' if not.
isInitSync() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: isInitSync.
isInWhichTier(Node) - Method in class edu.cmu.tetrad.data.Knowledge: Returns the index of the tier of node if it's in a tier, otherwise -1.
isInWhichTier(String) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns the index of the tier of this structure containing the variable, or -1.
isIpen() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: isIpen.
isIs() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: isIs.
isItr() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: isItr.
isJointlyMeasured(Node, Node) - Method in class edu.cmu.tetrad.search.IndTestIod: Returns true just in case some dataset contains both of the given variables, so that at least one component test can say anything at all about their relationship.
isJointMarginalSupported() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: isJointMarginalSupported.
isJointMarginalSupported() - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Returns the joint marginal probability of the given variables taking the given values, given the evidence.
isJointMarginalSupported() - Method in class edu.cmu.tetrad.bayes.Identifiability: isJointMarginalSupported.
isJointMarginalSupported() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Indicates whether this updater supports computing joint marginals.
isJointMarginalSupported() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater
isJointMarginalSupported() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Indicates whether this updater supports computing joint marginals.
isLaggedGraph(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Returns true if the graph is a lagged (time-series) graph, i.e., at least one node has a positive lag index in its name.
isLatentVariableAlgorithmByAnnotation(Algorithm) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Checks if the provided algorithm is a latent variable algorithm by inspecting the associated annotation.
isLater(Knowledge, String, String) - Static method in class edu.cmu.tetrad.search.rlcd.TierPrecedence: Whether knowledge places later in a strictly later tier than earlier, in the main tiers or in some additional tier structure, so that later cannot be an ancestor of earlier.
isLegal() - Method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Returns the value of the isLegal record component.
isLegalCpdag() - Method in class edu.cmu.tetrad.graph.Paths: Determines whether the current structure is a legal completed partially directed acyclic graph (CPDAG).
isLegalDag() - Method in class edu.cmu.tetrad.graph.Paths: Checks if the graph passed as parameter is a legal directed acyclic graph (DAG).
isLegalFirstEdge(Node, Node) - Method in interface edu.cmu.tetrad.search.utils.LegalPairs: isLegalFirstEdge.
isLegalMag() - Method in class edu.cmu.tetrad.graph.Paths: Checks if the given graph is a legal mag.
isLegalMag() - Method in class edu.cmu.tetrad.search.utils.PagLegalityCheck.LegalMagRet: Returns whether the graph is a legal MAG.
isLegalMag(Graph, Set) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Determines whether the given graph is a legal Maximal Ancestral Graph (MAG).
isLegalMag(Graph, Set, int) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Runs PagLegalityCheck.isLegalMag(Graph, Set) with a timeout.
isLegalMagQuiet(Graph, Set) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Determines whether the given graph is a legal Maximal Ancestral Graph (MAG) without providing detailed error messages.
isLegalMpag(boolean) - Method in class edu.cmu.tetrad.graph.Paths: Checks if the given Maximal Ancestral Graph (MAG) is legal.
isLegalName(String) - Static method in class edu.cmu.tetrad.util.NamingProtocol: Evaluates whether the given name conforms to the naming protocol.
isLegalPag() - Method in class edu.cmu.tetrad.graph.Paths: Checks if the given Directed Acyclic Graph (DAG) is a Legal Partial Ancestral Graph (PAG).
isLegalPag() - Method in class edu.cmu.tetrad.search.utils.PagLegalityCheck.LegalPagRet: Returns whether the graph is a legal PAG.
isLegalPag(Graph, Graph, Set) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: The same certificate with the Zhang MAG of pag supplied by the caller, for searches that have already computed that MAG for another purpose (to score it, say) and would otherwise pay for the conversion twice per candidate.
isLegalPag(Graph, Set) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Checks if the provided Partial Ancestral Graph (PAG) is a legal PAG.
isLegalPag(Graph, Set, int) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Runs PagLegalityCheck.isLegalPag(Graph, Set) with a timeout.
isLegalPagGivenKnowledge(Graph) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Returns true if g is acceptable to this search as a PAG, given the background knowledge currently set.
isLegalPagModuloKnowledge(Graph, Set, Knowledge, FciOrient, boolean) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Legality certificate for a PAG that carries background-knowledge endpoint marks: legal PAG *modulo background knowledge*.
isLegalPagQuiet(Graph, Set) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Determines whether the provided Partial Ancestral Graph (PAG) is a legal PAG without providing detailed error messages.
isLegalPagQuiet(Graph, Set, int) - Static method in class edu.cmu.tetrad.search.utils.PagLegalityCheck: Runs PagLegalityCheck.isLegalPagQuiet(Graph, Set) with a timeout.
isLegalPair(Node, Node, Node, List, List) - Method in interface edu.cmu.tetrad.search.utils.LegalPairs: isLegalPair.
isLegalPdag() - Method in class edu.cmu.tetrad.graph.Paths: Checks if the given graph is a legal Maximal Partial Directed Acyclic Graph (PDAG).
isLinear() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Determines if the strength kind is linear.
isLocallyAdjacent(int, int) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Whether two observed variables of the partition are adjacent in the stage-1 skeleton.
isLog() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: True iff log output should be produced.
isLogFinalOrientations() - Method in class edu.cmu.tetrad.search.StarFciB: Indicates whether the final FCI orientation engine logs its rule-by-rule output.
isLogFinalOrientations() - Method in class edu.cmu.tetrad.search.StarFciC: Indicates whether the final FCI orientation engine logs its rule-by-rule output.
isLogFinalOrientations() - Method in class edu.cmu.tetrad.search.StarFciD: Indicates whether the final FCI orientation engine logs its rule-by-rule output.
isLogFinalOrientations() - Method in class edu.cmu.tetrad.search.StarFciOriginal: Indicates whether the final FCI orientation engine logs its rule-by-rule output.
isLogging() - Method in class edu.cmu.tetrad.util.TetradLogger: States whether the logger is turned on or not.
isLogIntermediate() - Method in class edu.cmu.tetrad.data.UnmixSpec: Determines if intermediate logging is enabled.
isLogIntermediate() - Method in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Returns whether intermediate steps of the computation should be logged.
isManipulated(int) - Method in class edu.cmu.tetrad.bayes.Evidence: isManipulated.
isManipulated(int) - Method in class edu.cmu.tetrad.bayes.Manipulation: isManipulated.
isManipulated(int) - Method in class edu.cmu.tetrad.sem.SemEvidence: isManipulated.
isManipulated(int) - Method in class edu.cmu.tetrad.sem.SemManipulation: isManipulated.
isMaximal() - Method in class edu.cmu.tetrad.graph.Paths: Determines if the current graph configuration is maximal.
isMConnectedTo(Node, Node, Set, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Determmines whether x and y are d-connected given z.
isMConnectedTo(Node, Node, Set, Map, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Detemrmines whether x and y are d-connected given z.
isMConnectingPath(List, Set, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Checks if the given path is an m-connecting path.
isMConnectingPath(List, Set, Map, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Checks if the given path is an m-connecting path and doens't contain duplicate nodes.
isMeasuredDataSaved() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns 'true' if measured data is being saved out for the getModel simulation, 'false' if not.
isMeekPreventCycles() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: isMeekPreventCycles.
isMeekPreventCycles() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: isMeekPreventCycles.
isMeekPreventCycles() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: isMeekPreventCycles.
isMeekPreventCycles() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: isMeekPreventCycles.
isMissing(DataSet, int, int) - Static method in class edu.cmu.tetrad.data.missing.MissingDataAudit: Returns true just in case the given cell of the dataset is missing, using Tetrad's conventions: DiscreteVariable.MISSING_VALUE for discrete variables and Double.NaN otherwise.
isMissingCell(DataSet, int, int) - Static method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: True if the cell holds a missing value: NaN or an infinity for a continuous variable, or a negative category code for a discrete variable.
isMissingValue(Object) - Method in class edu.cmu.tetrad.data.AbstractVariable: Tests whether the given value is the missing data marker.
isMissingValue(Object) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Tests whether the given value is the missing data marker.
isMissingValue(Object) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Tests whether the given value is the missing data marker.
isMissingValue(Object) - Method in interface edu.cmu.tetrad.data.Variable: Tests whether the given value is the missing data marker.
isMixed() - Method in class edu.cmu.tetrad.data.BoxDataSet: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: isMixed.
isMixed() - Method in interface edu.cmu.tetrad.data.DataModel: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.DataModelList: isMixed.
isMixed() - Method in interface edu.cmu.tetrad.data.DataSet: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.IndependenceFacts: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: isMixed.
isMixed() - Method in class edu.cmu.tetrad.data.TimeSeriesData: isMixed.
isMpdag(Graph) - Static method in class edu.cmu.tetrad.search.GRegression: Returns true if the graph is an MPDAG (only directed and undirected edges, acyclic, and closed under Meek's rules R1-R4).
isMSeparated(Node, Node, Set) - Method in class edu.cmu.tetrad.search.test.MsepTest: Auxiliary method to calculate msep(x, y | z) directly from nodes instead of from variables.
isMSeparatedFrom(Node, Node, Set) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether x and y are d-separated given z.
isMSeparatedFrom(Node, Node, Set, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Determines whether one n ode is d-separated from another.
isMSeparatedFrom(Node, Node, Set, Map, boolean) - Method in class edu.cmu.tetrad.graph.Paths: Checks if two nodes are M-separated.
isNoData() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: isNoData.
isNonAtomic(Cover) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Whether the cover can be subdivided into existing atomic covers (Python isNonAtomic).
isNondirectedEdge(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: isNondirectedEdge.
isNonMonotone() - Method in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: True if the fitted effect changed sign on more than 10% of held-out rows (minority side of PartialEdgeStrengthResult.fracPositiveDeriv above 0.10), so the average marginal effect mixes regions of opposite sign.
isNoOp() - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Indicates whether this CandidateEdit instance represents a no-operation (no-op).
isNull() - Method in class edu.cmu.tetrad.graph.Edge: isNull.
isObserved() - Method in class edu.cmu.tetrad.search.rlcd.Cover: Returns the observed flag.
isOneEdgeFaithfulnessAssumed() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: isOneEdgeFaithfulnessAssumed.
isOnlyCanCauseNextTier(int) - Method in class edu.cmu.tetrad.data.Knowledge: isOnlyCanCauseNextTier.
isParallelized() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isParallelized.
isParameterBoundsEnforced() - Method in interface edu.cmu.tetrad.sem.ISemIm: isParameterBoundsEnforced.
isParameterBoundsEnforced() - Method in class edu.cmu.tetrad.sem.SemIm: isParameterBoundsEnforced.
isParameterizable(Node) - Method in class edu.cmu.tetrad.graph.Dag: Checks if the given node is parameterizable.
isParameterizable(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: isParameterizable.
isParameterizable(Node) - Method in interface edu.cmu.tetrad.graph.Graph: isParameterizable.
isParameterizable(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: isParameterizable.
isParameterizable(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: isParameterizable.
isParameterizable(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks if a node is parameterizable.
isParent(int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Returns true if node p is parent of node c.
isParentOf(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Determines if a given node is a parent of another node in the graph.
isParentOf(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Determines whether node1 is a parent of node2.
isParentOf(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Determines whether node1 is a parent of node2.
isParentOf(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Determines whether node1 is a parent of node2.
isParentOf(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Determines whether node1 is a parent of node2.
isParentOf(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Determines if a given node is a parent of another node in the graph.
isPartiallyOrientedEdge(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: isPartiallyOrientedEdge.
isPdag() - Method in class edu.cmu.tetrad.search.MarkovCheck: Checks whether the given graph is a CPDAG (Completed Partially Directed Acyclic Graph).
isPermissibleCombination(int[]) - Method in class edu.cmu.tetrad.bayes.Proposition: isPermissibleCombination.
isPlateauCapHit() - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Whether some plateau-escape attempt in the last projection hit its state cap (if so, an exit from the plateau may have been missed).
isPolishCapHit() - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Whether the last projection hit the polish-phase move cap (it should not; if it did, the result may not be at a local optimum).
isPositiveDefinite(Matrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Return true if the given matrix is symmetric positive definite--that is, if it would make a valid covariance matrix.
isPossibleAncestor(Node, Node) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns true if x is a possible ancestor of y in the PAG.
isPValueAProbability() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns true just in case IndependenceResult.getPValue() for this test is a probability, so that larger values mean stronger evidence of independence.
isPValueAProbability() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns false: the value this test reports in the p-value slot is a score difference (negative for independence, positive for dependence), not a probability, so SMALLER values mean stronger evidence of independence.
isRankTransform() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Whether continuous variables were rank-transformed before embedding.
isRawDataSaved() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns true if raw data is being saved in the getModel simulation, false if not.
isRawDataSaved() - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: isRawDataSaved.
isRefinementOf(Graph, Graph) - Static method in class edu.cmu.tetrad.search.PdagPagIda: Determines whether one graph is a refinement of another.
isReplicatingGraph() - Method in class edu.cmu.tetrad.search.StarFci: Returns whether graphs replicate across time lags.
isReplicatingGraph() - Method in class edu.cmu.tetrad.search.StarFciB: Returns whether the working PAG replicates across time lags.
isReplicatingGraph() - Method in class edu.cmu.tetrad.search.StarFciC: Returns whether the working PAG replicates across time lags.
isReplicatingGraph() - Method in class edu.cmu.tetrad.search.StarFciD: Returns whether the working PAG replicates across time lags.
isReplicatingGraph() - Method in class edu.cmu.tetrad.search.StarFciOriginal: Returns whether the working PAG replicates across time lags.
isRequired(Node) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: isRequired.
isRequired(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Determines whether the edge var1 --> var2 is required.
isRequiredByGroups(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Determines whether the edge var1 --> var2 is required by some required knowledge group.
isRobustScaleResiduals() - Method in class edu.cmu.tetrad.data.UnmixSpec: Indicates whether robust scaling of residuals is enabled.
isSatisfyBackDoorCriterion(Graph, Node, Node, Set) - Method in class edu.cmu.tetrad.graph.Paths: Check to see if a set of variables Z satisfies the back-door criterion relative to node x and node y.
isSaveCPDAGs() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Checks if the CPDAGs are saved.
isSaveCPDAGs() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isSaveCPDAGs.
isSaveData() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Returns the status of whether data is being saved or not.
isSaveDiagnostics() - Method in class edu.cmu.tetrad.data.UnmixSpec: Retrieves whether to save diagnostics.
isSaveGraphs() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isSaveGraphs.
isSavePags() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Checks if the "savePags" variable is true or false.
isSavePags() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isSavePags.
IsScore - Interface in edu.cmu.tetrad.search.is: Instance‑specific scoring contract used by IS‑FGES / IS‑GFCI and related algorithms.
isSelected(Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: isSelected.
isSelected(Node) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Checks if the specified node is selected in the covariance matrix.
isSelected(Node) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Checks if the specified node is selected in the covariance matrix.
isSelected(Node) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Checks if the specified node is selected in the covariance matrix.
isSelected(Node) - Method in interface edu.cmu.tetrad.data.DataSet: isSelected.
isSelected(Node) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Checks if the specified node is selected in the covariance matrix.
isSelected(Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: isSelected.
isShareVariance() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport: Whether continuous-child fits shared one residual variance across strata.
isShowErrorTerms() - Method in class edu.cmu.tetrad.graph.SemGraph: isShowErrorTerms.
isShowUtilities() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Checks if the utilities are currently being shown.
isShowUtilities() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isShowUtilities.
isSimulatedPositiveDataOnly() - Method in interface edu.cmu.tetrad.sem.ISemIm: isSimulatedPositiveDataOnly.
isSimulatedPositiveDataOnly() - Method in class edu.cmu.tetrad.sem.SemIm: isSimulatedPositiveDataOnly.
isSingular() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: isSingular.
isSkipChecks() - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Whether the trusted-caller fast path is on.
isSortByUtility() - Method in class edu.cmu.tetrad.algcomparison.Comparison: Returns whether utility does the sorting.
isSortByUtility() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isSortByUtility.
isSquare() - Method in class edu.cmu.tetrad.util.Matrix: isSquare.
isSquare(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: isSquare.
isStable(Matrix) - Static method in class edu.cmu.tetrad.search.LingD: Spectral-radius stability with small tolerance.
isStrictSubsetOf(Cover) - Method in class edu.cmu.tetrad.search.rlcd.Cover: Tests whether this cover is a strict subset of another.
isSubsetOf(Cover) - Method in class edu.cmu.tetrad.search.rlcd.Cover: Tests whether this cover is a subset of another.
isSubsetOf(Collection, boolean) - Method in class edu.cmu.tetrad.search.rlcd.Cover: Whether this cover's variables are a subset of the union of the variables of the given covers.
isSubtrek(List, List) - Method in class edu.cmu.tetrad.search.work_in_progress.Dci: Determines whether one trek is a subtrek of another trek
Issue(int, int, BlockSpecTextCodec.Severity, String, String) - Constructor for record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Creates an instance of a Issue record class.
issues() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.ParseResult: Returns the value of the issues record component.
isSupportConnected() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns a boolean indicating whether the support of the distribution is connected or not.
isSupportLowerBoundInclusive() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Deprecated.
This method is deprecated and will be removed in a future release.
isSupportUpperBoundInclusive() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Deprecated.
This method is deprecated and will be removed in a future release.
isSymmetric() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Indicates whether the independence test is symmetric.
isSymmetric() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Determines whether the test is symmetric with respect to two orientations.
isSymmetric(double[][], double) - Static method in class edu.cmu.tetrad.util.MatrixUtils: isSymmetric.
isSymmetrized() - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Indicates whether the test is symmetrized over the (x, y) pair.
isTabDelimitedTables() - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: isTabDelimitedTables.
isTierForbiddenWithin(int) - Method in class edu.cmu.tetrad.data.Knowledge: Checks whether it is the case that any variable is forbidden by any other variable within a given tier.
isTierForbiddenWithin(int) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns whether edges within the given tier are forbidden.
isTimeLagModel() - Method in class edu.cmu.tetrad.graph.Dag: isTimeLagModel.
isTimeLagModel() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: isTimeLagModel.
isTimeLagModel() - Method in interface edu.cmu.tetrad.graph.Graph: isTimeLagModel.
isTimeLagModel() - Method in class edu.cmu.tetrad.graph.LagGraph: isTimeLagModel.
isTimeLagModel() - Method in class edu.cmu.tetrad.graph.SemGraph: isTimeLagModel.
isTimeLagModel() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks if the model is based on time lag.
isTruthAdj() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.ArrowConfusion: Returns true if the truth graph is used to determine adjacency for arrowhead FP's.
isTruthAdj() - Method in class edu.cmu.tetrad.algcomparison.statistic.utils.CircleConfusion: Returns true if the truth graph is used to determine adjacency for circle FP's.
isUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Determines if a triple of nodes is underlined.
isUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: States whether r-s-r is an underline triple or not.
isUnderlineTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: States whether r-s-r is an underline triple or not.
isUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: States whether r-s-r is an underline triple or not.
isUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: States whether r-s-r is an underline triple or not.
isUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Checks whether a given triple (x, y, z) is an underline triple.
isUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: States whether r-s-r is an underline triple or not.
isUndirected(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Checks if the edge between two nodes in the graph is undirected.
isUndirectedEdge(Edge) - Static method in class edu.cmu.tetrad.graph.Edges: isUndirectedEdge.
isUnshieldedCollider(Graph, Node, Node, Node) - Method in interface edu.cmu.tetrad.search.utils.R0R4Strategy: Determines if a given triple is an unshielded collider based on an examination of the data.
isUnshieldedCollider(Graph, Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Checks if a collider is unshielded or not.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.DagSepsets: isUnshieldedCollider.
isUnshieldedCollider(Node, Node, Node, int) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: isUnshieldedCollider.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: isUnshieldedCollider.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: isUnshieldedCollider.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Determines if a node is an unshielded collider between two other nodes.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Checks if a given collider node is unshielded between two other nodes.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: isUnshieldedCollider.
isUnshieldedCollider(Node, Node, Node, int) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: isUnshieldedCollider.
isUseCosSinPairs() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Returns whether cosine and sine pairs are used for RFF ensembles.
isUseHalfZConcatForKx() - Method in class edu.cmu.tetrad.search.test.Kci: Determines whether the half Z concatenation is utilized for Kx operations.
isUseJointXZKernel() - Method in class edu.cmu.tetrad.search.test.Kci: Determines whether the Joint XZ Kernel is enabled.
isUseParentSuperset() - Method in class edu.cmu.tetrad.data.UnmixSpec: Indicates whether the parent superset feature is enabled or disabled.
isUsePseudoinverse() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Checks whether the pseudoinverse is being used in computations.
isUsingRobustStandardErrors() - Method in class edu.cmu.tetrad.sem.SemIm: Reports whether robust (sandwich) standard errors are currently set, in which case getStandardError returns them in place of the normal-theory standard errors.
isValid() - Method in class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Returns whether the result is valid or not.
isValid() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns whether the result is valid or not.
isVariableListEqual(int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Returns true iff the given variable list is equal to the variable list of this term.
isVerbose() - Method in class edu.cmu.tetrad.cluster.KMeans: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.Fasd: Returns the current value of the verbose flag.
isVerbose() - Method in class edu.cmu.tetrad.search.GfciOld: Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.IndTestIod: Returns true if the test is verbose.
isVerbose() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Retrieves the verbosity setting for the independence test.
isVerbose() - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Determines if verbose mode is enabled for logging or output.
isVerbose() - Method in class edu.cmu.tetrad.search.is.IsGFci: Indicates whether verbose mode is enabled for logging or detailed output.
isVerbose() - Method in class edu.cmu.tetrad.search.Pcd: True iff the algorithm should be run with verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.Rfci: Returns whether verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns true if verbose output should be sent to out.
isVerbose() - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns true if verbose output should be sent to out.
isVerbose() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns true if verbose output should be sent to out.
isVerbose() - Method in class edu.cmu.tetrad.search.StarFci: Returns whether verbose output is on.
isVerbose() - Method in class edu.cmu.tetrad.search.StarFciB: Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.StarFciC: Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.StarFciD: Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.StarFciOriginal: Indicates whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Determines whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Determines whether the underlying independence test is in verbose mode.
isVerbose() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns true if this test prints verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.test.FfCi: Determines whether the verbose mode is enabled or not.
isVerbose() - Method in class edu.cmu.tetrad.search.test.FfCi1: Determines whether the verbose mode is enabled or not.
isVerbose() - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Checks whether verbose output is enabled for the current instance.
isVerbose() - Method in class edu.cmu.tetrad.search.test.Gcm
isVerbose() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns true if the test prints verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Returns whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Indicates whether this instance is operating in verbose mode.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Indicates whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Indicates whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Checks if the verbosity flag is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Returns true if verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns true iff verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Returns whether verbose output is printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Returns whether verbose output is printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Retrieves the verbosity setting for the independence test.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Indicates whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Determines whether verbose output is enabled in the underlying independence test wrapped by this instance.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Retrieves the verbosity setting for this instance.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Return True if verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Returns True if verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestGin: Returns the current backend being used for the independence test.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Returns whether verbose output is enabled or not.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Determines if the verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Returns whether verbose output is to be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Returns true if the test prints verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Returns whether verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns the verbose flag indicating whether verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Checks if verbose output is enabled for the instance.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Returns true if the test prints verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns whether verbose output is printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Checks whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.Kci: Indicates whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Checks whether verbose output is enabled for the independence test.
isVerbose() - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Determines whether verbose mode is enabled for the current object.
isVerbose() - Method in class edu.cmu.tetrad.search.test.MsepTest: Returns True just in case verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Indicates whether verbose output is enabled for the PythonKciIndependenceTest.
isVerbose() - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Indicates whether verbose output is enabled for the PythonRcitIndependenceTest.
isVerbose() - Method in class edu.cmu.tetrad.search.test.Rcit: Determines whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns a boolean indicating whether verbose output is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Indicates whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Indicates whether verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Returns whether the object is in verbose mode.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Returns whether the object is in verbose mode.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Returns whether the object is in verbose mode.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Returns whether the object is in verbose mode.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: True iff verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Determines if verbose output is enabled or disabled.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Returns the value of the verbose flag.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Returns whether verbose output should be printed.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Returns true if the test prints verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Returns whether the verbose mode is enabled.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Returns true if the test prints verbose output.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: isVerbose.
isVerbose() - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
isVerbose() - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Checks if the program is in verbose mode.
isViolatedBy(Graph) - Method in class edu.cmu.tetrad.data.Knowledge: isViolatedBy.
isWithReplacement() - Method in class edu.cmu.tetrad.data.BootstrapSampler: This method takes a dataset and a sample size and creates a new dataset containing that number of samples by
iterate() - Method in class edu.cmu.tetrad.bayes.FactoredBayesStructuralEM: This iterate2 method also uses BdeMetricCache but it uses the factorScoreMD method which can handle missing data and latent variables.
iterations - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: The number of iterations performed.
iterations() - Method in class edu.cmu.tetrad.cluster.KMeans: iterations.
ITERATIVE_DEEPENING - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking.Strategy: Iterative deepening on recursion depth (RecursiveBlocking.blockPathsIterativeDeepening(Graph, Node, Node, Set, Set, int, int, int, int, boolean)).
iterator() - Method in class edu.cmu.tetrad.graph.ConsistentDagIterator
iterator() - Method in class edu.cmu.tetrad.graph.LegEnumerator
iterator() - Method in class edu.cmu.tetrad.graph.ValidOrderIterator
iterator() - Method in class edu.pitt.isp.sverchkov.data.DataTableImpl
ItkPredictorSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker: This class contains methods which implement the algorithm described in the paper " " by Ideker, Thorsen and Karp.
ItkPredictorSearch(int, int[][], String[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: Constructor for ItkPredictorSearch.
ItkPredictorSearch.Gene - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker: A gene.
ITR - Static variable in class edu.cmu.tetrad.util.Params: Constant ITR="itr"

J

j() - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Returns the value of the j record component.
jaccard(Set, Set) - Static method in class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer: Computes the Jaccard similarity between two sets.
JoinDatasets - Class in edu.cmu.tetrad.data: Joins two tabular data sets on a key, in the manner of a database join: each row of the left data set is looked up in the right data set by its key value(s), and the right data set's non-key columns are appended to it.
JoinDatasets(String) - Constructor for class edu.cmu.tetrad.data.JoinDatasets: Constructs a join on a single key with the same name on both sides.
JoinDatasets(List, List) - Constructor for class edu.cmu.tetrad.data.JoinDatasets: Constructs a join on the given keys, paired by position.
JoinDatasets.JoinType - Enum Class in edu.cmu.tetrad.data: Which unmatched rows to keep.
JoinDatasets.Result - Record Class in edu.cmu.tetrad.data: The joined data set and a findings-only report.
joined() - Method in record class edu.cmu.tetrad.data.JoinDatasets.Result: Returns the value of the joined record component.
jointEntropy(int[], int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: This method computes the joint entropy of two arrays.
jointEntropy(int[], int[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: jointEntropy.
jointEntropy(int[], int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: This method computes the joint entropy of two arrays.
jointEntropy(int[], int[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: jointEntropy.
jointEntropy(int[], int[][]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: jointEntropy.
jointEntropy(int[], int[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: jointEntropy.
JOptionUtils - Class in edu.cmu.tetrad.util: Stores some utility items for displaying JOptionPane messages.
JsonUtils - Class in edu.cmu.tetrad.util: Dec 9, 2016 5:43:47 PM
JsonUtils() - Constructor for class edu.cmu.tetrad.util.JsonUtils: Constructor for JsonUtils.
judgedIndependent(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: The I-map gate's verdict on one independence fact: the test's p-value against imapAlpha when the test reports a probability, else the test's own verdict.
JunctionTreeInference - Class in edu.cmu.tetrad.bayes: Exact junction-tree inference over a BayesIm supporting: Soft evidence via allowed categories mask (Proposition) Hard evidence via node=category Marginals, conditionals, and joint marginals (all conditional on evidence) This class is designed specifically to satisfy the methods required by JunctionTreeUpdater.
JunctionTreeInference(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.JunctionTreeInference: Constructs a JunctionTreeInference object for performing inference on a Bayesian network represented by the given BayesIm (Bayesian network model interface).
JunctionTreeUpdater - Class in edu.cmu.tetrad.bayes: RowSummingExactUpdater-style updater plumbing, but uses JunctionTreeInference for marginals/joints/conditionals.
JunctionTreeUpdater(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Constructs a new instance of JunctionTreeUpdater using the provided Bayesian network representation.
JunctionTreeUpdater(BayesIm, Evidence) - Constructor for class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Constructs a new instance of JunctionTreeUpdater, using the provided Bayesian network representation and associated evidence.
JunctionTreeUpdater(BayesIm, Evidence, BayesIm, BayesIm, JunctionTreeInference) - Constructor for class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Creates an instance of the JunctionTreeUpdater class which performs updates on a junction tree structure based on Bayesian networks and evidence.
JunctionTreeUpdaterTest - Class in edu.cmu.tetrad.bayes: Test class for verifying the functionality of operations related to junction tree updates in Bayesian networks.
JunctionTreeUpdaterTest() - Constructor for class edu.cmu.tetrad.bayes.JunctionTreeUpdaterTest: Test class for verifying the functionality of the JunctionTreeUpdater component, utilized for probabilistic reasoning and evidence propagation in Bayesian networks.
JUSTIFIED - Enum constant in enum class edu.cmu.tetrad.util.TextTable.Delimiter: Constant JUSTIFIED

K

k() - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Returns the value of the k record component.
K - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Specifies the number of clusters (K) used in the clustering process within the CausalUnmixer framework.
K - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: The number of clusters to be used in the EmUnmix algorithm.
K - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: The number of mixture components in the Gaussian Mixture Model (GMM).
K - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: Represents the number of mixture components in the Gaussian Mixture Model (GMM).
K - Variable in class edu.cmu.tetrad.search.unmix.UnmixResult: The number of clusters (K) determined or specified for a clustering algorithm.
K() - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Returns the value of the K record component.
KAMADA_KAWAI - Enum constant in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: Kamada-Kawai, with its standard parameters (natural edge length 80, spring constant 0.2, stop energy 1, not randomly initialized); see LayoutUtil.kamadaKawaiLayout(Graph, boolean, double, double, double).
kamadaKawaiLayout(Graph, boolean, double, double, double) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: kamadaKawaiLayout.
kamadaKawaiLayout(Graph, boolean, double, double, double, LayoutUtil.LayoutProgressListener) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Overload that accepts an optional progress listener.
KamadaKawaiLayout(Graph) - Constructor for class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Constructs a new Kamada-Kawai layout for the given graph.
kappa() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Returns the value of the kappa record component.
Kci - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for KCI test.
Kci - Class in edu.cmu.tetrad.search.test: The Kci class implements the Kernel-based Conditional Independence (KCI) test for statistical independence between variables.
Kci() - Constructor for class edu.cmu.tetrad.algcomparison.independence.Kci: `Kci` constructor.
Kci(DataSet) - Constructor for class edu.cmu.tetrad.search.test.Kci: Constructs a Kci instance with the given DataSet.
Kci(DataSet, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.Kci: Constructs a Kci instance with an explicit missing-data specification.
Kci(SimpleMatrix, Map, List) - Constructor for class edu.cmu.tetrad.search.test.Kci: Constructs a Kci instance using specified data, variable-to-row mapping, an optional hint matrix, and a list of row indices.
KCI_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant KCI_ALPHA="kciAlpha"
KCI_CUTOFF - Static variable in class edu.cmu.tetrad.util.Params: Constant KCI_CUTOFF="kciCutoff"
KCI_EPSILON - Static variable in class edu.cmu.tetrad.util.Params: Constant KCI_EPSILON="kciEpsilon"
KCI_NUM_BOOTSTRAPS - Static variable in class edu.cmu.tetrad.util.Params: Constant KCI_NUM_BOOTSTRAPS="kciNumBootstraps"
KCI_USE_APPROXIMATION - Static variable in class edu.cmu.tetrad.util.Params: Constant KCI_USE_APPROXIMATION="kciUseApproximation"
Kci.KernelType - Enum Class in edu.cmu.tetrad.search.test: Enum representing the type of kernel function used in kernel-based computations.
KciSmokeTest - Class in edu.cmu.tetrad.search: The KciSmokeTest class contains unit tests to evaluate the performance and correctness of the Kci class, specifically focusing on conditional independence tests.
KciSmokeTest() - Constructor for class edu.cmu.tetrad.search.KciSmokeTest: Default constructor for the KciSmokeTest class.
KcvBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for KVC BIC score.
KcvBicScore - Class in edu.cmu.tetrad.search.score: KCI-inspired local score for continuous variables.
KcvBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.KcvBicScore: Constructs a new instance of the SemBicScore.
KcvBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.KcvBicScore: Constructor for the KcvBicScore class.
KcvBicScore.BandwidthMode - Enum Class in edu.cmu.tetrad.search.score: Enumeration representing different modes for bandwidth computation.
kdDep() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the kdDep record component.
keepReservedLiteral - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Indicates whether reserved literals should be retained in their original form when processing or transforming text or structures.
keepReservedLiteral(boolean) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets whether reserved literal names should be kept in the resulting configuration.
KENDALL - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.SupersetScore: Represents Kendall's tau coefficient, a measure of ordinal association between two ranked variables.
KENDALL - Enum constant in enum class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.ScoreType: Kendall's tau correlation coefficient, which measures ordinal association between variables.
kendallsTau(double[], double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: kendallsTau.
kendallsTauB(double[], double[]) - Static method in class edu.cmu.tetrad.data.DataTransforms: Kendall's tau-b for two columns, computed in O(n log n) by merge-sort inversion counting, with pairwise-complete deletion of rows where either value is NaN.
kernel(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the kernel value for a given input.
Kernel - Interface in edu.cmu.tetrad.search.utils: Gives an implemented that is implemented by classes that evaluate scalar valued kernels
KERNEL_REGRESSION_SAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant KERNEL_REGRESSION_SAMPLE_SIZE="kernelRegressionSampleSize"
KERNEL_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant KERNEL_TYPE="kernelType"
KERNEL_WIDTH - Static variable in class edu.cmu.tetrad.util.Params: Constant KERNEL_WIDTH="kernelWidth"
kernel1(double) - Method in class edu.cmu.tetrad.search.Pairwise: Computes the value of the kernel function 1 for a given input.
kernel2(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the value of the kernel2 function for the given input.
kernel3(double) - Method in class edu.cmu.tetrad.search.Pairwise: This method calculates the value of the kernel function, kernel3, for a given input.
kernel4(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the result of kernel4 function.
kernel5(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the value of the kernel function, kernel5, for the given input.
kernel6(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the value of the kernel function 6 given a parameter z.
kernel7(double) - Method in class edu.cmu.tetrad.search.Pairwise: Computes the value of the kernel7 function for the given value.
kernel8(double) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the value of the kernel8 function for the given input.
KernelGaussian - Class in edu.cmu.tetrad.search.utils: Soces the Gaussian kernel for a given bandwidth.
KernelGaussian(double) - Constructor for class edu.cmu.tetrad.search.utils.KernelGaussian: Creates a new Gaussian kernel with the given bandwidth
KernelGaussian(DataSet, Node) - Constructor for class edu.cmu.tetrad.search.utils.KernelGaussian: Creates a new Gaussian kernel using the median distance between points to set the bandwidth
KernelMarginalLikelihoodScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Kernel Marginal Likelihood Score.
KernelMarginalLikelihoodScore - Class in edu.cmu.tetrad.search.score: KML: Kernel Marginal Likelihood score for mixed (continuous + discrete) parents
KernelMarginalLikelihoodScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.KernelMarginalLikelihoodScore: Constructs a new instance of the SemBicScore.
KernelMarginalLikelihoodScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Constructs a mixed KML score for the given dataset.
KernelMarginalLikelihoodScoreContinuous - Class in edu.cmu.tetrad.search.score: Kernel Marginal Likelihood (KML) score for continuous variables.
KernelMarginalLikelihoodScoreContinuous(DataSet) - Constructor for class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Constructs an instance of KernelMarginalLikelihoodScoreContinuous using the provided data set.
KernelUtils - Class in edu.cmu.tetrad.search.utils: Provides various kernel utilities.
key() - Method in class edu.cmu.tetrad.search.rlcd.Cover: A sortable key for this cover: its variable names, sorted and joined.
key() - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Retrieves the unique key identifying this CandidateEdit instance.
Key difference from OrderedLocalMarkovPropertySinkElimination - Section in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty
Key hyperparameters - Section in class edu.cmu.tetrad.search.test.Rcit
Key references - Section in class edu.cmu.tetrad.search.test.FfCi
keySet() - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Returns the set of all keys in the map.
kind() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Returns the value of the kind record component.
klDivBits - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Mean over configurations and randomized draws of KL(P(Y | pa) ‖ P(Y | pa, X randomized)) in bits.
klDivBitsSd - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Standard deviation of EdgeStrengthResult.klDivBits across repeats; NaN if one repeat or continuous.
kMax() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the kMax record component.
Kmax - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Specifies the minimum number of clusters (Kmin) that can be used in the clustering process within the CausalUnmixer framework.
KMeans - Class in edu.cmu.tetrad.cluster: Implements the "batch" version of the K Means clustering algorithm-- that is, in one sweep, assign each point to its nearest center, and then in a second sweep, reset each center to the mean of the cluster for that center, repeating until convergence.
KMeans - Class in edu.cmu.tetrad.search.unmix: Implements the K-Means clustering algorithm using the k-means++ initialization method and iterative refinement.
KMeans.Result - Class in edu.cmu.tetrad.search.unmix: Represents the result of a clustering operation using the KMeans algorithm.
kmeansRestarts - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: The number of restarts to be performed by the k-means clustering algorithm.
kmeansRestarts - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the number of restarts for the K-means clustering algorithm.
kmeansRestarts - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Specifies the number of restart attempts for the k-means algorithm during initialization.
kMin() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the kMin record component.
Kmin - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Specifies the minimum number of clusters (Kmin) that can be used in the clustering process within the CausalUnmixer framework.
KML_BANDWIDTH_MULTIPLIER - Static variable in class edu.cmu.tetrad.util.Params: Constant KML_BANDWIDTH_MULTIPLIER="kmlBandwidthMultiplier"
KML_FEATURE_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant KML_FEATURE_TYPE="kmlFeatureType"
KML_JITTER - Static variable in class edu.cmu.tetrad.util.Params: Constant KML_JITTER="kmlJitter"
KML_LAMBDA - Static variable in class edu.cmu.tetrad.util.Params: Constant KML_LAMBDA="kmlLambda"
KML_NUM_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant KML_NUM_FEATURES="kmlNumFeatures"
knowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the knowledge object for the builder.
knowledge(Knowledge) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the Knowledge instance to be used by the Builder.
Knowledge - Class in edu.cmu.tetrad.data: Stores information about required and forbidden edges and common causes for use in algorithm.
Knowledge() - Constructor for class edu.cmu.tetrad.data.Knowledge: Constructor for Knowledge.
Knowledge(Knowledge) - Constructor for class edu.cmu.tetrad.data.Knowledge: Constructor for Knowledge.
Knowledge(Collection) - Constructor for class edu.cmu.tetrad.data.Knowledge: Constructor for Knowledge.
KnowledgeEdge - Class in edu.cmu.tetrad.data: Implements a knowledge edge X-->Y as a simple ordered pair of strings.
KnowledgeEdge(String, String) - Constructor for class edu.cmu.tetrad.data.KnowledgeEdge: Constructs a knowledge edge for from-->to.
KnowledgeGroup - Class in edu.cmu.tetrad.data: Represents a "Other Group" in Knowledge, which can be understood as: Group1 -> Group2 where there are edges between all members of Group1 to Group2.
KnowledgeGroup(int) - Constructor for class edu.cmu.tetrad.data.KnowledgeGroup: Constructs an empty instance of a knowledge group.
KnowledgeGroup(int, Set, Set) - Constructor for class edu.cmu.tetrad.data.KnowledgeGroup: Constructs a group given the type.
KnowledgeSatisfied - Class in edu.cmu.tetrad.algcomparison.statistic: Implementation of the KnowledgeSatisfied class.
KnowledgeSatisfied() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied: Constructs the statistic.
KnowledgeTierStructure - Class in edu.cmu.tetrad.data: An additional, named tier structure over (a subset of) the variables of a Knowledge object, independent of the main temporal tiers and of every other tier structure.
KnowledgeTierStructure(String) - Constructor for class edu.cmu.tetrad.data.KnowledgeTierStructure: Constructs an empty tier structure with the given name.
KnowledgeTransferable - Interface in edu.cmu.tetrad.data: Interface implemented by classes that are capable of participating in the transfer of knowledge objects.
Kpc - Class in edu.cmu.tetrad.search.work_in_progress: Deprecated.
Kpc(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Constructs a new PC search using for the given dataset.
ksDep() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the ksDep record component.
ksInd() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the ksInd record component.
ksInd() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the ksInd record component.
ksPValue() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Uniformity: Returns the value of the ksPValue record component.
KURT - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: He4(Y)/sqrt(4!) = (Y^4-6Y^2+3)/sqrt(24): conditional-kurtosis / tail channel.
KURT - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: He4(Y)/sqrt(4!) = (Y^4-6Y^2+3)/sqrt(24): conditional-kurtosis / tail channel.
kurtosis - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The kurtosis.
kurtosis(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: kurtosis.
kurtosis(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: kurtosis.
kurtosis(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: kurtosis.
kurtosis(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: kurtosis.
KURTOSIS - Enum constant in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NonGaussianBonus: Represents a non-Gaussian bonus based on kurtosis.

L

l() - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Returns the value of the l record component.
l2 - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: L2 weight decay.
l2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Represents the regularization strength applied during model training.
l2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: L2 regularization parameter used to prevent overfitting during the training process.
l2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: L2 regularization parameter for the simulator.
l2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: L2 regularization parameter for the simulator.
l2Regularization - Variable in class edu.cmu.tetrad.search.CordEngine1: A regularization parameter used in the model to penalize large weights during optimization.
l2Regularization - Variable in class edu.cmu.tetrad.search.CordEngine2: L2 regularization parameter used to penalize large coefficients in model training.
l2Regularization - Variable in class edu.cmu.tetrad.search.CordEngine3: Represents the L2 regularization parameter used to penalize large model coefficients and prevent overfitting during the model training process.
l2Regularization - Variable in class edu.cmu.tetrad.search.CordEngine4: Specifies the L2 regularization parameter used for controlling the magnitude of model coefficients in regularized regression techniques.
l2Regularization - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the L2 regularization parameter used in optimization algorithms.
label() - Method in interface edu.cmu.tetrad.search.harness.tsc.AlgorithmRunner: Returns a short human-readable label for use in result tables.
label() - Method in class edu.cmu.tetrad.search.harness.tsc.BpcRunner
label() - Method in class edu.cmu.tetrad.search.harness.tsc.FofcRunner
label() - Method in class edu.cmu.tetrad.search.harness.tsc.FtfcRunner
label() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns the value of the label record component.
label() - Method in class edu.cmu.tetrad.search.harness.tsc.TscRunner
LabeledData() - Constructor for class edu.cmu.tetrad.search.unmix.TestCausalUnmixer.LabeledData: Constructs an empty instance of the LabeledData class.
labels - Variable in class edu.cmu.tetrad.search.unmix.KMeans.Result: An array representing the cluster assignments for each data point after the KMeans clustering operation.
labels - Variable in class edu.cmu.tetrad.search.unmix.UnmixResult: An array representing the cluster assignments (labels) for each data point.
labels - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Length-K constraint labels, of the form "X _||_ Y" or "X _||_ Y | Z1,Z2".
LAG - Static variable in interface edu.cmu.tetrad.graph.Node: Constant LAG
lagAscendingComparator() - Static method in class edu.cmu.tetrad.util.NaturalSort: Returns a comparator over possibly-lagged names that puts unlagged names first, then lagged names in order of increasing lag; within a lag group, and among the unlagged names, ordering is by the natural order of the base name, with the raw string as a final tiebreaker so that distinct names never compare equal.
LaggedEdge - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: wrapper class for passing factor+edge via a propertyChange event
LaggedEdge(String, LaggedFactor) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedEdge: Creates new LaggedEdge
LaggedFactor - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Identifies a particular factor (by name) at a particular lag (integer).
LaggedFactor(LaggedFactor) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Copy constructor- creates a new object with the same properties as the original
LaggedFactor(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Constructs a new lagged factor with a given name and time lag.
LagGraph - Class in edu.cmu.tetrad.graph: Implements a graph allowing nodes in the getModel time lag to have parents taken from previous time lags.
LagGraph - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.history: Lag graph.
LagGraph() - Constructor for class edu.cmu.tetrad.graph.LagGraph: Constructor for LagGraph.
LagGraphParams - Class in edu.cmu.tetrad.study.gene.tetrad.gene.graph: LagGraphParams class.
LagGraphParams(Parameters) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Constructor for LagGraphParams.
lagIndex(String) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Returns the lag index encoded in a lagged variable name of the form base:k (as produced by TsUtils.createLagData), or 0 if the name carries no integer lag suffix.
LagReplicationPolicy - Class in edu.cmu.tetrad.graph: SVAR-style lag mirroring policy.
LagReplicationPolicy() - Constructor for class edu.cmu.tetrad.graph.LagReplicationPolicy: Default constructor for the LagReplicationPolicy class.
LagReplicationPolicy(Integer, Integer, int) - Constructor for class edu.cmu.tetrad.graph.LagReplicationPolicy: Constructs a LagReplicationPolicy object with specified minimum lag, maximum lag, and lag step size.
LAMBDA1 - Static variable in class edu.cmu.tetrad.util.Params: Constant LAMBDA1="lambda1"
lambdaParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: A parameter that controls the interpolation between unconditional and structural mechanisms during simulation.
lambdaParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: A parameter representing the regularization strength applied to parent nodes in a learning model.
lambdaParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Interpolation factor for unconditional vs structural mechanism during simulation.
lambdaParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Interpolation factor for unconditional vs structural mechanism during simulation.
LAPLACE - Enum constant in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NoiseModel: A noise model based on the Laplace distribution, also known as the double exponential distribution.
LargeScaleSimulation - Class in edu.cmu.tetrad.sem: Stores a SEM model, pared down, for purposes of simulating data sets with large numbers of variables and sample sizes.
LargeScaleSimulation(Graph) - Constructor for class edu.cmu.tetrad.sem.LargeScaleSimulation: Constructor for LargeScaleSimulation.
LargeScaleSimulation(Graph, List, int[]) - Constructor for class edu.cmu.tetrad.sem.LargeScaleSimulation: Constructor for LargeScaleSimulation.
LAST - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: Keep the last row in file order.
LAST_ID - Static variable in class edu.cmu.tetrad.data.AbstractVariable: The last ID assigned to a variable.
LATENT - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant LATENT
LATENT - Enum constant in enum class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Answer: A further shared latent (or correlated errors).
LATENT_GROUP_SPECS - Static variable in class edu.cmu.tetrad.util.Params: Constant LATENT_GROUP_SPECS="latentGroupSpecs"
LATENT_MEASURED_IMPURE_PARENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant LATENT_MEASURED_IMPURE_PARENTS="latentMeasuredImpureParents"
LatentCommonAncestorFalseNegativeBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
LatentCommonAncestorFalseNegativeBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalseNegativeBidirected: Constructor for LatentCommonAncestorFalseNegativeBidirected.
LatentCommonAncestorFalsePositiveBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
LatentCommonAncestorFalsePositiveBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorFalsePositiveBidirected: Constructor for LatentCommonAncestorFalsePositiveBidirected.
LatentCommonAncestorRecallBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
LatentCommonAncestorRecallBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorRecallBidirected: Constructor for LatentCommonAncestorRecallBidirected.
LatentCommonAncestorTruePositiveBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
LatentCommonAncestorTruePositiveBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LatentCommonAncestorTruePositiveBidirected: Constructor for LatentCommonAncestorTruePositiveBidirected.
LatentGraphRefinement - Class in edu.cmu.tetrad.search.mimic: Shared post-processing refinements applied to the latent-variable graph produced by Trek-MIMIC-style searches.
LatentGraphRefinement(List, SimpleMatrix, DataSet, int, double) - Constructor for class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Constructs a LatentGraphRefinement with the correlation context needed to run both operations.
LatentGroups - Class in edu.cmu.tetrad.search.rlcd: The evolving latent structure discovered by RLCD over one partition of observed variables: a dictionary from covers (latent or observed non-sink groups) to their children, together with the bookkeeping sets the search reads (active set, non-sink set, children of non-atomic covers, pending rank-deficient sets and clusters).
LatentGroups(List, Collection, Set, boolean[][], String) - Constructor for class edu.cmu.tetrad.search.rlcd.LatentGroups: Constructs the structure over a partition of observed variables.
LatentGroups.AdjacencyResult - Record Class in edu.cmu.tetrad.search.rlcd: A signed adjacency matrix with its variable names.
LatentGroups.Entry - Class in edu.cmu.tetrad.search.rlcd: The value stored for each cover key: children, strict subcovers, "fake children" (the measured descendants not fully adjacent in the stage-1 skeleton; only used to decide whether an update changed anything), and the refined flag (always false in the reachable code).
LatentGroups.RankDefSet - Class in edu.cmu.tetrad.search.rlcd: One recorded rank-deficient set: the covers found rank deficient, the non-sinks used in the test, and the list of original rank-deficient sets merged into it (Python keeps this as the third list element; it is carried along but, as in the Python, not used by the downstream logic).
LatentGroupSpec(int, int, int) - Constructor for record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Constructs a specification for a block of groups sharing the same rank and number of children per latent.
LatentLatentEvaluator - Class in edu.cmu.tetrad.search.mimic: Evaluates the adequacy of latent-latent connections in an estimated MIMIC-style graph against a true graph.
LatentLatentEvaluator() - Constructor for class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator: Constructs a new evaluator.
LatentLatentEvaluator.LatentPair - Record Class in edu.cmu.tetrad.search.mimic: Unordered pair of latent names representing latent-latent adjacency.
LatentLatentEvaluator.LatentSignature - Record Class in edu.cmu.tetrad.search.mimic: Signature of a latent node based on its measured parents and measured children.
LatentLatentEvaluator.Report - Class in edu.cmu.tetrad.search.mimic: Result object for a latent-latent adequacy evaluation.
LatentNameAssigner - Class in edu.cmu.tetrad.search.blocks: The LatentNameAssigner class is responsible for assigning suitable latent names to blocks in a Block Specification (BlockSpec) based on provided clusters and naming modes.
LatentNameAssigner.Config - Class in edu.cmu.tetrad.search.blocks: A Config instance with the current builder's configuration settings.
LatentNameAssigner.Config.Builder - Class in edu.cmu.tetrad.search.blocks: Builder class for constructing instances of the Config class with customizable properties.
LatentPair(String, String) - Constructor for record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair: Constructs a LatentPair with two distinct, non-null latent names.
LatentParentRecovery - Class in edu.cmu.tetrad.search: Recovers measured parents of already recovered latent variables.
LatentParentRecovery(DataSet, Graph) - Constructor for class edu.cmu.tetrad.search.LatentParentRecovery: Constructs a latent-parent recovery procedure.
LatentPurifier - Class in edu.cmu.tetrad.search: Implements purification step of Silva et al.'s BuildPureClusters algorithm.
LatentPurifier(Graph, List, List) - Constructor for class edu.cmu.tetrad.search.LatentPurifier: Constructor for the LatentPurifier class, which implements the purification step of Silva et al.'s BuildPureClusters algorithm.
latents() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the latents record component.
latents() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the latents record component.
LatentSignature(Set, Set) - Constructor for record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentSignature: Creates an instance of a LatentSignature record class.
latentStructToEdgeListGraph(DMSearch.LatentStructure) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: latentStructToEdgeListGraph.
LatentStructure() - Constructor for class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: Constructor for LatentStructure.
LatentStructureAlgorithm - Interface in edu.cmu.tetrad.algcomparison.algorithm: A tagging interface for algorithms that can be used for latent structure search.
layoutByCausalOrder(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Lays the graph out in rows by causal order, using an estimate of each node's rendered size.
layoutByCausalOrder(Graph, LayoutUtil.NodeSize) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Lays the graph out in rows by causal order: the nodes with no parents in the top row, then the nodes all of whose parents are above, and so on down.
layoutByKnowledgeIndices(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes of the given graph in tiers based on numerical indices derived from the node names, and positions them accordingly in a 2D layout.
layoutByKnowledgeTiers(Graph, Knowledge) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the nodes of the given graph in tiers based on their grouping as defined in the provided knowledge object.
layoutInCenteredRows(List, LayoutUtil.NodeSize) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Places the given rows of nodes one under another, top row first, each row centered on the widest row.
LayoutUtil - Class in edu.cmu.tetrad.graph: LayoutUtil class.
LayoutUtil() - Constructor for class edu.cmu.tetrad.graph.LayoutUtil: Constructor.
LayoutUtil.DefaultLayout - Enum Class in edu.cmu.tetrad.graph: The layouts that can serve as the default layout: the ones that need nothing but the graph.
LayoutUtil.FruchtermanReingoldLayout - Class in edu.cmu.tetrad.graph: Lays out a graph by linearly summing repulsive force between all nodes and attractive force between adjacent nodes.
LayoutUtil.KamadaKawaiLayout - Class in edu.cmu.tetrad.graph: Lays out a graph by placing springs between the nodes and letting the system settle (one node at a time).
LayoutUtil.LayoutProgressListener - Interface in edu.cmu.tetrad.graph: Callback interface for reporting layout progress.
LayoutUtil.NodeSize - Interface in edu.cmu.tetrad.graph: Supplies the rendered box size of each node, so a layout can space nodes by what they actually take up on screen.
LayoutUtil.NoOpProgressListener - Class in edu.cmu.tetrad.graph: No-op listener for headless / non-GUI use.
LayoutUtil.SwingProgressListener - Class in edu.cmu.tetrad.graph: Swing adapter that wraps a ProgressMonitor.
LE - Enum constant in enum class edu.cmu.tetrad.search.test.IndTestBlocksLemma10.EqualityMode: Deprecated.

Represents the inequality mode "less than or equal to" for the Lemma-10 criterion.
LE - Enum constant in enum class edu.cmu.tetrad.sem.ParamComparison: An enum value representing the "LE" comparison type for a parameter in SEM estimation.
leakyRelu(Double) - Static method in class edu.cmu.tetrad.util.MathUtils: Applies the Leaky Rectified Linear Unit (Leaky ReLU) activation function to the given input.
learnBNRFCI(DataSet, int, Graph) - Method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: learnBNRFCI.
LEARNED_SINGLE - Enum constant in enum class edu.cmu.tetrad.search.blocks.BlocksUtil.NamingMode: Represents a naming mode where latent variables are named based on a single learning mechanism, typically resulting in consolidated names.
learnedSingle_prefersContentNames_andDropsB_whenRanked() - Method in class edu.cmu.tetrad.search.blocks.GiveGoodLatentNamesTest: This test verifies the behavior of the `giveGoodLatentNames` method in the case where the naming mode is set to `LEARNED_SINGLE`.
learningRate - Variable in class edu.cmu.tetrad.search.CordEngine1: The learning rate used by the CordEngine1 class for optimization processes such as gradient updates.
learningRate - Variable in class edu.cmu.tetrad.search.CordEngine2: Represents the learning rate parameter used in optimization algorithms within the CordEngine2 class.
learningRate - Variable in class edu.cmu.tetrad.search.CordEngine3: The learning rate used in the implementation of gradient-based algorithms.
learningRate - Variable in class edu.cmu.tetrad.search.CordEngine4: Represents the learning rate used in the CordEngine4 model training process.
learningRate - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the learning rate used in optimization algorithms or machine learning models to determine the step size during the weight update process.
learnStructureIteration() - Method in class edu.cmu.tetrad.search.Icd: Executes a single ICD iteration (one r-value), advancing the internal state so the next call processes r+1.
leaveOneOut(String) - Method in class edu.cmu.tetrad.data.audit.DeterministicClusters.Fitter: Fits the equation writing the named variable in terms of ALL other analyzed variables (screened to the ones that matter).
LEDOIT_WOLF - Enum constant in enum class edu.cmu.tetrad.search.test.IndTestFisherZ.ShrinkageMode: Represents the Ledoit-Wolf shrinkage mode.
ledoitWolfDelta() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns the value of the ledoitWolfDelta record component.
LEE_AND_HASTIE - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant LEE_AND_HASTIE="Mixed Lee & Hastie"
LeeHastieSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: A version of the Lee and Hastic simulation.
LeeHastieSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: Constructor for LeeHastieSimulation.
LEFT - Enum constant in enum class edu.cmu.tetrad.data.JoinDatasets.JoinType: Keep every left row; unmatched left rows get missing values in the right columns.
LEFT_JUSTIFIED - Static variable in class edu.cmu.tetrad.util.TextTable: Set justification to this if the columns should be left justified.
LEFT_RIGHT - Enum constant in enum class edu.cmu.tetrad.search.Fask.Origin: Oriented by the left-right statistic alone.
leftRight(double[], double[]) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

A left/right judgment for double[] arrays (data) as input.
leftRightDiff(double[], double[], int) - Static method in class edu.cmu.tetrad.search.Fask: Returns a signed left-right "difference/score" per rule.
leftRightDiffResidualized(int, Graph, Node, Node, List, double[][]) - Static method in class edu.cmu.tetrad.search.Fask: Computes the left-right difference residualized on covariates for the given nodes in a graph.
legal() - Static method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Creates a CpdagCheckResult instance representing a legal CPDAG.
LegalCpdag - Class in edu.cmu.tetrad.algcomparison.statistic: The LegalCpdag class implements the Statistic interface and provides methods to evaluate whether an estimated graph is a Legal CPDAG.
LegalCpdag() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LegalCpdag: Constructs a new instance of the LegalCpdag class, representing a statistic used to evaluate whether an estimated graph is a Legal CPDAG.
LEGALITY_PLUS_SEPARATOR - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.CommitGate: FCIT-ZM's gate: commit a removal on MAG legality plus the one test-confirmed separator for the removed pair.
LEGALITY_PLUS_SEPARATOR - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.CommitGate: FCIT-ZM's gate: commit a removal on MAG legality plus the one test-confirmed separator for the removed pair.
LEGALITY_PLUS_SEPARATOR - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.CommitGate: FCIT-ZM's gate: commit a removal on MAG legality plus the one test-confirmed separator for the removed pair.
LEGALITY_PLUS_SEPARATOR - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.CommitGate: FCIT-ZM's gate: commit a removal on MAG legality plus the one test-confirmed separator for the removed pair.
LegalMagRet(boolean, String) - Constructor for class edu.cmu.tetrad.search.utils.PagLegalityCheck.LegalMagRet: Constructs a new LegalMagRet object.
LegalPag - Class in edu.cmu.tetrad.algcomparison.statistic: Legal PAG
LegalPag() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LegalPag: Constructor for LegalPag.
LegalPagRet(boolean, String) - Constructor for class edu.cmu.tetrad.search.utils.PagLegalityCheck.LegalPagRet: Constructs a new LegalPagRet object.
LegalPairs - Interface in edu.cmu.tetrad.search.utils: Determines whether nodes indexed as (n1, center, n2) form a legal pair of edges in a graph for purposes of some algorithm that uses this information.
legendre(int, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the value of the Legendre polynomial of a given degree at a specified point x.
LegendreBicScore - Class in edu.cmu.tetrad.search.score: Legendre BIC score
LegendreBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.LegendreBicScore: Constructs an instance of the LegendreBicScore class.
LegendreBicScore.LocalFit - Record Class in edu.cmu.tetrad.search.score: Represents the result of a local fit in a statistical model.
LegendreLrIndependenceTest - Class in edu.cmu.tetrad.search.test: A likelihood-ratio conditional independence (CI) test built on top of LegendreBicScore local polynomial fits.
LegendreLrIndependenceTest(LegendreBicScore) - Constructor for class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Constructs a LegendreLrIndependenceTest instance with the provided scoring method.
LegendreLrIndependenceTest(LegendreBicScore, boolean) - Constructor for class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Constructs a LegendreLrIndependenceTest instance with the provided scoring mechanism and an option to disable interactions during the test.
LegEnumerator - Class in edu.cmu.tetrad.graph: Lazily enumerates the Loyal Equivalent Graphs (LEGs) of a directed Markov equivalence class by walking the class transformationally, via Zhang & Spirtes' legitimate directed-edge reversals rather than by re-orienting the circle component from scratch.
LegEnumerator(Graph) - Constructor for class edu.cmu.tetrad.graph.LegEnumerator: Enumerates over LEGs starting wiht the given LEG.
LG_MNAR_SIMULATION - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant LG_MNAR_SIMULATION="LG MNAR Simulation"
lgChiSquare() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Linear Gaussian Chi Square score for a given dataset and DAG.
LgMnarDataSimulator - Class in edu.cmu.tetrad.search.utils: The MNARDataSimulator class provides functionality to simulate Missing Not at Random (MNAR) data.
LgMnarSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a continuous MNAR simulation.
LgMnarSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.LgMnarSimulation: Constructs a SemSimulation object with the given RandomGraph object.
lgModelP() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Model P-Value for a given dataset and DAG.
lik() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Returns the value of the lik record component.
like() - Method in class edu.cmu.tetrad.data.BoxDataSet: Returns a dataset with the same dimensions as this dataset, but with no data.
like() - Method in class edu.cmu.tetrad.data.ByteDataBox: like.
like() - Method in interface edu.cmu.tetrad.data.DataBox: Returns a data box of the same dimensions as this one, without setting any values.
like() - Method in interface edu.cmu.tetrad.data.DataSet: Returns a dataset with the same dimensions as this dataset, but with no data.
like() - Method in class edu.cmu.tetrad.data.DoubleDataBox: like.
like() - Method in class edu.cmu.tetrad.data.FloatDataBox: like.
like() - Method in class edu.cmu.tetrad.data.IntDataBox: like.
like() - Method in class edu.cmu.tetrad.data.LongDataBox: like.
like() - Method in class edu.cmu.tetrad.data.MixedDataBox: Returns a data box of the same dimensions as this one, without setting any values.
like() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Returns a dataset with the same dimensions as this dataset, but with no data.
like() - Method in class edu.cmu.tetrad.data.ShortDataBox: like.
like() - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: like.
like() - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: like.
like() - Method in class edu.cmu.tetrad.util.Matrix: like.
like() - Method in class edu.cmu.tetrad.util.Vector: like.
like(String) - Method in class edu.cmu.tetrad.data.AbstractVariable: Creates a new node of the same type as this one with the given name.
like(String) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Creates a new node of the same type as this one with the given name.
like(String) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Creates a new node of the same type as this one with the given name.
like(String) - Method in class edu.cmu.tetrad.graph.GraphNode: Creates a new node of the same type as this one with the given name.
like(String) - Method in interface edu.cmu.tetrad.graph.Node: Creates a new node of the same type as this one with the given name.
LikelihoodResult(double, int, double, int) - Constructor for record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Creates an instance of a LikelihoodResult record class.
LikelihoodRet() - Constructor for class edu.cmu.tetrad.bayes.BayesProperties.LikelihoodRet: Default constructor for the LikelihoodRet class.
line() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns the value of the line record component.
LINEAR - Enum constant in enum class edu.cmu.tetrad.search.test.Kci.KernelType: Represents the linear kernel, useful for linear relationships.
LINEAR_GAUSSIAN_SEM - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant LINEAR_GAUSSIAN_SEM="Linear Gaussian SEM"
LINEAR_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant LINEAR_HIGH="linearHigh"
LINEAR_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant LINEAR_LOW="linearLow"
LINEAR_MIXED - Enum constant in enum class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Kind: Linear coefficient whose sign differs across discrete-parent strata.
LINEAR_NEGATIVE - Enum constant in enum class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Kind: Linear coefficient, non-positive in every stratum.
LINEAR_NON_GAUSSIAN_SEM - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant LINEAR_NON_GAUSSIAN_SEM="Linear Non-Gaussian SEM"
LINEAR_POSITIVE - Enum constant in enum class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Kind: Linear coefficient, non-negative in every stratum.
LINEAR_RIDGE - Enum constant in enum class edu.cmu.tetrad.search.test.Gcm.RegressorType: Indicates regularized linear ridge regression.
LINEAR_SEM - Enum constant in enum class edu.cmu.tetrad.search.MarkovCheckNullReference.SimulatorType: A linear-Gaussian SEM fitted with SemEstimator.
LinearFisherModel - Class in edu.cmu.tetrad.algcomparison.simulation: Linear Fisher Model.
LinearFisherModel(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Constructor for LinearFisherModel.
LinearFisherModel(RandomGraph, List) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.LinearFisherModel: Constructor for LinearFisherModel.
LinearFunction - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements a linear update function, Gi.0 = L(Parents(G0.0)) + ei, where P is a polynomial function and ei is a random noise term.
LinearFunction(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Constructs a polyomial function where each factor is given a zero polynomial.
LinearFunctionND - Class in edu.cmu.tetrad.search.utils: Represents a linear function in an n-dimensional space (R^n -> R).
LinearFunctionND(int, double, double, boolean) - Constructor for class edu.cmu.tetrad.search.utils.LinearFunctionND: Constructor to create a linear function.
LinearGaussian - Annotation Interface in edu.cmu.tetrad.annotation: Data with Gaussian variables that have linear relationships.
linearize() - Method in class edu.cmu.tetrad.data.Linearizer: Runs the transform and returns the linearized dataset (same variables, columns standardized).
Linearizer - Class in edu.cmu.tetrad.data: Linearizing ("un-warping") transform: estimates, for each variable, a monotone Yeo-Johnson transform chosen to make that variable's relations to the other variables as LINEAR as possible, and returns the dataset with all transforms applied (columns standardized).
Linearizer(DataSet) - Constructor for class edu.cmu.tetrad.data.Linearizer: Constructs a linearizer for the given continuous dataset.
LinearQRRegressor - Class in edu.cmu.tetrad.search.unmix: Linear regression using EJML's QR-based solve (stable).
LinearQRRegressor() - Constructor for class edu.cmu.tetrad.search.unmix.LinearQRRegressor: Constructor with default values for ridgeEps and condWarn.
LinearQRRegressor(double, double) - Constructor for class edu.cmu.tetrad.search.unmix.LinearQRRegressor: Constructs a LinearQRRegressor instance with specified ridgeEps and condWarn parameters.
LinearSimExp1 - Class in edu.cmu.tetrad.study.gene.tetrad.gene.simexp: Implements a particular simulation for experimental purposes.
LinearSimExp1(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.simexp.LinearSimExp1: Constructor for LinearSimExp1.
LinearSineSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: A simulation method based on the mixed variable polynomial assumption.
LinearSineSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Constructor for LinearSineSimulation.
Lingam - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: IcaLingam class implements the Algorithm and ReturnsBootstrapGraphs interface.
Lingam - Class in edu.cmu.tetrad.search: LiNGAM (Shimizu et al., 2006), with small numerical hardening to match the stabilized LiNG-D pipeline.
Lingam() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Constructs a new instance of the IcaLingam algorithm.
Lingam() - Constructor for class edu.cmu.tetrad.search.Lingam: Constructor for the IcaLingam class.
LINGAM_PRUNE_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant LINGAM_PRUNE_ALPHA="lingamPruneAlpha"
LINGAM_REFIT_AND_PRUNE - Static variable in class edu.cmu.tetrad.util.Params: Constant LINGAM_REFIT_AND_PRUNE="lingamRefitAndPrune"
LingamStudy - Class in edu.cmu.tetrad.study.examples.conditions: An example script to simulate data and run a comparison analysis on it.
LingD - Class in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag: IcaLingD is an implementation of the Algorithm interface that performs the LiNG-D algorithm for discovering causal models for the linear non-Gaussian case where the underlying model might be cyclic.
LingD - Class in edu.cmu.tetrad.search: LiNG-D (Lacerda, Spirtes, Ramsey, Hoyer, 2012).
LingD() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Constructs a new instance of the IcaLingD algorithm.
LingD() - Constructor for class edu.cmu.tetrad.search.LingD: Default constructor for the IcaLingD class.
listColliderTriples(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Generates a list of triples where a node acts as a collider in a given graph.
listDiscriminatingPaths(Graph, int, boolean) - Static method in class edu.cmu.tetrad.search.utils.FciOrient: Finds and returns a set of discriminating paths in the given graph.
listDiscriminatingPaths(Graph, Node, Node, int, boolean) - Static method in class edu.cmu.tetrad.search.utils.FciOrient: Finds and returns the set of discriminating paths in the given graph, based on the specified parameters.
listRankDeficientNtads(CorrelationMatrix, List, int, int, double, Cca) - Static method in class edu.cmu.tetrad.search.NtadExplorer: Identifies and returns a list of rank-deficient NTAD (near-total aggregate decomposition) results, based on the given correlation matrix and other input parameters.
listUnmeasuredLatents() - Method in interface edu.cmu.tetrad.sem.ISemIm: listUnmeasuredLatents.
listUnmeasuredLatents() - Method in class edu.cmu.tetrad.sem.SemIm: listUnmeasuredLatents.
listwise() - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec for listwise deletion.
LISTWISE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Delete every row containing at least one missing value, once, up front, and analyze the remaining complete cases.
listwiseDelete(DataSet) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: Returns a new dataset consisting of the rows of the given dataset that have no missing entries (listwise deletion).
littlesMcarTest() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: Computes Little's (1988) chi-square test of the MCAR hypothesis for a continuous dataset, using EM estimates of the mean and covariance under the saturated Gaussian model.
lngamma(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: This is a more literal (that is, exact) copy of the log gamma method from Numerical Recipes than the following one.
load(File) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgIo: Loads a model from a JSON file (UTF-8).
loadContinuousData(File, String, char, String, boolean, Delimiter, boolean) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Loads a continuous dataset from a file.
LoadContinuousDataAndGraphs - Class in edu.cmu.tetrad.data.simulation: Load data sets and graphs from a directory.
LoadContinuousDataAndGraphs(String) - Constructor for class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndGraphs: Constructor for LoadContinuousDataAndGraphs.
LoadContinuousDataAndSingleGraph - Class in edu.cmu.tetrad.data.simulation: Load data sets and graphs from a directory.
LoadContinuousDataAndSingleGraph(String) - Constructor for class edu.cmu.tetrad.data.simulation.LoadContinuousDataAndSingleGraph: Constructor for LoadContinuousDataAndSingleGraph.
LoadContinuousDataSmithSim - Class in edu.cmu.tetrad.data.simulation: Load data sets and graphs from a directory.
LoadContinuousDataSmithSim(String) - Constructor for class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: Constructor for LoadContinuousDataSmithSim.
loadCovarianceMatrix(char[], String, DelimiterType, char, String) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Parses a covariance matrix from a char[] array.
loadCovarianceMatrix(File, String, DelimiterType, char, String) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Parses the given files for a tabular data set, returning a RectangularDataSet if successful.
LoadDataAndGraphs - Class in edu.cmu.tetrad.data.simulation: Load data sets and graphs from a directory.
LoadDataAndGraphs(String) - Constructor for class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Constructor for LoadDataAndGraphs.
LoadDataFromFileWithoutGraph - Class in edu.cmu.tetrad.data.simulation: Load data sets and graphs from a directory.
LoadDataFromFileWithoutGraph(String) - Constructor for class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: Constructor for LoadDataFromFileWithoutGraph.
loadDiscreteData(File, String, char, String, boolean, Delimiter, boolean) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Loads a discrete dataset from a file.
LOADED_FROM_FILES - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant LOADED_FROM_FILES="Loaded From Files"
loadGraph(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadGraph.
loadGraphAmatCpdag(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Loads a CPDAG in the "amat.cpdag" format of PCALG.
loadGraphAmatPag(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Loads a PAG in the "amat.pag" format of PCALG.
loadGraphBNTPcMatrix(List, DataSet) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadGraphBNTPcMatrix.
loadGraphJson(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadGraphJson.
loadGraphRMatrix(Graph) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadGraphRMatrix.
loadGraphRuben(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadGraphRuben.
loadGraphTxt(Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: Converts a given graph to human-readable text format.
loadGraphTxt(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadGraphTxt.
loadKnowledge(File, DelimiterType, String) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Loads knowledge from a file.
loadKnowledge(Reader, DelimiterType, String) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Loads knowledge from an arbitrary character stream, in the same tetrad2 format SimpleDataLoader.loadKnowledge(File, DelimiterType, String) reads and DataWriter.saveKnowledge writes.
loadMixedData(File, String, char, String, boolean, int, Delimiter, boolean) - Static method in class edu.cmu.tetrad.data.SimpleDataLoader: Loads a mixed dataset from a file.
loadRSpecial(File) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: loadRSpecial.
LOCAL_MARKOV - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Local Markov property, conditioning on the parents of the target variable.
LOCAL_SWEEP - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.RepairStrategy: Represents the local sweep strategy for repairs.
localCounts(int) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Calculates and returns the local count of objects for a given node.
localCounts(int, int) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Computes the local counts for a given node and its single parent.
localCounts(int, int[]) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Computes the local counts for a given node and its parent set.
localFit(int, int...) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Computes and returns a LocalFit object representing the fit of a model with the specified child node and parent nodes.
LocalFit(double, double, int) - Constructor for record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Creates an instance of a LocalFit record class.
LocalFit(double, double, int) - Constructor for record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Creates an instance of a LocalFit record class.
localFitOnRows(int, int[], int[]) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Performs a local fit on rows for a given child node based on its parent nodes and data rows.
localFitOnRows(int, int[], int[]) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Computes the local fit for a specified child variable based on its parent variables and a subset of rows.
LocalGraphConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for local graph accuracy check --i.e.
LocalGraphConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.LocalGraphConfusion: Constructs a new LocalGraphConfusion object from the given graphs.
LocalGraphPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The LocalGraphPrecision class implements the Statistic interface and represents the Local Graph Precision statistic.
LocalGraphPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LocalGraphPrecision: The default constructor of the LocalGraphPrecision class.
LocalGraphRecall - Class in edu.cmu.tetrad.algcomparison.statistic: LocalGraphRecall implements the Statistic interface and represents the local graph recall statistic.
LocalGraphRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.LocalGraphRecall: The default constructor of the LocalGraphRecall class.
localMarkovInitializePValues(Graph, boolean, IndependenceTest, Map) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Initializes and evaluates p-values for local Markov properties in a given graph.
localScore() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: localScore.
localScore(int) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Calculates the local score for a given node based on the BDeu scoring metric.
localScore(int) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore
localScore(int) - Method in class edu.cmu.tetrad.search.score.GraphScore: Returns the local score of the gien node in the graph.
localScore(int) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore
localScore(int) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns the local score of the gien node in the graph.
localScore(int) - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns the local score of the gien node in the graph.
localScore(int) - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Calculates the local score for a given variable index.
localScore(int) - Method in interface edu.cmu.tetrad.search.score.Score: Returns the local score of the gien node in the graph.
localScore(int[]) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: localScore.
localScore(int, int) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Calculates the local score for a given node and a single parent based on the BDeu scoring metric.
localScore(int, int) - Method in class edu.cmu.tetrad.search.score.GraphScore: localScore.
localScore(int, int) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore
localScore(int, int) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns the (aggregate) local score for a variable given one of its parents, obtained as the weighted average of the local scores from each component score, under the weighting scheme in use.
localScore(int, int) - Method in class edu.cmu.tetrad.search.score.IndTestScore: localScore.
localScore(int, int) - Method in interface edu.cmu.tetrad.search.score.Score: Returns the local score of the graph.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Calculates the local score for a given node and its parent set based on the BDeu scoring metric.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Computes the local score of a given node based on its parents, factoring in an additional term weighted by a parameter `isAlpha`.
localScore(int, int...) - Method in interface edu.cmu.tetrad.search.score.AdditiveLocalScorer: Computes the local score for a target node's index given the indices of its parent nodes in a graphical model.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns log p(D_{A u B}) - log p(D_B) for the embedded columns A of the node and B of its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Calculates the local score for a given node and its parent nodes.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Calculates the local score for a given node and its parent nodes.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.BdeScore: Returns the score for the given parent given its parents, where these are specified as column indices into the dataset.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.BDeuScore: Calculates the local score of a node given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the log marginal likelihood of the family node | parents; see the class Javadoc for the formula.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Computes the local score for the given node index and its parent nodes.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Computes the local score for a given variable and its parent variables.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Calculates the Bayesian Information Criterion (BIC)-based score for a target variable given its index and optional parent variable indices.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Local BIC score for child i with parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: The score of a node given its parents.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: The score of a node given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.EbicScore: Returns the score of the node at index i, given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the local score of a node given its parents, with the prior term added.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Computes the local score for a given variable and its parent set in a Bayesian network.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Computes the local score for a given variable and its parent set in a Bayesian network.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.FfMl
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Computes the local score for a given variable and its parent set in a Bayesian network.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.GicScores: Calculates the sample likelihood and BIC score for index i given its parents in a simple SEM model.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.GraphScore: Calculates the sample likelihood and BIC score for y given its z in a simple SEM model.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Computes the local score for a given target variable and its set of parent variables.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Computes the local score for a given target variable and a set of parent variables.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns the (aggregate) local score for a variable given its parents, obtained as the weighted average of the local scores from each component score, under the weighting scheme in use.
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.IndTestScore: Calculates the sample likelihood and BIC score for i, given its parents in a simple SEM model
localScore(int, int[]) - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Calculates the local score for a given variable index and its parent set.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Computes the local score for a given variable and its parent variables based on a kernel-based regression model.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Computes the local score for a given node and its parent nodes based on the log-likelihood, effective degrees of freedom, and penalty discount.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.MvpScore: The local score of the child given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns the score of the node at index i, given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Computes the local score for a given target variable conditioned on its parent variables.
localScore(int, int...) - Method in interface edu.cmu.tetrad.search.score.Score: The score of a node given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the score for the given node and its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Computes the local score for a given variable and its parents based on specific scoring criteria.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns the score for the child given the parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: The score of a node given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: The score of a node given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: The score of a node given its parents.
localScore(int, int...) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model
localScore(int, int[], int) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns the local score for a variable given its parents from a single component score, unweighted.
localScore(int, int[], int[], int[]) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2
localScore(int, int[], int[], int[]) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Computes the local score for a given node in a Bayesian network based on the provided parent and child configurations.
localScore(int, int[], int[], int[]) - Method in interface edu.cmu.tetrad.search.is.IsScore: Local score for node given parent sets and children (population) context.
localScore(Node, Collection) - Method in interface edu.cmu.tetrad.search.score.AdditiveLocalScorer: Computes the local score for a target node given its set of parent nodes in a graphical model.
localScore(Node, Collection) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Local score: BIC(Y | parents) under additive P-splines, with λ_j by GCV via backfitting.
localScore(Node, List) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Computes the local score for a given node and its potential parent nodes.
localScore(Node, List) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Computes the local score for a given node and its parent nodes in a directed structure.
localScore1(int, int[], int[], int[]) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Local score without structure prior (pure IS+POP BDeu likelihoods).
localScore1(int, int[], int[], int[]) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Computes the local score for a given node using the BIC metric.
localScore1(int, int[], int[], int[]) - Method in interface edu.cmu.tetrad.search.is.IsScore: Deprecated.
localScoreDiff(int, int) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Computes the difference in the local score of a target node when considering a single node as its parent, based on the BDeu scoring metric.
localScoreDiff(int, int) - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the score difference for adding x to the parents of y with no other parents.
localScoreDiff(int, int) - Method in class edu.cmu.tetrad.search.score.GraphScore: Returns the local score difference of the graph.
localScoreDiff(int, int) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore
localScoreDiff(int, int) - Method in interface edu.cmu.tetrad.search.score.Score: Returns the local score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Calculates the difference in the local score of a given node when an additional node is appended to its parent set, based on the BDeu scoring metric.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BdeScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BDeuScore: Calculates the difference in local scores between two nodes y and x, when x is added to a set of nodes z.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BgeScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Computes the difference in local scores for a specified target node and its parent configurations.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Computes the difference in local scores when a new parent variable is added to the parent set of a given target variable.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Score difference localScore(y | z ∪ {x}) - localScore(y | z).
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Calculates localScore(y | z, x) - localScore(z).
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.EbicScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns the score difference for adding x to the parents of y given z.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Calculates the difference in local scores between two configurations of variables.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Calculates the difference in local scores between two configurations of variables.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.FfMl
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Calculates the difference in local scores between two configurations of variables.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.GicScores: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.GraphScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Computes the difference in local scores between two configurations of a target variable and its parent set.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Computes the difference between two local scores: `score(y | z ∪ {x})` and `score(y | z)`.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.IndTestScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Computes the local score difference for a specific variable and its parent set.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Computes the difference in local scores when adding a variable to the conditioning set.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Calculates the difference in local scores based on the given variables.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.MvpScore: Returns localScore(y | z, x) - localScore(y | z).
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Computes the difference in local scores when the variable `x` is added to the conditioning set `z` for variable `y`.
localScoreDiff(int, int, int[]) - Method in interface edu.cmu.tetrad.search.score.Score: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Computes the difference in local scores when a variable is added to the set of parent variables for a target variable.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.ZsbScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Returns the score difference of the graph.
localScoreDiff(int, int, int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Returns the score difference of the graph.
localScoreDiff(int, int, int[], int[], int[]) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2
localScoreDiff(int, int, int[], int[], int[]) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Computes the difference in local scores for a given variable y, when a variable x is added to a conditioning set z, compared to the original set z.
localScoreDiff(int, int, int[], int[], int[]) - Method in interface edu.cmu.tetrad.search.is.IsScore: Difference in local score for y when modifying the relationship w.r.t.
Local score formula - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
localScoreWithoutStructurePrior(int, int[], int[], int[]) - Method in interface edu.cmu.tetrad.search.is.IsScore: Local score for node without using structure priors (pure likelihood/penalty).
LOCK - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant LOCK
locus() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns the value of the locus record component.
log(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the base-2 logarithm of a double value using the specified implementation.
log(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a message if the tracer is enabled.
log(String) - Method in class edu.cmu.tetrad.util.TetradLogger: Logs the given message regardless of the logger's getModel settings.
LOG_COSH - Enum constant in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NoiseModel: A noise model derived from the logarithm of the hyperbolic cosine function, commonly used in robust regression techniques.
LOG_FINAL_ORIENTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant LOG_FINAL_ORIENTATIONS="logFinalOrientations"
LOG_ODDS - Enum constant in enum class edu.cmu.tetrad.search.utils.EdgePriors.Semantics: Prior log-odds beta_ij; neutral element 0.
log10(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the base-10 logarithm of a double value using the specified implementation.
log1p(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the logarithm of a double value using the specified implementation.
logbeta(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Calculates the log beta function of p and q.
logChoose(int, int) - Static method in class edu.cmu.tetrad.util.MathUtils: logChoose.
logCombinations(int, int) - Static method in class edu.cmu.tetrad.util.ChoiceGenerator: Returns the natural logarithm of the number of combinations of a choose b.
logcosh - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The logcosh.
LOGCOSH - Static variable in class edu.cmu.tetrad.search.FastIca: Neg-entropy nonlinearity: logcosh.
logCoshExp() - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the logarithm of the hyperbolic cosine of an exponential value.
logCoshScore(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the log-cosh score for a given array of data.
logData(DataSet, double, boolean, int) - Static method in class edu.cmu.tetrad.data.DataTransforms: Log or unlog data
logData(DataSet, Map) - Static method in class edu.cmu.tetrad.data.DataTransforms: Applies a logarithmic transform to selected variables of the given dataset, each with its own offset, base, and direction.
logDataModelList(String, DataModelList) - Static method in class edu.cmu.tetrad.data.LogDataUtils: logDataModelList.
LogDataUtils - Class in edu.cmu.tetrad.data: Sundry methods for logging data.
logFactorial(int) - Static method in class edu.cmu.tetrad.util.MathUtils: logFactorial.
logIntermediate - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: If true, logs intermediate EM state (iteration log-likelihoods, weights, means) and per-K model selection information to the Tetrad log.
logIntermediate - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: If true, intermediate EM state and per-K model selection information are written to the Tetrad log.
logistic(double) - Static method in class edu.cmu.tetrad.util.MathUtils: logistic.
LogisticRegression - Class in edu.cmu.tetrad.regression: Implements a logistic regression algorithm based on a Javascript implementation by John Pezzullo.
LogisticRegression(DataSet) - Constructor for class edu.cmu.tetrad.regression.LogisticRegression: A mixed data set.
LogisticRegression.Result - Class in edu.cmu.tetrad.regression: The result of a logistic regression.
logitFitIrlsV1(SimpleMatrix, int[], int, double, double) - Static method in class edu.cmu.tetrad.regression.v1.RegressionUtilV1: v1: Fit logistic regression using IRLS on design matrix X (n x p) with binary y01 (length n).
logLik() - Method in record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Returns the value of the logLik record component.
logLik() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Returns the value of the logLik record component.
logLikelihood - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: The observed-data log likelihood at the returned estimates, evaluated after the final M-step.
logLikelihood - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: Represents the overall log-likelihood of the Gaussian Mixture Model (GMM).
logLikelihood() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Returns the value of the logLikelihood record component.
logLikelihood(Graph) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: The maximized Gaussian log-likelihood of the graph, constants included, computed district by district with caching.
logLikelihoodTrace - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: The observed-data log likelihood at the parameters entering each iteration, in order, followed by the value at the returned estimates.
logLikMAG(DoubleMatrix2D, DoubleMatrix2D, DoubleMatrix2D, int[], ICovarianceMatrix) - Static method in class edu.cmu.tetrad.sem.Ricf: Calculates the log-likelihood for a Mixed Ancestral Graph (MAG) model given the input matrices and provides a measure of model fit.
LogNormal - Class in edu.cmu.tetrad.util.dist: Represents a lognormal distribution for purposes of sampling.
LogNormal(double) - Constructor for class edu.cmu.tetrad.util.dist.LogNormal: Constructor for LogNormal.
logSumExp(double[]) - Static method in class edu.cmu.tetrad.search.unmix.EmUtils: Computes the log-sum-exp of an array of doubles.
logSummary(String) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Logs the end-of-run summaries of the gates that are on, when verbose.
logSummary(String, boolean) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Logs the end-of-run summaries of the gates that are on.
LogTransformSpec - Record Class in edu.cmu.tetrad.data: A logarithmic transform to apply to a single variable: the direction (log or unlog), the offset a, and the base.
LogTransformSpec(double, int, boolean) - Constructor for record class edu.cmu.tetrad.data.LogTransformSpec: Creates an instance of a LogTransformSpec record class.
LogUtils - Class in edu.cmu.tetrad.util: Sets up streams for logging via the Java logging API.
LogUtilsSearch - Class in edu.cmu.tetrad.search.utils: Contains utilities for logging search steps.
LogUtilsSearch() - Constructor for class edu.cmu.tetrad.search.utils.LogUtilsSearch: Constructor.
LongDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of long data.
LongDataBox(long[][]) - Constructor for class edu.cmu.tetrad.data.LongDataBox: Constructs a new data box using the given 2D long data array as data.
LongToWide - Class in edu.cmu.tetrad.data: Reshapes a long-format data set (one row per unit per occasion, e.g., one row per student per assessment) into a wide-format data set (one row per unit, one column per occasion per measured variable).
LongToWide(String, String...) - Constructor for class edu.cmu.tetrad.data.LongToWide: Constructs a transform with the given row key(s) and column key.
LongToWide.Aggregation - Enum Class in edu.cmu.tetrad.data: How multiple long rows for the same (unit, level) combination are combined into one wide cell.
LongToWide.Result - Record Class in edu.cmu.tetrad.data: The wide data set, the unit keys in row order (one list of row-key values per wide row), and a findings-only report.
LOW_SAMPLE_RATIO - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The ratio of sample size to number of variables is small, limiting the conditioning depth that can be trusted.
LOWER - Enum constant in enum class edu.cmu.tetrad.sem.DagMetricResult.Better: Indicates that a lower value is considered preferable in the context of metric evaluation.
LOWER_BOUND - Static variable in class edu.cmu.tetrad.util.Params: Constant LOWER_BOUND="lowerBound"
LPAREN - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Left parenthesis.
lr - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: SGD learning rate.
lr - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Represents the learning rate used in optimization algorithms during model training.
lr - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Specifies the learning rate used in the optimization algorithm during training.
lr - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Learning rate for the simulator.
lr - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Learning rate for the simulator.
LT - Enum constant in enum class edu.cmu.tetrad.sem.ParamComparison: Represents the "LT" comparison type for a parameter in SEM estimation.
LT - Enum constant in enum class edu.cmu.tetrad.sem.ParamConstraintType: Represents a parameter constraint type LT (less than).
LTestBoolSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu: This is merely a main program used to read a binarized measurement data set and to instantiate a BoolSearch and run one or more search methods of that instance.
LTestBoolSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: This is merely a main program used to read a binarized measurement data set and to instantiate a BoolSearch and run one or more search methods of that instance.
LTester - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua: Class that makes some very simple tests on the classes LtMatrix, Graph, and Biolingua
LTestPredictorSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker: LTestPredictorSearch class.
LTestQnet3 - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu: LTestQnet3 class.
LTestReveal - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: LTestReveal class.
LTestRevealSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal: This is merely a main program used to read a binarized measurement data set and to instantiate a RevealSearch and run one or more search methods of that instance.
LTestRevealSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: This is merely a main program used to read a binarized measurement data set and to instantiate a RevealSearch and run one or more search methods of that instance.
LTestSsys1 - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu: LTestSsys1 class.
LTMatrix - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a space-efficient Lower Triangular Matrix of elements of type short
LTMatrix(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Creates a lower triangular matrix reading it from file fname.
LTMatrix(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Creates a lower triangular matrix with nrows rows.
LTMatrixF - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a space-efficient Lower Triangular Matrix of elements of type float
LTMatrixF(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Creates a lower triangular matrix reading it from file fname.
LTMatrixF(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Creates a lower triangular matrix with nrows rows.
LV_HEURISTIC_ONLY - Static variable in class edu.cmu.tetrad.util.Params: Constant LV_HEURISTIC_ONLY="lvHeuristicOnly"
LvBossExperimental - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Wrapper for LV-BOSS: the fully score-based latent-variable search that embeds a MAG projection inside a BOSS-style search over orders, scored by the exact RICF Gaussian MAG BIC (MagRicfBicScorer).
LvBossExperimental - Class in edu.cmu.tetrad.search.work_in_progress: LV-BOSS: a fully score-based latent-variable search that embeds the MAG projection inside a BOSS-style search over orders.
LvBossExperimental() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvBossExperimental: Constructs the wrapper.
LvBossExperimental(MagRicfBicScorer) - Constructor for class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: Constructs the search over the given scorer.
LvHeuristic - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: This class represents the FCIT algorithm, which is an implementation of the FGES-FCI algorithm for learning causal structures from observational data using the BOSS algorithm as an initial CPDAG and using all score-based steps afterward.
LvHeuristic - Class in edu.cmu.tetrad.search: LV-Heuristic is a class that implements the IGraphSearch interface.
LvHeuristic() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: This class represents LV-Heuristic algorithm.
LvHeuristic(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: FCIT is a class that represents a FCIT algorithm.
LvHeuristic(Score) - Constructor for class edu.cmu.tetrad.search.LvHeuristic: LV-Heuristic constructor.

M

MAG - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.COMMIT_ROUTE: Project back through the canonical Zhang MAG (the route the paper calls FCIT-ZM).
MAG - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.COMMIT_ROUTE: Project back through the canonical Zhang MAG (the route the paper calls FCIT-ZM).
MAG - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.COMMIT_ROUTE: Project back through the canonical Zhang MAG (the route the paper calls FCIT-ZM).
MAG - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.GraphType: Represents a Maximal Ancestral Graph (MAG) within the context of the graph analysis.
MAG - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: A maximal ancestral graph: directed and bidirected edges, admitting latent confounders, with an edge between every pair of vertices not m-separated by any set.
MagCgBicScore - Class in edu.cmu.tetrad.search.work_in_progress: Gives a BIC score for a linear, Gaussian MAG (Mixed Ancestral Graph).
MagCgBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Constructor.
MagCgBicScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Constructor.
MagCgScore - Class in edu.cmu.tetrad.algcomparison.statistic: Takes a MAG in a PAG using Zhang's method and then reports the MAG DG BIC score for it.
MagCgScore() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MagCgScore: Constructs the statistic.
MagDgBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for degenerate Gaussian BIC score
MagDgBicScore - Class in edu.cmu.tetrad.search.work_in_progress: Gives a BIC score for a linear, Gaussian MAG (Mixed Ancestral Graph).
MagDgBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.MagDgBicScore: Initializes a new instance of the DegenerateGaussianBicScore class.
MagDgBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Constructor.
MagDgBicScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Constructor.
MagDgScore - Class in edu.cmu.tetrad.algcomparison.statistic: Takes a MAG in a PAG using Zhang's method and then reports the MAG DG BIC score for it.
MagDgScore() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MagDgScore: Constructs the statistic.
MagOrderProjection - Class in edu.cmu.tetrad.search.work_in_progress: Projects a total order of the observed variables to a high-scoring ancestral graph consistent with that order: the latent-variable analog of BOSS's grow-shrink projection, and the inner step of the planned LV-BOSS.
MagOrderProjection(MagRicfBicScorer) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Constructs the projection over the given scorer.
MagRicfBicScorer - Class in edu.cmu.tetrad.search.score: Exact Gaussian BIC for a MAG (more generally, for any ADMG over the data's variables with directed and bidirected edges only), computed by maximum likelihood via RICF (RicfEjml), with a district-wise decomposition and a cache so that searches re-scoring many similar graphs pay only for the districts that changed.
MagRicfBicScorer(DataSet) - Constructor for class edu.cmu.tetrad.search.score.MagRicfBicScorer: Constructs the scorer for a continuous data set (its covariance matrix is taken).
MagRicfBicScorer(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.MagRicfBicScorer: Constructs the scorer for a covariance matrix.
MagSemBicScore - Class in edu.cmu.tetrad.search.work_in_progress: Gives a BIC score for a linear, Gaussian MAG (Mixed Ancestral Graph).
MagSemBicScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Constructor.
MagSemBicScore(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Constructor.
MagSemScore - Class in edu.cmu.tetrad.algcomparison.statistic: Takes a MAG in a PAG using Zhang's method and then reports the MAG SEM BIC score for it.
MagSemScore() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MagSemScore: Constructs the statistic.
magToPag(Graph, boolean, int) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Converts a maximal ancestral graph (MAG) into a partial ancestral graph (PAG).
MagToPag - Class in edu.cmu.tetrad.search.utils: Converts a MAG to a PAG.
MagToPag(Graph) - Constructor for class edu.cmu.tetrad.search.utils.MagToPag: Constructs a new FCI search for the given independence test and background knowledge.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.ExampleCompareFromFiles: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.ExampleCompareSimulation: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.ExampleCompareSimulation2: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.ExampleNonlinearSave: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.ExampleSave: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.MVPCompareFromFiles: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.RunConfig: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.RunKemmeren: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.Save: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.SaveDGSimulations: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.TestBoss: main.
main(String...) - Static method in class edu.cmu.tetrad.algcomparison.examples.TestDegenerateGaussian: The main method initializes various parameters, statistics, algorithms, simulations, and a comparison object.
main(String[]) - Static method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: main.
main(String[]) - Static method in class edu.cmu.tetrad.classify.ClassifierMbDiscrete: Runs MbClassify using moves-line arguments.
main(String[]) - Static method in class edu.cmu.tetrad.data.GeneralAndersonDarlingTest: main.
main(String[]) - Static method in class edu.cmu.tetrad.graph.Paths.AllCliquesAlgorithm: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.AdjustmentHarness: Entry point of the AdjustmentHarness program.
main(String[]) - Static method in class edu.cmu.tetrad.search.CordEngine1: The main entry point for the CordEngine1 program.
main(String[]) - Static method in class edu.cmu.tetrad.search.CordEngine2: Entry point for the CordEngine2 application.
main(String[]) - Static method in class edu.cmu.tetrad.search.CordEngine3: Main entry point for the CordEngine3 application.
main(String[]) - Static method in class edu.cmu.tetrad.search.CordEngine4: The main method serves as the entry point for the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.CordEngine5: Entry point for the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.BossIlsFlopStudy: The main entry point for the application, responsible for the execution and comparison of different algorithms (BOSS and FLOP variants) across a specified number of repetitions.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.ChemMaskHarness: Runs the comparison.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.CiTestHarness: Entry point of the program.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.CpwLingamStudy: Runs the four experiments, prints the summary tables, and optionally writes the per-replicate counts as CSV.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.DependentWildBootstrapUsage: Entry point; see the class Javadoc for arguments.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorFacadeCheck: Main method that evaluates various aspects of the EdgePriorFacade and its behavior, including independence testing, prior pair counts, and annotation effects under differing conditions.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorsAnchorCheck: Entry point for the EdgePriorsAnchorCheck program.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorsCheck: The main entry point for the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.EdgePriorsUsage: The entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.FaskCpwOrientationHarness: Runs the orientation harness for FASK and CPW across all five pairwise left-right rules and prints a comparative summary table.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.FaskLeftRightHarness: Main method for executing the FASK leftRightDiff harness.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.FaskTwoCycleRuleHarness: Prints the acceptance rate of the two-cycle test for each rule, system, noise shape and sample size.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.FcitBenchmarkHarness: Executes the main benchmark logic for evaluating graph structure learning algorithms under various conditions with controlled parameters.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.FcitBenchmarkHarness100: Entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.Figure4Harness: Entry point for the program, which generates synthetic data, runs analysis pipelines, and prints relevant results.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.GraphoidClosureHarness: The main method serves as the entry point for the program execution.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.GuidedWitnessHarness: Entry point.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.MissingDataRecoveryHarness: Runs the sweep.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.MissingDataScoreDiagnostics: Runs the diagnostics and prints one table per mechanism.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.MixedMissingDataHarness: Runs the comparison.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.MultiSubjectCyclicComparison: Runs the comparison.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomCounterexampleHarness: Main
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomFcitReachabilityHarness1: Main.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator10: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator11: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator12: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator13: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator14: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator15: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator16: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator17: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator2: Main method
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator3: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator4: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator5: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator6: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator7: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator8: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator9: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelEnumeratorSingleEdgeDeletions: Main method.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.PhantomKernelSampler: Runs the sampler.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.Pke5DeterminismCheck: Runs the check.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.RlcdAccuracyStudy: Runs the study.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.RlcdTwoIndicatorStudy: Runs the study.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.SepsetComparisonHarness: The entry point of the program.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.SepsetComparisonPagHarness: Entry point for the SepsetComparisonPagHarness application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.StratifiedRankHarness: Runs the demonstrations.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.StratifiedRealDataRunner: Runs the comparison; see the class comment for the arguments.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi: The entry point of the SimulationStudyTsbi program.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.tsc.SimulationStudy: Runs both studies with default settings.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.VarsortabilityHarness: Executes the main simulation and evaluation routine to assess the sensitivity of the BOSS algorithm to varsortability (VS).
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.VertexRepairBattery: The entry point for the VertexRepairBattery program.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.VertexRepairBattery2: The entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.VertexRepairSimulation: The entry point of the VertexRepairSimulation application.
main(String[]) - Static method in class edu.cmu.tetrad.search.harness.VertexRepairSpeedBenchmark: Entry point.
main(String[]) - Static method in class edu.cmu.tetrad.search.is.TestIGFCI_TCGA: The main entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.is.TestISFGS_TCGA: The entry point of the program.
main(String[]) - Static method in class edu.cmu.tetrad.search.mimic.RunEvaluation: The main method serves as the entry point for the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.NtadExplorerHarness: The entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.PagSigmaHatHarness: Runs the comparison.
main(String[]) - Static method in class edu.cmu.tetrad.search.RecursiveBlockingMemoEquivalenceHarness: The entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.ReproPke14Violation: The main method serves as the entry point for the program.
main(String[]) - Static method in class edu.cmu.tetrad.search.test.CordEngine: Main method that serves as the entry point for executing the statistical calibration, power sweep, and single-run tests.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.GaussianProcessRBF: The main method serves as the entry point for the Gaussian Process simulation demonstration.
main(String...) - Static method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: The main methods.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.HungarianAlgorithm: main.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.LgMnarDataSimulator: The entry point for the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.LinearFunctionND: The main method demonstrates the creation of a linear function with specified parameters, prints its coefficients and intercept, and evaluates the function for a set of sample inputs.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.MsepVertexCutFinder: The entry point for the MsepVertexCutFinder application.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptron: The main method serves as the entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunction1D: The entry point of the application that demonstrates the usage of the MultiLayerPerceptronFunction1D class by defining a random function with specific parameters and evaluating it over a range of inputs.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunctionND: The main method demonstrating the creation and evaluation of a multi-layer perceptron function with random initialization and specific parameters.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra: A simple test of the Dijkstra algorithm.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.RandomFourier: The main method demonstrates the usage of the RandomFourier class.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.RandomFunctions: Executes a series of demonstrations of random mathematical function generators.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.RandomMonotonicPiecewiseLinear: The main method serves as the entry point for testing the functionality of the RandomMonotonicPiecewiseLinear class.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.RandomPiecewiseLinear: The main method serves as the entry point of the program.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.RandomPiecewiseSpline: The main method serves as the entry point for the program.
main(String[]) - Static method in class edu.cmu.tetrad.search.utils.RandomRBF: The entry point of the application.
main(String[]) - Static method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Self-contained calibration demo.
main(String...) - Static method in class edu.cmu.tetrad.search.work_in_progress.DemixerMMLKun: main.
main(String[]) - Static method in class edu.cmu.tetrad.sem.PlotMatrixMimicDriver: Entry point for the PlotMatrixMimicDriver application.
main(String[]) - Static method in class edu.cmu.tetrad.sem.TrainAndResimulate: The entry point for the application.
main(String...) - Static method in class edu.cmu.tetrad.simulation.GdistanceApply: main.
main(String...) - Static method in class edu.cmu.tetrad.simulation.GdistanceRandomApply: main.
main(String...) - Static method in class edu.cmu.tetrad.simulation.GdistanceTest: The main method generates random graphs, loads a location map, calculates the distance between two graphs, and saves the output to a file.
main(String...) - Static method in class edu.cmu.tetrad.simulation.HsimCompareRepeat: main.
main(String[]) - Static method in class edu.cmu.tetrad.simulation.HsimEvalFromData: main.
main(String[]) - Static method in class edu.cmu.tetrad.simulation.HsimRepeatAuto: main.
main(String[]) - Static method in class edu.cmu.tetrad.simulation.HsimSchedule: main.
main(String[]) - Static method in class edu.cmu.tetrad.simulation.HsimStudy: main.
main(String[]) - Static method in class edu.cmu.tetrad.simulation.HsimStudyAuto: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.BryanSensitivityStudy: Run the example.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.ExampleCompareFromFiles: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.ExampleCompareSimulation: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.ExampleCompareSimulationDiscrete: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.ExampleFirstInflection: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.ExampleSave: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.ExampleStars: main.
main(String...) - Static method in class edu.cmu.tetrad.study.examples.conditions.LingamStudy: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.LTestBoolSearch: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.LTestQnet3: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.LTestSsys1: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaRunner: Main method, runs the Biolingua algorithm
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.LTester: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.LTestPredictorSearch: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ReadControl: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ReadIdeker: The main method processes input data and performs various calculations and predictions.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.LTestRevealSearch: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestBoolSearch: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestRevealSearch: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.SimulateNetwork: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.SimulateNetworkMain: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.simexp.LinearSimExp1: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.util.HandleyConvert: Converts the graph file from the moves line.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.BossMissingDataStudy: The entry point for the BossMissingDataStudy program, coordinating the execution of the missing data study under various conditions and configurations.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.BossMultipleImputationStudy: The entry point: for each mechanism, simulates data, injects missingness, runs every arm on every rep, and prints a summary table of adjacency precision and recall, arrowhead precision and recall, structural Hamming distance where defined, and seconds per rep.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.DropoutZeroDeletionStudy: Entry point.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.ImputationRegimeStudy: The entry point: runs the mixed phase (degenerate-Gaussian score) and the nonlinear phase (basis-function score), printing a summary table per phase.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.NabiBoundaryStudy: Entry point: one table per mechanism, reps in parallel.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.NabiIdentificationProbe: Entry point; runs the three checks and prints the results.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.StroblTestwiseFciStudy: Entry point: one table per (mechanism, n), scored against the unselected true PAG.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.TestwiseDeletionStudy: The entry point: for each mechanism, sample size, and missing rate, simulates data, injects missingness, runs every arm on every rep, and prints a summary table of adjacency precision and recall, arrowhead precision and recall, structural Hamming distance where defined, and seconds per rep.
main(String[]) - Static method in class edu.cmu.tetrad.study.missingness.WitteReplicationStudy: Entry point.
main(String...) - Static method in class edu.cmu.tetrad.study.performance.ComparisonScript: main.
main(String...) - Static method in class edu.cmu.tetrad.study.performance.ExploreComparison: main.
main(String...) - Static method in class edu.cmu.tetrad.study.performance.PerformanceTests: main.
main(String...) - Static method in class edu.cmu.tetrad.study.performance.PerformanceTestsDan: main.
main(String[]) - Static method in class edu.cmu.tetrad.study.RBExperiments: Main method for executing the RBExperiments class and running a series of experiments.
main(String[]) - Static method in class edu.cmu.tetrad.util.MultiDimensionalTaylorDegree2: The main method demonstrates the use of the MultiDimensionalTaylorDegree2 class to evaluate a second-degree Taylor series expansion at a specific point.
main(String[]) - Static method in class edu.cmu.tetrad.util.RandomPiecewiseLinearBijective: The entry point of the program that demonstrates the use of the RandomPiecewiseLinearBijective class.
main(String[]) - Static method in class edu.cmu.tetrad.util.RandomUtil: The main method serves as the entry point for the program execution.
main(String[]) - Static method in class edu.cmu.tetrad.util.TaylorSeriesDerivativeCheck: The main method initializes an array of derivatives and tests whether the first derivative f'(x) is positive in a specified range.
main(String[]) - Static method in class edu.cmu.tetrad.util.UniformityTest: The main method of the UniformityTest class.
main(String[]) - Static method in class edu.pitt.dbmi.algo.bayesian.constraint.inference.BayesianConstraintInference: Main method.
main(String[]) - Static method in class edu.pitt.dbmi.algo.bayesian.constraint.inference.BayesianConstraintInferenceTest: Main method.
main(String[]) - Static method in class edu.pitt.isp.sverchkov.data.AdTreeTest: Test the AD tree
majority - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Majority method
majority - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The majority method.
Majority - Enum constant in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Choose an edge iff its prob.
MAJORITY - Enum constant in enum class edu.cmu.tetrad.search.Ccd.ColliderRule: Orient by majority vote over the separating sets of (a, c); ties are left unoriented.
makeBayesIm(String) - Static method in class edu.cmu.tetrad.bayes.BayesBifParser: Parses a string in BayesBif format and converts it into a BayesIm object.
makeBlockVariables(List, DataSet) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Creates a list of block variables based on the provided list of blocks and the dataset.
makeDAG(int, double, int) - Method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: makeDAG.
makeDisjointBySize(List) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Creates a list of disjoint blocks from the provided list of blocks, prioritizing larger blocks first.
makeDisjointSpec(DataSet, List) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Constructs a BlockSpec object using the provided DataSet and block definitions, ensuring that the blocks are made disjoint by prioritizing larger blocks first.
makeGraph(Matrix, List) - Static method in class edu.cmu.tetrad.search.LingD: Constructs a directed graph based on the input binary adjacency matrix and a list of nodes.
makeGraph(List) - Method in class edu.cmu.tetrad.search.Cstar: Makes a graph of the estimated predictors to the effect.
makeLatexTable(String[][]) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: makeLatexTable.
makeMixedData(DataSet, Map) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: makeMixedData.
makeMixedData(DataSet, Map, int) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: makeMixedData.
makeMixedGraph(Graph, Map) - Static method in class edu.cmu.tetrad.algcomparison.simulation.LeeHastieSimulation: makeMixedGraph.
makeTable(LinkedList) - Method in class edu.cmu.tetrad.search.Cstar: Returns a text table from the given records
makeValidOrder(List) - Method in class edu.cmu.tetrad.graph.Paths: Reorders the given order into a valid causal order for either a DAG or a CPDAG.
makeVertIntBox(DataSet) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: makeVertIntBox.
ManipulatingBayesUpdater - Interface in edu.cmu.tetrad.bayes: Interface for a Bayes updating algorithm that's capable of doing manipulation.
Manipulation - Class in edu.cmu.tetrad.bayes: Stores information for a variable source about evidence we have for each variable as well as whether each variable has been manipulated.
Manipulation(Manipulation) - Constructor for class edu.cmu.tetrad.bayes.Manipulation: Copy constructor.
Manipulation(VariableSource) - Constructor for class edu.cmu.tetrad.bayes.Manipulation: Constructs a container for evidence for the given Bayes IM.
MANUAL - Enum constant in enum class edu.cmu.tetrad.bayes.MlBayesIm.InitializationMethod: Represents a manual initialization method.
MANUAL - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The lambda is set manually.
ManualLagGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.graph: Constructs as a (manual) update graph.
ManualLagGraph(ManualLagGraphParams) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Using the given parameters, constructs an BasicLagGraph.
ManualLagGraphParams - Class in edu.cmu.tetrad.study.gene.tetrad.gene.graph: Stores the parameters needed to generate a new lag graph, whether randomized or manually constructed.
ManualLagGraphParams() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraphParams: Constructs a new parameters object.
mapLabels(double[][]) - Static method in class edu.cmu.tetrad.search.unmix.EmUtils: Maps the most likely label for each data point based on the given responsibility matrix.
Mapping - Class in edu.cmu.tetrad.sem: Maps a parameter to the matrix element where its value is stored in the model.
Mapping(ISemIm, Parameter, Matrix, int, int) - Constructor for class edu.cmu.tetrad.sem.Mapping: Constructs matrix new mapping using the given freeParameters.
MAR - Enum constant in enum class edu.cmu.tetrad.data.MissingnessInjector.MechanismClass: Deletion depends only on values certain to be observed.
marginalY1() - Method in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: Computes the marginal probability density for the first dimension (Y1) by integrating the joint density along the second dimension (Y2).
marginalY2() - Method in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: Computes the marginal probability density for the second dimension (Y2) by integrating the joint density along the first dimension (Y1).
Mark(Node, Node) - Constructor for record class edu.cmu.tetrad.search.RecursiveBlocking.Mark: Creates an instance of a Mark record class.
MARK - Enum constant in enum class edu.cmu.tetrad.search.PcAR.RescueAction: Annotate the deletion as contested; skeleton is left exactly as FAS produced it.
MARKOV_BLANKET - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: The Markov blanket of the target variable.
markovAdjustPValues(Graph, boolean, IndependenceTest, Map, Pair) - Static method in class edu.cmu.tetrad.search.utils.PreserveMarkov: Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).
MarkovAuditFailure(Node, Node, Set, double) - Constructor for record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Creates an instance of a MarkovAuditFailure record class.
MarkovAuditUtils - Class in edu.cmu.tetrad.search: Search-agnostic Markov-audit utilities: given ANY graph and an independence test, find the conditional independencies the graph entails that the data reject, with the family-wise false discovery rate controlled, and derive from those failures the vertex set a targeted repair should be seeded at.
markovBlanket(Node) - Method in class edu.cmu.tetrad.graph.Paths: Returns the Markov Blanket of a given node in the graph.
markovBlanket(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Computes the Markov blanket for a given node in a graph.
markovBlanketSubgraph(Node, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the subgraph over the Markov blanket of a target node in a given DAG, CPDAG, MAG, or PAG.
MarkovCheck - Class in edu.cmu.tetrad.search: Checks whether a graph is Markov given a data set.
MarkovCheck(Graph, IndependenceTest, ConditioningSetType) - Constructor for class edu.cmu.tetrad.search.MarkovCheck: Constructor.
MarkovCheck.AllSubsetsIndependenceFacts - Class in edu.cmu.tetrad.search: Stores the set of m-separation facts and the set of m-connection facts for a graph, for the global check.
MarkovCheck.MarkovCheckRecord - Record Class in edu.cmu.tetrad.search: A single record for the results of the Markov check.
MarkovCheckAdPasses - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAdPasses(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPasses: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAdPassesBestOf10 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAdPassesBestOf10(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPassesBestOf10: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAndersonDarlingP - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAndersonDarlingP(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingP: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAndersonDarlingPBestOf10 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckAndersonDarlingPBestOf10(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingPBestOf10: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckBinomialP - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Binomial P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckBinomialP(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialP: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckBinomialPBestOf10 - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Binomial P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckBinomialPBestOf10(IndependenceWrapper, ConditioningSetType) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialPBestOf10: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckerStatistic - Interface in edu.cmu.tetrad.algcomparison.statistic: Tagging interface.
MarkovCheckFdrCutoffP - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckFdrCutoffP(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFdrCutoffP: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckFractionDependentH0 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckFractionDependentH0(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH0: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckFractionDependentH1 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckFractionDependentH1(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH1: Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKolmogorovSmirnoffP - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKolmogorovSmirnoffP(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffP: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKolmogorovSmirnoffPBestOf10 - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKolmogorovSmirnoffPBestOf10(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffPBestOf10: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKsPasses - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKsPasses(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPasses: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKsPassesBestOf10 - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a markov check statistic that calculates the Kolmogorov-Smirnoff P value for whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckKsPassesBestOf10(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPassesBestOf10: Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
MarkovCheckNullReference - Class in edu.cmu.tetrad.search: Parametric-bootstrap null reference for the Markov check
MarkovCheckNullReference.Result - Class in edu.cmu.tetrad.search: The real-data check statistics and the null distributions, with empirical p-values and component timings.
MarkovCheckNullReference.SimulatorType - Enum Class in edu.cmu.tetrad.search: The simulator used to generate null draws from the fitted (data, DAG) model.
MarkovCheckRecord(double, double, double, double, double, double, double, double, double, double, int, int) - Constructor for record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Creates an instance of a MarkovCheckRecord record class.
markovIndependence(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.PreserveMarkov: Checks the independence of two nodes given a set of conditioning nodes, and if Markov is to be preserved, checks to make sure the additional independence does not generate p-values that violate the Markov property.
MarkovStats(double, double, double, double, double, double, double, double, int, int) - Constructor for record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Creates an instance of a MarkovStats record class.
maskFromUndirected(Graph, List) - Static method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Constructs a mask matrix from an undirected graph, indicating the adjacency relationships between a given list of nodes.
mat() - Method in class edu.cmu.tetrad.util.MView: Creates and returns a new Matrix instance representing a submatrix based on the rows and columns specified in the MatrixView.
Matching convention - Section in class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer
materializeDataVariables(HybridCgModel.HybridCgPm) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgVars: Build data variables (ContinuousVariable / DiscreteVariable) from the PM’s schema.
MATH - Enum constant in enum class edu.cmu.tetrad.util.TMath.Impl: Represents the default mathematical implementation strategy in the system.
MathewsCorrAdj - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Matthew's correlation coefficient for adjacencies.
MathewsCorrAdj() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrAdj: Constructs a new instance of the statistic.
MathewsCorrArrow - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the Matthew's correlation coefficient for adjacencies.
MathewsCorrArrow() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MathewsCorrArrow: Constructs a new instance of the statistic.
MathUtils - Class in edu.cmu.tetrad.util: Some extra mathematical functions not contained in org.apache.commons.math3.util.TMath.
matrix() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the matrix record component.
Matrix - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a Matrix of elements of type short
Matrix - Class in edu.cmu.tetrad.util: Wraps the EJML linear algebra library for most uses in Tetrad.
Matrix(double[][]) - Constructor for class edu.cmu.tetrad.util.Matrix: Constructor for Matrix.
Matrix(int, int) - Constructor for class edu.cmu.tetrad.util.Matrix: Constructor for Matrix.
Matrix(Matrix) - Constructor for class edu.cmu.tetrad.util.Matrix: Constructor for Matrix.
Matrix(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Creates a matrix reading it from a file fname.
Matrix(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Creates a matrix with name mname, and nrows rows.
Matrix(SimpleMatrix) - Constructor for class edu.cmu.tetrad.util.Matrix: Constructs a Matrix object based on the given SimpleMatrix data.
MatrixF - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a Matrix of elements of type float
MatrixF(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Creates a matrix reading it from a file fname.
MatrixF(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Creates a matrix with name mname, and nrows rows.
MatrixUtils - Class in edu.cmu.tetrad.util: Class Matrix includes several public static functions performing matrix operations.
max() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the max record component.
max(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: max.
max(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: max.
max(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the maximum of two double values using the specified implementation.
max(float, float) - Static method in class edu.cmu.tetrad.util.TMath: Computes the maximum of two float values using the specified implementation.
max(int, int) - Static method in class edu.cmu.tetrad.util.TMath: Computes the maximum of two integers using the specified implementation.
max(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: max.
max(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: max.
max(long, long) - Static method in class edu.cmu.tetrad.util.TMath: Computes the maximum of two long integers using the specified implementation.
MAX - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: The maximum of the values (continuous only; missing values ignored).
MAX - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Constant MAX=1
MAX - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.history.SimpleRandomizer: Indicates maximum indegree.
MAX_BLOCKING_PATH_LENGTH - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_BLOCKING_PATH_LENGTH="maxBlockingPathLength"
MAX_CATEGORIES - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_CATEGORIES="maxCategories"
MAX_CELLS_PER_STRATUM - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_CELLS_PER_STRATUM="maxCellsPerStratum"
MAX_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_DEGREE="maxDegree"
MAX_DISCRIMINATING_PATH_LENGTH - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_DISCRIMINATING_PATH_LENGTH="maxDiscriminatingPathLength"
MAX_DISTINCT_VALUES_DISCRETE - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_DISTINCT_VALUES_DISCRETE="maxDistinctValuesDiscrete"
MAX_FLOAT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Maximum float value
MAX_INDEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_INDEGREE="maxIndegree"
MAX_INT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Maximum int value
MAX_ITERATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_ITERATIONS="maxIterations"
MAX_LATENT_SUBSET_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_LATENT_SUBSET_SIZE="maxLatentSubsetSize"
MAX_OBSERVED_LEVELS_PER_VAR - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_OBSERVED_LEVELS_PER_VAR="maxObservedLevelsPerVar"
MAX_OUTDEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_OUTDEGREE="maxOutdegree"
MAX_P - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.ColliderStyle: Represents the Max_P collider style in the graph learning process.
MAX_P - Enum constant in enum class edu.cmu.tetrad.search.Cdnod.ColliderOrientationStyle: Represents a collider orientation strategy in causal discovery that employs a strategy aimed at maximizing the probability of correctly orienting colliders in a causal graph, potentially at the cost of increased computational complexity.
MAX_P - Enum constant in enum class edu.cmu.tetrad.search.CdnodBoss.ColliderOrientationStyle: Decide collider orientation by comparing the maximum p-value margins between two sides (includes given variable vs excludes given variable).
MAX_P - Enum constant in enum class edu.cmu.tetrad.search.Fci.ColliderRule: Refers to the rule where the maximum p-value is considered when deciding colliders, often used in statistical tests or algorithms.
MAX_P - Enum constant in enum class edu.cmu.tetrad.search.Pc.ColliderOrientationStyle: Represents the collider orientation style for the PC-Max algorithm.
MAX_P - Enum constant in enum class edu.cmu.tetrad.search.PcAR.ColliderOrientationStyle: PC-Max (Ramsey 2016): among all candidate separating sets, take the one with the largest p-value; orient as a collider iff z is not in it.
MaX_PAX_P_ORIENTATION_HEURISTIC_MAX_LENGTH - Static variable in class edu.cmu.tetrad.util.Params: Constant MaX_PAX_P_ORIENTATION_HEURISTIC_MAX_LENGTH="maxPaxPOrientationHeuristicMaxLength"
MAX_POSSIBLE_SEP_PATH_LENGTH - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_POSSIBLE_SEP_PATH_LENGTH="maxPossibleSepPathLength"
MAX_RANK - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_RANK="maxRank"
MAX_RUNS - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_RUNS="maxRuns"
MAX_SCORE_DROP - Static variable in class edu.cmu.tetrad.util.Params: Constant MAX_SCORE_DROP="maxScoreDrop"
MAX_SHORT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Maximum short value
maxAbsCorrelation() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns the value of the maxAbsCorrelation record component.
maxAttemptsPerFact() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the maxAttemptsPerFact record component.
maxBins - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents the maximum number of bins that can be used for data discretization in processing or modeling tasks within the CordEngine1 class.
maxBins - Variable in class edu.cmu.tetrad.search.CordEngine2: The maximum number of discrete bins allowed for feature discretization or histogram-based computations.
maxBins - Variable in class edu.cmu.tetrad.search.CordEngine3: Specifies the maximum number of discrete bins that can be used for feature quantization in tree-based algorithms.
maxBins - Variable in class edu.cmu.tetrad.search.CordEngine4: The maximum number of discrete bins that the algorithm can use for representing and processing data.
maxBins - Variable in class edu.cmu.tetrad.search.CordEngine5: The maximum number of bins to be used, typically in the context of data discretization or histogram generation.
maxCliques() - Method in class edu.cmu.tetrad.graph.Paths: Returns a set of all maximum cliques in the graph.
maxCondSize(int) - Method in class edu.cmu.tetrad.search.Pcmci.Builder: Sets the maximum conditioning set size (|S|) to be used in the Pcmci algorithm.
maxCorrelatedWith() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns the value of the maxCorrelatedWith record component.
maxDiscreteLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Specifies the maximum number of discrete levels that a variable or feature can have within the context where this parameter is used.
maxDiscreteLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: The maximum number of discrete levels allowed for a variable or feature in the model or algorithm.
maxDiscreteLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Maximum number of discrete levels to consider for simulation.
maxDiscreteLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Maximum number of discrete levels to consider for simulation.
maxEntApprox(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the maximum entropy approximation of the given data array.
Maximal - Class in edu.cmu.tetrad.algcomparison.statistic: Checks whether a PAG is maximal.
Maximal() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.Maximal: Constructor for LegalPag.
maximalCliques(Graph, List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Finds all maximal cliques in a given graph.
MaximalityCondition - Class in edu.cmu.tetrad.algcomparison.statistic: MaximalMag statistic.
MaximalityCondition() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.MaximalityCondition: Constructs a new instance of the statistic.
maximization(double) - Method in class edu.cmu.tetrad.bayes.EmBayesEstimator: This method iteratively estimates the parameters of the Bayes net using the dataset until the parameters don't change.
maximization(double) - Method in class edu.cmu.tetrad.bayes.FactoredBayesStructuralEM: This method allows specification of the tolerance parameter used in Bayes EM estimation.
maximizeDiagonal(Matrix) - Static method in class edu.cmu.tetrad.search.LingD: Computes a permutation matrix pair that maximizes the diagonal elements of a given matrix W.
maxIrlsIter - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Maximum number of
MAXIT - Static variable in class edu.cmu.tetrad.util.Params: Constant MAXIT="maxit"
maxIters - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Specifies the maximum number of iterations for the Expectation-Maximization (EM) algorithm in the Gaussian Mixture Model (GMM) fitting process.
maxLag(int) - Method in class edu.cmu.tetrad.search.Pcmci.Builder: Sets the maximum lag to be considered in the Pcmci algorithm.
maxLeafNodes - Variable in class edu.cmu.tetrad.search.CordEngine1: Specifies the maximum number of leaf nodes permitted in a decision tree.
maxLeafNodes - Variable in class edu.cmu.tetrad.search.CordEngine2: The maximum number of leaf nodes allowed when growing decision trees.
maxLeafNodes - Variable in class edu.cmu.tetrad.search.CordEngine3: The maximum number of leaf nodes allowed for a tree in the CORD engine.
maxLeafNodes - Variable in class edu.cmu.tetrad.search.CordEngine4: The maximum number of leaf nodes allowed during the construction or growth of trees in the CordEngine4 framework.
maxLeafNodes - Variable in class edu.cmu.tetrad.search.CordEngine5: The maximum number of leaf nodes allowed in a data structure or algorithm, typically used to define limits in hierarchical or tree-based structures.
maxOverlapParts - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Specifies the maximum number of overlapping true clusters to display.
maxOverlapParts(int) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets the maximum number of overlapping parts allowed.
maxPMargin(double) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the maximum P-margin value for the algorithm.
maxPMargin(double) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the maximum p-value margin to be used by the Builder.
maxProp - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the minimum proportion of observations used in the effect estimate calculation.
maxRate() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the value of the maxRate record component.
maxResidualStrata - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Maximum number of residual strata to consider during simulation.
maxResidualStrata - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: The maximum number of residual strata to consider for stratification.
maxResidualStrata - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Maximum number of residual bootstrap strata to consider.
maxResidualStrata - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Maximum number of residual bootstrap strata to consider.
MAY_NOT_CONTAIN - Enum constant in enum class edu.cmu.tetrad.calculator.parser.ExpressionParser.RestrictionType: The expression may not contain parameters in the given list.
MAY_ONLY_CONTAIN - Enum constant in enum class edu.cmu.tetrad.calculator.parser.ExpressionParser.RestrictionType: The expression may only contain parameters in the given list.
MB - Static variable in class edu.cmu.tetrad.util.Params: Constant MB="mb"
MbUtils - Class in edu.cmu.tetrad.search.utils: Provides some useful utilities for dealing with Markov blankets and Markov blanket DAGs.
MCAR - Enum constant in enum class edu.cmu.tetrad.data.MissingnessInjector.MechanismClass: Deletion depends on no data value.
MCAR_TEST_ALPHA - Static variable in class edu.cmu.tetrad.data.missing.MissingDataAudit: The alpha level advice() uses to interpret Little's MCAR test.
McGetNumTestsH0 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the number of tests Kolmogorov-Smirnoff under the null hypothesis H0 of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
McGetNumTestsH0(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH0: Calculates the number of tests for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
McGetNumTestsH1 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the number of tests Kolmogorov-Smirnoff under the alternative hypothesis H1 of dependennce for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
McGetNumTestsH1(IndependenceWrapper, ConditioningSetType, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH1: Calculates the number of tests for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
mean(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: mean.
mean(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: mean.
mean(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: mean.
mean(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: mean.
mean(Matrix) - Static method in class edu.cmu.tetrad.data.DataUtils: mean.
mean(Vector, int) - Static method in class edu.cmu.tetrad.util.StatUtils: mean.
MEAN - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: The mean of the values (continuous only; missing values ignored).
MEAN - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: He1(Y) = Y: conditional-mean channel.
MEAN - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: He1(Y) = Y: conditional-mean channel.
MEAN - Enum constant in enum class edu.cmu.tetrad.sem.ParamType: Variable Mean parameter type for SEM models.
MEAN - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Constant MEAN=2
MEAN - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.history.SimpleRandomizer: Indicates mean indegree.
MEAN_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant MEAN_HIGH="meanHigh"
MEAN_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant MEAN_LOW="meanLow"
MEAN_PAIRWISE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataSpec.EffectiveSampleSizeMode: Use the mean, over variable pairs, of the number of rows on which both variables are observed.
meanAbsolute(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the squared difference between the mean of the absolute values of a standardized dataset and the theoretical mean of the standard normal distribution.
meanAdjF1() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the meanAdjF1 record component.
meanAdjPrecision() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the meanAdjPrecision record component.
meanAdjRecall() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the meanAdjRecall record component.
meanAhdF1() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the meanAhdF1 record component.
meanAhdPrecision() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the meanAhdPrecision record component.
meanAhdRecall() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the meanAhdRecall record component.
meanAlpha() - Method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Returns the average significance level over the pairs carrying a prior, before the mean-one rescaling.
meanARI - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.StabilityResult: Represents the mean Adjusted Rand Index (ARI) across all pairwise comparisons of cluster labelings in a clustering stability analysis.
meanEntropy - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.EntropyStats: Represents the mean entropy of clustering results, providing a measure of the uncertainty in cluster assignments.
meanF1() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns the value of the meanF1 record component.
meanImprovement - Variable in class edu.cmu.tetrad.sem.AdequacyReport: Represents the mean improvement of nodes over a baseline metric in the context of an adequacy report.
meanImprovement - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: Fold-mean paired improvement from this parent at the time it was removed: ΔR² for a continuous child, Δ cross-entropy in nats for a discrete child.
MeanInterpolator - Class in edu.cmu.tetrad.data: Returns a data set in which missing values in each column are filled using the mean of that column.
MeanInterpolator() - Constructor for class edu.cmu.tetrad.data.MeanInterpolator: Constructor for MeanInterpolator.
meanOosMmd2 - Variable in class edu.cmu.tetrad.sem.CVReport: Mean OOS MMD² across folds.
meanOosR2 - Variable in class edu.cmu.tetrad.sem.CVReport: Mean OOS R² across continuous non-root nodes.
meanOosXentImprovement - Variable in class edu.cmu.tetrad.sem.CVReport: Mean OOS cross-entropy improvement (baseline − model) across discrete non-root nodes.
meanPrecision() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns the value of the meanPrecision record component.
meanRecall() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns the value of the meanRecall record component.
meanRowMissing() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns the value of the meanRowMissing record component.
means - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: The mean vectors of the mixture components in the Gaussian Mixture Model (GMM).
means() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: means.
means(double[][]) - Static method in class edu.cmu.tetrad.data.DataUtils: Column major data.
means(Matrix) - Static method in class edu.cmu.tetrad.data.DataUtils: means.
meanWeight() - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns the mean of the stored weights, or 1 if none are stored.
MEASURED - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant MEASURED
MEASURED_MEASURED_IMPURE_ASSOCIATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant MEASURED_MEASURED_IMPURE_ASSOCIATIONS="measuredMeasuredImpureAssociations"
MEASURED_MEASURED_IMPURE_PARENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant MEASURED_MEASURED_IMPURE_PARENTS="measuredMeasuredImpureParents"
measuredChildren() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentSignature: Returns the value of the measuredChildren record component.
measuredHasNonSinks(Set, Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Whether the measured descendants of As include a non-sink (Python MeassuredHasNonSinks).
measuredParents() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentSignature: Returns the value of the measuredParents record component.
MEASUREMENT_MODEL_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant MEASUREMENT_MODEL_DEGREE="measurementModelDegree"
MEASUREMENT_VARIANCE - Static variable in class edu.cmu.tetrad.util.Params: Constant MEASUREMENT_VARIANCE="measurementVariance"
MeasurementBuildResult(BlockSpec, Graph, List, List, List, List, int, double) - Constructor for record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Creates an instance of a MeasurementBuildResult record class.
MeasurementBuildResult(BlockSpec, Graph, List, List, List, List, SimpleMatrix, int, double) - Constructor for record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Creates an instance of a MeasurementBuildResult record class.
MeasurementSimulator - Class in edu.cmu.tetrad.study.gene.tetrad.gene.simulation: Simulates measurement genetic data using an underlying GeneHistory object to generate individual cell data.
MeasurementSimulator(Parameters) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Constructs a measurement simulator using the given history.
MeasurementSimulatorParams - Class in edu.cmu.tetrad.study.gene.tetradapp.model: Wraps MeasurementSimulator so that it may be used as a parameter object.
MeasurementSimulatorParams(Parameters) - Constructor for class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: Constructs a measurement simulator using the given history.
mechanism() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Result: Returns the value of the mechanism record component.
Mechanism(List, List, int[]) - Constructor for record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Canonical constructor, making defensive copies.
Mechanism classes - Section in class edu.cmu.tetrad.data.MissingnessInjector
MechanismReport(MissingnessInjector.MechanismClass, List, boolean[], boolean[], int[], int, double) - Constructor for record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Creates an instance of a MechanismReport record class.
Mechanisms and what to expect - Section in class edu.cmu.tetrad.search.harness.MissingDataScoreDiagnostics
median(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Computes the median value of a given array of doubles.
median(SimpleMatrix) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the median of all the elements in the given SimpleMatrix.
Median - Enum constant in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Instead of assembling a composite of edges drawn from different bootstrap graphs (which in general is not a legal member of the algorithm's output class), display the single bootstrap graph closest to the ensemble edge frequencies.
medianNearestNeighborDistance(double[][]) - Static method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Median Euclidean distance from each row to its nearest neighbor; a scale reference for WildBootstrapMarkovCheck.setKernel(double[][], double) bandwidths and WildBootstrapMarkovCheck.gridBlocks(double[][], double) cell sizes.
MeekRules - Class in edu.cmu.tetrad.search.utils: Implements Meek's complete orientation rule set for PC (Chris Meek (1995), "Causal inference and causal explanation orienting.
MeekRules() - Constructor for class edu.cmu.tetrad.search.utils.MeekRules: Constructs the MeekRules with no logging.
members - Variable in class edu.cmu.tetrad.search.ScoredClusterFinder.ClusterHit: indices in dataSet columns (subset of Vsub)
Memorable - Interface in edu.cmu.tetrad.util: Tags a session node as "memorable", so that it won't be forgotten when a model is destroyed.
MERGE_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant MERGE_THRESHOLD="dmMergeThreshold"
message() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns the value of the message record component.
META_EDGE_CONNECTION_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant META_EDGE_CONNECTION_TYPE="mimMetaEdgeConnectionType"
method(Parameters) - Static method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Returns the p-value combining method selected by Params.POOLED_TEST_METHOD.
Metric definition - Section in class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer
Metrics - Section in class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi
MG_FAS - Enum constant in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: Pooled moral graph pruned by FAS-style sepset tests restricted to moral neighborhoods; cycle-safe.
MG_FAS_INTERSECT_LING - Enum constant in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: The intersection of MG_FAS and MG_LING.
MG_LING - Enum constant in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: Pooled moral graph filtered by per-dataset ICA (LiNG) estimates of B; requires non-Gaussian errors.
MGM_PARAM1 - Static variable in class edu.cmu.tetrad.util.Params: Constant MGM_PARAM1="mgmParam1"
MGM_PARAM2 - Static variable in class edu.cmu.tetrad.util.Params: Constant MGM_PARAM2="mgmParam2"
MGM_PARAM3 - Static variable in class edu.cmu.tetrad.util.Params: Constant MGM_PARAM3="mgmParam3"
MiceLiteImputer - Class in edu.cmu.tetrad.data.missing: A "lite" chained-equations (MICE-style) multiple imputer for continuous, discrete, or mixed data, using predictive mean matching (PMM) as the single imputation engine for both variable types.
MiceLiteImputer() - Constructor for class edu.cmu.tetrad.data.missing.MiceLiteImputer: Constructs an imputer with the defaults: 5 donors, 5 sweeps.
MiceLiteImputer(int, int) - Constructor for class edu.cmu.tetrad.data.missing.MiceLiteImputer: Constructs an imputer.
MiceLiteImputer(int, int, double) - Constructor for class edu.cmu.tetrad.data.missing.MiceLiteImputer: Constructs an imputer.
MillisecondTimes - Class in edu.cmu.tetrad.util: Reports elapsed time in wall time, user time, and CPU time in milliseconds.
MillisecondTimes.Type - Enum Class in edu.cmu.tetrad.util: An enum for the type of time.
MIM_LATENT_GROUP_SPECS - Static variable in class edu.cmu.tetrad.util.Params: Constant MIM_LATENT_GROUP_SPECS="mimLatentGroupSpecs"
MIM_LATENT_MEASURED_IMPURE_PARENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant MIM_LATENT_MEASURED_IMPURE_PARENTS="mimLatentMeasuredImpureParents"
MIM_MEASURED_MEASURED_IMPURE_ASSOCIATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant MIM_MEASURED_MEASURED_IMPURE_ASSOCIATIONS="mimMeasuredMeasuredImpureAssociations"
MIM_MEASURED_MEASURED_IMPURE_PARENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant MIM_MEASURED_MEASURED_IMPURE_PARENTS="mimMeasuredMeasuredImpureParents"
MIM_NUM_CHILDREN_PER_GROUP - Static variable in class edu.cmu.tetrad.util.Params: Constant MIM_NUM_CHILDREN_PER_GROUP="mimNumChildrenPerGroup"
MIMBUILD_TYPE - Static variable in class edu.cmu.tetrad.util.Params: Constant MIMBUILD_TYPE="mimbuildType"
MimbuildBollen - Class in edu.cmu.tetrad.algcomparison.algorithm.other: Mimbuild Bollen.
MimbuildBollen - Class in edu.cmu.tetrad.search: MimbuildBollen (BlockSpec version)
MimbuildBollen() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Constructs a new instance of the algorithm.
MimbuildBollen(BlockSpec) - Constructor for class edu.cmu.tetrad.search.MimbuildBollen: Constructor for the MimbuildBollen class.
MimbuildPca - Class in edu.cmu.tetrad.algcomparison.algorithm.other: Mimbuild PCA.
MimbuildPca - Class in edu.cmu.tetrad.search: Mimbuild over the first principal components of (pure) clusters, driven by a BlockSpec.
MimbuildPca() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Constructs a new instance of the algorithm.
MimbuildPca(BlockSpec) - Constructor for class edu.cmu.tetrad.search.MimbuildPca: Constructs an instance of MimbuildPca with the specified BlockSpec.
MimbuildTrek - Class in edu.cmu.tetrad.search: Deprecated.
MimbuildTrek() - Constructor for class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

Empty constructor.
MIMIC_GROUP_SPECS - Static variable in class edu.cmu.tetrad.util.Params: Constant MIMIC_GROUP_SPECS="mimicGroupSpecs"
MIMIC_NUM_SHARED_PARENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant MIMIC_NUM_SHARED_PARENTS="mimicNumSharedParents"
MimicBenchmark - Class in edu.cmu.tetrad.search.mimic: Benchmark harness for MIMIC-style algorithms.
MimicBenchmark() - Constructor for class edu.cmu.tetrad.search.mimic.MimicBenchmark: Constructs a benchmark with the default generator, evaluator, and the three current MIMIC-style runners.
MimicBenchmark(RandomMimicGraphGenerator, MimicEvaluator, List) - Constructor for class edu.cmu.tetrad.search.mimic.MimicBenchmark: Constructs a benchmark with the supplied generator, evaluator, and runners.
MimicBenchmarkReport - Class in edu.cmu.tetrad.search.mimic: Produces a readable text report for a MIMIC benchmark run.
MimicBenchmarkReport() - Constructor for class edu.cmu.tetrad.search.mimic.MimicBenchmarkReport: Constructs a report generator without per-trial latent details.
MimicBenchmarkReport(boolean) - Constructor for class edu.cmu.tetrad.search.mimic.MimicBenchmarkReport: Constructs a report generator.
MimicBenchmarkReportTdf - Class in edu.cmu.tetrad.search.mimic: Produces a tab-delimited benchmark report for a MIMIC benchmark run.
MimicBenchmarkReportTdf() - Constructor for class edu.cmu.tetrad.search.mimic.MimicBenchmarkReportTdf: Default constructor for the MimicBenchmarkReportTdf class.
MimicBenchmarkResult - Class in edu.cmu.tetrad.search.mimic: Stores the results of multiple benchmark trials and provides simple averages by algorithm.
MimicBenchmarkResult(List) - Constructor for class edu.cmu.tetrad.search.mimic.MimicBenchmarkResult: Constructs a new benchmark result.
MimicEvaluation - Class in edu.cmu.tetrad.search.mimic: Stores summary statistics from evaluating an estimated MIMIC graph against a true model.
MimicEvaluation(int, int, int, double, double, double) - Constructor for class edu.cmu.tetrad.search.mimic.MimicEvaluation: Constructs a new evaluation summary.
MimicEvaluator - Class in edu.cmu.tetrad.search.mimic: Evaluates estimated MIMIC-style graphs against a true MimicModel.
MimicEvaluator() - Constructor for class edu.cmu.tetrad.search.mimic.MimicEvaluator: Constructs a new instance of the MimicEvaluator class.
MimicGroupSpec(int, int, int) - Constructor for record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Constructs a new specification for a block of MIMIC groups.
MimicModel - Class in edu.cmu.tetrad.search.mimic: Stores a generated MIMIC model together with the designated input, latent, and output variables.
MimicModel(Graph, List, List, List) - Constructor for class edu.cmu.tetrad.search.mimic.MimicModel: Constructs a new MIMIC model container.
MimicSearchRunner - Interface in edu.cmu.tetrad.search.mimic: Interface for running a MIMIC-style search algorithm inside the benchmark harness.
MimicTrialResult - Class in edu.cmu.tetrad.search.mimic: Stores the complete result of one benchmark trial.
MimicTrialResult(MimicModel, DataSet, Knowledge, Map, Map, Map) - Constructor for class edu.cmu.tetrad.search.mimic.MimicTrialResult: Constructs a new trial result.
min() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the min record component.
min(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: min.
min(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: min.
min(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the minimum of two double values using the specified implementation.
min(float, float) - Static method in class edu.cmu.tetrad.util.TMath: Computes the minimum of two float values using the specified implementation.
min(int, int) - Static method in class edu.cmu.tetrad.util.TMath: Computes the minimum of two integer values using the specified implementation.
min(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: min.
min(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: min.
min(long, long) - Static method in class edu.cmu.tetrad.util.TMath: Computes the minimum of two long integers using the specified implementation.
MIN - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: The minimum of the values (continuous only; missing values ignored).
MIN_CATEGORIES - Static variable in class edu.cmu.tetrad.util.Params: Constant MIN_CATEGORIES="minCategories"
MIN_COUNT_PER_CELL - Static variable in class edu.cmu.tetrad.util.Params: Constant MIN_COUNT_PER_CELL="minCountPerCell"
MIN_EIGENVALUE_FRACTION - Static variable in class edu.cmu.tetrad.sem.ResidualCovariance: The smallest eigenvalue allowed, as a fraction of the mean residual variance; a ridge brings a smaller one up to this.
MIN_FLOAT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Minimum float value
MIN_INT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Minimum int value
MIN_PAIRWISE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataSpec.EffectiveSampleSizeMode: Use the minimum, over variable pairs, of the number of rows on which both variables are observed.
MIN_PARAM_SAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant MIN_PARAM_SAMPLE_SIZE="minParamSampleSize"
MIN_SAMPLE_SIZE_PER_CELL - Static variable in class edu.cmu.tetrad.util.Params: Constant MIN_SAMPLE_SIZE_PER_CELL="minSampleSizePerCell"
MIN_SHORT - Static variable in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Minimum short getValue
MIN_STRATUM_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant MIN_STRATUM_SIZE="minStratumSize"
minBinSize() - Method in record class edu.cmu.tetrad.search.utils.MinimaxBinningConfig: Returns the value of the minBinSize record component.
minCoveringSet(SortedSet[][]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: minCoveringSet.
minDetectablePartialCorrelation(double, int) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: The smallest |partial correlation| that the sign rule accepts at penalty discount c and sample size N, i.e.
minDetectablePartialCorrelation(double, int, int, int, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The smallest partial correlation a df-degree-of-freedom test detects at level alpha with the requested power, for a linear dependence carried by one component of the block.
minDetectableR() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns the value of the minDetectableR record component.
minDetectableRAt(double, int) - Method in class edu.cmu.tetrad.search.test.AlphaReport: The smallest partial correlation a pair in the given df class detects at a level, at the report's power.
minDetectableRMaxDf() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns the value of the minDetectableRMaxDf record component.
minDetectableRMinDf() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns the value of the minDetectableRMinDf record component.
minFoldSize - Variable in class edu.cmu.tetrad.search.CordEngine4: Minimum allowable size for each fold when splitting data into training and validation sets during cross-validation or other partitioning processes.
minFoldSize - Variable in class edu.cmu.tetrad.search.CordEngine5: The minimum allowable size of a fold when partitioning data into subsets.
minHoldoutRows - Variable in class edu.cmu.tetrad.sem.AdequacyParams: The minimum number of rows required to be held out from the dataset for testing or validation purposes, regardless of the specified holdout fraction.
MINIMAX_FF_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_FF_FEATURES="minimaxFfFeatures"
MINIMAX_FF_SIGMA - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_FF_SIGMA="minimaxFfSigma"
MINIMAX_IRLS_ITERS - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_IRLS_ITERS="minimaxIrlsIters"
MINIMAX_NU - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_NU="minimaxMaxNu"
MINIMAX_PERMUTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_PERMUTATIONS="minimaxPermutations"
MINIMAX_RIDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_RIDGE="minimaxRidge"
MINIMAX_SCALE - Static variable in class edu.cmu.tetrad.util.Params: Constant MINIMAX_SCALE="minimaxScale"
MinimaxBinning - Class in edu.cmu.tetrad.search.utils: Quantile-grid binning for conditioning variables Z, returning groups of row indices.
MinimaxBinningConfig - Record Class in edu.cmu.tetrad.search.utils: Configuration for minimax-style quantile-grid binning used to approximate conditioning on Z.
MinimaxBinningConfig(int, int) - Constructor for record class edu.cmu.tetrad.search.utils.MinimaxBinningConfig: Constructs a configuration for minimax-style quantile-grid binning, enforcing validity of parameters.
MinimaxCITest - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Minimax binning test.
MinimaxCITest - Class in edu.cmu.tetrad.search.test: Minimax CI test: a stratified, permutation-calibrated, nonparametric conditional independence test for mixed data (continuous + discrete).
MinimaxCITest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.MinimaxCITest: Constructor.
MinimaxCITest(DataSet, double) - Constructor for class edu.cmu.tetrad.search.test.MinimaxCITest: Constructs a MinimaxCITest object using the given dataset and significance level (alpha).
MinimaxGroupCache - Class in edu.cmu.tetrad.search.utils: Cache for quantile-grid binning groups used by minimax-style conditional independence tests/scores.
MinimaxGroupCache() - Constructor for class edu.cmu.tetrad.search.utils.MinimaxGroupCache: Default constructor for the MinimaxGroupCache class.
minimaxTrffBic() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Minimax t-RFF BIC metric for a given data set and directed acyclic graph (DAG).
minPairwiseCount() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns the value of the minPairwiseCount record component.
minProp - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the minimum proportion of observations used in the effect estimate calculation.
minRate() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the value of the minRate record component.
minSamplesLeaf - Variable in class edu.cmu.tetrad.search.CordEngine1: The minimum number of samples required to be in a leaf node when growing a decision tree.
minSamplesLeaf - Variable in class edu.cmu.tetrad.search.CordEngine2: Specifies the minimum number of samples that a leaf node must have during the construction of decision trees.
minSamplesLeaf - Variable in class edu.cmu.tetrad.search.CordEngine3: Specifies the minimum number of samples required to be at a leaf node in a decision tree.
minSamplesLeaf - Variable in class edu.cmu.tetrad.search.CordEngine4: Specifies the minimum number of samples required to form a leaf node in a tree-based model.
minSamplesLeaf - Variable in class edu.cmu.tetrad.search.CordEngine5: The minimum number of samples required to be present in a leaf node of a decision tree.
minus(Matrix) - Method in class edu.cmu.tetrad.util.Matrix: minus.
minus(Vector) - Method in class edu.cmu.tetrad.util.Vector: minus.
mirrorsFor(EdgeListGraph, Edge) - Method in interface edu.cmu.tetrad.graph.EdgeReplicationPolicy: Returns a set of mirrored edges in the given graph that should be kept in sync with the specified edge.
mirrorsFor(EdgeListGraph, Edge) - Method in class edu.cmu.tetrad.graph.LagReplicationPolicy: Generates a set of mirrored edges for a given edge in the graph, based on the lagging conventions of the node names.
MisclassificationUtils - Class in edu.cmu.tetrad.graph: Some utilities for generating misclassification tables for graphs.
MISSING_DATA - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The dataset contains missing values.
MISSING_DATA_POLICY - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_DATA_POLICY="missingDataPolicy".
MISSING_EM_MAX_ITERATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_EM_MAX_ITERATIONS="missingEmMaxIterations".
MISSING_EM_RIDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_EM_RIDGE="missingEmRidge".
MISSING_EM_TOLERANCE - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_EM_TOLERANCE="missingEmTolerance".
MISSING_ESS_MODE - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_ESS_MODE="missingEssMode".
MISSING_NUM_IMPUTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_NUM_IMPUTATIONS="missingNumImputations".
MISSING_TESTWISE_COMMON_ROWS - Static variable in class edu.cmu.tetrad.util.Params: Constant MISSING_TESTWISE_COMMON_ROWS="missingTestwiseCommonRows".
MISSING_VALUE - Static variable in class edu.cmu.tetrad.data.DiscreteVariable: This is the index in the data which represents missing data internally for this variable.
Missing data - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
MissingDataAudit - Class in edu.cmu.tetrad.data.missing: A descriptive audit of the missingness in a dataset, intended to be computed (and logged) before a search is run on real data, and to give the user the evidence needed to choose a MissingDataPolicy.
MissingDataAudit(DataSet) - Constructor for class edu.cmu.tetrad.data.missing.MissingDataAudit: Constructs the audit for the given dataset, computing all descriptive statistics.
MissingDataAudit.LittleResult - Class in edu.cmu.tetrad.data.missing: The result of Little's MCAR test: the chi-square statistic, its degrees of freedom, the p-value, and pattern accounting.
MissingDataPolicy - Enum Class in edu.cmu.tetrad.data.missing: An explicit statement of how missing values in a dataset are to be handled by a score, independence test, or search wrapper.
MissingDataRecoveryHarness - Class in edu.cmu.tetrad.search.harness: Stage 3 of the missing-data experiments: how well does BOSS recover the graph, and the causes of a designated target, under each missingness mechanism and each way Tetrad has of handling missing values?
MissingDataScoreDiagnostics - Class in edu.cmu.tetrad.search.harness: Stage 2 of the missing-data experiments: does each missing-data policy make the linear Gaussian BIC score rank the true DAG above its single-edge neighbours?
MissingDataSpec - Class in edu.cmu.tetrad.data.missing: An immutable specification of how missing data are to be handled: a MissingDataPolicy together with the parameters that policy needs (EM settings for MissingDataPolicy.EM_COVARIANCE, the number of imputations for MissingDataPolicy.MULTIPLE_IMPUTATION, and so on).
MissingDataSpec.EffectiveSampleSizeMode - Enum Class in edu.cmu.tetrad.data.missing: How the effective sample size used by penalized scores (e.g., the n in BIC) is determined when the analysis runs on a covariance matrix estimated from incomplete data.
MissingDataUtils - Class in edu.cmu.tetrad.data.missing: Small shared utilities for the missing-data policies: listwise deletion, resolution of a possibly-null MissingDataSpec to a policy (with a logged warning where the historical behavior was a silent switch), a cheap missingness summary suitable for logging from constructors, and the effective-sample-size calculation for analyses run on covariance matrices estimated from incomplete data.
Missingness - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM
Missingness - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM
Missingness - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1
Missingness - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2
MissingnessIndicatorAdder - Class in edu.cmu.tetrad.search.utils: Appends missingness indicators to a dataset.
MissingnessIndicatorAdder.Result - Record Class in edu.cmu.tetrad.search.utils: The augmented dataset together with a record of what was done, for logging and for the session box to display.
MissingnessIndicatorAdder.Spec - Record Class in edu.cmu.tetrad.search.utils: Options controlling which indicators are added and what is attached to the result.
MissingnessInjector - Class in edu.cmu.tetrad.data: Injects missing values into a dataset under a per-cell logistic propensity, with optional terms that make the mechanism MAR or MNAR as well as MCAR.
MissingnessInjector.Effect - Record Class in edu.cmu.tetrad.data: One term in the deletion propensity of a target variable: the effect of a driver variable's standardized value, or of its missingness indicator, on the log odds that the target is deleted.
MissingnessInjector.Mechanism - Record Class in edu.cmu.tetrad.data: The terms of the deletion mechanism beyond the per-variable level and the row effect.
MissingnessInjector.MechanismClass - Enum Class in edu.cmu.tetrad.data: The mechanism class of an injection.
MissingnessInjector.MechanismReport - Record Class in edu.cmu.tetrad.data: What the mechanism was, and how monotone the realized pattern is.
MissingnessInjector.Report - Record Class in edu.cmu.tetrad.data: A report of what was actually injected, for checking that the realized pattern is the intended one.
MissingnessInjector.Result - Record Class in edu.cmu.tetrad.data: The injected data together with its reports.
MissingnessInjector.Spec - Record Class in edu.cmu.tetrad.data: The injection specification.
MissingnessThreshold - Class in edu.cmu.tetrad.data.missing: Works out what dropping every variable whose missingness rate exceeds a threshold would do to a dataset, without dropping anything.
MissingnessThreshold.Candidate - Record Class in edu.cmu.tetrad.data.missing: A variable that a threshold would drop, with what is needed to judge whether dropping it is safe.
MissingnessThreshold.Effect - Record Class in edu.cmu.tetrad.data.missing: The consequence of applying one threshold.
missingNodes - Variable in class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: Names present in G* but missing from the seed graph entirely.
missingRate() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns the value of the missingRate record component.
missingTrueEdges - Variable in class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: True edges of G* absent from the seed AND confirmed m-inseparable in truth.
MissingValueSupport - Enum Class in edu.cmu.tetrad.data.missing: A capability declaration for scores and independence tests, stating what the component itself can do with data containing missing values.
Mixed - Enum constant in enum class edu.cmu.tetrad.data.DataType: Mixed.
Mixed - Annotation Interface in edu.cmu.tetrad.annotation: Data with variables that have linear relationship.
MIXED - Enum constant in enum class edu.cmu.tetrad.graph.EdgeShading.Hue: Effect whose sign varies by context (Okabe-Ito reddish purple).
MixedDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of double continuousData.
MixedDataBox(List, int) - Constructor for class edu.cmu.tetrad.data.MixedDataBox: The variables here are used only to determine which columns are discrete and which are continuous; bounds checking is not done.
MixedDataBox(List, int, double[][], int[][]) - Constructor for class edu.cmu.tetrad.data.MixedDataBox: This constructor allows other data readers to populate the fields directly.
Mixed datasets - Section in class edu.cmu.tetrad.search.test.FfCi
Mixed data support - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM
Mixed data support - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM
Mixed data support - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1
Mixed data support - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2
MixedMissingDataHarness - Class in edu.cmu.tetrad.search.harness: Stage 4 of the missing-data experiments: on mixed continuous and discrete data, which way of handling missing values works best with the embedded scores, degenerate Gaussian BIC (DG-BIC) and optionally basis-function BIC (BF-BIC)?
MixtureModel - Class in edu.cmu.tetrad.search.work_in_progress: Represents a Gaussian mixture model -- a dataset with data sampled from two or more multivariate Gaussian distributions.
MixtureModel(DataSet, double[][], double[][], double[], Matrix[], double[][]) - Constructor for class edu.cmu.tetrad.search.work_in_progress.MixtureModel: Constructs a mixture model from a mixed data set, a means matrix, a weights array, a variance matrix, and a gamma matrix.
MixtureOfGaussians - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
MixtureOfGaussians(double, double, double, double, double) - Constructor for class edu.cmu.tetrad.util.dist.MixtureOfGaussians: Constructor for MixtureOfGaussians.
mkRandSEMDAG(int, int) - Static method in class edu.cmu.tetrad.simulation.HsimUtils: mkRandSEMDAG.
MlBayesEstimator - Class in edu.cmu.tetrad.bayes: Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
MlBayesEstimator(double) - Constructor for class edu.cmu.tetrad.bayes.MlBayesEstimator: Create an instance of MlBayesEstimator with the given prior.
MlBayesEstimatorOld - Class in edu.cmu.tetrad.bayes: Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
MlBayesEstimatorOld() - Constructor for class edu.cmu.tetrad.bayes.MlBayesEstimatorOld: Constructor for MlBayesEstimator.
MlBayesIm - Class in edu.cmu.tetrad.bayes: Stores a table of probabilities for a Bayes net and, together with BayesPm and Dag, provides methods to manipulate this table.
MlBayesIm(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.MlBayesIm: Copy constructor.
MlBayesIm(BayesPm) - Constructor for class edu.cmu.tetrad.bayes.MlBayesIm: Constructs a new BayesIm from the given BayesPm, initializing all values as Double.NaN ("?").
MlBayesIm(BayesPm, boolean) - Constructor for class edu.cmu.tetrad.bayes.MlBayesIm: Constructs an instance of MlBayesIm.
MlBayesIm(BayesPm, BayesIm, MlBayesIm.InitializationMethod) - Constructor for class edu.cmu.tetrad.bayes.MlBayesIm: Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM, but using values from the old BayesIm provided where posssible.
MlBayesIm(BayesPm, MlBayesIm.InitializationMethod) - Constructor for class edu.cmu.tetrad.bayes.MlBayesIm: Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM.
MlBayesIm.CptMapType - Enum Class in edu.cmu.tetrad.bayes: An enumeration representing the different types of CptMap.
MlBayesIm.InitializationMethod - Enum Class in edu.cmu.tetrad.bayes: The InitializationMethod enum represents different methods of initializing a class object.
MlBayesImObs - Class in edu.cmu.tetrad.bayes: Stores a table of probabilities for a Bayes net and, together with BayesPm and Dag, provides methods to manipulate this table.
MlBayesImObs(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.MlBayesImObs: Constructor for MlBayesImObs.
MlBayesImObs(BayesPm) - Constructor for class edu.cmu.tetrad.bayes.MlBayesImObs: Constructs a new BayesIm from the given BayesPm, initializing all values as Double.NaN ("?").
MlBayesImObs(BayesPm, int) - Constructor for class edu.cmu.tetrad.bayes.MlBayesImObs: Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM.
MlBayesImObs(BayesPm, BayesIm, int) - Constructor for class edu.cmu.tetrad.bayes.MlBayesImObs: Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM, but using values from the old BayesIm provided where posssible.
mle(HybridCgModel.HybridCgPm, DataSet) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.HybridEstimator: Estimates a HybridCgIm model from the given HybridCgPm and DataSet.
mmd2 - Variable in class edu.cmu.tetrad.sem.AdequacyReport: Represents the global Maximum Mean Discrepancy squared (MMD²) value between the real and simulated data distributions in the context of an adequacy report.
mmd2 - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Mean over parent configurations of MMD² between draws of the child with the configuration fixed and draws with the parent's input randomized.
mmd2() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Maximum Mean Discrepancy (MMD)² score for a given dataset and DAG.
mmd2(DataSet, DataSet, TrainedDagSimulatorGNM, AdequacyParams) - Static method in class edu.cmu.tetrad.sem.TrainedDagAdequacy: Evaluates the adequacy of a trained DAG simulator by comparing simulated data with real data.
mmd2ExactRbf(DataSet, DataSet, double, int) - Static method in class edu.cmu.tetrad.sem.DagScores: Computes the Maximum Mean Discrepancy (MMD) score using the Radial Basis Function (RBF) kernel between two datasets: real and simulated.
mmd2ExactRbf(DataSet, Graph) - Static method in class edu.cmu.tetrad.sem.DagScores: Computes the Maximum Mean Discrepancy (MMD) score using the Radial Basis Function (RBF) kernel between the real dataset and a simulated dataset.
mmd2Sd - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Standard deviation of EdgeStrengthResult.mmd2 across repeats; NaN if one repeat.
mmdFeatures - Variable in class edu.cmu.tetrad.sem.AdequacyParams: The number of features to be used for the Maximum Mean Discrepancy (MMD) calculation.
mmdFeatures - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Number of random Fourier features used by the MMD² approximation.
mmdMaxRows - Variable in class edu.cmu.tetrad.sem.AdequacyParams: Specifies the maximum number of rows to be used for the Maximum Mean Discrepancy (MMD) calculation.
mmdMaxRows - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Maximum number of rows used when computing MMD².
mmdSeed - Variable in class edu.cmu.tetrad.sem.AdequacyParams: Seed value used in random number generation during the Maximum Mean Discrepancy (MMD) calculation.
mmdSeed - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Random seed for the MMD² random feature approximation.
Mmhc - Class in edu.cmu.tetrad.search.work_in_progress: Implements the MMHC algorithm.
Mmhc(IndependenceTest, DataSet) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Mmhc: Constructor for Mmhc.
Mmmb - Class in edu.cmu.tetrad.search.work_in_progress: Implements the Min-Max Markov Blanks (MMMB) algorithm as defined in Tsamardinos, Aliferis, and Statnikov, Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations (KDD 2003).
Mmmb(IndependenceTest, int, boolean) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Mmmb: Constructs.
MNAR - Enum constant in enum class edu.cmu.tetrad.data.MissingnessInjector.MechanismClass: Not certified MAR; includes self-masking.
MNAR_NUM_EXTRA_INFLUENCES - Static variable in class edu.cmu.tetrad.util.Params: Constant MNAR_NUM_EXTRA_INFLUENCES="mnarNumExtraInfluences"
MNAR_NUM_VARIABLES_WITH_MISSING - Static variable in class edu.cmu.tetrad.util.Params: Constant MNAR_NUM_VARIABLES_WITH_MISSING="mnarNumVariablesWithMissing"
MNAR_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant MNAR_THRESHOLD="mnarThreshold"
mObs - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: The observed value of max_j |T_j|.
mode() - Method in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: Computes the mode of the density function.
ModeInterpolator - Class in edu.cmu.tetrad.bayes: Creates a data set in which missing values in each column are filled using the mode of that column.
ModeInterpolator() - Constructor for class edu.cmu.tetrad.bayes.ModeInterpolator: Constructor for ModeInterpolator.
Model(int, int, GaussianMixtureEM.CovarianceType, double[], double[][], double[][][], double, double[][]) - Constructor for class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: Constructs an instance of the Gaussian Mixture Model (GMM) with the specified parameters.
Model family (ANM) - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM
Model family (GNM) - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM
Model family (GNM) - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1
Model family (GNM) - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2
modelFit(HybridCgModel.HybridCgIm, DataSet, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: Convenience overload of HybridCgEdgeSignificance.modelFit(HybridCgPm, DataSet, boolean) from an IM.
modelFit(HybridCgModel.HybridCgPm, DataSet, boolean) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: The BIC of the Hybrid CG model family over the PM's structure against the data, at the maximum-likelihood fit — the same MLE, available-case, and identifiable-parameter conventions as the per-edge tests in this class, extended to whole families and to parentless nodes (which contribute their marginal fit).
ModelFit(double, double, int, String) - Constructor for record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Creates an instance of a ModelFit record class.
ModelGenerator - Class in edu.cmu.tetrad.bayes: Provides static methods for generating variants of an input graph.
ModelObserver - Interface in edu.cmu.tetrad.search: The ModelObserver interface is implemented by classes that want to observe changes in a model.
modelPAfter() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the modelPAfter record component.
modelPBefore() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the modelPBefore record component.
modifiedR0(Graph) - Method in class edu.cmu.tetrad.search.is.IsGFci: Modifies the orientation of edges in the given graph based on a set of rules and conditions that respect definite colliders, adjacency, and separation sets derived from FGES analysis.
monotoneRows() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the monotoneRows record component.
moralize(Graph) - Static method in class edu.cmu.tetrad.bayes.GraphTools: Create a moral graph.
Move(Edge, Edge, HbsmsBeam.Move.Type) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move: Constructor for Move.
Move(Edge, HbsmsBeam.Move.Type) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move: Constructor for Move.
moveTo(Node, int) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Moves v to a new index.
moveToEnd() - Method in interface edu.cmu.tetrad.util.TetradLogger.LogDisplayOutputStream: Should move the log to the end of the stream.
moveType() - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Returns the structural category of this edit (ADD_EDGE, REMOVE_EDGE, REORIENT_SIMPLE, or OTHER).
mpdagProblem(Graph) - Static method in class edu.cmu.tetrad.search.GRegression: Returns null if the graph is an MPDAG, or else a description of why it is not.
MSeparationTest - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for M-separation test.
MSeparationTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Use this empty constructor to satisfy the java reflection
MSeparationTest(Graph) - Constructor for class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Constructor for MSeparationTest.
MsepOracleChangeTest - Class in edu.cmu.tetrad.search.cdnod_pag: Oracle ChangeTest backed by a known true graph, for exhaustive verification harnesses.
MsepOracleChangeTest(Graph) - Constructor for class edu.cmu.tetrad.search.cdnod_pag.MsepOracleChangeTest: Constructs the oracle over the given true graph.
MSepScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for Fisher Z test.
MSepScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.MSepScore: Use this empty constructor to satisfy the java reflection
MSepScore(Graph) - Constructor for class edu.cmu.tetrad.algcomparison.score.MSepScore: Constructor for MSeparationScore.
MsepTest - Class in edu.cmu.tetrad.search.test: Checks independence facts for variables associated with the nodes in a given graph by checking m-separation facts on the underlying nodes.
MsepTest(IndependenceFacts) - Constructor for class edu.cmu.tetrad.search.test.MsepTest: Constructor.
MsepTest(IndependenceFacts, boolean) - Constructor for class edu.cmu.tetrad.search.test.MsepTest: Constructor.
MsepTest(IndependenceFacts, List) - Constructor for class edu.cmu.tetrad.search.test.MsepTest: Constructor.
MsepTest(Graph) - Constructor for class edu.cmu.tetrad.search.test.MsepTest: Constructor.
MsepTest(Graph, boolean) - Constructor for class edu.cmu.tetrad.search.test.MsepTest: Constructor.
MsepVertexCutFinder - Class in edu.cmu.tetrad.search.utils: The MsepVertexCutFinder class is responsible for finding "choke points" within a given directed acyclic graph (DAG) using d-separation principles.
MsepVertexCutFinder(Graph) - Constructor for class edu.cmu.tetrad.search.utils.MsepVertexCutFinder: Constructs an instance of the MsepVertexCutFinder class with the specified graph.
mseTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Mean Squared Error (MSE) for the training data associated with the node.
mseTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Mean Squared Error (MSE) for the training dataset associated with the node.
mseTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Mean Squared Error (MSE) for the training dataset associated with the node.
mseTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Mean Squared Error (MSE) for the training dataset associated with the node.
mu - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: The estimated means.
mu(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: mu.
mu(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: mu.
mu(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: mu.
mu(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: mu.
mudholkergeorge - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Mudholker and George's method
mudholkergeorge - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The mudholkergeorge method.
mudholkergeorge2 - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Mudholker and George's method
mudholkergeorge2 - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The mudholkergeorge2 method.
muHat(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: muHat.
muHat(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: muHat.
muHat(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: muHat.
muHat(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: muHat.
MultiDataSetAlgorithm - Interface in edu.cmu.tetrad.algcomparison.algorithm: Implements an algorithm that takes multiple data sets as input.
MultiDataSetScoreWrapper - Interface in edu.cmu.tetrad.algcomparison.utils: A score wrapper that can construct its per-data-set scores jointly, coordinating any data-dependent representation choices across the data sets.
MultidataUtils - Class in edu.cmu.tetrad.util: Aug 7, 2018 10:21:59 AM
MultiDimensionalTaylorDegree2 - Class in edu.cmu.tetrad.util: Represents a multidimensional second-degree Taylor series approximation for a differentiable function.
MultiDimensionalTaylorDegree2(double[], double, double[], double[][]) - Constructor for class edu.cmu.tetrad.util.MultiDimensionalTaylorDegree2: Constructs a representation of a second-degree Taylor series expansion for a multi-dimensional function.
MultiDimIntTable - Class in edu.cmu.tetrad.util: Stores a table of cells with int values of arbitrary dimension.
MultiDimIntTable(int[]) - Constructor for class edu.cmu.tetrad.util.MultiDimIntTable: Constructs a new multidimensional table of integer cells, with the given (fixed) dimensions.
MultiGeneralAndersonDarlingTest - Class in edu.cmu.tetrad.data: Implements the Anderson-Darling test against the given CDF, with P values calculated as in R's ad.test method (in package nortest).
MultiGeneralAndersonDarlingTest(List, List) - Constructor for class edu.cmu.tetrad.data.MultiGeneralAndersonDarlingTest: Constructs an Anderson-Darling test for the given column of data.
MultiLayerPerceptron - Class in edu.cmu.tetrad.search.utils: The MultiLayerPerceptron class represents a simple feedforward neural network with one output and support for multiple hidden layers.
MultiLayerPerceptron(int, int[], Function, double) - Constructor for class edu.cmu.tetrad.search.utils.MultiLayerPerceptron: Constructor to initialize a random multi-layer perceptron.
MultiLayerPerceptronDjl - Class in edu.cmu.tetrad.search.utils: The MultiLayerPerceptronDjl class provides a customizable implementation of a Multi-Layer Perceptron (MLP) for tasks like regression or classification using the Deep Java Library (DJL).
MultiLayerPerceptronDjl(int, List, String, float) - Constructor for class edu.cmu.tetrad.search.utils.MultiLayerPerceptronDjl: Constructs a MultiLayerPerceptronDjl object with the specified input dimension, hidden layers, variable type, and input scaling factor.
MultiLayerPerceptronFunction1D - Class in edu.cmu.tetrad.search.utils: This class implements a 1-dimensional Multi-Layer Perceptron (MLP) function.
MultiLayerPerceptronFunction1D(int, double, Function) - Constructor for class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunction1D: Constructor to initialize a random function.
MultiLayerPerceptronFunctionND - Class in edu.cmu.tetrad.search.utils: Represents a random Multi-layer Perceptron (MLP) function from R^n to R.
MultiLayerPerceptronFunctionND(int, int, Function, double) - Constructor for class edu.cmu.tetrad.search.utils.MultiLayerPerceptronFunctionND: Constructor to initialize a random function.
MultinomialLogisticRegressionWald - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Fisher Z test.
MultinomialLogisticRegressionWald() - Constructor for class edu.cmu.tetrad.algcomparison.independence.MultinomialLogisticRegressionWald: Constructs a new instance of the test.
multinormalProb(double[], double[], double[][]) - Static method in class edu.cmu.tetrad.util.ProbUtils: Compute the probability over a rectangular region with correlation matrix c.
MULTIPLE_IMPUTATION - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Impute m completed datasets, run the analysis on each, and pool the results (for graph searches, by edge frequency across imputations).
multipleImputation(int) - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec for multiple imputation with the given number of imputations.
MultipleImputer - Interface in edu.cmu.tetrad.data.missing: An interface for multiple-imputation methods: given a dataset with missing values, produce m completed copies in which every missing entry has been replaced by a draw from an imputation model, with observed entries unchanged.
Multiple trials - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
multiplier - Variable in class edu.cmu.tetrad.search.CordEngine4: Represents the type of multiplier to be used in statistical computations or algorithms within the CordEngine4 class.
multiplier - Variable in class edu.cmu.tetrad.search.CordEngine5: Specifies the type of multiplier to be used in a particular context.
MultiSubjectCyclicComparison - Class in edu.cmu.tetrad.search.harness: Harness comparing the multi-subject cyclic methods in Tetrad on a common cyclic simulation: CCD and CPW, each run with the multi-dataset Fisher-pooled Fisher Z test, and FASK-Vote and FASK-Pool, each run with all five adjacency settings of PooledAdjacencySearch.Method.
mutualInformation(int, int[], int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: This method computes the mutual information between a gene and a set of presumptive causes (other genes).
mutualInformation(int, int[], int) - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.LTestReveal: mutualInformation.
mutualInformation(int, int[], int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: This method computes the mutual information between a gene and a set of presumptive causes (other genes).
mutualInformation(int, int[], int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: This method computes the mutual information between a gene and a set of presumptive causes (other genes).
MView - Class in edu.cmu.tetrad.util: Allows a view of a matrix to be created that is a subset of the original matrix, to allow parts of a matrix to be set using indices in the view.
MvnImputer - Class in edu.cmu.tetrad.data.missing: A multiple imputer for continuous data under a saturated multivariate normal model.
MvnImputer() - Constructor for class edu.cmu.tetrad.data.missing.MvnImputer: Constructs an imputer with default EM settings.
MvnImputer(MissingDataSpec) - Constructor for class edu.cmu.tetrad.data.missing.MvnImputer: Constructs an imputer whose EM settings (ridge, tolerance, max iterations) are taken from the given spec.
MVPBicScore - Class in edu.cmu.tetrad.algcomparison.score: Deprecated.
MVPBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.MVPBicScore: Deprecated.

Constructs a new instance of the score.
MVPCompareFromFiles - Class in edu.cmu.tetrad.algcomparison.examples: An example script to load in data sets and graphs from files and analyze them.
MVPCompareFromFiles() - Constructor for class edu.cmu.tetrad.algcomparison.examples.MVPCompareFromFiles: Constructor for MVPCompareFromFiles.
MvpLikelihood - Class in edu.cmu.tetrad.search.score: Calculates Mixed Variables Polynomial likelihood.
MvpLikelihood(DataSet, double, int, boolean) - Constructor for class edu.cmu.tetrad.search.score.MvpLikelihood: Constructs the score using a data set.
Mvplrt - Class in edu.cmu.tetrad.algcomparison.independence: Deprecated.
Mvplrt() - Constructor for class edu.cmu.tetrad.algcomparison.independence.Mvplrt: Deprecated.

Constructs a new instance of the test.
MvpScore - Class in edu.cmu.tetrad.search.score: Implements a mixed variable polynomial BIC score.
MvpScore(DataSet, double, int, boolean, int) - Constructor for class edu.cmu.tetrad.search.score.MvpScore: Constructs an MvpScore instance with the given dataset and parameters.
MvpScore(DataSet, double, int, boolean, int, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.MvpScore: Constructs the score from a dataset with an explicit missing-data specification.
MyContext() - Constructor for class edu.cmu.tetrad.sem.GeneralizedSemEstimator.MyContext: Constructs a new MyContext.

N

n - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: The sample size used in the effect estimation process.
n - Variable in class edu.cmu.tetrad.search.CordEngine1.Result: Represents the sample size used in a statistical test.
n - Variable in class edu.cmu.tetrad.search.CordEngine2.Result: The sample size used in the hypothesis test.
n - Variable in class edu.cmu.tetrad.search.CordEngine3.Result: Represents the sample size used in the statistical test.
n - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: The sample size used in the statistical test.
n - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: The sample size or count associated with the result.
n - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Sample size.
n() - Method in record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Returns the value of the n record component.
n() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns the value of the n record component.
n() - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Returns the value of the n record component.
N - Enum constant in enum class edu.cmu.tetrad.search.FaskPool.Weighting: Sample-size weights, w_k = n_k.
N() - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Returns the value of the N record component.
N(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: N.
N(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: N.
n_j - Variable in class edu.cmu.tetrad.search.is.BDeuScoreWOprior.CountObjects: An array of integers representing the count of objects at the first level of a hierarchy.
n_jk - Variable in class edu.cmu.tetrad.search.is.BDeuScoreWOprior.CountObjects: A two-dimensional array of integers representing the count of objects at the second level of a hierarchy.
n12() - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Returns the value of the n12 record component.
n21() - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Returns the value of the n21 record component.
NA - Enum constant in enum class edu.cmu.tetrad.sem.DagMetricResult.Better: Indicates that the metric's value is not applicable or does not influence the evaluation criteria in any meaningful way.
NabiBoundaryStudy - Class in edu.cmu.tetrad.study.missingness: Companion study to NabiIdentificationProbe: does the Nabi et al.
NabiIdentificationProbe - Class in edu.cmu.tetrad.study.missingness: Numerical probe of the identification boundary in Nabi, Bhattacharya and Shpitser, "Full Law Identification in Graphical Models of Missing Data: Completeness Results" (PMLR 119, 2020).
name() - Element in annotation interface edu.cmu.tetrad.annotation.Algorithm: Name of the algorithm.
name() - Element in annotation interface edu.cmu.tetrad.annotation.Score: The name of the score.
name() - Element in annotation interface edu.cmu.tetrad.annotation.TestOfIndependence: Name of the test.
name() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns the value of the name record component.
name() - Method in class edu.cmu.tetrad.search.test.IndTestGin.DistanceCorrTest: Returns the name of the test.
name() - Method in class edu.cmu.tetrad.search.test.IndTestGin.OlsRidge: Returns the name of the regression model, including the ridge parameter value.
name() - Method in class edu.cmu.tetrad.search.test.IndTestGin.PearsonCorrTest: Returns the name of the test represented by this class.
name() - Method in interface edu.cmu.tetrad.search.test.IndTestGin.Regressor: Returns the name of the regressor.
name() - Method in interface edu.cmu.tetrad.search.test.IndTestGin.UncondIndTest: Returns the name of the independence test.
name() - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Returns the value of the name record component.
names() - Method in record class edu.cmu.tetrad.search.rlcd.LatentGroups.AdjacencyResult: Returns the value of the names record component.
NamingProtocol - Class in edu.cmu.tetrad.util: Specifies the protocol used in Tetrad for variable names.
NANDY - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: BIC using Nandy et al.'s formulation.
NANDY - Enum constant in enum class edu.cmu.tetrad.search.score.SemBicScore.RuleType: The formulation of the standard BIC score given in Nandy et al.
nandyBic(int, int, int[]) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Calculates the BIC score of a partial correlation based on the specified variables.
NATIVE_MAR - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingValueSupport: The component handles missing values natively in a manner valid under MAR, e.g., a full-information maximum likelihood score that computes row-wise likelihood contributions over the observed entries of each row.
NATURAL_NAME_COMPARATOR - Static variable in class edu.cmu.tetrad.util.NaturalSort: A comparator that sorts strings in natural order (e.g.
naturalComparator() - Static method in class edu.cmu.tetrad.util.NaturalSort: Returns a comparator that sorts objects of any type in natural order, using each object's Object.toString() representation as the sort key.
NaturalSort - Class in edu.cmu.tetrad.util: Provides functionality for natural sorting of strings and objects.
NaturalSort.LaggedNaturalKey - Class in edu.cmu.tetrad.util: Represents a natural key for a possibly-lagged node name of the form "name" or "name:lag".
NaturalSort.NaturalKey - Class in edu.cmu.tetrad.util: Represents a natural key, which consists of a string prefix and an optional numeric suffix.
NbComponent - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbComponent interface.
NbFunction - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbFunction class.
NbFunction(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunction: Constructor for NbFunction.
NbFunctionAnd - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbFunctionAnd class.
NbFunctionAnd(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionAnd: Constructor for NbFunctionAnd.
NbFunctionOr - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbFunctionOr class.
NbFunctionOr(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionOr: Constructor for NbFunctionOr.
NbFunctionSum - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbFunctionSum class.
NbFunctionSum(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionSum: Constructor for NbFunctionSum.
NbFunctionSV - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbFunctionSV class.
NbFunctionSV(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionSV: Constructor for NbFunctionSV.
NbGene - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbGene class.
NbGene(double, double, NbComponent[], int[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbGene: Constructor for NbGene.
NbGeneAnd - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbGeneAnd class.
NbGeneAnd(double, double, NbComponent[], int[], String, double) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbGeneAnd: Constructor for NbGeneAnd.
NbGeneOr - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NbGeneOr class.
NbGeneOr(double, double, NbComponent[], int[], String, double) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbGeneOr: Constructor for NbGeneOr.
NC - Enum constant in enum class edu.cmu.tetrad.sem.ParamComparison: Represents the "Non-comparable" comparison type for a parameter in SEM estimation.
NEAR_CONSTANT - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A variable varies but is nearly constant (a continuous variable with negligible but nonzero variance, or a discrete variable with more than one observed category but almost all mass on one of them).
NEAR_DETERMINISM_DISCRETE_CONTINUOUS - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable is nearly determined by a discrete variable (eta-squared above threshold): the discrete variable is close to a deterministic coarsening of the continuous one.
NEAR_DETERMINISM_LINEAR - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable is nearly a linear function of the other continuous variables (multiple R-squared above threshold), a near-faithfulness violation that destabilizes conditional independence judgments.
NEAR_DETERMINISM_NONLINEAR - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable is nearly a smooth (generally nonlinear) function of a small set of other continuous variables: the leave-one-out cross-validated R-squared of a spline regression of the variable on the set is at or above threshold, while the linear check (NEAR_DETERMINISM_LINEAR) did not fire for the variable.
NEGATIVE - Enum constant in enum class edu.cmu.tetrad.graph.EdgeShading.Hue: Negative effect (Okabe-Ito vermillion).
negativeInfinity() - Static method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: negativeInfinity.
NEITHER - Enum constant in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: The expression can occur in neither the prefix nor infix position.
NestedFits(TRffBicScore.LocalFit, TRffBicScore.LocalFit) - Constructor for record class edu.cmu.tetrad.search.score.TRffBicScore.NestedFits: Creates an instance of a NestedFits record class.
nestedLocalFits(int, int[], int, int[]) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Computes reduced (child ~ Z) and full (child ~ Z + X) local fits on a single shared design matrix, guaranteeing that the reduced model's column space is contained in the full model's column space.
Nesting guarantee - Section in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest
NetBuilderModel - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: NetBuilderModel class.
NetBuilderModel(double[], int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NetBuilderModel: Constructor for NetBuilderModel.
neutral() - Method in enum class edu.cmu.tetrad.search.utils.EdgePriors.Semantics: Returns the neutral (no-information) value for this semantics.
neutral(EdgePriors.Semantics) - Static method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns an empty store: every pair takes the neutral value.
neutralMatrix(int, EdgePriors.Semantics) - Static method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns a matrix of the given size filled with the neutral value for the given semantics, as a starting point for a caller who intends to fill in only the pairs they care about.
newGraph(boolean) - Static method in class edu.cmu.tetrad.graph.GraphFactoryUtil: Creates a new instance of a Graph with the specified replication policy.
newGraph(Graph) - Static method in class edu.cmu.tetrad.graph.GraphFactoryUtil: Creates a new instance of a Graph based on the given graph.
newGraph(List, boolean) - Static method in class edu.cmu.tetrad.graph.GraphFactoryUtil: Creates a new instance of a Graph initialized with the provided list of nodes.
NEWLINE - Static variable in class edu.pitt.isp.sverchkov.data.DataTools: Constant NEWLINE="System.getProperty(line.separator)"
next() - Method in class edu.cmu.tetrad.graph.ConsistentDagIterator
next() - Method in class edu.cmu.tetrad.graph.LegEnumerator
next() - Method in class edu.cmu.tetrad.graph.ValidOrderIterator
next() - Method in class edu.cmu.tetrad.search.utils.DagInCpcagIterator: Successive calls to this method return successive DAGs in the CPDAG, in a more or less natural enumeration of them in which an arbitrary undirected edge is picked, oriented one way, Meek rules applied, then a remaining unoriented edge is picked, oriented one way, and so on, until a DAG is obtained, and then by backtracking the other orientation of each chosen edge is tried.
next() - Method in class edu.cmu.tetrad.search.utils.DagIterator: Successive calls to this method return successive DAGs in the CPDAG, in a more or less natural enumeration of them in which an arbitrary undirected edge is picked, oriented one way, Meek rules applied, then a remaining unoriented edge is picked, oriented one way, and so on, until a DAG is obtained, and then by backtracking the other orientation of each chosen edge is tried.
next() - Method in class edu.cmu.tetrad.util.ChoiceGenerator: next.
next() - Method in class edu.cmu.tetrad.util.CombinationGenerator: next.
next() - Method in class edu.cmu.tetrad.util.CombinationIterator: next.
next() - Method in class edu.cmu.tetrad.util.PermutationGenerator: next.
next() - Method in class edu.cmu.tetrad.util.SelectionGenerator: next.
next() - Method in class edu.cmu.tetrad.util.SublistGenerator: next.
nextAfter(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the floating-point number that is adjacent to the first argument in the direction of the second argument.
nextBeta(double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value sampled from a Beta distribution with the specified shape parameters.
nextBoolean() - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random boolean value.
nextBytes(byte[]) - Method in class edu.cmu.tetrad.util.RandomUtil: Fills the given byte array with random bytes.
nextChiSquare(double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value from a chi-squared distribution with the specified degrees of freedom.
nextDouble() - Method in class edu.cmu.tetrad.util.RandomUtil: Generates the next pseudorandom double value between 0.0 (inclusive) and 1.0 (exclusive) from the underlying random number generator.
nextDown(double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the floating-point value adjacent to the argument in the direction of negative infinity.
nextExponential(double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value following an exponential distribution with the specified scale parameter.
nextFloat() - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random float value uniformly distributed in the range [0, 1).
nextGamma(double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value following a Gamma distribution parameterized by the given shape and scale.
nextGaussian() - Method in class edu.cmu.tetrad.util.RandomUtil: Generates the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence.
nextGaussian(double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value from a normal (Gaussian) distribution with the specified mean and standard deviation.
nextGumbel(double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random sample from the Gumbel distribution with the specified location and scale parameters.
nextIncrementalRelease() - Method in class edu.cmu.tetrad.util.Version: nextIncrementalRelease.
nextInt() - Method in class edu.cmu.tetrad.util.RandomUtil: Generates the next pseudorandom integer.
nextInt(int) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random integer between 0 (inclusive) and the specified value (exclusive).
nextLaplace(double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random sample from the Laplace (double exponential) distribution with the specified location and scale parameters.
nextLong() - Method in class edu.cmu.tetrad.util.RandomUtil: Generates and returns the next pseudorandom long value from the underlying random number generator.
nextMajorVersion() - Method in class edu.cmu.tetrad.util.Version: nextMajorVersion.
nextMinorSubversion() - Method in class edu.cmu.tetrad.util.Version: nextMinorSubversion.
nextMinorVersion() - Method in class edu.cmu.tetrad.util.Version: nextMinorVersion.
nextParameterName(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Given base b (a String), returns the first name in the sequence "b1", "b2", "b3", etc., which is not already the name of a node in the workbench.
nextPoisson(double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value sampled from a Poisson distribution with the specified mean (lambda).
nextRandom() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.RandomDistribution: Draws a new noise value from the underlying distribution.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Beta: Returns the next random.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.ChiSquare: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Discrete: nextRandom.
nextRandom() - Method in interface edu.cmu.tetrad.util.dist.Distribution: Returns the next random number from the distribution.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Exponential: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Gamma: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.GaussianPower: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Indicator: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.LogNormal: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Normal: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Poisson: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.SingleValue: Returns the next random number from the distribution.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Split: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: nextRandom.
nextRandom() - Method in class edu.cmu.tetrad.util.dist.Uniform: nextRandom.
nextT(double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value sampled from a Student's t-distribution with the specified degrees of freedom.
nextToken() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionLexer: nextToken.
nextToken() - Method in class edu.cmu.tetrad.data.RegexTokenizer: nextToken.
nextTokenIncludingWhitespace() - Method in class edu.cmu.tetrad.calculator.parser.ExpressionLexer: nextTokenIncludingWhitespace.
nextTruncatedNormal(double, double, double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random value from a truncated normal (Gaussian) distribution with the specified mean, standard deviation, lower bound, and upper bound.
nextUniform(double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Generates a random double value uniformly distributed within the specified range [low, high].
nextUp(double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the floating-point value adjacent to the input in the direction of positive infinity.
nFactsDep() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the nFactsDep record component.
nFactsIndep() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the nFactsIndep record component.
nil - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: No edge
nIterNoChange - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents the number of consecutive training iterations without improvement in the monitored metric before early stopping is triggered.
nIterNoChange - Variable in class edu.cmu.tetrad.search.CordEngine2: Specifies the number of iterations with no improvement in the early stopping criterion before the model stops training.
nIterNoChange - Variable in class edu.cmu.tetrad.search.CordEngine3: Specifies the number of iterations with no improvement in model performance after which the training process may be halted early.
nIterNoChange - Variable in class edu.cmu.tetrad.search.CordEngine4: Specifies the number of consecutive iterations during which no improvement is observed before an early stopping criterion is triggered.
nIterNoChange - Variable in class edu.cmu.tetrad.search.CordEngine5: The maximum number of consecutive iterations allowed without any change in the desired outcome or criteria before stopping a process.
nl - Enum constant in enum class edu.cmu.tetrad.graph.Edge.Property: No latent confounder.
NNEstimator - Class in edu.cmu.tetrad.sem: Pure-library neural-network estimator for a DAG factorization.
NNEstimator(DataSet, Graph) - Constructor for class edu.cmu.tetrad.sem.NNEstimator: Creates an estimator with default parameters.
NNEstimator(DataSet, Graph, NNEstimatorParams) - Constructor for class edu.cmu.tetrad.sem.NNEstimator: Creates an estimator with explicit parameters.
NNEstimatorParams - Class in edu.cmu.tetrad.sem: Tuning parameters for NNEstimator.
NNEstimatorParams() - Constructor for class edu.cmu.tetrad.sem.NNEstimatorParams: Creates a parameter object with all defaults.
NO_PD_PATHS - Enum constant in enum class edu.cmu.tetrad.search.GacTotalEffectElibility.Status: No potentially directed paths from X to Y exist (within maxPathLength); total effect is forced to 0.
NO_RANDOMLY_DETERMINED_INDEPENDENCE - Static variable in class edu.cmu.tetrad.util.Params: Constant NO_RANDOMLY_DETERMINED_INDEPENDENCE="noRandomlyDeterminedIndependence"
NO_TYPE - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant NO_TYPE
NO_VALID_SINK - Enum constant in enum class edu.cmu.tetrad.graph.Paths.CpdagViolation: Indicates a violation where the graph does not contain a valid sink.
NoAlmostCyclicPathsCondition - Class in edu.cmu.tetrad.algcomparison.statistic: No almost cyclic paths condition.
NoAlmostCyclicPathsCondition() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NoAlmostCyclicPathsCondition: Constructs a new instance of the statistic.
NoCyclicPathsCondition - Class in edu.cmu.tetrad.algcomparison.statistic: No cyclic paths condition.
NoCyclicPathsCondition() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NoCyclicPathsCondition: Constructs a new instance of the statistic.
node - Variable in class edu.cmu.tetrad.sem.NodeAdequacySummary: The name or identifier of the node being evaluated within the model.
node - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Variable name.
node - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Name of the node.
node - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Node identifier.
node - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Node identifier.
node - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Node identifier.
Node - Interface in edu.cmu.tetrad.graph: Represents an object with a name, node type, and position that can serve as a node in a graph.
NodeAdequacySummary - Class in edu.cmu.tetrad.sem: Represents the adequacy summary of a specific node in a model with information about its characteristics, performance metrics, and improvement over a baseline.
NodeCVSummary - Class in edu.cmu.tetrad.sem: Immutable per-node summary produced by k-fold cross-validation in NNEstimator#crossValidate(int).
NodeId(String, int) - Constructor for class edu.cmu.tetrad.graph.TimeLagGraph.NodeId: Constructor for NodeId.
nodePAfter() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the nodePAfter record component.
NodePair - Class in edu.cmu.tetrad.graph: An unordered pair of nodes.
NodePair(Node, Node) - Constructor for class edu.cmu.tetrad.graph.NodePair: Constructor for NodePair.
NodesInCyclesPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
NodesInCyclesPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesPrecision: Constructs a new instance of the statistic.
NodesInCyclesRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
NodesInCyclesRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NodesInCyclesRecall: Constructs a new instance of the statistic.
nodeSummaries - Variable in class edu.cmu.tetrad.sem.AdequacyReport: Represents a collection of adequacy summaries for individual nodes within a model.
nodeSummaries - Variable in class edu.cmu.tetrad.sem.CVReport: Per-node OOS summaries, one per non-root node.
NodeType - Enum Class in edu.cmu.tetrad.graph: An enum of the node types in a graph (MEASURED, LATENT, ERROR).
NodeVariableType - Enum Class in edu.cmu.tetrad.graph: Node variable type.
noEdgeRequired(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: noEdgeRequired.
NOISE_EXPRESSION - Static variable in class edu.cmu.tetrad.util.Params: Constant NOISE_EXPRESSION="noiseExpression"
Noise models - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
noLagConvention_noMirroringNoException() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Nodes whose names don't follow the "X" / "X:N" convention should be treated as unrelated singletons — no mirroring, no exception.
NOMINAL - Static variable in class edu.cmu.tetrad.data.DiscreteVariableType: Constant NOMINAL
NON_GAUSSIAN - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable's marginal distribution deviates from Gaussian by the Anderson-Darling test.
NON_GAUSSIAN_NOTE - Static variable in class edu.cmu.tetrad.data.audit.DataAudit: The note attached by DataAudit.notes() when at least one FindingCode.NON_GAUSSIAN finding fired.
nonAmenabilityWitness(Node, Node) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns a witness to non-amenability: a possibly directed path from x to y whose first edge is not a visible directed edge out of x.
NonancestorPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: Number of NOT X~~>Y in true graph for which also NOT X~~>Y in estimated graph.
NonancestorPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NonancestorPrecision: Constructs a new instance of the statistic.
NonancestorRecall - Class in edu.cmu.tetrad.algcomparison.statistic: Number of NOT X~~>Y in true graph for which also NOT X~~>Y in estimated graph.
NonancestorRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NonancestorRecall: Constructs a new instance of the statistic.
nonApprox_independent_vs_dependent() - Method in class edu.cmu.tetrad.search.KciSmokeTest: Tests the accuracy and behavior of the non-approximate conditional independence testing method using the Kci framework.
noncentralChiSquareUpperTail(int, double, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: P(chi-square(df, lambda) > x) for the noncentral chi-square, by the Poisson mixture sum over j of Poisson(j; lambda/2) * P(chi-square(df + 2j) > x).
noncentrality(double, int, int) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The noncentrality a linear dependence of partial correlation r delivers to the likelihood-ratio statistic, -(N - |S| - 3) ln(1 - r^2).
NONCOLLIDER - Enum constant in enum class edu.cmu.tetrad.search.utils.GraphSearchUtils.CpcTripleType: A noncollider triple.
NONCOLLIDER - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.VcPc.CpcTripleType: The triple is a noncollider.
NONCOLLIDER - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.VcPcFast.CpcTripleType: Constant NONCOLLIDER
NONCOLLIDER_FIRST - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.ColliderPolicy: Specifies a policy where non-colliding entities or scenarios are given strict priority in collision-handling decisions.
nondirectedEdge(Node, Node) - Static method in class edu.cmu.tetrad.graph.Edges: Constructs a new nondirected edge from nodeA to nodeB (o-o).
nondirectedGraph(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: undirectedGraph.
none() - Static method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: The empty mechanism: no value effects, no indicator effects, no attrition.
None - Enum constant in enum class edu.cmu.tetrad.search.utils.ClusterSignificance.CheckType: Do not do the check.
NONE - Enum constant in enum class edu.cmu.tetrad.calculator.parser.ExpressionParser.RestrictionType: The expression may contain any parameters.
NONE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingValueSupport: The component has no support for missing values.
NONE - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

No initial adjacencies.
NONE - Enum constant in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: No normalisation: the entries are the adjustments, passed through unchanged (NaN still means no information).
NONE - Enum constant in enum class edu.cmu.tetrad.search.Rlcd.Stage1Method: No stage-1 search: the complete graph over the observed variables is used, so all variables form one partition and the stage-2 rank search runs unconstrained.
NONE - Enum constant in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NonGaussianBonus: Represents the absence of a non-Gaussian bonus.
NONE - Enum constant in enum class edu.cmu.tetrad.search.test.IndTestFisherZ.ShrinkageMode: Represents the absence of any shrinkage mode.
NONE - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: This option doesn't do any purify.
NONE - Enum constant in enum class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear.DistortionType: No distortion applied to the data.
NONE - Enum constant in enum class edu.cmu.tetrad.sem.ParamConstraintType: Represents a parameter constraint type NONE.
NONE - Static variable in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Constant NONE=3
Nonexecutable - Annotation Interface in edu.cmu.tetrad.annotation: Indicates an algorithm is nonexecutable.
Non-Gaussian bonus - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
NonGaussianSemSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: A linear SEM simulation whose exogenous noises are drawn independently from the distribution given by the customNoiseExpression parameter (default Exp(1)) rather than from Gaussians.
NonGaussianSemSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.NonGaussianSemSimulation: Constructs a NonGaussianSemSimulation object with the given RandomGraph object.
nonIdentificationWitness(Graph, Set, Node) - Static method in class edu.cmu.tetrad.search.GRegression: Returns a witness to non-identification, if any: a proper possibly causal path from a treatment to the outcome that starts with an undirected edge (Theorem 2).
nonIdentificationWitness(Collection, Node) - Method in class edu.cmu.tetrad.search.GRegression: Instance form of GRegression.nonIdentificationWitness(Graph, Set, Node), resolving nodes by name.
NonlinearFunctionOfLinear - Class in edu.cmu.tetrad.sem: Represents a nonlinear function of linear model for generating synthetic data based on a directed acyclic graph (DAG).
NonlinearFunctionOfLinear(Graph, int, RealDistribution, double, double, double, double, boolean, int, double) - Constructor for class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear: Constructs a NonlinearFunctionOfLinear instance, which generates synthetic data based on a directed acyclic graph (DAG) using post-nonlinear causal relationships and associated modeling parameters.
NonlinearFunctionOfLinear.DistortionType - Enum Class in edu.cmu.tetrad.sem: Represents the type of distortion applied in a nonlinear additive causal model.
NonlinearFunctionsOfLinear - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a nonlinear functions of linear simulation.
NonlinearFunctionsOfLinear(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.NonlinearFunctionsOfLinear: Constructs a SemSimulation object with the given RandomGraph object.
NonlinearityTests - Class in edu.cmu.tetrad.search.utils: Practical nonlinearity tests for conditional mean E(Y|X).
NonlinearityTests.CleanData - Class in edu.cmu.tetrad.search.utils: Represents cleaned data for nonlinearity tests, with NaNs removed from y and X.
NonlinearityTests.TestResult - Class in edu.cmu.tetrad.search.utils: Encapsulates the result of a statistical test, including the test statistic, p-value, and the decision to reject the null hypothesis.
NONPOSITIVE_VARIANCE - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A diagonal entry of a supplied covariance matrix is zero or negative.
noOp() - Static method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Creates a no-operation CandidateEdit, indicating no changes to be applied to a graph.
NoOpProgressListener() - Constructor for class edu.cmu.tetrad.graph.LayoutUtil.NoOpProgressListener: A no-operation implementation of the LayoutProgressListener interface.
NoPotentiallyDirectedF1 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for adjacencies.
NoPotentiallyDirectedF1() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NoPotentiallyDirectedF1: Constructs a new instance of the statistic.
norm1() - Method in class edu.cmu.tetrad.util.Matrix: norm1.
Normal - Class in edu.cmu.tetrad.util.dist: A normal distribution that allows its parameters to be set and allows random sampling.
Normal(double, double) - Constructor for class edu.cmu.tetrad.util.dist.Normal: Constructor for Normal.
normalCdf(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Normal cumulative distribution function (the value which results by integrating the normal distribution function from negative infinity up to y).
normalCdf(double, double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Computes the cumulative distribution function (CDF) of a normal (Gaussian) distribution with the specified mean and standard deviation at the given value.
normalizeAll() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Normalizes all rows in the tables associated with each of node in turn.
normalizeAll() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Normalizes all rows in the tables associated with each of node in turn.
normalizeAll() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Normalizes all rows in the tables associated with each of node in turn.
normalizeAll() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Normalizes all rows in the tables associated with each of node in turn.
normalizeAll() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: normalizeAll.
NORMALIZED - Enum constant in enum class edu.cmu.tetrad.search.utils.EdgePriors.Origin: Already normalised to mean one.
normalizedToMeanOne() - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns a copy of this WEIGHTS store rescaled so that the stored weights average one.
normalizeInPlace(double[]) - Static method in class edu.cmu.tetrad.search.unmix.EmUtils: Normalizes the elements of the given array in-place so that their sum equals 1.
normalizeNode(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Normalizes all rows in the table associated with a given node.
normalizeNode(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Normalizes all rows in the table associated with a given node.
normalizeNode(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Normalizes the specified node by invoking the MlBayesIm.normalizeRow(int, int) method on each row of the node.
normalizeNode(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Normalizes all rows in the table associated with a given node.
normalizeNode(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Normalizes all rows in the table associated with a given node.
normalizeRow(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Normalizes the given row.
normalizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Normalizes the given row.
normalizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Normalizes the probabilities of a given row in a node.
normalizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Normalizes the given row.
normalizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Normalizes the given row.
normalizeRow(int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Normalizes the probabilities in a row of the local table for a discrete child node.
normalPdf(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: normalPdf.
normalPdf(double, double, double) - Method in class edu.cmu.tetrad.util.RandomUtil: Computes the value of the Probability Density Function (PDF) of a normal (Gaussian) distribution with the specified mean and standard deviation at the given value.
normalQuantile(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: normalQuantile.
normalRand() - Static method in class edu.cmu.tetrad.util.ProbUtils: Normal random generator
NoSemidirectedPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NoSemidirectedPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedPrecision: Constructs a new instance of the statistic.
NoSemidirectedRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NoSemidirectedRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NoSemidirectedRecall: Constructs a new instance of the statistic.
NOT_AMENABLE - Enum constant in enum class edu.cmu.tetrad.search.GacTotalEffectElibility.Status: The graph is not amenable for (X, Y) under the chosen graph type / constraints.
NOT_EQUAL_TO_CPDAG - Enum constant in enum class edu.cmu.tetrad.graph.Paths.CpdagViolation: Indicates a violation where the graph does not equal the CPDAG of its induced DAG.
NOT_FOUND - Static variable in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: The family was exhausted without finding a separator.
note() - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Returns the value of the note record component.
notes() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns cross-references to other diagnostics bearing on the findings that fired, in a fixed order, or an empty list if none apply.
Notes - Section in class edu.cmu.tetrad.search.AdjustmentHarness
Notes and limitations - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM
Notes and limitations - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM
Notes and limitations - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1
Notes and limitations - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2
notifyObservers() - Method in class edu.cmu.tetrad.search.MarkovCheck: Notifies all registered ModelObservers by invoking their update() method.
noUnshieldedColliderDag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Orients an undirected chordal graph into a DAG with no unshielded colliders by directing every edge from the earlier to the later endpoint of a Maximum Cardinality Search order.
nRooks(boolean[][]) - Static method in class edu.cmu.tetrad.search.utils.NRooks: Solves the N-Rooks problem for the given board or allowable positions.
NRooks - Class in edu.cmu.tetrad.search.utils: Returns row positions for placing rooks for an n x n matrix so the rooks cannot attach each other, with a given boolean[][] specification of where rooks are allowed to be placed.
nRotations - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: The number of valid rotation p-values aggregated in the computation process.
nRowsUsed - Variable in class edu.cmu.tetrad.util.EssLikePython.Result: Represents the number of rows used in the computation.
nSamples - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Represents the total number of samples generated during a simulation.
nSamples - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Number of samples simulated in this result.
nSamples - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Number of samples simulated in this result.
nSamples - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Number of samples simulated in this result.
ntad(int[][]) - Method in class edu.cmu.tetrad.search.ntad_test.Ark: Computes a statistical measure using the specified indices and correlation matrices.
ntad(int[][]) - Method in class edu.cmu.tetrad.search.ntad_test.BollenTing: Computes the NTAD statistic for a given 2D array of integer pairs.
ntad(int[][]) - Method in class edu.cmu.tetrad.search.ntad_test.Cca: Computes the aggregate statistical measure based on a list of tetrad configurations.
ntad(int[][]) - Method in class edu.cmu.tetrad.search.ntad_test.NtadTest: Computes the value of a statistical test based on the input ntad configuration.
ntad(int[][]) - Method in class edu.cmu.tetrad.search.ntad_test.Wishart
ntad(int[][], boolean, double) - Method in class edu.cmu.tetrad.search.ntad_test.Ark: Computes a statistical measure based on the specified indices and correlation matrices.
ntad(int[][], boolean, double) - Method in class edu.cmu.tetrad.search.ntad_test.BollenTing: Computes the NTAD statistic for a given 2D array of integer pairs.
ntad(int[][], boolean, double) - Method in class edu.cmu.tetrad.search.ntad_test.Cca: Computes the aggregate statistical measure based on a list of tetrad configurations.
ntad(int[][], boolean, double) - Method in class edu.cmu.tetrad.search.ntad_test.NtadTest: Computes the value of a statistical test based on the given ntad configuration, with optional resampling.
ntad(int[][], boolean, double) - Method in class edu.cmu.tetrad.search.ntad_test.Wishart: Computes the p-value for testing the null hypothesis of tetrad constraints in a given correlation matrix.
NtadExplorer - Class in edu.cmu.tetrad.search: NtadExplorer contains utility methods for identifying and analyzing rank-deficient m×m variable subsets (n-tads) within a dataset.
NtadExplorer.NtadResult - Class in edu.cmu.tetrad.search: Represents the result of a near-total aggregate decomposition (NTAD) analysis.
NtadExplorerHarness - Class in edu.cmu.tetrad.search: Simple harness / demo driver for NtadExplorer.
NtadResult(List, List, int, int, double) - Constructor for class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Constructs an instance of NtadResult with specified properties.
ntads(int[][]...) - Method in class edu.cmu.tetrad.search.ntad_test.Ark: Computes a statistical measure based on a variable number of 2D integer arrays, where each array contains tetrad configurations.
ntads(int[][]...) - Method in class edu.cmu.tetrad.search.ntad_test.BollenTing: Computes the NTADS (Non-Testable Assumption Detection Score) for a given set of NTAD structures, each passed as a 2D array.
ntads(int[][]...) - Method in class edu.cmu.tetrad.search.ntad_test.Cca: Returns the p-value for the tetrad.
ntads(int[][]...) - Method in class edu.cmu.tetrad.search.ntad_test.NtadTest: Computes the statistical test results for multiple sets of ntad configurations.
ntads(int[][]...) - Method in class edu.cmu.tetrad.search.ntad_test.Wishart: Returns the p-value for the tetrad.
ntads(List) - Method in class edu.cmu.tetrad.search.ntad_test.Ark: Computes a statistical measure based on a list of tetrad configurations.
ntads(List) - Method in class edu.cmu.tetrad.search.ntad_test.BollenTing
ntads(List) - Method in class edu.cmu.tetrad.search.ntad_test.Cca: Returns the p-value for the tetrad.
ntads(List) - Method in class edu.cmu.tetrad.search.ntad_test.NtadTest: Computes a statistical measure based on the input list of ntad configurations.
ntads(List) - Method in class edu.cmu.tetrad.search.ntad_test.Wishart: Returns the p-value for the tetrad.
ntads(List, boolean, double) - Method in class edu.cmu.tetrad.search.ntad_test.BollenTing: Computes the NTADS for a given list of NTAD (Non-Testable Assumption Detection) structures.
NtadTest - Class in edu.cmu.tetrad.search.ntad_test: NtadTest is an abstract base class for implementing ntad-based statistical tests.
NtadTest(SimpleMatrix, boolean, int) - Constructor for class edu.cmu.tetrad.search.ntad_test.NtadTest: Constructs an instance of NtadTest using the provided data matrix, whether to compute correlations, and the effective sample size (ESS).
nu() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Returns the value of the nu record component.
NULL - Enum constant in enum class edu.cmu.tetrad.graph.Endpoint: No endpoint.
NullFit(long, double, double) - Constructor for record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Creates an instance of a NullFit record class.
nullMmd2 - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Refit-noise null: mean over refits of the same conditional MMD² between the child's original mechanism and a refit with the same parents under a new seed.
nullMmd2Sd - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Standard deviation of EdgeStrengthResult.nullMmd2 across refits; NaN if fewer than two.
nullRefits - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Number of refits the null is based on; 0 if skipped.
NUM_BASIS_FUNCTIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_BASIS_FUNCTIONS="numBasisFunctions"
NUM_BSC_BOOTSTRAP_SAMPLES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_BSC_BOOTSTRAP_SAMPLES="numBscBootstrapSamples"
NUM_CATEGORIES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_CATEGORIES="numCategories"
NUM_CATEGORIES_TO_DISCRETIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_CATEGORIES_TO_DISCRETIZE="numCategoriesToDiscretize"
NUM_FACTORS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_FACTORS="numFactors"
NUM_FF_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_FF_FEATURES="numFfFeatures"
NUM_LAGS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_LAGS="numLags"
NUM_LATENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_LATENTS="numLatents"
NUM_MEASURES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_MEASURES="numMeasures"
NUM_RANDOMIZED_SEARCH_MODELS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_RANDOMIZED_SEARCH_MODELS="numRandomizedSearchModels"
NUM_ROUNDS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_ROUNDS="numRounds"
NUM_RUNS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_RUNS="numRuns"
NUM_STARTS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_STARTS="numStarts"
NUM_STRUCTURAL_EDGES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_STRUCTURAL_EDGES="mimNumStructuralEdges"
NUM_STRUCTURAL_NODES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_STRUCTURAL_NODES="mimNumStructuralNodes"
NUM_SUBSAMPLES - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_SUBSAMPLES="numSubsamples"
NUM_THREADS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUM_THREADS="numThreads"
NumAmbiguousTriples - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
NumAmbiguousTriples() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumAmbiguousTriples: Constructs a new instance of the statistic.
NUMBER - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Number.
NUMBER_OF_EXPANSIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant NUMBER_OF_EXPANSIONS="numberOfExpansions"
NUMBER_RESAMPLING - Static variable in class edu.cmu.tetrad.util.Params: Constant NUMBER_RESAMPLING="numberResampling"
NumberArrowsEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of arrows in the estimated graph.
NumberArrowsEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsEst: Constructs the statistic.
NumberArrowsNotInUnshieldedCollidersEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of arrows not in unshielded colliders in the estimated graph.
NumberArrowsNotInUnshieldedCollidersEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsNotInUnshieldedCollidersEst: Constructs the statistic.
NumberCollidersEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of unshielded colliders in the estimated graph.
NumberCollidersEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberCollidersEst: Constructs the statistic.
NumberEdgesEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of edges in the estimated graph.
NumberEdgesEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesEst: Constructs the statistic.
NumberEdgesInCollidersEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of edges in colliders in the estimated graph.
NumberEdgesInCollidersEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInCollidersEst: Constructs the statistic.
NumberEdgesInUnshieldedCollidersEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of edges in unshielded colliders in the estimated graph.
NumberEdgesInUnshieldedCollidersEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInUnshieldedCollidersEst: Constructs the statistic.
NumberEdgesTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The NumberEdgesTrue class is an implementation of the Statistic interface.
NumberEdgesTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesTrue: Constructs the statistic.
NumberFormatUtil - Class in edu.cmu.tetrad.util: Provides an application-wide "memory" of the number format to be used.
NumberObjectDataSet - Class in edu.cmu.tetrad.data: Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.
NumberObjectDataSet(Number[][], List) - Constructor for class edu.cmu.tetrad.data.NumberObjectDataSet: Constructor for NumberObjectDataSet.
NumberOfEdgesEst - Class in edu.cmu.tetrad.algcomparison.statistic: Prints the number of edges in the estimated graph.
NumberOfEdgesEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesEst: Constructs a new instance of the statistic.
NumberOfEdgesTrue - Class in edu.cmu.tetrad.algcomparison.statistic: Prints the number of edges in the true graph.
NumberOfEdgesTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesTrue: Constructs a new instance of the statistic.
NumberTailsEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of tails in the estimated graph.
NumberTailsEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberTailsEst: Constructs the statistic.
NumberUnshieldedCollidersEst - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the NumberEdgesEst statistic, which calculates the number of unshielded colliders in the estimated graph.
NumberUnshieldedCollidersEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumberUnshieldedCollidersEst: Constructs the statistic.
NumBidirectedBothNonancestorAncestor - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected edge precision.
NumBidirectedBothNonancestorAncestor() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedBothNonancestorAncestor: Constructs a new instance of the statistic.
NumBidirectedEdgesEst - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
NumBidirectedEdgesEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesEst: Constructs a new instance of the statistic.
NumBidirectedEdgesTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
NumBidirectedEdgesTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesTrue: Constructs a new instance of the statistic.
numBlocks(int[]) - Static method in class edu.cmu.tetrad.search.utils.PerFactEss: Convenience: the number of distinct blocks in a block-id assignment.
numBootstrap - Variable in class edu.cmu.tetrad.search.CordEngine4: The number of bootstrap multiplier draws to perform when generating resampled datasets.
numBootstrap - Variable in class edu.cmu.tetrad.search.CordEngine5: The number of bootstrap multiplier draws per rotation.
numBootstraps - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Number of bootstrap replicates used, B.
NumColoredDD - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumColoredDD() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumColoredDD: Constructs a new instance of the statistic.
NumColoredNL - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumColoredNL() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumColoredNL: Constructs a new instance of the statistic.
NumColoredPD - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumColoredPD() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumColoredPD: Constructs a new instance of the statistic.
NumColoredPL - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumColoredPL() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumColoredPL: Constructs a new instance of the statistic.
numCols() - Method in class edu.cmu.tetrad.data.ByteDataBox: numCols.
numCols() - Method in interface edu.cmu.tetrad.data.DataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.DoubleDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.FloatDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.IntDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.LongDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.MixedDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.ShortDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: numCols.
numCols() - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: numCols.
NumCommonMeasuredAncestorBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCommonMeasuredAncestorBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCommonMeasuredAncestorBidirected: Constructs a new instance of the statistic.
NumCompatibleDefiniteDirectedEdgeAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatibleDefiniteDirectedEdgeAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDefiniteDirectedEdgeAncestors: Constructs a new instance of the statistic.
NumCompatibleDirectedEdgeConfounded - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatibleDirectedEdgeConfounded() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeConfounded: Constructs a new instance of the statistic.
NumCompatibleDirectedEdgeNonAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatibleDirectedEdgeNonAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleDirectedEdgeNonAncestors: Constructs a new instance of the statistic.
NumCompatibleEdges - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatibleEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleEdges: Constructs a new instance of the statistic.
NumCompatiblePossiblyDirectedEdgeAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The former name of the statistic now called NumCompatiblePotentiallyDirectedEdgeAncestors.
NumCompatiblePossiblyDirectedEdgeAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeAncestors: No-arg constructor.
NumCompatiblePossiblyDirectedEdgeNonAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The former name of the statistic now called NumCompatiblePotentiallyDirectedEdgeNonAncestors.
NumCompatiblePossiblyDirectedEdgeNonAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePossiblyDirectedEdgeNonAncestors: No-arg constructor.
NumCompatiblePotentiallyDirectedEdgeAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatiblePotentiallyDirectedEdgeAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeAncestors: Constructs a new instance of the statistic.
NumCompatiblePotentiallyDirectedEdgeNonAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatiblePotentiallyDirectedEdgeNonAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatiblePotentiallyDirectedEdgeNonAncestors: Constructs a new instance of the statistic.
NumCompatibleVisibleNonancestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCompatibleVisibleNonancestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCompatibleVisibleNonancestors: Constructs a new instance of the statistic.
numConstraints - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Number of constraints tested, K.
NumCorrectBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: Counts the number of X<->Y edges for which a latent confounder of X and Y exists.
NumCorrectBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCorrectBidirected: Counts the number of bidirectional edges for which a latent confounder of X and Y exists.
NumCorrectDDAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCorrectDDAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCorrectDDAncestors: Constructs a new instance of the statistic.
NumCorrectPDAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumCorrectPDAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCorrectPDAncestors: Constructs a new instance of the statistic.
NumCorrectVisibleEdges - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a statistic that calculates the number of correct visible ancestors in the true graph that are also visible ancestors in the estimated graph.
NumCorrectVisibleEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCorrectVisibleEdges: Constructs a new instance of the statistic.
NumCoveringAdjacenciesInPag - Class in edu.cmu.tetrad.algcomparison.statistic: The number of covering adjacencies in an estimated PAG compared to the true PAG.
NumCoveringAdjacenciesInPag() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumCoveringAdjacenciesInPag: Constructs the statistic.
numDataSets() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the numDataSets record component.
NumDefinitelyDirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDefinitelyDirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyDirected: Constructs a new instance of the statistic.
NumDefinitelyNotDirectedPaths - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDefinitelyNotDirectedPaths() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyNotDirectedPaths: Constructs a new instance of the statistic.
NumDirectedEdgeAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedEdgeAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeAncestors: Constructs a new instance of the statistic.
NumDirectedEdgeBnaMeasuredCounfounded - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedEdgeBnaMeasuredCounfounded() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeBnaMeasuredCounfounded: Constructs a new instance of the statistic.
NumDirectedEdgeNoMeasureAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedEdgeNoMeasureAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNoMeasureAncestors: Constructs a new instance of the statistic.
NumDirectedEdgeNotAncNotRev - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedEdgeNotAncNotRev() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeNotAncNotRev: Constructs a new instance of the statistic.
NumDirectedEdgeReversed - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedEdgeReversed() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdgeReversed: Constructs a new instance of the statistic.
NumDirectedEdges - Class in edu.cmu.tetrad.algcomparison.statistic: Number of X-->Y in est.
NumDirectedEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdges: Constructs a new instance of the statistic.
NumDirectedPathsEst - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedPathsEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsEst: Constructs a new instance of the statistic.
NumDirectedPathsTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedPathsTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsTrue: Constructs a new instance of the statistic.
NumDirectedShouldBePartiallyDirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumDirectedShouldBePartiallyDirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumDirectedShouldBePartiallyDirected: Constructs a new instance of the statistic.
numDroppedNoAdjustmentSet() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns the value of the numDroppedNoAdjustmentSet record component.
numDroppedNotAmenable() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns the value of the numDroppedNotAmenable record component.
NumEdgeInEstInTrue - Class in edu.cmu.tetrad.algcomparison.statistic: The number of adjacencies in the estimated graph but not in the true graph.
NumEdgeInEstInTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumEdgeInEstInTrue: Constructs the statistic.
numericSep - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Represents the separator used for numeric disambiguation in configurations.
numericSep(String) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets the string to be used as the separator for numeric parts in the resulting configuration.
numEstimators - Variable in class edu.cmu.tetrad.search.CordEngine1: Specifies the number of estimators (or iterations) used in the model training process.
numEstimators - Variable in class edu.cmu.tetrad.search.CordEngine2: Specifies the number of estimators (trees) to be used in a tree-based model.
numEstimators - Variable in class edu.cmu.tetrad.search.CordEngine3: The maximum number of estimators (iterations) used in constructing the model.
numEstimators - Variable in class edu.cmu.tetrad.search.CordEngine4: The number of estimators to be used in the ensemble learning algorithm.
numEstimators - Variable in class edu.cmu.tetrad.search.CordEngine5: The number of estimators or iterations to be used in a model.
numFailed() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns the value of the numFailed record component.
numFolds - Variable in class edu.cmu.tetrad.search.CordEngine4: Number of folds used for splitting the data in cross-validation or other similar iterative processes.
numFolds - Variable in class edu.cmu.tetrad.search.CordEngine5: The number of folds (numFolds) specifies the parameter used for dividing a dataset or performing operations that involve partitioning data, typically in the context of cross-validation or other algorithms requiring data splits.
numFolds - Variable in class edu.cmu.tetrad.sem.CVReport: Number of folds used.
numFolds - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Number of folds used.
numFolds - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Number of CV folds used.
numForcedPairs(double, int) - Method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Returns the number of variable pairs whose prior is at or beyond the required-knowledge limit for a score with penalty discount lambda and sample size n: pairs with 2 * beta >= lambda * log(n), whose BIC penalty is fully rebated, so the edge is added unconditionally by the forward phase and can never be removed by the backward phase.
NumGenuineAdjacenciesInPag - Class in edu.cmu.tetrad.algcomparison.statistic: The number of genuine adjacencies in an estimated PAG compared to the true PAG.
NumGenuineAdjacenciesInPag() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumGenuineAdjacenciesInPag: Constructs the statistic.
NumIncorrectDDAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumIncorrectDDAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectDDAncestors: Constructs a new instance of the statistic.
NumIncorrectPDAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumIncorrectPDAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectPDAncestors: Constructs a new instance of the statistic.
NumIncorrectVisibleAncestors - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumIncorrectVisibleAncestors() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumIncorrectVisibleAncestors: Constructs a new instance of the statistic.
NumLatentCommonAncestorBidirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumLatentCommonAncestorBidirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumLatentCommonAncestorBidirected: Constructs a new instance of the statistic.
numLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Number of discrete levels for the node.
numLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Number of discrete levels for the node.
numLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Number of discrete levels for the node.
numLevels - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Number of discrete levels for the node.
NumNondirectedEdges - Class in edu.cmu.tetrad.algcomparison.statistic: Number of X---Y in est.
NumNondirectedEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumNondirectedEdges: Constructs a new instance of the statistic.
numNullPairs(int, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The number of pairs that are non-adjacent in the true graph, p(p-1)/2 minus the expected number of true edges.
numPairs - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.StabilityResult: Represents the total number of pairwise comparisons considered in the context of a clustering stability analysis.
numParameters(int, int[]) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Returns the number of parameters for a node given its parents.
NumParametersEst - Class in edu.cmu.tetrad.algcomparison.statistic: Number of parameters for a discrete Bayes model of the data.
NumParametersEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumParametersEst: Constructor for NumParametersEst.
NumPartiallyOrientedEdges - Class in edu.cmu.tetrad.algcomparison.statistic: Number of Xo->Y in est.
NumPartiallyOrientedEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumPartiallyOrientedEdges: Constructs a new instance of the statistic.
numPatternsSkipped - Variable in class edu.cmu.tetrad.data.missing.MissingDataAudit.LittleResult: The number of patterns skipped because their observed-variable covariance submatrix was singular.
numPatternsUsed - Variable in class edu.cmu.tetrad.data.missing.MissingDataAudit.LittleResult: The number of missingness patterns that contributed to the statistic.
NumPotentiallyDirected - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
NumPotentiallyDirected() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumPotentiallyDirected: Constructs a new instance of the statistic.
numPriorPairs() - Method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Returns the number of variable pairs carrying a non-neutral prior at the locus.
numPriorPairs() - Method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Returns the number of variable pairs carrying a non-neutral prior at the locus.
numRefinementsEvaluated() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns the value of the numRefinementsEvaluated record component.
numRepeats - Variable in class edu.cmu.tetrad.search.CordEngine4: The number of repetitions or iterations for repeated cross-fitting, where results are averaged over multiple partitions to improve stability and reduce variance.
numRepeats - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the number of repetitions or iterations for a process, such as repeated cross-validation partitions in machine learning or other scenarios requiring multiple runs for accuracy or validation.
numRepeats - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Number of independent repeats the means and SDs are taken over.
numRows() - Method in class edu.cmu.tetrad.data.ByteDataBox: numRows.
numRows() - Method in interface edu.cmu.tetrad.data.DataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.DoubleDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.FloatDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.IntDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.LongDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.MixedDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.ShortDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: numRows.
numRows() - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: numRows.
numRowsUsed - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: The number of rows having at least one observed value.
numStarts(int) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the number of random restarts for BOSS.
numTestsDep() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the numTestsDep record component.
numTestsDep() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the numTestsDep record component.
numTestsInd() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns the value of the numTestsInd record component.
numTestsInd() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns the value of the numTestsInd record component.
numThreads(int) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the number of threads BOSS may use.
numThresholds - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents the number of thresholds used in the CORD algorithm for splitting or binning data.
numThresholds - Variable in class edu.cmu.tetrad.search.CordEngine2: The number of thresholds (or partitions) used in the analysis process.
numThresholds - Variable in class edu.cmu.tetrad.search.CordEngine3: The number of thresholds used in model training or scoring processes.
numThresholds - Variable in class edu.cmu.tetrad.search.CordEngine4: The number of thresholds used in the CordEngine4 algorithm.
numThresholds - Variable in class edu.cmu.tetrad.search.CordEngine5: The number of thresholds to be used for a specific operation or computation.
NumUndirectedEdges - Class in edu.cmu.tetrad.algcomparison.statistic: Number of X---Y in est.
NumUndirectedEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumUndirectedEdges: Constructs a new instance of the statistic.
NumVisibleEdges - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a statistic that calculates the number of correct visible ancestors in the true graph that are also visible ancestors in the estimated graph.
NumVisibleEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdges: Constructs a new instance of the statistic.
NumVisibleEdgeTrue - Class in edu.cmu.tetrad.algcomparison.statistic: A class that implements the Statistic interface to calculate the number of visible edges in the true PAG.
NumVisibleEdgeTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdgeTrue: A class that calculates the number of visible edges in the true PAG.
nUsed() - Method in record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Returns the value of the nUsed record component.
nUsed() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Returns the value of the nUsed record component.
nVars() - Method in class edu.cmu.tetrad.util.ConditionalKDE.BatchResult: Returns the total number of variables involved in the batch operation.

O

O_COMPATIBLE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.RaMode: Represents a mode in the RaMode enumeration.
OBSERVATIONAL_STUDY - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant OBSERVATIONAL_STUDY="Observational Study"
ObservationalStudySimulation - Class in edu.cmu.tetrad.algcomparison.simulation: Simulates data with the anatomy of a real observational scientific dataset (the archetype is the Algerian Forest Fires dataset, but with all complications defaulted off this is a plain mixed-data cross-sectional study).
ObservationalStudySimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.ObservationalStudySimulation: Constructs an ObservationalStudySimulation.
observed(String) - Static method in class edu.cmu.tetrad.search.rlcd.Cover: Creates a measured (observed) cover over a single variable, with the leaf flag undecided.
observedAncestors(Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: The observed variables among the given variables and their ancestors, in the variable-level graph that LatentGroups.toAdjacency() writes (every variable of a key is a parent of every variable of each of its children).
observedChildren() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the observedChildren record component.
observedChildren() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the observedChildren record component.
observedDescendants(Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: The observed variables among the given variables and their descendants, in the variable-level graph that LatentGroups.toAdjacency() writes.
observedLogLikelihood(double[][], double[], double[][]) - Static method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Returns the observed-data log likelihood of the given data under a multivariate normal with the given mean and covariance, ignoring missing entries in the manner appropriate under MAR: each row contributes the marginal density of its observed coordinates.
of(MissingDataPolicy) - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec with the given policy and default parameters.
of(String, DagMetric.MetricComputer) - Static method in interface edu.cmu.tetrad.sem.DagMetric: Creates a new instance of a DagMetric that computes a metric with the specified name using the provided MetricComputer.
OFF - Enum constant in enum class edu.cmu.tetrad.search.PcAR.RescueAction: Run detection but take no action; PcAR.getContestedDeletions() stays empty.
OFF - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.ColliderPolicy: Represents a configuration where collisions are ignored or not considered.
ok(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a success message prefixed with a checkmark symbol if the tracer is enabled.
OK - Enum constant in enum class edu.cmu.tetrad.search.GacTotalEffectElibility.Status: OK to proceed (e.g., compute adjustment sets, then estimate).
olsFitRidgeV1(SimpleMatrix, SimpleMatrix, double) - Static method in class edu.cmu.tetrad.regression.v1.RegressionUtilV1: v1: OLS fit with ridge (Tikhonov) via normal equations: (X'X + λI)β = X'y.
olsFitV1(SimpleMatrix, SimpleMatrix) - Static method in class edu.cmu.tetrad.regression.v1.RegressionUtilV1: v1: OLS fit via QR-based least squares: beta = argmin ||X*beta - y||_2.
OlsResidualizer() - Constructor for class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.OlsResidualizer: Constructs an OLS residualizer with the default ridge of 1e-10.
OlsResidualizer(double) - Constructor for class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.OlsResidualizer: Constructs an OLS residualizer with a given ridge.
OlsRidge(double) - Constructor for class edu.cmu.tetrad.search.test.IndTestGin.OlsRidge: Constructs an instance of OlsRidge with the specified ridge parameter.
onCommonSupport(List, Collection) - Method in class edu.cmu.tetrad.data.audit.DeterministicClusters.Fitter: Fits, for each target, its equation in terms of the SAME support universe -- the audit's retained-form report, where every removed variable is written in terms of the retained variables.
oneFactorTest(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: oneFactorTest.
oneFactorTest(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: oneFactorTest.
oneFactorTest(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: oneFactorTest.
oneFactorTest(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: oneFactorTest.
oneFactorTest(int, int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: oneFactorTest.
oneFactorTest(int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: oneFactorTest.
oneFactorTest(int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: oneFactorTest.
oneFactorTest(int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: oneFactorTest.
oneLine() - Method in class edu.cmu.tetrad.search.rlcd.RlcdStabilitySummary: One line for a graph attribute: the run count and the modal number of latents.
onProgress(int, String) - Method in interface edu.cmu.tetrad.graph.LayoutUtil.LayoutProgressListener: Reports the progress of a layout operation.
onProgress(int, String) - Method in class edu.cmu.tetrad.graph.LayoutUtil.NoOpProgressListener
onProgress(int, String) - Method in class edu.cmu.tetrad.graph.LayoutUtil.SwingProgressListener
oosMse - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Mean OOS MSE across folds: average of (observed - predicted)² on held-out rows.
oosR2 - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: OOS R²: 1 - oosMse / baselineMse.
oosXent - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Mean OOS cross-entropy across folds on held-out rows.
OPERATOR - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Operator.
OPTIMAL - Enum constant in enum class edu.cmu.tetrad.search.Ida.IDA_TYPE: Represents the OPTIMAL classification of IDA_TYPE (Witte et al.
OPTIMAL - Enum constant in enum class edu.cmu.tetrad.search.PdagPagIda.IDA_TYPE: Represents the optimal algorithmic approach in the IDA (Intervention Distribution Adjustment) process.
OPTIMAL_ACROSS_BRANCHES - Static variable in class edu.cmu.tetrad.util.Params: Constant OPTIMAL_ACROSS_BRANCHES="optimalAcrossBranches"
optimize(SemIm) - Method in interface edu.cmu.tetrad.sem.SemOptimizer: Optimizes the fitting function of a Sem by adjusting its parameter values.
optimize(SemIm) - Method in class edu.cmu.tetrad.sem.SemOptimizerEm: Optimizes an unoptimized Sem object by minimizing the chi-square statistic.
optimize(SemIm) - Method in class edu.cmu.tetrad.sem.SemOptimizerPowell: Optimizes the fitting function of a Sem by adjusting its parameter values.
optimize(SemIm) - Method in class edu.cmu.tetrad.sem.SemOptimizerRegression: Optimizes the fitting function of a Sem by adjusting its parameter values.
optimize(SemIm) - Method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Optimizes the given structural equation model by utilizing the RICF (Regression Iterative Conditional Fitting) algorithm based on the EJML library, tailored for models with ADMG (Acyclic Directed Mixed Graphs) and no selection bias.
optimize(SemIm) - Method in class edu.cmu.tetrad.sem.SemOptimizerScattershot: Optimizes the fitting function of a Sem by adjusting its parameter values.
order() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the order record component.
ORDERED_LOCAL_MARKOV_PROPERTY - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Full ordered local Markov property (Richardson)
ORDERED_LOCAL_MARKOV_PROPERTY_SINK_ELIMINATION - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Sink elimination ordered local Markov property (Andrews)
OrderedLocalMarkovProperty - Class in edu.cmu.tetrad.search: Computes the complete Ordered Local Markov Property (OLMP) for an acyclic directed mixed graph (ADMG), as defined and proved sound and complete by Richardson (2003, Scandinavian Journal of Statistics 30:145-157, Theorem 2).
OrderedLocalMarkovPropertySinkElimination - Class in edu.cmu.tetrad.search: Utility class for computing the ordered local Markov property of a maximal ancestral graph (MAG).
OrderedPair<E> - Class in edu.cmu.tetrad.graph: An ordered pair of objects.
OrderedPair(E, E) - Constructor for class edu.cmu.tetrad.graph.OrderedPair: Constructor for OrderedPair.
orderedVarsString(Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: The variable names of the covers, sorted and space-joined (Python getOrderedVarsString).
orderRowsToReduceCrossings(Graph, List) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Orders the nodes within each of the given rows to reduce the crossings of the edges between rows, by the ordering step of Richard's layout (see LayoutUtil.orderTiers(Graph, List, List)).
ORF - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.FeatureType: Denotes the type of Orthogonal Random Features (ORF) used in random feature mappings for machine learning or statistical models.
ORF - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.FeatureType: Denotes the type of Orthogonal Random Features (ORF) used in random feature mappings for machine learning or statistical models.
ORF - Enum constant in enum class edu.cmu.tetrad.search.score.FfMl.FeatureType: Represents the Orthogonal Random Features (ORF) option within the enumeration.
ORF - Enum constant in enum class edu.cmu.tetrad.search.score.FfMlContinuous.FeatureType: Denotes the type of Orthogonal Random Features (ORF) used in random feature mappings for machine learning or statistical models.
ORF - Enum constant in enum class edu.cmu.tetrad.search.test.FfCiContinuous.FeatureType: Represents the Orthogonal Random Features (ORF) feature generation method.
ORF - Enum constant in enum class edu.cmu.tetrad.search.test.Rcit.FeatureType: Represents the Orthogonal Random Features (ORF) method for approximating shift-invariant kernel functions, such as the Radial Basis Function (RBF) kernel.
orient() - Method in class edu.cmu.tetrad.search.Pairwise: Orients the graph and returns the oriented graph.
orient(Graph) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: orient.
orient(Graph, Set, boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Performs FCI orientation on the given graph, including R0 and either the Spirtes or Zhang final orientation rules.
ORIENT_AND_PRUNE - Static variable in class edu.cmu.tetrad.util.Params: Constant ORIENT_AND_PRUNE="orientAndPrune"
orient_pairwise - Enum constant in enum class edu.cmu.tetrad.annotation.AlgType: If algorithm orients edges pairwise.
ORIENT_TOWARD_DCONNECTIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant ORIENT_TOWARD_DCONNECTIONS="orientTowardDConnections"
ORIENT_VISIBLE_FEEDBACK_LOOPS - Static variable in class edu.cmu.tetrad.util.Params: Constant ORIENT_VISIBLE_FEEDBACK_LOOPS="orientVisibleFeedbackLoops"
orientAndPruneEdges(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Orients and prunes the latent structure of the given graph.
orientArrowheadAt(Graph, Node, Node) - Static method in class edu.cmu.tetrad.search.cdnod_pag.PagEdgeUtils: Orients the edge in the given PAG (Partial Ancestral Graph) such that there is an arrowhead at the specified child node coming from the specified parent candidate node.
Orientation - Section in class edu.cmu.tetrad.search.test.IndTestCord
Orientation - Section in class edu.cmu.tetrad.search.test.IndTestCordEric
ORIENTATION_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant ORIENTATION_ALPHA="orientationAlpha"
OrientationClash(Node, Node, Set) - Constructor for record class edu.cmu.tetrad.search.PcAR.OrientationClash: Creates an instance of a OrientationClash record class.
OrientationConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for orientations:
OrientationConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.OrientationConfusion: Constructor for OrientationConfusion.
orientationCovariates(Graph, Node, Node, int) - Static method in class edu.cmu.tetrad.search.Fask: Identifies a set of covariates (nodes) that help orient causal relationships between two given nodes xi and xj within a graph.
OrientationPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The orientation precision.
OrientationPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.OrientationPrecision: Constructs a new instance of the statistic.
OrientationRecall - Class in edu.cmu.tetrad.algcomparison.statistic: Represents an implementation of the Statistic interface that calculates the Orientation Recall.
OrientationRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.OrientationRecall: Constructs a new instance of the statistic.
orientCollider(Graph, Node, Node, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Orients the edges of the given graph by setting both specified nodes as arrow endpoints directed towards the specified target node.
orientCollidersUsingSepsets(SepsetMap, Knowledge, Graph, boolean, boolean) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Step C of PC; orients colliders using specified sepset.
orientImplied(Graph) - Method in class edu.cmu.tetrad.search.utils.MeekRules: Uses the Meek rules to do as many orientations in the given graph as possible.
orientImpureCluster(List) - Method in class edu.cmu.tetrad.search.Tgin.TginOrientationStages: Deprecated.

Orients all latent-latent edges within one impure cluster using the LaHiCaSl Algorithm 6 procedure (Xie et al.
orientIntraClusterEdges() - Method in class edu.cmu.tetrad.search.Tgin.TginOrientationStages: Deprecated.

Adjusts the orientation of intra-cluster edges within a graph based on a causal ordering derived from independent component analysis (ICA) and pairwise LiNGAM.
orientLatentEdges(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Orients undirected latent-latent edges using asymmetric correlations between measured parents and measured children.
orientLatentEdgesAnyNone(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Orients undirected latent-latent edges using asymmetric correlations between measured parents and measured children.
orientLatentEdgesUsingRanks(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Orients latent edges in the given graph using statistical rank-based tests.
origin() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the origin record component.
originalData() - Method in record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Returns the value of the originalData record component.
OS_DEGRADE_CENSOR_PROP - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_DEGRADE_CENSOR_PROP="osDegradeCensorProp"
OS_DEGRADE_CENSOR_QUANTILE - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_DEGRADE_CENSOR_QUANTILE="osDegradeCensorQuantile"
OS_DEGRADE_MISSING_MECHANISM - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_DEGRADE_MISSING_MECHANISM="osDegradeMissingMechanism"
OS_DEGRADE_MISSING_PROP - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_DEGRADE_MISSING_PROP="osDegradeMissingProp"
OS_DEGRADE_ORDINALIZE_PROP - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_DEGRADE_ORDINALIZE_PROP="osDegradeOrdinalizeProp"
OS_FORM_INDEX_NOISE - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_FORM_INDEX_NOISE="osFormIndexNoise"
OS_FORM_INTERACTION - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_FORM_INTERACTION="osFormInteraction"
OS_FORM_NONLINEARITY - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_FORM_NONLINEARITY="osFormNonlinearity"
OS_GRAPH_AVG_SYSTEM_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_AVG_SYSTEM_DEGREE="osGraphAvgSystemDegree"
OS_GRAPH_EDGE_DENSITY - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_EDGE_DENSITY="osGraphEdgeDensity"
OS_GRAPH_NUM_CONTEXT - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_NUM_CONTEXT="osGraphNumContext"
OS_GRAPH_NUM_HIDDEN_CONTEXT - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_NUM_HIDDEN_CONTEXT="osGraphNumHiddenContext"
OS_GRAPH_NUM_INDICES - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_NUM_INDICES="osGraphNumIndices"
OS_GRAPH_NUM_OUTCOMES - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_NUM_OUTCOMES="osGraphNumOutcomes"
OS_GRAPH_NUM_SYSTEM - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_GRAPH_NUM_SYSTEM="osGraphNumSystem"
OS_PANEL_EMIT_SUBJECT_COLUMN - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_PANEL_EMIT_SUBJECT_COLUMN="osPanelEmitSubjectColumn"
OS_PANEL_EMIT_SUBJECTS_AS_DATA_SETS - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_PANEL_EMIT_SUBJECTS_AS_DATA_SETS="osPanelEmitSubjectsAsDataSets"
OS_PANEL_NUM_SUBJECTS - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_PANEL_NUM_SUBJECTS="osPanelNumSubjects"
OS_PANEL_SUBJECT_SHIFT_SD - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_PANEL_SUBJECT_SHIFT_SD="osPanelSubjectShiftSd"
OS_SERIAL_AR_COEF - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_SERIAL_AR_COEF="osSerialArCoef"
OS_SERIAL_INDEX_MEMORY_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_SERIAL_INDEX_MEMORY_HIGH="osSerialIndexMemoryHigh"
OS_SERIAL_INDEX_MEMORY_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_SERIAL_INDEX_MEMORY_LOW="osSerialIndexMemoryLow"
OS_SERIAL_MAX_LAG - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_SERIAL_MAX_LAG="osSerialMaxLag"
OS_SERIAL_PROP_CROSS_LAG - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_SERIAL_PROP_CROSS_LAG="osSerialPropCrossLag"
OS_TYPE_DISCRETE_OUTCOME - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_TYPE_DISCRETE_OUTCOME="osTypeDiscreteOutcome"
OS_TYPE_NUM_CATEGORIES - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_TYPE_NUM_CATEGORIES="osTypeNumCategories"
OS_TYPE_PROP_CONTEXT_DISCRETE - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_TYPE_PROP_CONTEXT_DISCRETE="osTypePropContextDiscrete"
OS_TYPE_PROP_SYSTEM_DISCRETE - Static variable in class edu.cmu.tetrad.util.Params: Constant OS_TYPE_PROP_SYSTEM_DISCRETE="osTypePropSystemDiscrete"
OSet - Class in edu.cmu.tetrad.search: Utilities for computing the Henckel–Perković–Maathuis O-set (optimal adjustment set) in DAGs and amenable CPDAGs.
oSetCpdag(Graph, Node, Node, int) - Static method in class edu.cmu.tetrad.search.OSet: O_G(X -> Y) for an amenable CPDAG or maxPDAG.
oSetDag(Graph, Node, Node) - Static method in class edu.cmu.tetrad.search.OSet: O_G(X -> Y) for a DAG.
OSetTest - Class in edu.cmu.tetrad.search: Unit tests for OSet (Henckel–Perković–Maathuis O-sets).
OSetTest() - Constructor for class edu.cmu.tetrad.search.OSetTest: Default constructor for the OSetTest class.
other - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: Other score.
outcome() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns the value of the outcome record component.
outcome() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns the value of the outcome record component.
Output - Section in class edu.cmu.tetrad.search.harness.MissingDataRecoveryHarness
Output - Section in class edu.cmu.tetrad.search.harness.MixedMissingDataHarness
Output - Section in class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi
OUTPUT_CPDAG - Static variable in class edu.cmu.tetrad.util.Params: Constant OUTPUT_CPDAG="outputCpdag"
OUTPUT_RBD - Static variable in class edu.cmu.tetrad.util.Params: Constant OUTPUT_RBD="outputRBD"
Output files - Section in class edu.cmu.tetrad.search.AdjustmentHarness
OutputGraph - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Represents a graph that's output by a genetic search algorithm.
overall() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the overall record component.
overflowPrefix - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Represents the prefix string to be applied when the configured overlap or part limit is exceeded.
overflowPrefix(String) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets the string to be used as the prefix for overflow information in the resulting configuration.
overlapJoiner - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Specifies the string used to join parts in cases of overlap within a configuration.
overlapJoiner(String) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets the string that will be used as the joiner for overlapping parts.
overlapPaCh(Set) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Whether some cover in Vs shares a variable with a parent of another cover in Vs (Python overlapPaCh).
OVERRIDE - Enum constant in enum class edu.cmu.tetrad.search.Fask.Origin: The external graph's compelled orientation was overturned by the left-right statistic under the bootstrap sign-consensus gate.
OVERRIDE - Enum constant in enum class edu.cmu.tetrad.search.FaskPool.Origin: The default orientation from the evidence graphs was overturned by the pooled left-right statistic.
Overview - Section in class edu.cmu.tetrad.search.test.FfCi

P

p - Variable in class edu.cmu.tetrad.bayes.BayesProperties.LikelihoodRet: Represents the p-value from a statistical test, indicating the probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true.
PAG - Enum constant in enum class edu.cmu.tetrad.graph.GraphUtils.GraphType: The PAG graph type.
PAG - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.COMMIT_ROUTE: Reorient the PAG directly; endpoints stay circles until a rule commits them.
PAG - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.COMMIT_ROUTE: Reorient the PAG directly; endpoints stay circles until a rule commits them.
PAG - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.COMMIT_ROUTE: Reorient the PAG directly; endpoints stay circles until a rule commits them.
PAG - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.GraphType: Represents a Partial Ancestral Graph (PAG) within the context of the graph analysis.
PAG - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: A partial ancestral graph: the equivalence class of a MAG, admitting latent confounders and using circle endpoints for undetermined orientations.
PAG - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.ResultType: Constant for PAG result type.
PAG_of_the_true_DAG - Enum constant in enum class edu.cmu.tetrad.algcomparison.Comparison.ComparisonGraph: Constant for the PAG of the true DAG.
PAG_of_the_true_DAG - Enum constant in enum class edu.cmu.tetrad.algcomparison.TimeoutComparison.ComparisonGraph: The pag of the true dag.
PAG_SAMPLING_RFCI_PARAMETERS - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Constant PAG_SAMPLING_RFCI_PARAMETERS
PagAdjacencyPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The PagAdjacencyPrecision class implements the Statistic interface and represents the adjacency precision compared to the true PAG (Partially Ancestral Graph).
PagAdjacencyPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyPrecision: Constructs a new instance of the statistic.
PagAdjacencyRecall - Class in edu.cmu.tetrad.algcomparison.statistic: A class that implements the PagAdjacencyRecall statistic.
PagAdjacencyRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.PagAdjacencyRecall: Constructs a new instance of the statistic.
PagCache - Class in edu.cmu.tetrad.util: A thread-safe, identity-preserving cache that converts DAGs or MAGs to PAGs.
PagCacheTest - Class in edu.cmu.tetrad.util: Unit tests for PagCache: 1) Repeated calls on same source return the SAME PAG object (identity preserved).
PagCacheTest() - Constructor for class edu.cmu.tetrad.util.PagCacheTest: Constructs a new instance of the PagCacheTest class.
PagEdgeUtils - Class in edu.cmu.tetrad.search.cdnod_pag: Utility class for handling operations related to partially oriented edges in a PAG (Partial Ancestral Graph).
PagEffects(LinkedList, int, int, int) - Constructor for record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Creates an instance of a PagEffects record class.
PagLegalityCheck - Class in edu.cmu.tetrad.search.utils: The PagLegalityCheck class provides utility methods for validating the legality of Partial Ancestral Graphs (PAGs) and Maximal Ancestral Graphs (MAGs).
PagLegalityCheck.LegalMagRet - Class in edu.cmu.tetrad.search.utils: Stores a result for checking whether a graph is a legal MAG--(a) whether it is (a boolean), and (b) the reason why it is not, if it is not (a String).
PagLegalityCheck.LegalPagRet - Class in edu.cmu.tetrad.search.utils: Stores a result for checking whether a graph is a legal PAG--(a) whether it is (a boolean), and (b) the reason why it is not, if it is not (a String).
pagProblem(Graph) - Static method in class edu.cmu.tetrad.search.GRegressionPag: Returns null if the graph can be used by this estimator, or else a description of why it cannot.
PagSampleRfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: PAG-Sampling-RFCI, the former name of the class now called PagSamplingRfci.
PagSampleRfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: No-arg constructor.
PagSamplingRfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Jan 29, 2023 3:45:09 PM
PagSamplingRfci - Class in edu.pitt.dbmi.algo.bayesian.constraint.search: Jan 29, 2023 4:10:52 PM
PagSamplingRfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Constructs a new instance of the PagSampleRfci algorithm.
PagSamplingRfci(DataSet) - Constructor for class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Constructor.
PagSigmaHatHarness - Class in edu.cmu.tetrad.search: Decides, empirically, whether evaluating a covariate-adjustment functional at the RICF-fitted covariance matrix beats evaluating it at the sample covariance matrix, when the causal structure is known as a PAG.
pairDofHistogram(int[]) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: Builds the df histogram over unordered pairs from per-variable parameter block sizes, taking the df of the pair (x, y) to be size[x] * size[y].
pairs() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Returns the value of the pairs record component.
pairs() - Method in class edu.cmu.tetrad.util.ConditionalKDE.BatchResult: Returns the list of variable index pairs involved in the batch operation.
pairsUsed - Variable in class edu.cmu.tetrad.util.RowCorrelationEffN.Result: The number of row pairs used in the calculation.
Pairwise - Class in edu.cmu.tetrad.search: Implements a number of methods which take a fixed graph as input and use linear, non-Gaussian methods to orient the edges in the graph.
Pairwise(Graph, List) - Constructor for class edu.cmu.tetrad.search.Pairwise: Constructor.
PAIRWISE_CORRELATION_NOT_PSD - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The pairwise-complete correlation matrix of the continuous variables has at least one negative eigenvalue.
PAIRWISE_COVARIANCE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Estimate the covariance matrix once by pairwise deletion (see PairwiseCovarianceEstimator) and run the analysis on it as if it were complete data: each correlation from the rows complete on its pair, rescaled by each variable's standard deviation over all its observed rows, and shrunk toward the identity just enough to be positive definite.
PAIRWISE_MARKOV_PROPERTY - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Pairwise Markov property, conditioing on the anteriority of the endpoints.
Pairwise.Rule - Enum Class in edu.cmu.tetrad.search: Give a list of options for rules for doing the non-Gaussian orientations.
Pairwise.Score - Enum Class in edu.cmu.tetrad.search: Gives a list of options for non-Gaussian transformations that can be used for some scores.
pairwiseCovariance() - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec for a pairwise-deletion covariance matrix; see MissingDataPolicy.PAIRWISE_COVARIANCE and PairwiseCovarianceEstimator.
PairwiseCovarianceEstimator - Class in edu.cmu.tetrad.data.missing: Estimates a covariance matrix from data with missing values by pairwise deletion, without a model of the data.
pairwiseCovarianceMatrix(List) - Method in class edu.cmu.tetrad.data.missing.TestwiseCovariance: Returns a pairwise-deletion covariance matrix over the given variables: entry (i, j) is the covariance of columns i and j over the rows complete on both, with each diagonal entry computed over the rows complete on that column alone.
PairwiseMarkovProperty - Class in edu.cmu.tetrad.search: Computes the pairwise Markov property for an acyclic directed mixed graph (ADMG).
pairwiseN() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns the value of the pairwiseN record component.
PAPER_CANCELLATION_C0 - Static variable in class edu.cmu.tetrad.search.PcAR: Boole upper bound on the per-triangle probability that a true triangle is silently collapsed by genuine cancellation (not a weak-edge miss), summed over the three cancellation loci (Secs.
paperCancellationBaseRate(int) - Static method in class edu.cmu.tetrad.search.PcAR: q_cancel(n) ~= PAPER_CANCELLATION_C0 / sqrt(n - 3), per Secs.
paperCancellationBaseRateOdds(int) - Static method in class edu.cmu.tetrad.search.PcAR: The base rate above, expressed as odds (q/(1-q)) rather than a probability -- i.e.
PARALLEL - Static variable in class edu.cmu.tetrad.search.FastIca: Extract components simultaneously (symmetric decorrelation).
PARALLELIZED - Static variable in class edu.cmu.tetrad.util.Params: Constant PARALLELIZED="parallelized"
ParamComparison - Enum Class in edu.cmu.tetrad.sem: An enum of the types of the various comparisons a parameter may have with respect to one another for SEM estimation.
ParamConstraint - Class in edu.cmu.tetrad.sem: A class for implementing constraints on the values of the freeParameters of of instances of the SemIm class.
ParamConstraint(SemIm, Parameter, ParamConstraintType, double) - Constructor for class edu.cmu.tetrad.sem.ParamConstraint: The first constructor specifies the parameter and a number and the type of relation imposed by the constraint.
ParamConstraintType - Enum Class in edu.cmu.tetrad.sem: Enum for representing different types of parameter constraints.
ParamDescription - Class in edu.cmu.tetrad.util: Describes a parameter.
ParamDescription(String, String, String, Serializable) - Constructor for class edu.cmu.tetrad.util.ParamDescription: Constructor for ParamDescription.
ParamDescription(String, String, String, Serializable, double, double) - Constructor for class edu.cmu.tetrad.util.ParamDescription: Constructor for ParamDescription.
ParamDescription(String, String, String, Serializable, int, int) - Constructor for class edu.cmu.tetrad.util.ParamDescription: Constructor for ParamDescription.
ParamDescriptions - Class in edu.cmu.tetrad.util: Stores descriptions of the parameters for the simulation box.
Parameter - Class in edu.cmu.tetrad.sem: Stores information about the identity of a SEM parameter--its name, its type (COEF, COVAR), and the node(s) it is associated with.
Parameter(String, ParamType, Node, Node) - Constructor for class edu.cmu.tetrad.sem.Parameter: Constructor for Parameter.
PARAMETER - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Parameter.
ParameterColumn - Class in edu.cmu.tetrad.algcomparison.statistic: Adds a column to the output table in which values for the given parameter are listed.
ParameterColumn(String) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ParameterColumn: Constructor for ParameterColumn.
Parameter conventions for the simulation study - Section in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer
parameterize(Graph) - Method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Creates a SemIm from graph using the current parameter ranges.
parameterizeAndSimulate(Graph, int) - Method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Parameterises graph and simulates sampleSize rows of linear Gaussian data in a single call.
ParameterPair - Class in edu.cmu.tetrad.sem: Implements an ordered pair of objects (a, b) suitable for storing in HashSets.
ParameterPair(Parameter, Parameter) - Constructor for class edu.cmu.tetrad.sem.ParameterPair: Constructs a new ordered pair (a, b).
ParameterRange(Edge, double, double, double) - Constructor for class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: Constructs a new parameter range.
parameters() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Returns the value of the parameters record component.
Parameters - Section in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython
Parameters - Section in class edu.cmu.tetrad.algcomparison.independence.ClKciPython
Parameters - Section in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython
Parameters - Section in class edu.cmu.tetrad.search.Tsc
Parameters - Class in edu.cmu.tetrad.util: Stores a list of named parameters with their values.
Parameters() - Constructor for class edu.cmu.tetrad.util.Parameters: Constructor for Parameters.
Parameters(Parameters) - Constructor for class edu.cmu.tetrad.util.Parameters: Constructor for Parameters.
ParameterSettingsText - Class in edu.cmu.tetrad.algcomparison.utils: Renders the effective parameter settings of a configured algorithm as plain text suitable for copying into an email, a lab notebook, or a script - one name = value line per parameter, grouped into the same sections the parameter panel displays (algorithm, score, test, bootstrapping), with section titles taken from the annotations.
ParameterSettingsText.ApplyResult - Class in edu.cmu.tetrad.algcomparison.utils: The result of applying pasted settings text: the section titles encountered, the names of parameters whose values were applied, and the lines that were skipped (with reasons).
ParameterSweep - Class in edu.cmu.tetrad.algcomparison.sweep: A harness for evaluating a search algorithm over a grid of parameter settings and recording, per setting, the evidence a parameter choice should rest on: the point-estimate graph on the full data, a probability-annotated aggregate of searches on shared resamples (edge probabilities displaying identically to bootstrap results elsewhere in Tetrad), the StARS-style adjacency instability across those resamples, and Markov-check statistics for the point graph if a Markov-check test is configured.
ParameterSweep(Algorithm, Parameters) - Constructor for class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Constructs a sweep harness for the given algorithm and base parameters.
ParameterUtils - Class in edu.cmu.tetrad.util: A utility to create/modify parameters.
parameterValues() - Method in interface edu.cmu.tetrad.algcomparison.utils.ParameterValues: parameterValues.
parameterValues() - Method in class edu.cmu.tetrad.data.simulation.LoadDataFromFileWithoutGraph: parameterValues.
ParameterValues - Interface in edu.cmu.tetrad.algcomparison.utils: Tags a class that can return values for parameters.
Parametric-bootstrap null reference for the Markov check - Section in class edu.cmu.tetrad.search.MarkovCheckNullReference
params() - Method in record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Returns the value of the params record component.
Params - Class in edu.cmu.tetrad.util: Static final string constants for use in parameter maps.
Params() - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Default constructor for the Params class.
Params() - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Constructs a new parameter set for the trained DAG simulator.
Params() - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Default constructor for the Params class.
Params() - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Default constructor for the Params class.
ParamType - Enum Class in edu.cmu.tetrad.sem: An enum of the free parameter types for SEM models (COEF, MEAN, VAR, COVAR).
parent(Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: parent.
PARENT_SET_MEDIAN - Enum constant in enum class edu.cmu.tetrad.search.score.KcvBicScore.BandwidthMode: Compute median pairwise distance in the parent space (more RCIT-like, more expensive).
PARENT_SET_MEDIAN - Enum constant in enum class edu.cmu.tetrad.search.score.RffBicScore.BandwidthMode: Compute median pairwise distance in the parent space (more RCIT-like, more expensive).
parentCardinality(Set) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: The cardinality of Vs after replacing any cluster within it by its atomic latent parent, recursively (Python parentCardinality).
Parent encoding - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM
Parent encoding - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM
Parent encoding - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1
Parent encoding - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2
parentName - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Name of the parent variable (tail of the edge).
parentName - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Name of the parent variable (tail of the edge).
parentName - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: Name of the parent variable (tail of the deleted edge).
parentPool() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the parentPool record component.
parentPool() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the parentPool record component.
parents - Variable in class edu.cmu.tetrad.sem.NodeAdequacySummary: List of parent nodes for the node being evaluated.
parents - Variable in class edu.cmu.tetrad.sem.NodeCVSummary: Parent variable names (empty for root nodes).
parents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: A list of parent nodes that influence the current node in the simulation report.
parents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: List of parent nodes for the current node.
parents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: List of parent nodes for the current node.
parents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: List of parent nodes for the current node.
PARENTS_AND_NEIGHBORS - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Causal Markov property, conditioning on the parents and neighbors of the target variable.
parentsPerGroup() - Method in record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Returns the value of the parentsPerGroup record component.
ParentSupersetBuilder - Class in edu.cmu.tetrad.search.unmix: Builds a parent *superset* per node without assuming a single pooled DAG.
ParentSupersetBuilder.Config - Class in edu.cmu.tetrad.search.unmix: Represents the configuration settings used for statistical screening and optional bagging operations in the parent superset construction process.
ParentSupersetBuilder.ScoreType - Enum Class in edu.cmu.tetrad.search.unmix: Represents the type of correlation score used for statistical calculations or comparisons.
parse(String, DataSet) - Static method in class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec: Parses an input textual representation of block specifications into a structured format.
parseEquation(String) - Method in class edu.cmu.tetrad.calculator.parser.ExpressionParser: Parses an equation of the form Variable = Expression.
parseExpression(String) - Method in class edu.cmu.tetrad.calculator.parser.ExpressionParser: Parses the given expression, or throws an exception if its not possible.
parseGraphXml(Element, Map) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: parseGraphXml.
parseJSONArrayToTetradEdges(Graph, JSONArray) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONArrayToTetradEdges.
parseJSONArrayToTetradNodes(JSONArray) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONArrayToTetradNodes.
parseJSONArrayToTetradTriple(JSONObject) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONArrayToTetradTriple.
parseJSONArrayToTetradTriples(JSONArray) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONArrayToTetradTriples.
parseJSONObjectToEdgeProperty(String) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONObjectToEdgeProperty.
parseJSONObjectToEdgeTypeProperty(JSONObject) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONObjectToEdgeTypeProperty.
parseJSONObjectToTetradEdge(Graph, JSONObject) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONObjectToTetradEdge.
parseJSONObjectToTetradGraph(String) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONObjectToTetradGraph.
parseJSONObjectToTetradGraph(JSONObject) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONObjectToTetradGraph.
parseJSONObjectToTetradNode(JSONObject) - Static method in class edu.cmu.tetrad.util.JsonUtils: parseJSONObjectToTetradNode.
parseLatentGroupSpecs(String) - Static method in class edu.cmu.tetrad.graph.RandomMim: Parses a specification string defining latent group configurations into a list of LatentGroupSpec objects.
parseMimicGroupSpecs(String) - Static method in class edu.cmu.tetrad.graph.RandomMimic: Parses a specification string defining MIMIC group configurations into a list of MimicGroupSpec objects.
parseRateProfile(String, List, double) - Static method in class edu.cmu.tetrad.data.MissingnessInjector: Parses a hand-specified rate profile.
ParseResult(BlockSpec, List) - Constructor for record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.ParseResult: Creates an instance of a ParseResult record class.
partialCorrelation(Matrix, double) - Static method in class edu.cmu.tetrad.util.StatUtils: Assumes that the given covariance matrix was extracted in such a way that the order of the variables (in either direction) is X, Y, Z1, ..., Zn, where the partial correlation one wants is correlation(X, Y | Z1,...,Zn).
partialCorrelation(Matrix, double, int, int, int...) - Static method in class edu.cmu.tetrad.util.StatUtils: partialCorrelation.
PartialCorrelation - Class in edu.cmu.tetrad.search.utils: Calculates partial correlation using the recursive method.
PartialCorrelation(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.utils.PartialCorrelation: Constructor
PartialCorrelation(List, Matrix, int) - Constructor for class edu.cmu.tetrad.search.utils.PartialCorrelation: Constructor.
partialCorrelationForNoncentrality(double, int, int) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The inverse of AlphaCalibration.noncentrality(double, int, int): r = sqrt(1 - exp(-lambda / (N - |S| - 3))).
PartialCorrelationPdf - Class in edu.cmu.tetrad.util: Frequency function of partial correlation r(12|34...k), assuming that the true partial correlation is equal to zero.
PartialCorrelationPdf(int, int) - Constructor for class edu.cmu.tetrad.util.PartialCorrelationPdf: Constructs a new zero partial correlation distribution function with the given values for n and k.
partialCorrelationPrecisionMatrix(Matrix, double) - Static method in class edu.cmu.tetrad.util.StatUtils: partialCorrelationPrecisionMatrix.
partialCovarianceWhittaker(Matrix) - Static method in class edu.cmu.tetrad.util.StatUtils: Assumes that the given covariance matrix was extracted in such a way that the order of the variables (in either direction) is X, Y, Z1, ..., Zn, where the partial covariance one wants is covariance(X, Y | Z1,...,Zn).
partialCovarianceWhittaker(Matrix, int, int, int...) - Static method in class edu.cmu.tetrad.util.StatUtils: partialCovarianceWhittaker.
PartialEdgeStrengthResult - Class in edu.cmu.tetrad.sem: Result of a partial edge strength computation performed by NNEstimator.computePartialEdgeStrength(String, String, int).
partiallyOriented(Graph) - Static method in class edu.cmu.tetrad.search.cdnod_pag.PagEdgeUtils: Retrieves a list of edges from the specified graph (PAG) that are partially oriented.
partiallyOrientedEdge(Node, Node) - Static method in class edu.cmu.tetrad.graph.Edges: Constructs a new partially oriented edge from nodeA to nodeB (o->).
partialR2 - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Held-out R² of the full model minus held-out R² of the model with X removed from Y's parents, on the same k folds as the CV table.
partialStandardDeviation(Matrix, int, int...) - Static method in class edu.cmu.tetrad.util.StatUtils: partialStandardDeviation.
partialVariance(Matrix, int, int...) - Static method in class edu.cmu.tetrad.util.StatUtils: partialVariance.
partialXentImprovement - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: OOS cross-entropy improvement of predicting R from X vs.
passesGuards() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the passesGuards record component.
PATCHY_CONNECTIONS - Enum constant in enum class edu.cmu.tetrad.graph.RandomMim.LatentLinkMode: Roughly half of the possible Cartesian-product links are chosen, but at least one.
Patel - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Patel rule.
Patel25 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Patel25 rule.
Patel50 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Patel50 rule.
Patel75 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Patel75 rule.
Patel90 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Patel90 rule.
paths() - Method in class edu.cmu.tetrad.graph.Dag: paths.
paths() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: paths.
paths() - Method in interface edu.cmu.tetrad.graph.Graph: paths.
paths() - Method in class edu.cmu.tetrad.graph.LagGraph: paths.
paths() - Method in class edu.cmu.tetrad.graph.SemGraph: paths.
paths() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns the instance of Paths.
Paths - Class in edu.cmu.tetrad.graph: Paths class.
Paths(Graph) - Constructor for class edu.cmu.tetrad.graph.Paths: Constructor for Paths.
Paths.AllCliquesAlgorithm - Class in edu.cmu.tetrad.graph: An algorithm to find all cliques in a graph.
Paths.CpdagCheckResult - Record Class in edu.cmu.tetrad.graph: Represents the result of a CPDAG legality check.
Paths.CpdagViolation - Enum Class in edu.cmu.tetrad.graph: Enumeration representing various violations associated with CPDAGs (Completed Partially Directed Acyclic Graphs).
pathString(Graph, Node...) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Generates a string representation of a path in a given graph, starting from the specified nodes.
pathString(Graph, Node, Node, Node) - Static method in class edu.cmu.tetrad.graph.Triple: pathString.
pathString(Graph, List) - Static method in class edu.cmu.tetrad.search.GRegression: Renders a path as a string using the graph's edge marks, e.g.
pathString(Graph, List, boolean) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Constructs a string representation of a path in a graph.
pathString(Graph, List, Set) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns a string representation of the given path in the graph, considering the conditioning variables.
pathString(Graph, List, Set, boolean, boolean) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Returns a string representation of the given path in the graph, with additional information about conditioning variables.
Pc - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Peter/Clark algorithm (PC).
Pc - Class in edu.cmu.tetrad.search: Implements the PC (Peter–Clark) causal discovery algorithm for learning a causal graph from conditional independence information.
Pc() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Constructor for Pc.
Pc(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Constructor for Pc.
Pc(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Pc: Constructs a new instance of the Pc algorithm with the specified independence test.
PC - Enum constant in enum class edu.cmu.tetrad.search.harness.VertexRepairSimulation.StartingGraph: Run PC and use its output as the starting graph.
PC - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Constant for the PC algorithm.
PC_MAX - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.GraphLearner: Represents the PC-Max graph learning algorithm.
PC_ORDER_REFINEMENT - Static variable in class edu.cmu.tetrad.util.Params: Constant PC_ORDER_REFINEMENT="pcOrderRefinement"
PC_REFINEMENT_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant PC_REFINEMENT_ALPHA="pcRefinementAlpha"
PC_STABLE - Enum constant in enum class edu.cmu.tetrad.search.Cstar.CpdagAlgorithm: The PC_STABLE algorithm.
Pc.AllowBidirected - Enum Class in edu.cmu.tetrad.search: Enum representing whether bidirected edges are allowed in the graph.
Pc.ColliderOrientationStyle - Enum Class in edu.cmu.tetrad.search: An enumeration that defines the styles of collider orientation in a graph or network.
Pc.Triple - Class in edu.cmu.tetrad.search: A utility class that encapsulates a triplet of nodes.
pcAlpha - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Represents the alpha parameter for controlling the significance level in hypothesis testing or statistical calculations.
PcAr - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Peter/Clark algorithm with adjacency-rescue (PcAR): PC augmented with detection, and optionally repair, of cancelled unfaithful-triangle edges.
PcAr() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Constructor for PcAr.
PcAr(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Constructor for PcAr.
PcAR - Class in edu.cmu.tetrad.search: Implements the PC (Peter–Clark) causal discovery algorithm for learning a causal graph from conditional independence information.
PcAR(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.PcAR: Constructs a new instance of the PcAR algorithm with the specified independence test.
PcAR.AllowBidirected - Enum Class in edu.cmu.tetrad.search: Enum representing whether bidirected edges are allowed in the graph.
PcAR.ColliderOrientationStyle - Enum Class in edu.cmu.tetrad.search: How unshielded triples x - z - y with x and y nonadjacent are decided to be colliders.
PcAR.ContestedDeletion - Record Class in edu.cmu.tetrad.search: A candidate rescuer: the deleted pair, the sepset that removed it, which clash test caught it, and whether a RECOVER action was actually taken for this deletion.
PcAR.DiscriminatingTestOddsEstimator - Class in edu.cmu.tetrad.search: A PcAR.RecoveryOddsEstimator implementing Sec.
PcAR.MarkovAuditFailure - Record Class in edu.cmu.tetrad.search: A diagnostic Markov-audit flag: an implied independence x _||_ y | conditioningSet that did not hold under a fresh test.
PcAR.MarkovAuditor - Interface in edu.cmu.tetrad.search: Functional hook for a real Markov-audit implication pass, given the finished graph and the test.
PcAR.OrientationClash - Record Class in edu.cmu.tetrad.search: A bidirected orientation clash: the edge u-z, present in the graph, that two different unshielded triples demand carry an arrowhead in opposite directions (CLASH-PASS lines 6-7).
PcAR.RecoveryOddsEstimator - Interface in edu.cmu.tetrad.search: Functional hook supplying posterior odds that a flagged deletion is a genuine edge, for the RECOVER decision.
PcAR.RescueAction - Enum Class in edu.cmu.tetrad.search: What to do with a tier-1 (orientation-clash) positive.
PcAR.Triple - Class in edu.cmu.tetrad.search: A utility class that encapsulates a triplet of nodes.
pcColliderStyle - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Defines the orientation style for the collider in the configuration.
Pcd - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: PC.
Pcd - Class in edu.cmu.tetrad.search: Modifies the PC algorithm to handle the deterministic case.
Pcd() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Constructor for Pcd.
Pcd(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Pcd: Constructs a new PC search using the given independence test as oracle.
pcdOrientC(IndependenceTest, Knowledge, Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Performs step C of the algorithm, as indicated on page xxx of CPS, with the modification that X--W--Y is oriented as X-->W<--Y if W is *determined by* the sepset of (X, Y), rather than W just being *in* the sepset of (X, Y).
PcMax - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: PC-Max.
PcMax() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Constructor for PcMax.
PcMax(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Constructor for PcMax.
PcMb - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: PC.
PcMb - Class in edu.cmu.tetrad.search: Searches for a CPDAG representing all the Markov blankets for a given target T consistent with the given independence information.
PcMb() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Constructor for PcMb.
PcMb(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Constructor for PcMb.
PcMb(IndependenceTest, int) - Constructor for class edu.cmu.tetrad.search.PcMb: Constructs a new search.
Pcmci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: PCMCI wrapper for algcomparison.
Pcmci - Class in edu.cmu.tetrad.search: PCMCI (Runge et al.) — minimal, time-series implementation with lagged edges only (τ≥1).
Pcmci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Default constructor for the Pcmci class.
Pcmci(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Constructs an instance of the Pcmci class with a specified independence test.
Pcmci.Builder - Class in edu.cmu.tetrad.search: Builder class for constructing instances of the Pcmci class.
pcOrientbk(Knowledge, Graph, List, boolean) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Orients according to background knowledge.
pd - Enum constant in enum class edu.cmu.tetrad.graph.Edge.Property: Possibly direct.
PDAG - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.GraphType: Represents a Partially Directed Acyclic Graph (PDAG) within the context of the graph analysis.
PDAG - Enum constant in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: A partially directed acyclic graph: directed and undirected edges, no directed cycle.
PdagPagIda - Class in edu.cmu.tetrad.search: Implements PDAG/PAG IDA with **incremental FCI propagation** after each local orientation at X.
PdagPagIda(DataSet, Graph, List) - Constructor for class edu.cmu.tetrad.search.PdagPagIda: Constructs a new instance of the PdagPagIda class which is used for processing a dataset and graph combination while ensuring the provided graph is a valid DAG, CPDAG/PDAG, or PAG, and the dataset is continuous.
PdagPagIda.IDA_TYPE - Enum Class in edu.cmu.tetrad.search: The IDA_TYPE enum represents the different algorithmic approaches available in IDA (Intervention Distribution Adjustment) processes.
PdagPagIda.NodeEffects - Class in edu.cmu.tetrad.search: Convenience container used elsewhere in Tetrad.
PdagPagIda.PagEffects - Record Class in edu.cmu.tetrad.search: The result of the PAG branch: the possible total effects, together with a count of the refinements that could not be estimated.
PdagPagIdaTest - Class in edu.cmu.tetrad.search: Provides a suite of unit tests for evaluating the functionality of the PagIda algorithm when applied to partially directed acyclic graphs (PAGs).
PdagPagIdaTest() - Constructor for class edu.cmu.tetrad.search.PdagPagIdaTest: Default constructor for the PdagPagIdaTest class.
PEARSON - Enum constant in enum class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.ScoreType: Pearson correlation coefficient, which measures linear correlation between two variables.
PearsonCorrTest() - Constructor for class edu.cmu.tetrad.search.test.IndTestGin.PearsonCorrTest: Default constructor for the PearsonCorrTest class.
PENALTY_DISCOUNT - Static variable in class edu.cmu.tetrad.util.Params: Constant PENALTY_DISCOUNT="penaltyDiscount"
PENALTY_DISCOUNT_DEFAULT_1 - Static variable in class edu.cmu.tetrad.util.Params: Constant PENALTY_DISCOUNT_DEFAULT_1="penaltyDiscountDefault1"
PENALTY_DISCOUNT_ZS - Static variable in class edu.cmu.tetrad.util.Params: Constant PENALTY_DISCOUNT_ZS="penaltyDiscountZs"
penaltyDiscount() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns the value of the penaltyDiscount record component.
penaltyDiscount() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Returns the value of the penaltyDiscount record component.
penaltyDiscount() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Returns the penalty discount of the gate score.
penaltyDiscount(double) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the penalty discount used when the default SEM BIC score is constructed.
PenaltyDiscountCalibration - Class in edu.cmu.tetrad.search.score: Chooses a BIC penalty discount so that the expected number of spurious edges over a whole search meets a budget, for any score whose gain from adding one parent has the form
PenaltyDiscountCalibration.NullFit - Record Class in edu.cmu.tetrad.search.score: The null distribution of the per-pair LRT for one degrees-of-freedom class, as a scaled chi-square kappa * chi-square(nu) fitted to its first two moments (Satterthwaite), together with how many null pairs belong to the class.
penaltyDiscountForAlpha(double, int) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: Inverse of SemBicScore.impliedAlpha(): the penalty discount at which this score's sign rule operates at significance level alpha, i.e.
penaltyDiscountForAlpha(double, int, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: Inverse of PenaltyDiscountCalibration.alpha(double, int, int): the penalty discount at which a df-degree-of-freedom test runs at level alpha, i.e.
penaltyDiscountForExpectedFalseEdges(int, int, double) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: The penalty discount that holds the expected number of spurious edges over the whole search at approximately expectedFalseEdges, for numVariables variables and sample size sampleSize.
penaltyDiscountForExpectedFalseEdges(Map, int, double) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The penalty discount c at which PenaltyDiscountCalibration.expectedFalseEdges(double, Map, int) equals expectedFalseEdges, found by bisection; the expected count is strictly decreasing in c so the root is unique.
penaltyDiscountForExpectedFalseEdgesFitted(Map, int, double) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The penalty discount holding PenaltyDiscountCalibration.expectedFalseEdgesFitted(double, Map, int) at the budget, by bisection.
penaltyDiscountForFalseDiscoveryRate(int, int, double, double) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: The penalty discount that holds the expected number of spurious edges at a fraction fdr of the expected number of true edges, p * expectedDegree / 2.
penaltyDiscountForFalseDiscoveryRate(Map, int, int, double, double) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The penalty discount that holds the expected number of spurious edges at fdr times the expected number of true edges, p * expectedDegree / 2.
penaltyDiscountForFalseDiscoveryRateFitted(Map, int, int, double, double) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: Fractional-budget version of PenaltyDiscountCalibration.penaltyDiscountForExpectedFalseEdgesFitted(Map, int, double).
penaltyDiscountForMinPartialCorrelation(double, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: The penalty discount at which the sign rule ignores any parameter whose partial correlation with the child is below minPartialCorrelation: the c solving -N ln(1 - r^2) = c ln N, i.e.
penaltyDiscountForMinPartialCorrelation(double, int) - Static method in class edu.cmu.tetrad.search.score.SemBicScore: Inverse of SemBicScore.minDetectablePartialCorrelation(double, int): the penalty discount at which partial correlations below minPartialCorrelation are ignored.
PenaltyDiscountReport - Class in edu.cmu.tetrad.search.score: A calibrated penalty discount together with everything needed to interpret it: the degrees-of-freedom histogram it was computed from, the per-class false-positive levels it implies, the recall-side effect floor, and a sweep of the same quantities over a range of penalty discounts.
PenaltyDiscountReport(int[], int, double, double, double, Map) - Constructor for class edu.cmu.tetrad.search.score.PenaltyDiscountReport: Builds a report.
PenaltyDiscountReport.Row - Record Class in edu.cmu.tetrad.search.score: One row of the sweep: a penalty discount and what it implies.
PER_VARIABLE_MEDIAN - Enum constant in enum class edu.cmu.tetrad.search.score.KcvBicScore.BandwidthMode: Precompute per-variable medians; parent-set sigma = median of parent sigmas.
PER_VARIABLE_MEDIAN - Enum constant in enum class edu.cmu.tetrad.search.score.RffBicScore.BandwidthMode: Precompute per-variable medians; parent-set sigma = median of parent sigmas.
PERCENT_DISCRETE - Static variable in class edu.cmu.tetrad.util.Params: Constant PERCENT_DISCRETE="percentDiscrete"
PERCENT_RESAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant PERCENT_RESAMPLE_SIZE="percentResampleSize"
PERCENT_STABILITY - Static variable in class edu.cmu.tetrad.util.Params: Constant PERCENT_STABILITY="percentStability"
PERCENT_SUBSAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant PERCENT_SUBSAMPLE_SIZE="percentSubsampleSize"
PercentAmbiguous - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
PercentAmbiguous() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.PercentAmbiguous: Constructs a new instance of the statistic.
PercentBidirectedEdges - Class in edu.cmu.tetrad.algcomparison.statistic: The adjacency precision.
PercentBidirectedEdges() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.PercentBidirectedEdges: Constructs a new instance of the statistic.
perClusterGraphFn - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: A function that generates a cluster-specific graph creation method.
Per-fact effective sample sizes from block structure - Section in class edu.cmu.tetrad.search.utils.PerFactEss
PerFactEss - Class in edu.cmu.tetrad.search.utils: Per-fact effective sample sizes from block structure
PerformanceTests - Class in edu.cmu.tetrad.study.performance: Runs some basic performance tests of various algorithm.
PerformanceTests() - Constructor for class edu.cmu.tetrad.study.performance.PerformanceTests: This class represents a set of performance tests for a certain application.
PerformanceTestsDan - Class in edu.cmu.tetrad.study.performance: Contains some tests for Dan Malinsky, that might be of interest to others.
PerformanceTestsDan() - Constructor for class edu.cmu.tetrad.study.performance.PerformanceTestsDan: Constructor for PerformanceTestsDan.
PERMUTATION - Enum constant in enum class edu.cmu.tetrad.search.test.FfCiContinuous.Approx: Represents the permutation-based approximation method.
PERMUTATION - Enum constant in enum class edu.cmu.tetrad.search.test.Rcit.Approx: Represents the permutation-based approximation
permutation_independent_vs_dependent() - Method in class edu.cmu.tetrad.search.KciSmokeTest: Tests the behavior and accuracy of the permutation-based conditional independence testing method in the Kci framework.
PermutationGenerator - Class in edu.cmu.tetrad.util: Generates all of the permutations of [0,..., numObjects - 1], where numObjects is numObjects nonnegative integer.
PermutationGenerator(int) - Constructor for class edu.cmu.tetrad.util.PermutationGenerator: Constructs numObjects new choice generator for numObjects choose b.
PermutationMatrixPair - Class in edu.cmu.tetrad.search.utils: Stores a matrix together with a row and column permutation.
PermutationMatrixPair(Matrix, int[], int[]) - Constructor for class edu.cmu.tetrad.search.utils.PermutationMatrixPair: Constructs with a given matrix M and a row and column permutation (which may be null).
PermutationSearch - Class in edu.cmu.tetrad.search: Implements common elements of a permutation search.
PermutationSearch(SuborderSearch) - Constructor for class edu.cmu.tetrad.search.PermutationSearch: Constructs a new PermutationSearch using the given SuborderSearch.
PermutationSearchTopK - Class in edu.cmu.tetrad.search: Implements common elements of a permutation search.
PermutationSearchTopK(SuborderSearchTopK) - Constructor for class edu.cmu.tetrad.search.PermutationSearchTopK: Constructs a new PermutationSearchTopK using the given SuborderSearch, retaining a single (top) model.
PermutationSearchTopK(SuborderSearchTopK, int, double) - Constructor for class edu.cmu.tetrad.search.PermutationSearchTopK: Constructs a new PermutationSearchTopK using the given SuborderSearch, configured to retain the top k models and to split the search using threshold delta.
permutationTest(Node, Node, Set, int) - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Performs a permutation test to empirically determine the distribution of p-values under the null hypothesis.
permuteRows() - Method in class edu.cmu.tetrad.data.BoxDataSet: Randomly permutes the rows of the dataset.
permuteRows() - Method in interface edu.cmu.tetrad.data.DataSet: Randomizes the rows of the data set.
permuteRows() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Randomly permutes the rows of the dataset.
perVariable() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the perVariable record component.
PhantomCounterexampleHarness - Class in edu.cmu.tetrad.search.harness: Searches for a legal-but-non-genuine PAG reachable by a single spurious-edge deletion from a genuine legal Markov PAG; reports a discriminating-path census (now over all phantom DDPs) and dumps near-misses for inspection.
PhantomCounterexampleHarness() - Constructor for class edu.cmu.tetrad.search.harness.PhantomCounterexampleHarness: Constructor.
PhantomFcitReachabilityHarness1 - Class in edu.cmu.tetrad.search.harness: Runs the real FCIT on every small latent DAG (oracle independence) and compares the result to the true PAG, to test whether the constructed cold deadlocks are actually reachable by FCIT's own edge-removal trajectory.
PhantomFcitReachabilityHarness1() - Constructor for class edu.cmu.tetrad.search.harness.PhantomFcitReachabilityHarness1: Constructor.
PhantomKernelEnumerator - Class in edu.cmu.tetrad.search.harness: Parallel exhaustive enumeration over small latent DAGs that either proves no legal-non-genuine PAG exists up to a given size or prints a witness, while censusing phantom spine length and probing the under-commitment mechanism.
PhantomKernelEnumerator() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator: Default constructor.
PhantomKernelEnumerator10 - Class in edu.cmu.tetrad.search.harness: Exhaustive enumeration over DISTINCT TRUE PAGs, running PKE9's analysis once per PAG.
PhantomKernelEnumerator10() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator10: Default constructor.
PhantomKernelEnumerator11 - Class in edu.cmu.tetrad.search.harness: PKE11: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real FcitSl from a GRaSP start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator11() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator11: Default constructor.
PhantomKernelEnumerator12 - Class in edu.cmu.tetrad.search.harness: pke12: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real Fcit from a GRaSP start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator12() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator12: Default constructor.
PhantomKernelEnumerator13 - Class in edu.cmu.tetrad.search.harness: pke13: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real GraspFci from a GRaSP start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator13() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator13: Default constructor.
PhantomKernelEnumerator14 - Class in edu.cmu.tetrad.search.harness: pke14: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real FcitZm from a GRaSP start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator14() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator14: Default constructor.
PhantomKernelEnumerator15 - Class in edu.cmu.tetrad.search.harness: pke15: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real BfciC from a BOSS start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator15() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator15: Default constructor.
PhantomKernelEnumerator16 - Class in edu.cmu.tetrad.search.harness: pke16: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real BfciBryan from a BOSS start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator16() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator16: Default constructor.
PhantomKernelEnumerator17 - Class in edu.cmu.tetrad.search.harness: pke17: enumerate DISTINCT TRUE PAGs (magspace or dagsweep, as in PKE10) and, once per PAG, run the real Bfci from a BOSS start, oracle-driven, reporting every PAG whose terminal graph is not exactly G* (PKE8's end-to-end check over PKE10's population).
PhantomKernelEnumerator17() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator17: Default constructor.
PhantomKernelEnumerator2 - Class in edu.cmu.tetrad.search.harness: Parallel exhaustive enumeration over small latent DAGs that either proves no legal-non-genuine PAG exists up to a given size or prints a witness, while censusing phantom spine length and probing the under-commitment mechanism.
PhantomKernelEnumerator2() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator2: Default constructor.
PhantomKernelEnumerator3 - Class in edu.cmu.tetrad.search.harness: Parallel exhaustive enumeration over small latent DAGs that either proves no legal-non-genuine PAG exists up to a given size or prints a witness, while censusing phantom spine length and probing the under-commitment mechanism.
PhantomKernelEnumerator3() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator3: Default constructor.
PhantomKernelEnumerator4 - Class in edu.cmu.tetrad.search.harness: Enumerator.
PhantomKernelEnumerator4() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator4: Default contructor.
PhantomKernelEnumerator5 - Class in edu.cmu.tetrad.search.harness: Enumerator.
PhantomKernelEnumerator5() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator5: Default constructor.
PhantomKernelEnumerator6 - Class in edu.cmu.tetrad.search.harness: Enumerator.
PhantomKernelEnumerator6() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator6: Default constructor.
PhantomKernelEnumerator7 - Class in edu.cmu.tetrad.search.harness: PKE7: does any gated Markov state exist that the latest FCIT-SL move set cannot solve?
PhantomKernelEnumerator7() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator7: Default constructor.
PhantomKernelEnumerator8 - Class in edu.cmu.tetrad.search.harness: PKE8: run the real FcitSl from GRaSP starts, oracle-driven, and report every class whose terminal PAG is not exactly G*.
PhantomKernelEnumerator8() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator8: Default constructor.
PhantomKernelEnumerator9 - Class in edu.cmu.tetrad.search.harness: Parallel exhaustive enumeration over small latent DAGs, run under the corrected (spanning, fully-covered) sepset regime, that either certifies the reachability appendix's claims at a given size or prints witnesses.
PhantomKernelEnumerator9() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumerator9: Default constructor.
PhantomKernelEnumeratorSingleEdgeDeletions - Class in edu.cmu.tetrad.search.harness: Parallel exhaustive enumeration over small latent DAGs that either proves no legal-non-genuine PAG exists up to a given size or prints a witness, while censusing phantom spine length and probing the under-commitment mechanism.
PhantomKernelEnumeratorSingleEdgeDeletions() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelEnumeratorSingleEdgeDeletions: Default constructor.
PhantomKernelSampler - Class in edu.cmu.tetrad.search.harness: Seeded random-sampling search that runs the PhantomKernelEnumerator probes on PhantomCounterexampleHarness-style models, to surface diverse witnesses (in particular deeper-spine phantoms) for analysis.
PhantomKernelSampler() - Constructor for class edu.cmu.tetrad.search.harness.PhantomKernelSampler: Default constructor.
phase1_controlledSweep() - Method in class edu.cmu.tetrad.search.unmix.RoadmapTest: This method performs a controlled evaluation process for the "Phase 1" experiments involving statistical clustering and graph learning methods.
phase2_robustness() - Method in class edu.cmu.tetrad.search.unmix.RoadmapTest: Evaluates the robustness of clustering algorithms under different simulated conditions by sweeping over multiple parameters and configurations.
phase3_semisynthetic() - Method in class edu.cmu.tetrad.search.unmix.RoadmapTest: Phase 3 of a study using semi-synthetic data to evaluate causal structure learning methods.
phase3_semisynthetic() - Method in class edu.cmu.tetrad.search.unmix.TestCausalUnmixer: This test evaluates the performance of a causal unmixing algorithm in a semi-synthetic setting.
PI - Static variable in class edu.cmu.tetrad.calculator.expression.ConstantExpression: Constant expression for PI.
PI - Static variable in class edu.cmu.tetrad.util.TMath: A constant holding the value of π (pi), the ratio of a circle's circumference to its diameter.
pickAllMeasures(Collection) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: All measured (observed) descendants of a set of covers, including observed variables named inside mixed covers and descendants reached through non-atomic covers whose variables have all been visited (Python pickAllMeasures).
Pke5DeterminismCheck - Class in edu.cmu.tetrad.search.harness: Checks determinism for PhantomKernelEnumerator5.
Pke5DeterminismCheck() - Constructor for class edu.cmu.tetrad.search.harness.Pke5DeterminismCheck: Default constructor.
pl - Enum constant in enum class edu.cmu.tetrad.graph.Edge.Property: Possible latent confounder.
PlotMatrixMimicDriver - Class in edu.cmu.tetrad.sem: Simple command-line driver for PlotMatrixMimicSimulator.
PlotMatrixMimicSimulator - Class in edu.cmu.tetrad.sem: PlotMatrixMimicSimulator (continuous-only, v1)
PlotMatrixMimicSimulator(DataSet) - Constructor for class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Constructs a new instance of PlotMatrixMimicSimulator, designed for processing continuous-only datasets and generating matrices used in copula normal-score simulations.
plus(Matrix) - Method in class edu.cmu.tetrad.util.Matrix: plus.
plus(Vector) - Method in class edu.cmu.tetrad.util.Vector: plus.
Pm - Interface in edu.cmu.tetrad.util: Tagging interface for parametric models.
pMax - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: The p-value associated with the GLS max-type test statistic (T_max) for the statistical test.
pMax - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Wild-bootstrap p-value for the omnibus statistic max_j |T_j|.
pMean - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: The p-value corresponding to the T_mean statistic.
pmForGraph(HybridCgModel.HybridCgPm, Graph) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance: Builds a PM over the given graph with the typing, categories, and node order of src, carrying over cutpoints for whichever continuous parents each discrete child retains.
Point - Class in edu.cmu.tetrad.util: Implements an n-dimensional point.
Point(Vector) - Constructor for class edu.cmu.tetrad.util.Point: Constructs a point with coordinates as in the given vector.
pointsTowards(Node) - Method in class edu.cmu.tetrad.graph.Edge: pointsTowards.
PointXy - Class in edu.cmu.tetrad.util: Stores a (x, y) point without having to use awt classes.
PointXy(int, int) - Constructor for class edu.cmu.tetrad.util.PointXy: Constructs a new point with the given coordinates.
PointXy(PointXy) - Constructor for class edu.cmu.tetrad.util.PointXy: Copy constructor.
poisson(double, double, boolean) - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the Poisson Distribution for mean x and k events for doubles.
Poisson - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
POISSON_LAMBDA - Static variable in class edu.cmu.tetrad.util.Params: Constant POISSON_LAMBDA="poissonLambda"
PoissonBicTest - Class in edu.cmu.tetrad.algcomparison.independence: The SemBicTest class implements the IndependenceWrapper interface and represents a test for independence based on SEM BIC algorithm.
PoissonBicTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.PoissonBicTest: Constructs a new instance of the SEM BIC test.
poissonCdf(int, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: poissonCdf.
poissonPmf(int, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: poissonPmf.
PoissonPriorScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for the Poisson prior score (Bryan)
PoissonPriorScore - Class in edu.cmu.tetrad.search.score: Implements Poisson prior score, a novel (unpubished) score that replaces the penalty term in BIC by the log of the Poisson distribution.
PoissonPriorScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.PoissonPriorScore: Constructs a new instance of the PoissonPriorScore.
PoissonPriorScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.score.PoissonPriorScore: Constructs the score using a covariance matrix.
PoissonPriorScore(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.PoissonPriorScore: Constructs the score using a covariance matrix.
poissonQuantile(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: poissonQuantile.
poissonRand(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: Poisson random generator from Numerical Recipes
policiesReachableThroughWrapper() - Method in class edu.cmu.tetrad.test.TestMissingDataPolicyWiring: Each wrapper accepts exactly the policies it is expected to, through the same call path an interface uses.
policyParametersAreDeclared() - Method in class edu.cmu.tetrad.test.TestMissingDataPolicyWiring: A wrapper that gates a policy must also declare the parameters that select it, or the control never renders and the policy is unreachable however well the score implements it.
Polynomial - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements a polynomial as a sum of a list of terms whose variables are identified as integers in the set {0, 1, 2, ...}.
Polynomial(List) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Constructs a polynomial from a list of terms.
POLYNOMIAL - Enum constant in enum class edu.cmu.tetrad.search.test.Kci.KernelType: Represents the polynomial kernel, which generalizes the linear kernel by introducing polynomial terms.
POLYNOMIAL_CONSTANT - Static variable in class edu.cmu.tetrad.util.Params: Constant POLYNOMIAL_CONSTANT="polynomialConstant"
POLYNOMIAL_DEGREE - Static variable in class edu.cmu.tetrad.util.Params: Constant POLYNOMIAL_DEGREE="polynomialDegree"
PolynomialFunction - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements a polynomial update function, Gi.0 = P(Parents(G0.0)) + ei, where P is a polynomial function and ei is a random noise term.
PolynomialFunction(LagGraph) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Constructs a polyomial function where each factor is given a zero polynomial.
PolynomialTerm - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements a term in a polymonial whose variables are mapped to indices in in the set {0, 1, 2, ...}.
PolynomialTerm(double, int[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Constructs a term.
pool(double[], double[], double) - Static method in class edu.cmu.tetrad.data.missing.RubinsRules: Pools one parameter.
POOL_DATA_SETS - Static variable in class edu.cmu.tetrad.util.Params: Constant POOL_DATA_SETS="poolDataSets" Whether, given several data sets, to pool them into ONE search (IMaGES-style: the score is summed across data sets, the test combines p-values by Fisher's method) instead of running the algorithm once per data set.
POOL_IMPUTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant POOL_IMPUTATIONS="poolImputations" Whether, given several data sets that are imputations of one data set with missing values, to run ONE search over them with the imputation uncertainty carried into each test, rather than one search per imputation.
Pooled(double, double, double, double, double, double, double, double, double, double) - Constructor for record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Creates an instance of a Pooled record class.
POOLED - Enum constant in enum class edu.cmu.tetrad.search.FaskPool.Origin: Oriented (or left undirected by abstention) from the pooled left-right statistic alone.
POOLED_FAS - Enum constant in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: Pooled FAS: stable FAS with a composite test combining per-dataset Fisher Z p-values by Fisher's method.
POOLED_TEST_METHOD - Static variable in class edu.cmu.tetrad.util.Params: Constant POOLED_TEST_METHOD="pooledTestMethod" How per-data-set p-values are combined when data sets are pooled for a test-based search: "fisher" (default; most powerful when the dependence is shared by all data sets) or "tippett" (min-p, Sidak-adjusted; more powerful when the dependence is present in only some of the data sets).
PooledAdjacencySearch - Class in edu.cmu.tetrad.search: Estimates a common adjacency (skeleton) structure across multiple datasets sharing the same variables.
PooledAdjacencySearch(ScoreWrapper) - Constructor for class edu.cmu.tetrad.search.PooledAdjacencySearch: Constructs the search with the given score wrapper (used only by the IMAGES method; may be null for the other methods).
PooledAdjacencySearch.Method - Enum Class in edu.cmu.tetrad.search: How the common adjacency structure is estimated.
pooledCovariance(List) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Pools several imputations of one data set into a single covariance matrix: the elementwise average of the imputations' covariance matrices, at the imputations' (shared) sample size.
PooledDgLrtTest - Class in edu.cmu.tetrad.study.missingness: Degenerate-Gaussian likelihood-ratio conditional-independence testing under multiple imputation, pooled by the rules for likelihood-ratio statistics: Meng and Rubin (1992) ("D3", the default, which is what the micd package Witte et al.
PooledDgLrtTest(List, double, PooledDgLrtTest.Pooling) - Constructor for class edu.cmu.tetrad.study.missingness.PooledDgLrtTest: Constructs the pooled test over completed datasets.
PooledDgLrtTest.Pooling - Enum Class in edu.cmu.tetrad.study.missingness: The pooling rule.
pooledEffectiveSampleSize(List) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Estimates an effective sample size for analyses run on the pooled covariance of several imputations, from the disagreement among the imputations themselves.
pooledGraph - Variable in class edu.cmu.tetrad.data.missing.ImputationSearch.Result: The pooled graph.
pooledGraphFn - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: A function that generates a pooled graph model based on a given configuration and dataset.
PooledIndependenceWrapper - Class in edu.cmu.tetrad.algcomparison.utils: The test-side counterpart of PooledScoreWrapper: builds the inner test on each pooled data set and combines them with IndTestMulti using Fisher's method (default) or Tippett's method on the per-data-set p-values, per Params.POOLED_TEST_METHOD (data sets are assumed independent of one another, e.g.
PooledIndependenceWrapper(IndependenceWrapper, List) - Constructor for class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Constructs a pooled independence wrapper.
pooledLr() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the pooledLr record component.
pooledPValue(double[], int) - Static method in class edu.cmu.tetrad.search.rlcd.PooledRankTest: Combines chi-square statistics from M imputations by the D2 rule; see the class comment.
PooledRankTest - Class in edu.cmu.tetrad.search.rlcd: The RLCD rank test over several imputations of one data set with missing values: each question RLCD asks is answered once, from all of the imputations, instead of once per imputation.
PooledRankTest(List) - Constructor for class edu.cmu.tetrad.search.rlcd.PooledRankTest: Constructs the test from the imputed data sets.
PooledScoreWrapper - Class in edu.cmu.tetrad.algcomparison.utils: A score wrapper that pools several data sets into one IMaGES score built from an inner score wrapper.
PooledScoreWrapper(ScoreWrapper, List) - Constructor for class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Constructs a pooled score wrapper.
pooledStatistic(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: The pooled statistic for the rank ≤ r test: the sum of the per-domain Lawley statistics over the usable domains, to be referred to a chi-square distribution whose degrees of freedom are (|P| - r)(|Q| - r) times the number of usable domains.
PoolsImputations - Interface in edu.cmu.tetrad.algcomparison.utils: An algorithm that can take several imputations of one data set with missing values and run ONE search over them, with the uncertainty from the missing values carried into each of its statistical decisions.
pop(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Decreases the indentation level and optionally logs a closing trailer message if the tracer is enabled.
POPULATION - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Population test.
POSITIVE - Enum constant in enum class edu.cmu.tetrad.graph.EdgeShading.Hue: Positive effect (Okabe-Ito blue).
possibleAncestor(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: possibleAncestor.
possibleDsep(Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Identifies and returns a list of possible d-separating nodes relative to a specified node in the graph.
PossibleDsepFci - Class in edu.cmu.tetrad.search.utils: Implements the Possible-M-Sep search step of Spirtes, et al's (1993) FCI algorithm (pp 144-145).
PossibleDsepFci(Graph, IndependenceTest) - Constructor for class edu.cmu.tetrad.search.utils.PossibleDsepFci: Creates a new SepSet and assumes that none of the variables have yet been checked.
PossibleMConnectingPath - Class in edu.cmu.tetrad.search.utils: Finds possible d-connecting undirectedPaths for the IonSearch.
POST_NONLINEAR - Enum constant in enum class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear.DistortionType: Noise added before the nonlinear distortion.
posteriorOdds(Node, Node, Node, Set) - Method in class edu.cmu.tetrad.search.PcAR.DiscriminatingTestOddsEstimator: Computes base-rate odds times the Vovk-Sellke likelihood-ratio bound from the discriminating test of (x, y) given the sepset with the pivot's membership toggled; see the class javadoc for the derivation and its limits.
posteriorOdds(Node, Node, Node, Set) - Method in interface edu.cmu.tetrad.search.PcAR.RecoveryOddsEstimator: Returns the posterior odds that the deleted pair (x, y) is a genuine edge, given the clash that flagged it.
PostnonlinearSem - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a post-nonlinear SEM model.
PostnonlinearSem - Class in edu.cmu.tetrad.sem: Represents a Post-nonlinear Causal Model (Zhang and Hyvarinen, 2009, 2012).
PostnonlinearSem(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.PostnonlinearSem: Constructs a SemSimulation object with the given RandomGraph object.
PostnonlinearSem(Graph, int, RealDistribution, int, double) - Constructor for class edu.cmu.tetrad.sem.PostnonlinearSem: Constructs a PostnonlinearCausalModel object.
potentiallyDirectedPaths() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility: Potentially directed paths from X to Y, as returned by graph.paths().potentiallyDirectedPaths(...).
potentiallyDirectedPaths(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds all potentially directed paths between two nodes up to a maximum length.
pow() - Static method in class edu.cmu.tetrad.util.StatUtils: Calculates the average of 1000 random absolute values generated from a normal distribution with a mean of 0 and standard deviation of 1.
pow(double, double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the value of the first argument raised to the power of the second argument using the specified internal implementation.
power(double, int, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The power of a level-alpha test with a chi-square(df) null against noncentrality lambda.
power(SimpleMatrix, int) - Static method in class edu.cmu.tetrad.search.Dagma: Computes the matrix power of the given square matrix A raised to the power n.
powerset(List) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: The powerset of the list in the order produced by the Python recipe: by subset size, empty set first, then all singletons, and so on up to the full set (Python powerset in misc.py).
powerSet(List) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: powerSet.
PRAOerrors - Class in edu.cmu.tetrad.simulation: Created by Erich on 8/21/2016.
PRAOerrors(double[], String) - Constructor for class edu.cmu.tetrad.simulation.PRAOerrors: Constructor for PRAOerrors.
PRAOerrors(List, String) - Constructor for class edu.cmu.tetrad.simulation.PRAOerrors: Constructor for PRAOerrors.
PRE_NOISE - Enum constant in enum class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear.DistortionType: Noise added after the nonlinear distortion.
precision() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns the value of the precision record component.
PRECOMPUTE_COVARIANCES - Static variable in class edu.cmu.tetrad.util.Params: Constant PRECOMPUTE_COVARIANCES="precomputeCovariances"
predict(DataSet, Node, List) - Method in class edu.cmu.tetrad.search.unmix.LinearQRRegressor: Predicts the regression output for the given dataset, target node, and parent nodes using the model parameters.
predict(DataSet, Node, List) - Method in interface edu.cmu.tetrad.search.unmix.ResidualRegressor: Predicts fitted values for all rows for the given target using the current fitted model.
PredictionPruneReport - Class in edu.cmu.tetrad.sem: Result of NNEstimator.pruneByPredictiveContribution(int, double): a per-child backward elimination of parents whose held-out predictive contribution is not distinguishable from zero.
PredictionPruneReport.Deletion - Class in edu.cmu.tetrad.sem: One proposed edge deletion.
predictNode(int, DataSet, int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Returns the predicted value for the given node at the given row of the supplied dataset, using the already-fitted mechanism for that node.
predictNodeProbs(int, DataSet, int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Returns the predicted probability distribution for the given node at the given row of the supplied dataset.
predictor(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ItkPredictorSearch: predictor.
predictProbV1(SimpleMatrix) - Method in class edu.cmu.tetrad.regression.v1.RegressionUtilV1.FitLogitV1: v1: Predict probabilities p = sigmoid(X*w).
predictV1(SimpleMatrix) - Method in class edu.cmu.tetrad.regression.v1.RegressionUtilV1.FitOlsV1: v1: Predict yhat = X*beta.
PREFER_NONCOLLIDERS - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.ColliderPolicy: Denotes a policy where preference is given to non-colliding entities or scenarios in the context of collision handling.
PREFIX - Enum constant in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: The expression can occur in the prefix position.
prepareMagForVertexFacts(Graph, ConditioningSetType) - Static method in class edu.cmu.tetrad.search.MarkovCheck: Precomputes the graph-level transform (the MAG) used by the ordered-local-Markov conditioning types, so that callers evaluating facts for many vertices of the same graph can run the graph-type legality checks and the CPDAG-to-DAG-to-MAG conversion once rather than once per vertex.
PRESERVE_MARKOV - Static variable in class edu.cmu.tetrad.util.Params: Constant PRESERVE_MARKOV="preserveMarkov"
Preserved - Enum constant in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Choose an edge iff there is an edge that its prob.
PreserveMarkov - Class in edu.cmu.tetrad.search.utils: A helper class to encapsulate logic for preserving a Markov property for subsequent testing after an initial local Markov graph has been found.
PreserveMarkov(Graph, IndependenceTest, boolean) - Constructor for class edu.cmu.tetrad.search.utils.PreserveMarkov: Constructs an PreserveMarkov class for a given Markov graph.
PreserveMarkov(PreserveMarkov) - Constructor for class edu.cmu.tetrad.search.utils.PreserveMarkov: Creates a new instance of the PreserveMarkov class by copying the fields from another PreserveMarkov object.
PreviousStepOnly - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Initializes a graph by adding the previous time step only of each variable.
PreviousStepOnly() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.PreviousStepOnly: Constructor for PreviousStepOnly.
printClusterPValues(Set) - Method in class edu.cmu.tetrad.search.utils.ClusterSignificance: Prints the p-values for the given clusters.
printEdges() - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Prints the parent matrix for the graph.
printGraphDegrees() - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: printGraphDegrees.
printSeries() - Method in class edu.cmu.tetrad.util.TaylorSeries: Print the Taylor series.
printStuffForKlea() - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: printStuffForKlea.
PRIOR_EQUIVALENT_SAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant PRIOR_EQUIVALENT_SAMPLE_SIZE="priorEquivalentSampleSize"
PROB_CYCLE - Static variable in class edu.cmu.tetrad.util.Params: Constant PROB_CYCLE="probCycle"
PROB_MAP - Enum constant in enum class edu.cmu.tetrad.bayes.MlBayesIm.CptMapType: Represents a probabilistic CptMap type.
PROB_REMOVE_COLUMN - Static variable in class edu.cmu.tetrad.util.Params: Constant PROB_REMOVE_COLUMN="probRemoveColumn"
PROB_TWO_CYCLE - Static variable in class edu.cmu.tetrad.util.Params: Constant PROB_TWO_CYCLE="probTwoCycle"
PROBABILISTIC_TEST_PARAMETERS - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Constant PROBABILISTIC_TEST_PARAMETERS
ProbabilisticMapIndependence - Class in edu.cmu.tetrad.search.work_in_progress: Uses BCInference by Cooper and Bui to calculate probabilistic conditional independence judgments.
ProbabilisticMapIndependence(DataSet) - Constructor for class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Initializes the test using a discrete data sets.
ProbabilisticTest - Class in edu.cmu.tetrad.algcomparison.independence: Dec 17, 2018 3:44:46 PM
ProbabilisticTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.ProbabilisticTest: Constructs a new instance of the ProbabilisticTest class.
probability(double) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Calculates the probability mass function (PMF) of a given point.
PROBABILITY_OF_EDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant PROBABILITY_OF_EDGE="probabilityOfEdge"
probConstraint(BCCausalInference.OP, int, int, int[]) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCCausalInference: This function takes a constraint, which has a value of either OP.dependent or OP.independent, of the form "X independent Y given Z" or "X dependent Y given Z" and returns a probability for that constraint given the data in cases and assumed prior probability for that constraint given the data in cases and assumed prior probabilities.
probConstraint(BCInference.OP, int, int, int[]) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference: This function takes a constraint, which has a value of either OP.dependent or OP.independent, of the form "X independent Y given Z" or "X dependent Y given Z" and returns a probability for that constraint given the data in cases and assumed prior probability for that constraint given the data in cases and assumed prior probabilities.
probConstraint(BCInference.OP, Node, Node, Node[]) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: probConstraint.
probConstraint(BCInference, BCInference.OP, Node, Node, Node[], Map) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Returns the probability of the constraint x op y | z.
ProbUtils - Class in edu.cmu.tetrad.util: Implements a number of important functions from probability and statistics.> 0
ProcessPythonCiService - Class in edu.cmu.tetrad.search.test: Spawns a persistent Python process running kci_server.py and communicates using JSONL over stdin/stdout.
ProcessPythonCiService(String, String) - Constructor for class edu.cmu.tetrad.search.test.ProcessPythonCiService: Constructor for ProcessPythonCiService, which sets up the Python executable and the server script path for the service.
produce_undirected_graphs - Enum constant in enum class edu.cmu.tetrad.annotation.AlgType: If an algorithm produces undirected graphs.
product(double[][], double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: product.
product(double[][], double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: product.
product(double[], double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: product.
product(Vector, Matrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: product.
progressEvery() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the progressEvery record component.
project(List) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Projects the order to a high-scoring ancestral graph consistent with it.
project(List, Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Projects the order, warm-started from an earlier projection's graph: the warm start's order-consistent edges (directed edges whose tail precedes their head in the new order, and all bidirected edges) seed the state, the forward phases are skipped, and only the polish and plateau-escape phases run.
propensityClipEps - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Propensity clipping epsilon for handling extreme propensity scores.
ProportionSemidirectedPathsNotReversedEst - Class in edu.cmu.tetrad.algcomparison.statistic: Proportion of semi(X, Y) in estimated graph for which there is no semi(Y, X) in true graph.
ProportionSemidirectedPathsNotReversedEst() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedEst: Constructs a new instance of the statistic.
ProportionSemidirectedPathsNotReversedTrue - Class in edu.cmu.tetrad.algcomparison.statistic: Proportion of semi(X, Y) in true graph for which there is no semi(Y, Z) in estimated graph.
ProportionSemidirectedPathsNotReversedTrue() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.ProportionSemidirectedPathsNotReversedTrue: Constructs a new instance of the statistic.
Proposition - Class in edu.cmu.tetrad.bayes: Represents propositions over the variables of a particular BayesIm describing an event of a fairly general sort--namely, conjunctions of propositions that particular variables take on values from a particular disjunctive list of categories.
Proposition(Proposition) - Constructor for class edu.cmu.tetrad.bayes.Proposition: Copies the info out of the old proposition into a new proposition for the new BayesIm.
Proposition(VariableSource, Proposition) - Constructor for class edu.cmu.tetrad.bayes.Proposition: Copies the info out of the old proposition into a new proposition for the new BayesIm.
providesCalibratedPValue() - Method in interface edu.cmu.tetrad.search.score.ProvidesCalibratedPValue: Returns true just in case this score, as currently configured, can honor the contract of this interface.
providesCalibratedPValue() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns true just in case the likelihood-ratio statistic can be recovered from the local score difference, which requires a zero structure prior.
ProvidesCalibratedPValue - Interface in edu.cmu.tetrad.search.score: Implemented by a Score whose local score difference can be mapped back to a likelihood-ratio statistic with a known reference distribution, so that the score can report a genuine p-value in addition to its accept/reject verdict.
providesCalibratedPValues() - Method in class edu.cmu.tetrad.search.MarkovCheck: Returns true just in case the base independence test of this Markov check reports p-values that can be compared against a threshold and tested for uniformity.
ProvidesKnowledge - Interface in edu.cmu.tetrad.algcomparison.utils: A simulation whose design implies background knowledge that an analyst of the resulting data would legitimately have - typically tiers given by the roles of the variables (design factors before derived quantities before responses; context before system before indices before outcomes) - and that can supply it for each emitted data model.
pruneByPredictiveContribution(int, double) - Method in class edu.cmu.tetrad.sem.NNEstimator: Backward elimination of parents whose held-out predictive contribution is not distinguishable from zero, per child, on the same k folds as NNEstimator.crossValidate(int).
pruneInputLatentEdges(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Removes "latent" edges from the provided graph based on specific conditions.
pruneLatentLatentEdges(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Removes latent-latent edges whose cross-block dependence is fully explained by the recovered measured parents of the two endpoint latents.
pruneTransitiveInputEdgesByLatentAncestry(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Optionally removes input edges X -> L that are explained by a directed latent path X -> La -> ...
pruneTransitiveLatentEdges(Graph) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Removes transitive latent-latent edges after orientation.
pruneUninformativeBasisColumns(DataSet, Map, ICovarianceMatrix) - Static method in class edu.cmu.tetrad.search.utils.Embedding: Prunes uninformative basis columns from an embedding using a BIC-crossing screen, so that the effective truncation of each continuous variable is chosen by the data rather than by the truncationLimit parameter.
pseudoinverse() - Method in class edu.cmu.tetrad.util.Matrix: Returns the Moore-Penrose pseudoinverse of the matrix.
pSumSquares - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Wild-bootstrap p-value for the omnibus statistic sum_j T_j^2.
purify() - Method in class edu.cmu.tetrad.search.LatentPurifier: Purifies the graph by ensuring that each measured variable has at most one latent parent and removes latent variables that have no measured children.
purify(List) - Method in interface edu.cmu.tetrad.search.utils.IPurify: purify.
purify(List) - Method in class edu.cmu.tetrad.search.utils.PurifyTetradBased: purify.
purify(List) - Method in class edu.cmu.tetrad.search.utils.PurifySextadBased: purify.
PurifySextadBased - Class in edu.cmu.tetrad.search.utils: Implments a sextad-based Purify method.
PurifySextadBased(DeltaSextadTest, double) - Constructor for class edu.cmu.tetrad.search.utils.PurifySextadBased: Constructor for PurifySextadBased.
PurifyTetradBased - Class in edu.cmu.tetrad.search.utils: Implements a tetrad-based purify method.
PurifyTetradBased(TetradTest) - Constructor for class edu.cmu.tetrad.search.utils.PurifyTetradBased: Constructor for PurifyTetradBased.
push(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a message if the tracer is enabled.
put(String, ParamDescription) - Method in class edu.cmu.tetrad.util.ParamDescriptions: put.
putAll(Parameters) - Method in class edu.cmu.tetrad.util.Parameters: putAll.
putParameterValue(String, double) - Method in class edu.cmu.tetrad.sem.GeneralizedSemEstimator.MyContext: Puts a parameter value into the parameterValues map of the MyContext class.
putVariableValue(String, double) - Method in class edu.cmu.tetrad.sem.GeneralizedSemEstimator.MyContext: Puts a variable value into the variableValues map of the MyContext class.
pvalue - Variable in class edu.cmu.tetrad.search.CordEngine1.Result: Represents the p-value of a statistical hypothesis test, which quantifies the probability of obtaining a test statistic at least as extreme as the one observed, under the null hypothesis.
pvalue - Variable in class edu.cmu.tetrad.search.CordEngine2.Result: Represents the p-value of a statistical hypothesis test, corresponding to the one-sided upper probability (1 - Phi(T)), where Phi is the cumulative distribution function (CDF) of the standard normal distribution, and T is the test statistic.
pvalue - Variable in class edu.cmu.tetrad.search.CordEngine3.Result: The p-value representing the one-sided upper bound of the statistical test result.
pvalue - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: The reported p-value, typically representing the calibrated minimum p-value for the statistical test.
pvalue - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: Represents the aggregated one-sided p-value obtained from a statistical analysis, calculated based on a specified aggregation method.
pValue - Variable in class edu.cmu.tetrad.data.missing.MissingDataAudit.LittleResult: The p-value, or NaN if the degrees of freedom were nonpositive or fewer than two usable patterns were found.
pValue - Variable in class edu.cmu.tetrad.search.utils.NonlinearityTests.TestResult: Represents the p-value of a statistical hypothesis test.
pValue() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the pValue record component.
pValue() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns the value of the pValue record component.
pValue() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the pValue record component.
pValue() - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Returns the value of the pValue record component.
pValue() - Method in record class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Eval: Returns the value of the pValue record component.
pValue() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns the value of the pValue record component.
pValue() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetResult: Returns the value of the pValue record component.
pValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.IndTestGin.DistanceCorrTest: Computes the p-value for the given input arrays using a distance correlation test.
pValue(double[], double[]) - Method in class edu.cmu.tetrad.search.test.IndTestGin.PearsonCorrTest: Computes the p-value for testing the null hypothesis that two datasets are uncorrelated, based on the Pearson correlation coefficient.
pValue(double[], double[]) - Method in interface edu.cmu.tetrad.search.test.IndTestGin.UncondIndTest: Calculates the p-value to test the unconditional independence between the variables represented by the input arrays.
pValue(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.Chi2RankTest: The p-value of the rank ≤ r test (not used by RLCD's decision, which compares the statistic with the critical value for numerical robustness, but useful for inspection).
pValue(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.EmCovarianceRankTest
pValue(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.PooledRankTest: The pooled p-value of the rank ≤ r test.
pValue(int[], int[], int) - Method in interface edu.cmu.tetrad.search.rlcd.RankTester: The p-value of the test that the cross-covariance has rank at most r.
pValue(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: The p-value of the pooled rank ≤ r test, or 0 for degenerate inputs or when no domain is usable.
pValue(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest
pValue(int, int, int[], double) - Method in class edu.cmu.tetrad.search.test.ProcessPythonCiService
pValue(int, int, int[], double) - Method in interface edu.cmu.tetrad.search.test.PythonCiService: Computes the p-value for a conditional independence test based on the specified variables and parameters.
pValue(IndependenceFact) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Computes the p-value associated with a given independence fact by evaluating its statistical significance.
P-value approximation - Section in class edu.cmu.tetrad.search.test.Rcit
PvalueDistanceToAlpha - Class in edu.cmu.tetrad.algcomparison.statistic: Estimates whether the p-values under the null are Uniform usign the Markov Checker.
PvalueDistanceToAlpha(double) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.PvalueDistanceToAlpha: Constructor for PvalueDistanceToAlpha.
pValueIndepConditioned(SimpleMatrix, int[], int[], int[], int) - Static method in class edu.cmu.tetrad.util.RankTests: p-value for H0: rank(X â Y | Z) â¤ 0 using Wilks/Bartlett on partial CCA.
pValuePermuted(double[], double[], int) - Method in class edu.cmu.tetrad.search.test.IndTestGin.DistanceCorrTest: Computes the permutation-based p-value for the distance correlation statistic.
pValueRankLe(int[], int[], int) - Method in class edu.cmu.tetrad.util.StratifiedRankTests: The p-value for the null hypothesis rank(Σ_X,Y|T) ≤ r, pooling the Bartlett statistic over strata.
pValueRankLe(int[], int[], int[], int, int) - Method in class edu.cmu.tetrad.util.StratifiedPermutationRankTest: The permutation p-value for the null that the cross-covariance of X and Y given Z has rank at most r within every domain.
pValueRankLeConditioned(int[], int[], int[], int) - Method in class edu.cmu.tetrad.util.StratifiedRankTests: The p-value for the null hypothesis rank(Σ_X,Y|Z,T) ≤ r, where within each stratum the cross-covariance of X and Y is first conditioned on the variable set Z by Schur complement, and the Bartlett statistic is then pooled over strata.
P-values - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
pValuesAdP(Map) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates the p-value using the Anderson-Darling test for a given set of p-values from an independence test.
pValuesForAllVertices(Collection, CachedIndependenceQueries.ImpliedFactProvider) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Computes and returns a list of p-values for all given vertices based on the independence facts provided by the specified ImpliedFactProvider.
pValuesForFacts(Collection, CachedIndependenceQueries.Dedup) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Computes the p-values for a collection of independence facts after evaluating them.
pValuesForVertex(CachedIndependenceQueries.ImpliedFactProvider, Node) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Computes and returns a list of p-values associated with the given vertex based on implied independence facts provided by the given provider.
PvalueUniformityUnderNull - Class in edu.cmu.tetrad.algcomparison.statistic: Estimates whether the p-values under the null are Uniform using the Markov Checker.
PvalueUniformityUnderNull(double) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.PvalueUniformityUnderNull: Constructor for PvalueUniformityUnderNull.
PYTHON_CI_SERVER - Static variable in class edu.cmu.tetrad.util.Params: Constant PYTHON_CI_SERVER="pythonCiServer"
PYTHON_EXE - Static variable in class edu.cmu.tetrad.util.Params: Constant PYTHON_EXE="pythonExe"
PythonCiService - Interface in edu.cmu.tetrad.search.test: An interface for performing conditional independence tests using Python.
Python environment - Section in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython
Python environment - Section in class edu.cmu.tetrad.algcomparison.independence.ClKciPython
Python environment - Section in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython
PythonGin - Class in edu.cmu.tetrad.algcomparison.algorithm.other: Deprecated.
PythonGin() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

Default constructor for the PythonGin class.
PythonKciIndependenceTest - Class in edu.cmu.tetrad.search.test: The PythonKciIndependenceTest class implements an independence test using the kernel conditional independence (KCI) test provided through a Python backend, such as causallearn.
PythonKciIndependenceTest(DataSet, PythonCiService) - Constructor for class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Constructs a PythonKciIndependenceTest object for performing conditional independence tests using a Python backend via the provided PythonCiService.
PythonRcitIndependenceTest - Class in edu.cmu.tetrad.search.test: The PythonRcitIndependenceTest class implements an independence test using the kernel conditional independence (RCIT) test provided through a Python backend, such as causallearn.
PythonRcitIndependenceTest(DataSet, PythonCiService) - Constructor for class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Constructs a PythonRcitIndependenceTest object for performing conditional independence tests using a Python backend via the provided PythonCiService.
PythonResource - Class in edu.cmu.tetrad.util: A utility class for working with Python-related resources.

Q

q() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the q record component.
qObs - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: The observed value of sum_j T_j^2.
QuadraticFormPValues - Class in edu.cmu.tetrad.search.test: Utilities for converting a quadratic-form statistic Q = sum_i lambda_i * Z_i^2, Z_i ~ N(0,1), into a p-value using fast analytic transforms that use the full eigen-spectrum.
quartile(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: quartile.
quartile(double[], int, int) - Static method in class edu.cmu.tetrad.util.StatUtils: quartile.
quartile(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: quartile.
quartile(long[], int, int) - Static method in class edu.cmu.tetrad.util.StatUtils: quartile.
quickDemo() - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Quick demo: build a graph, stabilize, simulate.

R

r() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns the value of the r record component.
R0R4Strategy - Interface in edu.cmu.tetrad.search.utils: The FCI orientation rules are almost entirely taken up with an examination of the FCI graph, but there are two rules that require looking at the distribution.
R0R4StrategyTestBased - Class in edu.cmu.tetrad.search.utils: The FciOrientDataExaminationStrategyTestBased class implements the FciOrientDataExaminationStrategy interface and provides methods for checking unshielded colliders and determining orientations based on the Discriminating Path Rule.
R0R4StrategyTestBased(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Creates a new instance of FciOrientDataExaminationStrategyTestBased.
R0R4StrategyTestBased(IndependenceTest, long) - Constructor for class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Constructs an instance of R0R4StrategyTestBased with the specified IndependenceTest and timeout.
R0R4StrategyTestBased.BlockingType - Enum Class in edu.cmu.tetrad.search.utils: Enum representing the different types of blocking strategies.
R1 - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: R1.
R1 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The R1 rule.
R1() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Constructor for R1.
R1(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Constructor for R1.
R1TimeLag - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The R1 Time Lag rule.
R2 - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: R2.
R2 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The R2 rule.
R2() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Constructor for R2.
R2(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Constructor for R2.
R3 - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: R3.
R3 - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The R3 rule.
R3() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Constructor for R3.
R3(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Constructor for R3.
R5 - Enum constant in enum class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Rule: The R5 rule.
R5R9Dijkstra - Class in edu.cmu.tetrad.search.utils: A modified implementation of Dijkstra's shortest path algorithm for the R5 and R9 rules from Zhang, J.
R5R9Dijkstra.DijkstraEdge - Class in edu.cmu.tetrad.search.utils: Represents a node in Dijkstra's algorithm.
R5R9Dijkstra.Graph - Class in edu.cmu.tetrad.search.utils: Represents a graph for Dijkstra's algorithm.
R5R9Dijkstra.Rule - Enum Class in edu.cmu.tetrad.search.utils: The rule that is being implemented, R5 or R9.
R9 - Enum constant in enum class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Rule: The R9 rule.
RA_RADIUS - Static variable in class edu.cmu.tetrad.util.Params: Constant RA_RADIUS="raRadius"
RADEMACHER - Enum constant in enum class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Multiplier: Signs drawn uniformly from {-1, +1}.
random - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Random method
random - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The random method.
random() - Static method in class edu.cmu.tetrad.util.TMath: Generates a random double value between 0.0 (inclusive) and 1.0 (exclusive).
RANDOM - Enum constant in enum class edu.cmu.tetrad.bayes.MlBayesIm.InitializationMethod: Represents a random initialization method.
RANDOM - Static variable in class edu.cmu.tetrad.bayes.MlBayesIm: Represents a constant value for a random number.
RANDOM_FOWARD_DAG - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant RANDOM_FOWARD_DAG="Random Foward DAG (Fixed Average Degree"{trunked}
RANDOM_MIM - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant RANDOM_MIM="Random MIM"
RANDOM_MIMIC - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant RANDOM_MIMIC="Random MIMIC"
RANDOM_ONE_FACTOR_MIM - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant RANDOM_ONE_FACTOR_MIM="Random One Factor MIM"
RANDOM_SELECTION_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant RANDOM_SELECTION_SIZE="randomSelectionSize"
RANDOM_TWO_FACTOR_MIM - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant RANDOM_TWO_FACTOR_MIM="Random Two Factor MIM"
randomClusters(int) - Static method in class edu.cmu.tetrad.cluster.KMeans: Constructs a new KMeansBatch, initializing the algorithm by randomly assigning each point in the data to one of the numCenters clusters, then calculating the centroid of each cluster.
randomCyclicGraph2(int, int, int) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Makes a cyclic graph by repeatedly adding cycles of length of 3, 4, or 5 to the graph, then finally adding two cycles.
randomCyclicGraph3(int, int, int, double, double) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Makes a cyclic graph by repeatedly adding cycles of length of 3, 4, or 5 to the graph, then finally adding two cycles.
randomDag(int, int, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random Directed Acyclic Graph (DAG) with the specified parameters.
randomDag(int, int, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random Directed Acyclic Graph (DAG) based on specified constraints.
randomDag(List, int, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random Directed Acyclic Graph (DAG).
randomDag(List, int, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random Directed Acyclic Graph (DAG) based on the given parameters.
RandomDistribution - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.history: A distribution from which noise values are drawn.
RandomForward - Class in edu.cmu.tetrad.algcomparison.graph: Creates a random graph by adding forward edges.
RandomForward() - Constructor for class edu.cmu.tetrad.algcomparison.graph.RandomForward: Constructs a new instance of the RandomForward.
randomFourier(double, int, double, boolean) - Static method in class edu.cmu.tetrad.search.utils.RandomFunctions: Generates a random Fourier series and evaluates it at a given input value x.
RandomFourier - Class in edu.cmu.tetrad.search.utils: The RandomFourier class generates a random Fourier series with specified cosine and sine amplitudes and frequency terms.
RandomFourier(int, double) - Constructor for class edu.cmu.tetrad.search.utils.RandomFourier: Initializes a RandomFourier instance with the specified number of terms and frequency scale.
RandomFunctions - Class in edu.cmu.tetrad.search.utils: A utility class for generating and evaluating random mathematical functions, including polynomials, Fourier series, piecewise linear functions, radial basis functions, and sampled Gaussian processes.
randomGraph(int, int, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph based on the given parameters.
randomGraph(int, int, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph based on the specified parameters.
randomGraph(List, int, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph based on the given parameters.
randomGraph(List, int, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph with the specified parameters and properties.
RandomGraph - Class in edu.cmu.tetrad.graph: The RandomGraph class provides methods for generating random graphs.
RandomGraph - Interface in edu.cmu.tetrad.algcomparison.graph: An interface to represent a random graph of some sort.
RandomGraph.UniformGraphGenerator - Class in edu.cmu.tetrad.graph: Generates random DAGs uniformly with certain classes of DAGs using variants of Markov chain algorithm by Malancon, Dutour, and Philippe.
randomGraphRandomForwardEdges(int, int, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph with the given parameters and random forward edges.
randomGraphRandomForwardEdges(List, int, int, int, int, int, boolean) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph with forward edges.
randomGraphRandomForwardEdges(List, int, int, int, int, int, boolean, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph with forward edges.
randomGraphRandomForwardEdges(List, int, int, int, int, int, boolean, long) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random directed acyclic graph with specified properties, including constraints on maximum degree, indegree, and outdegree, with random forward edges.
randomGraphUniform(List, int, int, int, int, int, boolean, int) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random graph using UniformGraphGenerator with the specified parameters.
randomIm(HybridCgModel.HybridCgPm) - Static method in class edu.cmu.tetrad.hybridcg.TestHybridCgModel: Generates a random HybridCgIm model.
randomize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Chooses a random function by flipping a coin for each value in table.
RANDOMIZE - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant RANDOMIZE
RANDOMIZE_COLUMNS - Static variable in class edu.cmu.tetrad.util.Params: Constant RANDOMIZE_COLUMNS="randomizeColumns"
randomizeGraph(LagGraph) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.GraphRandomizer: Randomizes the given lag graph--in other words, chooses random edges for the graph according to a particlar scheme (see instantiations).
randomizeIncompleteRows(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Randomizes any row in the table for the given node index that has a Double.NaN value in it.
randomizeIncompleteRows(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Randomizes any row in the table for the given node index that has a Double.NaN value in it.
randomizeIncompleteRows(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Randomizes the incomplete rows in the specified node's table.
randomizeIncompleteRows(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Randomizes any row in the table for the given node index that has a Double.NaN value in it.
randomizeIncompleteRows(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Randomizes any row in the table for the given node index that has a Double.NaN value in it.
randomizeRow(int, int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Assigns random probability values to the child values of this row that add to 1.
randomizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Assigns random probability values to the child values of this row that add to 1.
randomizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Randomizes the values of a row in a table for a given node.
randomizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Assigns random probability values to the child values of this row that add to 1.
randomizeRow(int, int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Assigns random probability values to the child values of this row that add to 1.
randomizeTable(int) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Randomizes every row in the table for the given node index.
randomizeTable(int) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Randomizes every row in the table for the given node index.
randomizeTable(int) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Randomizes the table for a given node.
randomizeTable(int) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Randomizes every row in the table for the given node index.
randomizeTable(int) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Randomizes every row in the table for the given node index.
RandomMim - Class in edu.cmu.tetrad.algcomparison.graph: Creates a random graph by adding forward edges.
RandomMim - Class in edu.cmu.tetrad.graph: Flexible MIM (Multiple Indicator Model) generator.
RandomMim() - Constructor for class edu.cmu.tetrad.algcomparison.graph.RandomMim: Constructs a new instance of the RandomTwoFactorMim.
RandomMim.LatentGroupSpec - Record Class in edu.cmu.tetrad.graph: Specification for a block of groups sharing the same rank and number of children per latent.
RandomMim.LatentLinkMode - Enum Class in edu.cmu.tetrad.graph: Enum representing the mode of linking between latent nodes across groups in the meta-DAG.
RandomMimic - Class in edu.cmu.tetrad.algcomparison.graph: Creates a random graph by adding forward edges.
RandomMimic - Class in edu.cmu.tetrad.graph: Flexible MIMIC (Multiple Indicators Multiple Causes) graph generator.
RandomMimic() - Constructor for class edu.cmu.tetrad.algcomparison.graph.RandomMimic: Constructs a new instance of the RandomTwoFactorMim.
RandomMimic.MimicGroupSpec - Record Class in edu.cmu.tetrad.graph: Specification for a block of MIMIC groups sharing the same structure.
RandomMimicGraphGenerator - Class in edu.cmu.tetrad.search.mimic: Generates random MIMIC models.
RandomMimicGraphGenerator() - Constructor for class edu.cmu.tetrad.search.mimic.RandomMimicGraphGenerator: Default constructor for the RandomMimicGraphGenerator class.
RandomMonotonicPiecewiseLinear - Class in edu.cmu.tetrad.search.utils: A class that generates and evaluates a monotonic piecewise linear function defined over a specified domain and range.
RandomMonotonicPiecewiseLinear(int, double, double, double, double) - Constructor for class edu.cmu.tetrad.search.utils.RandomMonotonicPiecewiseLinear: Constructor to initialize a monotonic piecewise linear function.
randomPiecewiseLinear(double, double, double, int, boolean) - Static method in class edu.cmu.tetrad.search.utils.RandomFunctions: Generates a random piecewise linear function and evaluates it at a specified input value x.
RandomPiecewiseLinear - Class in edu.cmu.tetrad.search.utils: Represents a piecewise linear function with randomly generated breakpoints and values.
RandomPiecewiseLinear(int, double, double) - Constructor for class edu.cmu.tetrad.search.utils.RandomPiecewiseLinear: Constructs a RandomPiecewiseLinear instance.
RandomPiecewiseLinearBijective - Class in edu.cmu.tetrad.util: Represents a bijective, piecewise linear function whose control points are generated randomly.
RandomPiecewiseLinearBijective(int) - Constructor for class edu.cmu.tetrad.util.RandomPiecewiseLinearBijective: Constructs a RandomPiecewiseLinearBijective object with a specified number of segments and a seed for random generation.
RandomPiecewiseSpline - Class in edu.cmu.tetrad.search.utils: This class generates and evaluates a random piecewise cubic spline function.
RandomPiecewiseSpline(int, double, double, double, double) - Constructor for class edu.cmu.tetrad.search.utils.RandomPiecewiseSpline: Constructor to initialize a piecewise cubic spline function.
randomPoints(int) - Static method in class edu.cmu.tetrad.cluster.KMeans: Constructs a new KMeansBatch, initializing the algorithm by picking numCeneters centers randomly from the data itself.
randomPolynomial(double, int, double, boolean) - Static method in class edu.cmu.tetrad.search.utils.RandomFunctions: Generates a random polynomial function and evaluates it at a specified input value.
randomRBF(double, double, double, int, double, boolean) - Static method in class edu.cmu.tetrad.search.utils.RandomFunctions: Generates a random Radial Basis Function (RBF) and evaluates it at a given input value x.
RandomRBF - Class in edu.cmu.tetrad.search.utils: A class that represents a Random Radial Basis Function (RBF) system.
RandomRBF(int, double) - Constructor for class edu.cmu.tetrad.search.utils.RandomRBF: Constructs a RandomRBF instance with randomly initialized centers and amplitudes.
RandomSampler - Class in edu.cmu.tetrad.data: Provides a static method for sampling without replacement from a dataset to create a new dataset with a sample size supplied by the user.
RandomSampler() - Constructor for class edu.cmu.tetrad.data.RandomSampler: Constructs a new instance of the RandomSampler.
randomScaleFreeGraph(int, int, double, double, double, double) - Static method in class edu.cmu.tetrad.graph.RandomGraph: Generates a random scale-free graph.
randomSeed - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the seed value used to initialize a random number generator.
randomSimulation(int) - Method in class edu.cmu.tetrad.simulation.GdistanceRandom: randomSimulation.
RandomSingleFactorMim - Class in edu.cmu.tetrad.algcomparison.graph: Creates a random graph by adding forward edges.
RandomSingleFactorMim() - Constructor for class edu.cmu.tetrad.algcomparison.graph.RandomSingleFactorMim: Constructs a new instance of the RandomSingleFactorMim.
RandomTwoFactorMim - Class in edu.cmu.tetrad.algcomparison.graph: Creates a random graph by adding forward edges.
RandomTwoFactorMim() - Constructor for class edu.cmu.tetrad.algcomparison.graph.RandomTwoFactorMim: Constructs a new instance of the RandomTwoFactorMim.
RandomUtil - Class in edu.cmu.tetrad.util: Provides a common random number generator to be used throughout Tetrad, to avoid problems that happen when random number generators are created more often than once per millisecond.
randomZhangMagFromPag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Convenience overload with a fresh Random; use the seeded overload for reproducibility.
randomZhangMagFromPag(Graph, Random) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Returns a random Zhang MAG of a PAG.
range(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: range.
range(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: range.
range(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: range.
range(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: range.
rank() - Method in record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Returns the value of the rank record component.
rank() - Method in class edu.cmu.tetrad.util.Matrix: rank.
rank(int[][], double) - Method in class edu.cmu.tetrad.search.ntad_test.Cca: Determines the smallest rank value for which the hypothesis test returns a result below the specified alpha threshold using Wilks' lambda test.
RANK - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: Normal-scored ranks of Y: rank-robust omnibus channel (CORD-style).
RANK - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: Normal-scored ranks of Y: rank-robust omnibus channel (CORD-style).
RANK_TRANSFORM - Static variable in class edu.cmu.tetrad.search.utils.Embedding: Sentinel for basisScale: rank-transform each continuous column to a uniform grid on [-1, 1] before applying the basis.
RankBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for linear, Gaussian SEM BIC score.
RankBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.RankBicScore: Constructs a new instance of the SemBicScore.
rankCorrelation(double[], double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: rankCorrelation.
Rank engines - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
RankIndependenceTestLemma10Singletons - Class in edu.cmu.tetrad.algcomparison.independence: Deprecated.
RankIndependenceTestLemma10Singletons() - Constructor for class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestLemma10Singletons: Deprecated.

Constructs a new instance of the algorithm.
RankIndependenceTestTs - Class in edu.cmu.tetrad.algcomparison.independence: Rank Independence Test TS, the former name of the test now called Rank Independence Test TS Singletons.
RankIndependenceTestTs() - Constructor for class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTs: No-arg constructor.
RankIndependenceTestTsSingletons - Class in edu.cmu.tetrad.algcomparison.independence: Deprecated.
RankIndependenceTestTsSingletons() - Constructor for class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestTsSingletons: Deprecated.

Constructs a new instance of the algorithm.
RankIndependenceTestWilkesSingletons - Class in edu.cmu.tetrad.algcomparison.independence: Deprecated.
RankIndependenceTestWilkesSingletons() - Constructor for class edu.cmu.tetrad.algcomparison.independence.RankIndependenceTestWilkesSingletons: Deprecated.

Constructs a new instance of the algorithm.
rankLeByPermutation(double[][], int[], int[], int, int) - Static method in class edu.cmu.tetrad.util.RankTests: Permutation-based test for rank ≤ r in the linear non-Gaussian setting.
rankLeByWilks(SimpleMatrix, int[], int[], int, int) - Static method in class edu.cmu.tetrad.util.RankTests: Determines whether the rank is less than or equal to a specified value r using a Wilks' lambda test.
ranks() - Method in class edu.cmu.tetrad.search.blocks.BlockSpec: Returns the list of ranks associated with this BlockSpec instance.
ranks(double[]) - Static method in class edu.cmu.tetrad.data.DataUtils: ranks.
RankTester - Interface in edu.cmu.tetrad.search.rlcd: A rank test over two (possibly overlapping) column sets of a data matrix, as used by RLCD.
RankTests - Class in edu.cmu.tetrad.util: The RankTests class provides a suite of methods and utilities for performing rank estimation and hypothesis testing in Canonical Correlation Analysis (CCA) and Regularized Canonical Correlation Analysis (RCCA).
RankTests.RccaEntry - Class in edu.cmu.tetrad.util: Represents an entry in the RCCA (Regularized Canonical Correlation Analysis) data structure.
rankTransformToUnitInterval(DataSet) - Static method in class edu.cmu.tetrad.search.utils.Embedding: Replaces each continuous column by its mid-ranks mapped to a uniform grid on [-1, 1]: the k-th smallest value (0-based) becomes -1 + 2 (k + 0.5) / N.
rates() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Spec: Returns a copy of the rates, so the array cannot be mutated through the record.
rates() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns the value of the rates record component.
RawMarginalIndependenceTest - Interface in edu.cmu.tetrad.search: Functional interface for performing a raw marginal independence test.
RB_RADIUS - Static variable in class edu.cmu.tetrad.util.Params: Constant RB_RADIUS="rbRadius"
RBExperiments - Class in edu.cmu.tetrad.study: RBExperiments class.
RBExperiments() - Constructor for class edu.cmu.tetrad.study.RBExperiments: Constructor for RBExperiments.
rbfGramContinuous(int[], int[], int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Public accessor for the exact RBF Gram matrix over the given (continuous) parents, so composite scores can build product kernels without re-implementing the kernel.
RC - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The RC rule.
Rcit - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for RCIT test.
Rcit - Class in edu.cmu.tetrad.search.test: A Java implementation of the RCIT (Randomized Conditional Independence Test) and its variant RCoT (Randomized Conditional correlation Test), following the architecture of FfCiContinuous but with the feature-block construction described in: Strobl, Zhang, and Visweswaran (2019).
Rcit() - Constructor for class edu.cmu.tetrad.algcomparison.independence.Rcit: `Kci` constructor.
Rcit(DataSet) - Constructor for class edu.cmu.tetrad.search.test.Rcit: Constructs an instance of the Rcit class using the provided DataSet and default parameters.
Rcit(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.test.Rcit: Constructs an instance of the Rcit class using the provided DataSet and Parameters.
Rcit(DataSet, Parameters, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.test.Rcit: Constructs an instance with an explicit missing-data specification.
RCIT_APPROX - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_APPROX="rcit.approx" Approximation method for the null distribution: one of {"lpd4", "hbe", "gamma", "chi2", "perm"}.
RCIT_CENTER_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_CENTER_FEATURES="rcit.centerFeatures" Whether to center feature matrices before regression and HSIC.
RCIT_LAMBDA - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_LAMBDA="rcit.lambda" Ridge regularization parameter (λ) used during residualization.
RCIT_MODE - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_MODE="rcit.rcitMode" Whether to use RCIT (true) or RCoT (false).
RCIT_NUM_F - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_NUM_F="rcitNumF"
RCIT_NUM_F2 - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_NUM_F2="rcitNumF2"
RCIT_NUM_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_NUM_FEATURES="rcitNumFeatures"
RCIT_NUM_FEATURES_XY - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_NUM_FEATURES_XY="rcit.numFeaturesXY" Number of random Fourier features for the test variables X and Y (num_f2).
RCIT_NUM_FEATURES_Z - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_NUM_FEATURES_Z="rcit.numFeaturesZ" Number of random Fourier features for the conditioning set Z (num_f).
RCIT_PERMUTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant RCIT_PERMUTATIONS="rcit.permutations" Number of permutations used when RCIT_APPROX is "perm".
Rcit.Approx - Enum Class in edu.cmu.tetrad.search.test: The Approx enumeration defines the types of approximation methods that can be used for statistical computations or tests within the Rcit class.
Rcit.FeatureType - Enum Class in edu.cmu.tetrad.search.test: An enumeration representing the types of features that can be used for generating random feature mappings for RBF kernels.
RCIT vs. RCoT - Section in class edu.cmu.tetrad.search.test.Rcit
ReadControl - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker: ReadControl class.
ReadControl() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ReadControl: Constructor for ReadControl.
readerToGraphJson(Reader) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: readerToGraphJson.
readerToGraphRuben(Reader) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: readerToGraphRuben.
readerToGraphTxt(Reader) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: readerToGraphTxt.
readerToGraphTxt(String) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: readerToGraphTxt.
readGraph(File) - Method in class edu.cmu.tetrad.data.simulation.LoadContinuousDataSmithSim: readGraph.
ReadIdeker - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker: ReadIdeker class.
ReadIdeker() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.ideker.ReadIdeker: Constructor for ReadIdeker.
Reading the result - Section in class edu.cmu.tetrad.search.MarkovCheckNullReference
RealCovariance - Interface in edu.cmu.tetrad.stat.correlation: Jan 27, 2016 4:46:07 PM
RealCovarianceMatrix - Class in edu.cmu.tetrad.stat.correlation: Jan 27, 2016 5:35:01 PM
RealCovarianceMatrix(double[][]) - Constructor for class edu.cmu.tetrad.stat.correlation.RealCovarianceMatrix: Constructor for RealCovarianceMatrix.
RealCovarianceMatrixForkJoin - Class in edu.cmu.tetrad.stat.correlation: Jan 27, 2016 5:37:40 PM
RealCovarianceMatrixForkJoin(double[][], int) - Constructor for class edu.cmu.tetrad.stat.correlation.RealCovarianceMatrixForkJoin: Constructor for RealCovarianceMatrixForkJoin.
realizedRates() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns the value of the realizedRates record component.
RealVariance - Interface in edu.cmu.tetrad.stat: Feb 9, 2016 3:13:19 PM
RealVarianceVector - Class in edu.cmu.tetrad.stat: Feb 9, 2016 3:14:08 PM
RealVarianceVector(double[][]) - Constructor for class edu.cmu.tetrad.stat.RealVarianceVector: Constructor for RealVarianceVector.
RealVarianceVectorForkJoin - Class in edu.cmu.tetrad.stat: Feb 9, 2016 3:15:29 PM
RealVarianceVectorForkJoin(double[][], int) - Constructor for class edu.cmu.tetrad.stat.RealVarianceVectorForkJoin: Constructor for RealVarianceVectorForkJoin.
reason() - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Returns the value of the reason record component.
reason() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility: Retrieves the reason associated with the eligibility result.
recall() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns the value of the recall record component.
recallInitialColliders(Graph, Set, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Recall unshielded triples in a given graph.
recallUnshieldedTriples(Graph) - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Recalls unshielded triples in the given graph.
recodeToMissing(DataSet, String, double) - Static method in class edu.cmu.tetrad.data.audit.DataAudit: Sets every cell of the named continuous variable that holds the given value to missing (NaN), in place, and returns the number of cells changed.
recommended() - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Returns the value of the recommended record component.
recompute(List) - Method in class edu.cmu.tetrad.search.IdaCheck: Recomputes the total effects and absolute total effects for all ordered pairs of nodes in the graph.
RECOVER - Enum constant in enum class edu.cmu.tetrad.search.PcAR.RescueAction: Reinstate the edge and re-run orientation, subject to PcAR.recoveryOddsThreshold.
RECOVER_CORROBORATED - Enum constant in enum class edu.cmu.tetrad.search.PcAR.RescueAction: Reinstate a contested deletion only when the Markov audit independently flags the SAME pair -- i.e.
recovered() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns the value of the recovered record component.
recoveredCount() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns the value of the recoveredCount record component.
RECOVERY_ODDS_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant RECOVERY_ODDS_THRESHOLD="recoveryOddsThreshold"
RECURSIVE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking.Strategy: Depth-first with backtracking (RecursiveBlocking.blockPathsRecursivelyFull(Graph, Node, Node, Set, Set, int, int, int, int, boolean)).
RECURSIVE - Enum constant in enum class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased.BlockingType: Recursive blocking.
RECURSIVE_BLOCKING - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Recursive blocking.
RECURSIVE_BLOCKING_WITH_COMPOSITIONAL_GRAPHOID_CLOSURE - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Recursive blocking with compositional graphoid closure.
RECURSIVE_BLOCKING_WITH_GRAPHOID_CLOSURE - Enum constant in enum class edu.cmu.tetrad.search.ConditioningSetType: Recursive blocking with graphoid closure.
RECURSIVE_DEPTH - Static variable in class edu.cmu.tetrad.util.Params: Constant RECURSIVE_DEPTH="recursiveDepth"
RecursiveAdjustment - Class in edu.cmu.tetrad.search: Represents an adjustment mechanism for defining adjustment sets in causal inference across different types of graphical models.
RecursiveAdjustment(Graph) - Constructor for class edu.cmu.tetrad.search.RecursiveAdjustment: Constructs a RecursiveAdjustment instance with the specified causal graph.
RecursiveAdjustment.ColliderPolicy - Enum Class in edu.cmu.tetrad.search: Represents the policy to be used for handling collisions in a specific context.
RecursiveAdjustment.GraphType - Enum Class in edu.cmu.tetrad.search: Represents the type of graph being analyzed for adjustment set definition.
RecursiveAdjustment.NoAmenablePolicy - Enum Class in edu.cmu.tetrad.search: NoAmenablePolicy is an enumeration that defines the possible policies to handle cases where a certain condition or requirement is not amenable.
RecursiveAdjustment.RaMode - Enum Class in edu.cmu.tetrad.search: The RaMode enum represents the modes in which a specific operation or functionality can be executed.
RecursiveAdjustmentMultiple - Class in edu.cmu.tetrad.search: Recursive adjustment for the case of multiple treatments X and multiple outcomes Y.
RecursiveAdjustmentMultiple(Graph) - Constructor for class edu.cmu.tetrad.search.RecursiveAdjustmentMultiple: Constructs an instance of AdjustmentMultiple with the provided graph.
RecursiveAdjustmentMultipleTest - Class in edu.cmu.tetrad.search: Sanity checks for AdjustmentMultiple:
RecursiveAdjustmentMultipleTest() - Constructor for class edu.cmu.tetrad.search.RecursiveAdjustmentMultipleTest: Constructor for the RecursiveAdjustmentMultipleTest class.
RecursiveBlocking - Class in edu.cmu.tetrad.search: Implements a procedure for constructing candidate separating sets between two nodes under PAG semantics.
RecursiveBlocking.Blockable - Enum Class in edu.cmu.tetrad.search: Three-valued result of path-blocking analysis.
RecursiveBlocking.BlockingResult - Record Class in edu.cmu.tetrad.search: The result of a blocking-set search.
RecursiveBlocking.Mark - Record Class in edu.cmu.tetrad.search: A single endpoint mark: the endpoint at at on the edge between from and at.
RecursiveBlocking.Strategy - Enum Class in edu.cmu.tetrad.search: Selects the search strategy used by RecursiveBlocking.blockPathsRecursively(Graph, Node, Node, Set, Set, int, int, int, int, boolean).
RecursiveBlocking1 - Class in edu.cmu.tetrad.search: Implements a recursive procedure for constructing candidate separating sets between two nodes under PAG semantics.
RecursiveBlocking1.Blockable - Enum Class in edu.cmu.tetrad.search: The Blockable enum represents the state of an entity in relation to its ability to be blocked.
RecursiveBlockingChokePointA - Class in edu.cmu.tetrad.search: This class provides methods for identifying a minimal set of blocking nodes (a "choke point") that prevent all paths between two specified nodes in a graph using recursive and iterative mechanisms.
RecursiveBlockingChokePointB - Class in edu.cmu.tetrad.search: Option B, but â completely ignores the direct edge x *-* y while searching, and â returns null if it proves impossible to block the remaining xây paths with eligible non-colliders.
RecursiveBlockingMemoEquivalenceHarness - Class in edu.cmu.tetrad.search: Differential / equivalence harness for the memoized continuation cache added to RecursiveBlocking.findPathToTargetVisit(Graph, Node, Node, Node, Set, Set, int, Set, Map, Set, int, int, long, int[]).
RecursiveBlockingMemoEquivalenceHarness() - Constructor for class edu.cmu.tetrad.search.RecursiveBlockingMemoEquivalenceHarness: Default constructor for the RecursiveBlockingMemoEquivalenceHarness class.
RecursiveBlockingVerbose - Class in edu.cmu.tetrad.search: The RecursiveBlocking class implements a recursive procedure for constructing candidate separating sets between two nodes under PAG semantics, with optional verbose tracing for debugging and pedagogy.
RecursiveBlockingVerbose.Blockable - Enum Class in edu.cmu.tetrad.search: The Blockable enum represents the state of an entity in relation to its ability to be blocked.
RecursiveBlockingVerbose.Tracer - Class in edu.cmu.tetrad.search: Lightweight indented tracer.
RecursiveDiscriminatingPathRule - Class in edu.cmu.tetrad.search: Finds separating sets for the discriminating path rule (R4 of the final FCI orientation rules, Zhang 2008) by recursive path blocking rather than by searching over subsets of adjacents.
REDIRECT - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Redirect an edge.
reduced() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.NestedFits: Returns the value of the reduced record component.
References - Section in class edu.cmu.tetrad.search.OrderedLocalMarkovProperty
References - Section in class edu.cmu.tetrad.search.PairwiseMarkovProperty
References - Section in class edu.cmu.tetrad.search.Tsc
Refinement stage - Section in class edu.cmu.tetrad.search.Tsc
RegexTokenizer - Class in edu.cmu.tetrad.data: Tokenizes the given input character sequence using the type of delimiter specified bythe given CPDAG.
RegexTokenizer(CharSequence, Pattern, char) - Constructor for class edu.cmu.tetrad.data.RegexTokenizer: Constructs a tokenizer for the given input line, using the given Pattern as delimiter.
regress(double[], double[][]) - Static method in class edu.cmu.tetrad.regression.RegressionDataset: regress.
regress(DiscreteVariable, List) - Method in class edu.cmu.tetrad.regression.LogisticRegression: x must be binary; regressors must be continuous or binary.
regress(Node, Node...) - Method in interface edu.cmu.tetrad.regression.Regression: Regresses target on the regressors, yielding a regression plane.
regress(Node, Node...) - Method in class edu.cmu.tetrad.regression.RegressionCovariance: regress.
regress(Node, Node...) - Method in class edu.cmu.tetrad.regression.RegressionDataset: regress.
regress(Node, List) - Method in interface edu.cmu.tetrad.regression.Regression: Regresses target on the regressors, yielding a regression plane.
regress(Node, List) - Method in class edu.cmu.tetrad.regression.RegressionCovariance: Regresses the given target on the given regressors, yielding a regression plane, in which coefficients are given for each regressor plus the constant (if means have been specified, that is, for the last), and se, t, and p values are given for each regressor.
regress(Node, List) - Method in class edu.cmu.tetrad.regression.RegressionDataset: Regresses the target on the given regressors.
regressAndPrune(Matrix, DataSet) - Method in class edu.cmu.tetrad.search.Lingam: The regression refit-and-prune step of Shimizu et al.
Regression - Interface in edu.cmu.tetrad.regression: Implements a multiple regression model, allowing data to be specified either as a tabular data set or as a covariance matrix plus list of means.
RegressionCovariance - Class in edu.cmu.tetrad.regression: Implements a regression model from correlations--that is, from a correlation matrix, a list of standard deviations, and a list of means.
RegressionCovariance(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.regression.RegressionCovariance: Constructs a covariance-based regression model using the given covariance matrix, assuming that no means are specified.
RegressionDataset - Class in edu.cmu.tetrad.regression: Implements a regression model from tabular continuous data.
RegressionDataset(DataSet) - Constructor for class edu.cmu.tetrad.regression.RegressionDataset: Constructs a linear regression model for the given tabular data set.
RegressionDataset(Matrix, List) - Constructor for class edu.cmu.tetrad.regression.RegressionDataset: Constructor for RegressionDataset.
Regression details - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
RegressionResult - Class in edu.cmu.tetrad.regression: Stores the various components of a regression result so they can be passed around together more easily.
RegressionResult(boolean, String[], int, double[], double[], double[], double[], double, double, double, Vector) - Constructor for class edu.cmu.tetrad.regression.RegressionResult: A result for a variety of regression algorithm.
RegressionUtils - Class in edu.cmu.tetrad.regression: Sundry utilities for regression.
RegressionUtils() - Constructor for class edu.cmu.tetrad.regression.RegressionUtils: Constructs a new instance of the RegressionUtils.
RegressionUtilV1 - Class in edu.cmu.tetrad.regression.v1: Utility class for regression-related functionalities, including linear and logistic regression.
RegressionUtilV1.FitLogitV1 - Class in edu.cmu.tetrad.regression.v1: v1: Represents the result of a logistic regression fit using IRLS.
RegressionUtilV1.FitOlsV1 - Class in edu.cmu.tetrad.regression.v1: v1: OLS fit via QR-based least squares: beta = argmin ||X*beta - y||_2.
REGULAR - Enum constant in enum class edu.cmu.tetrad.search.Ida.IDA_TYPE: Represents the REGULAR classification of IDA_TYPE.
REGULAR - Enum constant in enum class edu.cmu.tetrad.search.PdagPagIda.IDA_TYPE: Represents the standard algorithmic approach in the IDA (Intervention Distribution Adjustment) process.
REGULARIZATION_LAMBDA - Static variable in class edu.cmu.tetrad.util.Params: Constant REGULARIZATION_LAMBDA="regularizationLambda"
regularize(double) - Method in class edu.cmu.tetrad.util.Matrix: Regularizes the diagonal of the matrix by adding a scaled identity matrix to it.
REHARVEST_SEPSETS - Static variable in class edu.cmu.tetrad.util.Params: Constant REHARVEST_SEPSETS="reharvestSepsets".
ReidentifyVariables - Class in edu.cmu.tetrad.sem: Utility for reidentifying variables for multiple indicator structure searches.
ReidentifyVariables() - Constructor for class edu.cmu.tetrad.sem.ReidentifyVariables: Constructs a new instance of the ReidentifyVariables.
reidentifyVariables1(List, Graph) - Static method in class edu.cmu.tetrad.sem.ReidentifyVariables: reidentifyVariables1.
reidentifyVariables2(List, Graph, DataSet) - Static method in class edu.cmu.tetrad.sem.ReidentifyVariables: reidentifyVariables2.
reject - Variable in class edu.cmu.tetrad.search.utils.NonlinearityTests.TestResult: Indicates whether the null hypothesis of the statistical hypothesis test is rejected.
reject(double) - Method in class edu.cmu.tetrad.search.CordEngine1.Result: Evaluates whether to reject the null hypothesis H0: Y is independent of Z given X.
reject(double) - Method in class edu.cmu.tetrad.search.CordEngine2.Result: Determines whether a null hypothesis can be rejected based on the p-value and a given significance level.
reject(double) - Method in class edu.cmu.tetrad.search.CordEngine3.Result: Determines whether to reject the null hypothesis based on the given significance level.
reject(double) - Method in class edu.cmu.tetrad.search.CordEngine4.Result: Determines if the null hypothesis should be rejected based on the given significance level.
reject(double) - Method in class edu.cmu.tetrad.search.CordEngine5.Result: Determines whether the result should be rejected based on the given threshold.
Relationship to other procedures - Section in class edu.cmu.tetrad.search.PairwiseMarkovProperty
relativeIncreaseInVariance(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.PooledRankTest: The relative increase in variance due to the missing values, as estimated for one test: the r of the class comment.
relu(Double) - Static method in class edu.cmu.tetrad.util.MathUtils: Applies the Rectified Linear Unit (ReLU) activation function to the given input.
remove() - Method in class edu.cmu.tetrad.util.CombinationIterator: remove.
remove(int) - Method in class edu.cmu.tetrad.data.DataModelList
remove(IndependenceFact) - Method in class edu.cmu.tetrad.data.IndependenceFacts: remove.
remove(String) - Method in class edu.cmu.tetrad.util.Parameters: Removes the specified parameter from the list of parameters.
REMOVE - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Remove an edge.
REMOVE_ALMOST_CYCLES - Static variable in class edu.cmu.tetrad.util.Params: Constant REMOVE_ALMOST_CYCLES="removeAlmostCycles"
REMOVE_COLLIDER - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Remove a collider.
REMOVE_EFFECT_NODES - Static variable in class edu.cmu.tetrad.util.Params: Constant REMOVE_EFFECT_NODES="removeEffectNodes"
removeAlmostCycles(Graph) - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Removes almost cycles from the Graph.
removeAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Removes an ambiguous triple from the list of ambiguous triples.
removeAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: removeAmbiguousTriple.
removeAmbiguousTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: removeAmbiguousTriple.
removeAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: removeAmbiguousTriple.
removeAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: removeAmbiguousTriple.
removeAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes an ambiguous triple from the collection.
removeAmbiguousTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: removeAmbiguousTriple.
removeAttribute(String) - Method in class edu.cmu.tetrad.data.ContinuousVariable: removeAttribute.
removeAttribute(String) - Method in class edu.cmu.tetrad.data.DiscreteVariable: removeAttribute.
removeAttribute(String) - Method in class edu.cmu.tetrad.graph.Dag: Removes an attribute from the graph.
removeAttribute(String) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: removeAttribute.
removeAttribute(String) - Method in interface edu.cmu.tetrad.graph.Graph: removeAttribute.
removeAttribute(String) - Method in class edu.cmu.tetrad.graph.GraphNode: removeAttribute.
removeAttribute(String) - Method in class edu.cmu.tetrad.graph.LagGraph: removeAttribute.
removeAttribute(String) - Method in interface edu.cmu.tetrad.graph.Node: removeAttribute.
removeAttribute(String) - Method in class edu.cmu.tetrad.graph.SemGraph: removeAttribute.
removeAttribute(String) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes the attribute with the specified key from the object.
removeBidirectedOrientations(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: removeBidirectedOrientations.
removeByPossibleDsep(IndependenceTest, SepsetMap) - Method in class edu.cmu.tetrad.graph.Paths: Remove edges by the possible m-separation rule.
removeCategory(int, int) - Method in class edu.cmu.tetrad.bayes.Proposition: removeCategory.
removeCols(int[]) - Method in class edu.cmu.tetrad.data.BoxDataSet: Removes the given columns from the data set.
removeCols(int[]) - Method in interface edu.cmu.tetrad.data.DataSet: Removes the given columns from the data set.
removeCols(int[]) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Removes the given columns from the data set.
removeColumn(int) - Method in class edu.cmu.tetrad.data.BoxDataSet: Removes the variable (and data) at the given index.
removeColumn(int) - Method in interface edu.cmu.tetrad.data.DataSet: Removes the variable (and data) at the given index.
removeColumn(int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Removes the variable (and data) at the given index.
removeColumn(Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: Removes the given variable, along with all of its data.
removeColumn(Node) - Method in interface edu.cmu.tetrad.data.DataSet: Removes the given variable, along with all of its data.
removeColumn(Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Removes the given variable, along with all of its data.
removeConditioningVariable(String) - Method in class edu.cmu.tetrad.data.Histogram: Removes a conditioning variable from the histogram.
removeConstantColumns(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: removeConstantColumns.
removeCrossLagEdge_removesAllMirrors() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Removing a cross-lag edge should remove its mirror too.
removeCycles(Graph) - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Removes cycles from the Graph.
removeDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Removes a dotted underline triple from the set of triples.
removeDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: removeDottedUnderlineTriple.
removeDottedUnderlineTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: removeDottedUnderlineTriple.
removeDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: removeDottedUnderlineTriple.
removeDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: removeDottedUnderlineTriple.
removeDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes a triple of nodes from the set of dottedUnderLineTriples.
removeDottedUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: removeDottedUnderlineTriple.
removeEdge(Edge) - Method in class edu.cmu.tetrad.graph.Dag: Removes a given edge from the graph.
removeEdge(Edge) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Removes the given edge from the graph.
removeEdge(Edge) - Method in interface edu.cmu.tetrad.graph.Graph: Removes the given edge from the graph.
removeEdge(Edge) - Method in class edu.cmu.tetrad.graph.LagGraph: Removes the given edge from the graph.
removeEdge(Edge) - Method in class edu.cmu.tetrad.graph.ReplicatingGraph: Removes an edge from the graph, and if applicable, removes its mirrored counterparts according to the edge replication policy.
removeEdge(Edge) - Method in class edu.cmu.tetrad.graph.SemGraph: Removes the given edge from the graph.
removeEdge(Edge) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes an edge from the graph.
removeEdge(Edge) - Static method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Creates a CandidateEdit operation to remove the specified edge from a graph.
removeEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Removes the edge between two nodes in the graph.
removeEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Removes an edge between two given nodes.
removeEdge(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Removes an edge between two given nodes.
removeEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Removes an edge between two given nodes.
removeEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.ReplicatingGraph: Removes an edge between the specified nodes, if such an edge exists in the graph.
removeEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Removes an edge between two given nodes.
removeEdge(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes the edge between two given nodes.
removeEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Removes the lagged factor from the list of lagged factors associated with the given factor.
removeEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Removes the lagged factor from the list of lagged factors associated with the given factor.
removeEdge(String, LaggedFactor) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Removes the lagged factor from the list of lagged factors associated with the given factor.
removeEdge(String, LaggedFactor) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Removes the lagged factor from the list of lagged factors associated with the given factor.
removeEdge_removesAllMirrors() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Removing one slice of a mirrored contemporaneous edge should remove all slices.
removeEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Removes an edge between two nodes.
removeEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Removes all edges connecting node A to node B.
removeEdges(Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: Removes all edges connecting node A to node B.
removeEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Removes all edges connecting node A to node B.
removeEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Removes all edges connecting node A to node B.
removeEdges(Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes edges between two nodes.
removeEdges(Collection) - Method in class edu.cmu.tetrad.graph.Dag: Removes the given edges from the graph.
removeEdges(Collection) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Iterates through the list and removes any permissible edges found.
removeEdges(Collection) - Method in interface edu.cmu.tetrad.graph.Graph: Iterates through the list and removes any permissible edges found.
removeEdges(Collection) - Method in class edu.cmu.tetrad.graph.LagGraph: Iterates through the list and removes any permissible edges found.
removeEdges(Collection) - Method in class edu.cmu.tetrad.graph.SemGraph: Iterates through the list and removes any permissible edges found.
removeEdges(Collection) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes the specified collection of edges from the graph.
removeFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Removes a factor from the graph.
removeFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Removes a factor from the graph.
removeFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Removes a factor from the graph.
removeFactor(String) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Removes a factor from the graph.
removeForbidden(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Marks the edge var1 --> var2 as not forbid.
removeFromTiers(String) - Method in class edu.cmu.tetrad.data.Knowledge: Removes the given variable by name or search string from all tiers.
removeFromTiers(String) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Removes the given variable from every tier of this structure.
removeHighLagEdges(int) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: removeHighLagEdges.
removeKnowledgeGroup(int) - Method in class edu.cmu.tetrad.data.Knowledge: Removes the knowledge group at the given index.
removeLatent(Node) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: removeLatent.
removeNaN(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: removeNaN.
removeNextOutputStream() - Method in class edu.cmu.tetrad.util.TetradLogger: removeNextOutputStream.
removeNode(Node) - Method in class edu.cmu.tetrad.graph.Dag: Removes the specified node from the graph.
removeNode(Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Removes a node from the graph.
removeNode(Node) - Method in interface edu.cmu.tetrad.graph.Graph: Removes a node from the graph.
removeNode(Node) - Method in class edu.cmu.tetrad.graph.LagGraph: Removes a node from the graph.
removeNode(Node) - Method in class edu.cmu.tetrad.graph.SemGraph: Removes a node from the graph.
removeNode(Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes the given node from the graph.
removeNodes(List) - Method in class edu.cmu.tetrad.graph.Dag: Removes the specified nodes from the graph.
removeNodes(List) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Iterates through the list and removes any permissible nodes found.
removeNodes(List) - Method in interface edu.cmu.tetrad.graph.Graph: Iterates through the list and removes any permissible nodes found.
removeNodes(List) - Method in class edu.cmu.tetrad.graph.LagGraph: Iterates through the list and removes any permissible nodes found.
removeNodes(List) - Method in class edu.cmu.tetrad.graph.SemGraph: Iterates through the list and removes any permissible nodes found.
removeNodes(List) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes the specified nodes from the graph.
removeNonSkeletonEdges(Graph, Knowledge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Removes non-skeleton edges from the given graph based on the provided knowledge.
removeObserver(ModelObserver) - Method in class edu.cmu.tetrad.search.MarkovCheck: Removes the specified observer from the list of observers.
removeOutputStream(OutputStream) - Method in class edu.cmu.tetrad.util.TetradLogger: Removes the given stream from the logger.
removeProperty(Edge.Property) - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: removeProperty.
removePropertyChangeListener(PropertyChangeListener) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Unregisters a listener for events concerning the lag graph.
removeRandomColumns(DataSet, double) - Static method in class edu.cmu.tetrad.data.DataTransforms: removeRandomColumns.
removeRepairListener(VertexRepairSearch.RepairListener) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Removes a previously registered listener.
removeRequired(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Marks the edge var1 --> var2 as not required.
removeRows(int[]) - Method in class edu.cmu.tetrad.data.BoxDataSet: Removes the specified rows from the dataBox, updates the selection, multipliers, and knowledge accordingly.
removeRows(int[]) - Method in interface edu.cmu.tetrad.data.DataSet: Removes the given rows from the data set.
removeRows(int[]) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Removes the given rows from the data set.
removeTetradLoggerListener(TetradLoggerListener) - Method in class edu.cmu.tetrad.util.TetradLogger: Removes a TetradLoggerListener from the TetradLogger.
removeTierStructure(int) - Method in class edu.cmu.tetrad.data.Knowledge: Removes the additional tier structure at the given index.
removeTriplesNotInGraph() - Method in class edu.cmu.tetrad.graph.Dag: Removes triples from the graph that contain nodes not present in the graph or are not adjacent to each other.
removeTriplesNotInGraph() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: removeTriplesNotInGraph.
removeTriplesNotInGraph() - Method in interface edu.cmu.tetrad.graph.Graph: removeTriplesNotInGraph.
removeTriplesNotInGraph() - Method in class edu.cmu.tetrad.graph.LagGraph: removeTriplesNotInGraph.
removeTriplesNotInGraph() - Method in class edu.cmu.tetrad.graph.SemGraph: removeTriplesNotInGraph.
removeTriplesNotInGraph() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes triples from the object's internal lists if any of the nodes in the triple is not present in the graph, or if any of the nodes are not adjacent to each other in the graph.
removeTriplesNotInGraph() - Method in class edu.cmu.tetrad.graph.Underlines: removeTriplesNotInGraph.
removeUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Dag: Removes an underline triple from the list of underline triples.
removeUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: removeUnderlineTriple.
removeUnderlineTriple(Node, Node, Node) - Method in interface edu.cmu.tetrad.graph.Graph: removeUnderlineTriple.
removeUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.LagGraph: removeUnderlineTriple.
removeUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.SemGraph: removeUnderlineTriple.
removeUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Removes the specified triple (x, y, z) from the list of underline triples.
removeUnderlineTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.graph.Underlines: removeUnderlineTriple.
removeVariables(List) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Removes variables from the covariance matrix, retaining only the variables specified in the provided list.
removeVariables(List) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Removes variables from the covariance matrix, retaining only the variables specified in the provided list.
removeVariables(List) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Removes variables from the covariance matrix, retaining only the variables specified in the provided list.
removeVariables(List) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Removes variables from the covariance matrix, retaining only the variables specified in the provided list.
removeZeroEdges(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: removeZeroEdges.
renameFactor(String, String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Renames a factor, changing all occurances of the old name to the new one
renameFactor(String, String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Renames a factor, changing all occurances of the old name to the new one
renameFactor(String, String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Renames a factor, changing all occurances of the old name to the new one
renameFactor(String, String) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Renames a factor, changing all occurances of the old name to the new one
render(Algorithm, Parameters, boolean) - Static method in class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText: Renders the settings for the given configured algorithm.
render(BayesIm) - Static method in class edu.cmu.tetrad.bayes.BayesBifRenderer: Renders the given BayesIm object as a Bayesian network in the BIF (Bayesian Interchange Format) format.
render(String, Collection, Parameters) - Static method in class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText: Renders a single titled section of parameter settings as plain text, in the same format as the algorithm rendering: the title on its own line, then one " name = value" line per parameter, in the iteration order of the given collection.
reorientAllWith(Endpoint) - Method in class edu.cmu.tetrad.graph.Dag: Reorients all edges in a Directed Acyclic Graph (DAG) with a single endpoint type.
reorientAllWith(Endpoint) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Reorients all edges in the graph with the given endpoint.
reorientAllWith(Endpoint) - Method in interface edu.cmu.tetrad.graph.Graph: Reorients all edges in the graph with the given endpoint.
reorientAllWith(Endpoint) - Method in class edu.cmu.tetrad.graph.LagGraph: Reorients all edges in the graph with the given endpoint.
reorientAllWith(Endpoint) - Method in class edu.cmu.tetrad.graph.ReplicatingGraph: Reorients all applicable edges in the graph to align with the specified endpoint.
reorientAllWith(Endpoint) - Method in class edu.cmu.tetrad.graph.SemGraph: Reorients all edges in the graph with the given endpoint.
reorientAllWith(Endpoint) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Reorients all edges in the graph to point towards the specified endpoint.
reorientWithCircles(Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Reorients all edges in a Graph as o-o.
repairConverged(int, String) - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.RepairListener: Called when the repair terminates normally (convergence or empty queue).
repairMaximality(Graph, boolean, Set, FciOrient, Knowledge, Set) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Repairs the maximality of a PAG (Partial Ancestral Graph) by ensuring that any inducing path between two nodes not currently adjacent in the graph results in an added non-directed edge.
repairNode(Node) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Repairs the graph at one vertex only: repeatedly applies the top-ranked candidate edit at that vertex until none improves on the current graph.
replaceEdge(Edge, Edge) - Static method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Replaces an existing edge in a graph with a new edge, creating a candidate edit operation.
replaceEdges(String, List, List) - Static method in interface edu.cmu.tetrad.search.VertexRepairSearch.CandidateEdit: Creates a CandidateEdit operation to replace multiple edges in a graph.
replaceMissingWithRandom(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: replaceMissingWithRandom.
replaceNodes(Graph, List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the given graph, originalGraph, to use the new variables (with the same names as the old).
replaceNodes(List, Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the given list of nodes, originalNodes, to use the replacement nodes for them by the same name in the given graph.
replaceNodes(List, List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts the given list of nodes, originalNodes, to use the new variables (with the same names as the old).
ReplicatingGraph - Class in edu.cmu.tetrad.graph: EdgeListGraph subclass that applies an EdgeReplicationPolicy whenever edges are added/removed or endpoints are oriented.
ReplicatingGraph(EdgeReplicationPolicy) - Constructor for class edu.cmu.tetrad.graph.ReplicatingGraph: Creates an empty repeating graph using the given policy.
ReplicatingGraph(Graph, EdgeReplicationPolicy) - Constructor for class edu.cmu.tetrad.graph.ReplicatingGraph: Creates a new ReplicatingGraph by copying the structure and attributes from the provided graph and applying the specified edge replication policy.
ReplicatingGraph(ReplicatingGraph) - Constructor for class edu.cmu.tetrad.graph.ReplicatingGraph: Copies nodes+edges from g and installs the policy.
ReplicatingGraph(List, EdgeReplicationPolicy) - Constructor for class edu.cmu.tetrad.graph.ReplicatingGraph: Constructs a new ReplicatingGraph using the provided list of nodes and edge replication policy.
ReplicatingGraphTest - Class in edu.cmu.tetrad.graph: Tests for ReplicatingGraph with LagReplicationPolicy.
ReplicatingGraphTest() - Constructor for class edu.cmu.tetrad.graph.ReplicatingGraphTest: Default constructor for the ReplicatingGraphTest class.
report() - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit: Returns a human-readable multi-section report of the audit.
report() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns a human-readable multi-section report of the audit.
report() - Method in record class edu.cmu.tetrad.data.JoinDatasets.Result: Returns the value of the report record component.
report() - Method in record class edu.cmu.tetrad.data.LongToWide.Result: Returns the value of the report record component.
report() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: A human-readable, multi-line summary of the audit, suitable for logging via TetradLogger before a search is run.
report() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Result: Returns the value of the report record component.
report() - Method in class edu.cmu.tetrad.search.rlcd.RlcdStabilitySummary: The full report.
report() - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: A plain-text summary: the inputs, the two criteria, the chosen discount, and the per-class levels at it.
report() - Method in class edu.cmu.tetrad.search.test.AlphaReport: A plain-text summary.
report() - Method in class edu.cmu.tetrad.search.utils.IndependenceCheckCounter: Human-readable breakdown, sorted by descending count.
report() - Method in class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: Generates a detailed report summarizing the consistency of a seed graph with respect to its Markov and I-map properties.
Report(double[], double, double, double, int) - Constructor for record class edu.cmu.tetrad.data.MissingnessInjector.Report: Creates an instance of a Report record class.
repositionLatents(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Repositions latent nodes in the given graph based on their non-latent neighbors.
repositionLatents(Graph) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Repositions each latent node to the average (x, y) location of its adjacent measured nodes (parents and children combined).
repositionLatents(Graph, LayoutUtil.NodeSize) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Places each latent near its adjacent nodes without overlapping any other node's box.
ReproPke14Violation - Class in edu.cmu.tetrad.search: Reproduces the PKE14 SKELETON_DIFF violation ca....aaaa.acccacacacaccc and diagnoses it.
REQUIRE_IMAP_STATES - Static variable in class edu.cmu.tetrad.util.Params: Constant REQUIRE_IMAP_STATES="requireImapStates".
REQUIRED - Static variable in class edu.cmu.tetrad.data.KnowledgeGroup: The types of groups (Can an enum be used instead?)
requiredEdgesIterator() - Method in class edu.cmu.tetrad.data.Knowledge: Iterator over the KnowledgeEdge's representing required edges.
requiredViolations(Graph, Knowledge) - Static method in class edu.cmu.tetrad.search.CheckKnowledge: Returns a sorted list of edges that are required by knowledge but which do not appear in the graph.
requireImapStates() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Reports whether the I-map gate is on.
requireNonNulls(DataSet, Node, Set) - Static method in interface edu.cmu.tetrad.search.cdnod_pag.ChangeTest: Ensures that the specified objects are not null.
requiresIndependenceTest(Class) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Checks if the algorithm requires independence test.
requiresScore(Class) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Checks if the algorithm requires a score.
RESAMPLING_ENSEMBLE - Static variable in class edu.cmu.tetrad.util.Params: Constant RESAMPLING_ENSEMBLE="resamplingEnsemble"
RESAMPLING_WITH_REPLACEMENT - Static variable in class edu.cmu.tetrad.util.Params: Constant RESAMPLING_WITH_REPLACEMENT="resamplingWithReplacement"
ResamplingEdgeEnsemble - Enum Class in edu.pitt.dbmi.algo.resampling: Sep 12, 2018 4:07:46 PM
RESCUE_ACTION - Static variable in class edu.cmu.tetrad.util.Params: Constant RESCUE_ACTION="rescueAction"
reseedRandomGenerator(long) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Reseeds the random number generator used by the empirical cumulative distribution function (CDF).
RESERVED_VARIABLE_NAME - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A variable name collides with a naming convention that Tetrad's own machinery gives special meaning.
reservedLiteralNames - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: A set of reserved literal names that must be preserved as-is during processing.
reservedLiteralNames(Set) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets the reserved literal names for the configuration.
reset() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Resets all per-run state.
reset() - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: reset.
reset() - Method in class edu.cmu.tetrad.search.utils.IndependenceCheckCounter: Resets the state of the counter by clearing all recorded counts.
reset() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Resets the history initialization array to that a new data set can be generated.
reset() - Method in class edu.cmu.tetrad.util.TetradLogger: Resets the logger by removing any configuration info set with setTetradLoggerConfig.
resetCache() - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: resetCache.
resetErrorPositions() - Method in class edu.cmu.tetrad.graph.SemGraph: resetErrorPositions.
resetMag() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Sets the MAG to null.
resetMag() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Sets the MAG to null.
resetMag() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Sets the MAG to null.
resetOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Sets the order ot null.
resetOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Sets the order ot null.
resetOrder() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Sets the order ot null.
resetTest(double[], double[][], double) - Static method in class edu.cmu.tetrad.search.utils.NonlinearityTests: Performs the RESET (Regression Equation Specification Error Test) to assess model specification.
residual(SemIm) - Static method in class edu.cmu.tetrad.sem.ResidualCovariance: The residual covariance of the measured variables under the given estimated model.
ResidualCovariance - Class in edu.cmu.tetrad.sem: The covariance among the measured variables that an estimated SEM leaves unexplained: the sample covariance minus the covariance the model implies, with the model's error variances put back on the diagonal.
residualize(double[], double[][]) - Static method in class edu.cmu.tetrad.search.Fask: Computes the residuals of a dependent variable after removing the effects of specified covariates.
residualMatrix(DataSet, Graph, ResidualRegressor) - Static method in class edu.cmu.tetrad.search.unmix.ResidualUtils: Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given graph.
residualMatrix(DataSet, Map, ResidualRegressor) - Static method in class edu.cmu.tetrad.search.unmix.ResidualUtils: Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given parent map.
residualMean - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Mean Squared Error (MSE) for the residual data associated with the node.
residualMean - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Mean and standard deviation of residuals for the training dataset associated with the node.
residualMean - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Mean and standard deviation of residuals for the training dataset associated with the node.
residualMean - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Mean and standard deviation of residuals for the training dataset associated with the node.
ResidualRegressor - Interface in edu.cmu.tetrad.search.unmix: Interface for local regressors used to compute residual signatures: X ~ f(Pa).
residuals(double[], double[][]) - Method in class edu.cmu.tetrad.search.test.IndTestGin.OlsRidge: Computes the residuals of a regression model by fitting the target values to the predictors using a least-squares approach with ridge stabilization.
residuals(double[], double[][]) - Method in interface edu.cmu.tetrad.search.test.IndTestGin.Regressor: Computes the residuals, which are the differences between the target values and the predicted values obtained using the given predictors.
residuals(double[], double[][]) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.OlsResidualizer: Regresses y on the predictors (with an intercept) and returns the residuals.
residuals(double[], double[][]) - Method in interface edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Residualizer: Residualizes a target column on a set of predictor columns.
residuals(DataSet, Graph) - Static method in class edu.cmu.tetrad.regression.RegressionUtils: residuals.
residuals(DataSet, Node, List) - Method in interface edu.cmu.tetrad.search.unmix.ResidualRegressor: Convenience method computing residuals = y - yhat for all rows.
residualSd - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Standard Deviation of the residual data associated with the node.
residualSd - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Standard deviation of residuals for the training dataset associated with the node.
residualSd - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Standard deviation of residuals for the training dataset associated with the node.
residualSd - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Standard deviation of residuals for the training dataset associated with the node.
ResidualUtils - Class in edu.cmu.tetrad.search.unmix: Utilities for computing residual matrices given a regressor and either (a) a graph with parent sets, or (b) an explicit parent map.
residualVariance - Variable in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Held-out MSE of the reduced model (Y predicted from its other parents, without X).
resolve(DataSet, List) - Static method in class edu.cmu.tetrad.data.missing.MissingnessThreshold: Resolves the dropped names to the dataset's variables, skipping any that are no longer present.
resolve(List) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Resolves this store against a variable list into a dense symmetric matrix indexed by that list's order.
resolveDeletionPolicy(DataSet, MissingDataSpec, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: Applies a missing-data specification to a dataset for a component that supports only the deletion policies natively: returns the dataset itself when it is complete or the policy is TESTWISE (the component then does its own per-family or per-test row selection), its complete cases under LISTWISE, and throws otherwise.
resolveDomainColumn(DataSet, String) - Static method in class edu.cmu.tetrad.util.StratifiedRankTests: Resolves the name of a domain column for a data set: the named column if it exists; otherwise, if the data set has exactly one discrete column, that column, with a note logged; otherwise an exception listing the discrete columns found.
resolveOrWarn(DataSet, MissingDataSpec, String) - Static method in class edu.cmu.tetrad.data.missing.MissingDataUtils: Resolves a possibly-null spec to a policy for a component that historically switched silently to test-wise deletion on missing data.
resolveSepsets(List, List, ResolveSepsetsDci.Method, SepsetMapDci, SepsetMapDci) - Static method in class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Resolves all inconsistencies between sepsets using a paricular method.
ResolveSepsets - Class in edu.cmu.tetrad.search.utils: Provides some utilities for resolving inconsistencies that arise between sepsets learned for overlapping datasets.
ResolveSepsets() - Constructor for class edu.cmu.tetrad.search.utils.ResolveSepsets: The method to use for resolving sepsets
ResolveSepsets.Method - Enum Class in edu.cmu.tetrad.search.utils: Gives the method to be used to resolve sepsets when they conflict.
ResolveSepsetsDci - Class in edu.cmu.tetrad.search.work_in_progress: Utilities for resolving inconsistencies that arise between sepsets learned for overlapping datasets.
ResolveSepsetsDci() - Constructor for class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci: Resolves the separation sets using the Decision Component Interface (DCI) algorithm.
ResolveSepsetsDci.Method - Enum Class in edu.cmu.tetrad.search.work_in_progress: A method for combining p values.
responsibilities - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: A two-dimensional array representing the responsibilities or soft cluster assignments for each data point in the Gaussian Mixture Model (GMM).
RESTRICTED_BOSS - Enum constant in enum class edu.cmu.tetrad.search.Cstar.CpdagAlgorithm: The RESTRICTED_BOSS algorithm.
RestrictedBoss - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Implements a restricted version of the BOSS (Best Order Score Search) algorithm that focuses search on a set of target variables and their immediate causes.
RestrictedBoss() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Constructor for RestrictedBoss.
RestrictedBoss(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Constructor for RestrictedBoss.
restrictTo(List) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns a copy of this store keeping only the pairs both of whose variables occur among the given ones.
restrictToGraph(DataSet, Graph) - Static method in class edu.cmu.tetrad.sem.NNEstimator: Restricts a dataset to the columns named by the nodes of a graph, in the dataset's column order, so that a graph over a subset of the dataset's variables can be estimated.
restrictToMeasured(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: restrictToMeasured.
restrictToMeasured(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Removes all latent nodes from the graph and returns the modified graph.
restrictToProposition(Proposition) - Method in class edu.cmu.tetrad.bayes.Proposition: Restricts this proposition to the categories for each variable that are true in the given proposition.
result() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: Returns the value of the result record component.
Result() - Constructor for class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: Constructs a new instance of the Result class.
Result(double, double, double, double, double) - Constructor for record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Creates an instance of a Result record class.
Result(double, double, int, boolean) - Constructor for class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Constructs a new g square result using the given parameters.
Result(double, double, int, boolean, boolean) - Constructor for class edu.cmu.tetrad.search.test.ChiSquareTest.Result: Constructs a new g square result using the given parameters.
Result(double, double, int, int, String) - Constructor for record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Creates an instance of a Result record class.
Result(int[], double[][]) - Constructor for class edu.cmu.tetrad.search.unmix.KMeans.Result: Constructs a Result object containing the clustering assignments of data points to clusters and the centroids of the clusters computed by a clustering algorithm.
Result(DataSet, MissingnessInjector.Report, MissingnessInjector.MechanismReport) - Constructor for record class edu.cmu.tetrad.data.MissingnessInjector.Result: Creates an instance of a Result record class.
Result(DataSet, String) - Constructor for record class edu.cmu.tetrad.data.JoinDatasets.Result: Creates an instance of a Result record class.
Result(DataSet, List, Map, List) - Constructor for record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Creates an instance of a Result record class.
Result(DataSet, List, String) - Constructor for record class edu.cmu.tetrad.data.LongToWide.Result: Creates an instance of a Result record class.
Result(CiTestHarness.Config, List, double[][], int[][][], List, double[][], int[][][], CiTestHarness.Uniformity[]) - Constructor for class edu.cmu.tetrad.search.harness.CiTestHarness.Result: Constructs a new Result instance, representing the outcome of a computation or analysis process.
Result(TwoIndicatorTest.Answer, double, double, int) - Constructor for record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Creates an instance of a Result record class.
Result(IndTestFisherZ.ShrinkageMode, DataSet, int, SemIm) - Constructor for record class edu.cmu.tetrad.sem.SemIm.Result: Creates an instance of a Result record class.
Result(Matrix) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Glasso.Result: Constructor for Result.
Result(String, List, double[], double[], int, int, int, double[], double[], double[], double, double, double, double) - Constructor for class edu.cmu.tetrad.regression.LogisticRegression.Result: Constructs a new LinRegrResult.
Result(SimpleMatrix, Graph) - Constructor for class edu.cmu.tetrad.search.TwoStep.Result: Deprecated.

Constructs a Result object that encapsulates the output of the TwoStep algorithm.
resultGraph() - Method in class edu.cmu.tetrad.search.PcMb: Returns the result graph.
results() - Method in class edu.cmu.tetrad.util.ConditionalKDE.BatchResult: Returns the array of ConditionalResult2D objects that represent the results of the 2D conditional density estimations for all variable pairs in the batch operation.
retained() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns the value of the retained record component.
retainValues(SemIm, SemGraph) - Static method in class edu.cmu.tetrad.sem.SemIm: Constructs a new SEM IM with the given graph, retaining parameter values from semIm for nodes of the same name and edges connecting nodes of the same names.
RETHROW - Enum constant in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.ErrorPolicy: Indicates that the error should be rethrown to the calling context for further handling or propagation.
RETURN_EMPTY_SET - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.NoAmenablePolicy: Represents a policy option within the NoAmenablePolicy enumeration that signifies returning an empty set when a certain condition or requirement is not amenable instead of deriving alternative solutions.
ReturnsBootstrapGraphs - Interface in edu.cmu.tetrad.algcomparison.algorithm: ReturnsBootstrapGraphs interface.
RevealEvaluator - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal: Provides the methods for computing mutual information between expression levels between genes and, for a given gene, between points in time determined by a lag value.
RevealEvaluator - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: Provides the methods for computing mutual information between expression levels between genes and, for a given gene, between points in time determined by a lag value.
RevealEvaluator(int[][]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealEvaluator: Constructor for RevealEvaluator.
RevealEvaluator(int[][]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealEvaluator: Constructor for RevealEvaluator.
RevealOutputGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu: RevealOutputGraph class.
RevealOutputGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal: RevealOutputGraph class.
RevealOutputGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: RevealOutputGraph class.
RevealOutputGraph(int, int[][], int[][], String[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.RevealOutputGraph: Constructor for RevealOutputGraph.
RevealOutputGraph(int, int[][], int[][], String[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealOutputGraph: Constructor for RevealOutputGraph.
RevealOutputGraph(int, int[][], int[][], String[], String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealOutputGraph: Constructor for RevealOutputGraph.
RevealSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal: This class contains as a member variable (cases) the time series data stored in an int array of microarray measurements.
RevealSearch - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: This class contains as a member variable (cases) the time series data stored in an int array of microarray measurements.
RevealSearch(int[][], String[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.RevealSearch: Constructor for RevealSearch.
RevealSearch(int[][], String[]) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.RevealSearch: Constructor for RevealSearch.
reverse() - Method in class edu.cmu.tetrad.graph.Edge: Reverses the edge, so that it if was X->Y it's now Y->X, or vice-versa.
revertSeed(long) - Method in class edu.cmu.tetrad.util.RandomUtil: Resets the internal random number generator with the specified seed value, ensuring deterministic behavior for subsequent random operations starting from this seed.
revertToUnshieldedColliders(Graph, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Reverts the provided graph to its unshielded colliders, with other endpoints oriented either as circles (PAG case) or tails (CPDAG case).
Rfci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: RFCI.
Rfci - Class in edu.cmu.tetrad.search: Implements the Really Fast Causal Inference (RFCI) algorithm, which aims to do a correct inference of inferrable causal structure under the assumption that unmeasured common causes of variables in the data may exist.
Rfci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Initializes a new instance of the Rfci class.
Rfci(IndependenceWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Creates an instance of Rfci with the given IndependenceWrapper.
Rfci(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.Rfci: Constructs a new RFCI search for the given independence test and background knowledge.
Rfci(IndependenceTest, List) - Constructor for class edu.cmu.tetrad.search.Rfci: Constructs a new RFCI search for the given independence test and background knowledge and a list of variables to search over.
RFCI_PARAMETERS - Static variable in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Constant RFCI_PARAMETERS
RfciBsc - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Runs RFCI-BSC, which is RFCI with bootstrap sampling of PAGs.
RfciBsc - Class in edu.pitt.dbmi.algo.bayesian.constraint.search: Implements the RFCI-BSC (Really Fast Causal Inference with Bayesian Scoring of Constraints) algorithm for learning PAGs (Partial Ancestral Graphs) from discrete data with probabilistic uncertainty quantification.
RfciBsc() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Blank constructor.
RfciBsc(Rfci) - Constructor for class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Constructor for RfciBsc.
RFF - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.FeatureType: Denotes the type of Random Fourier Features (RFF) used in random feature mappings for machine learning or statistical models.
RFF - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.FeatureType: Denotes the type of Random Fourier Features (RFF) used in random feature mappings for machine learning or statistical models.
RFF - Enum constant in enum class edu.cmu.tetrad.search.score.FfMl.FeatureType: Represents the Random Fourier Features (RFF) option within the enumeration.
RFF - Enum constant in enum class edu.cmu.tetrad.search.score.FfMlContinuous.FeatureType: Denotes the type of Random Fourier Features (RFF) used in random feature mappings for machine learning or statistical models.
RFF - Enum constant in enum class edu.cmu.tetrad.search.test.FfCiContinuous.FeatureType: Represents the Random Fourier Features (RFF) feature generation method.
RFF - Enum constant in enum class edu.cmu.tetrad.search.test.Rcit.FeatureType: Represents the Random Fourier Features (RFF) method for approximating shift-invariant kernel functions such as the Radial Basis Function (RBF) kernel.
RFF_RIDGE - Enum constant in enum class edu.cmu.tetrad.search.test.Gcm.RegressorType: Represents random Fourier features ridge regression.
RffBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for linear, Gaussian SEM BIC score.
RffBicScore - Class in edu.cmu.tetrad.search.score: RCIT-inspired local score for continuous variables.
RffBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.RffBicScore: Constructs a new instance of the SemBicScore.
RffBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.RffBicScore: Constructs an instance of the RffBicScore class.
RffBicScore.BandwidthMode - Enum Class in edu.cmu.tetrad.search.score: Defines the bandwidth selection modes for sigma calculation in the model.
RIC - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The RIC rule.
RICc - Enum constant in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: The RICc rule.
ricf(Graph, ICovarianceMatrix) - Method in class edu.cmu.tetrad.sem.RicfEjml: Runs RICF with the default tolerance and iteration cap.
ricf(Graph, ICovarianceMatrix, double, int) - Method in class edu.cmu.tetrad.sem.RicfEjml: Runs Residual Iterative Conditional Fitting for a Gaussian ADMG/MAG.
ricf(SemGraph, ICovarianceMatrix, double) - Method in class edu.cmu.tetrad.sem.Ricf: Calculates the Restricted Information Criterion Fusion (RICF) for a given SemGraph.
Ricf - Class in edu.cmu.tetrad.sem: Implements ICF as specified in Drton and Richardson (2003), Iterative Conditional Fitting for Gaussian Ancestral Graph Models, using hints from previous implementations by Drton in the ggm package in R and by Silva in the Purify class.
Ricf() - Constructor for class edu.cmu.tetrad.sem.Ricf: Represents the Ricf class.
Ricf.FitConGraphResult - Class in edu.cmu.tetrad.sem: The fit con graph result.
Ricf.RicfResult - Class in edu.cmu.tetrad.sem: RICF result.
ricf2(Graph, ICovarianceMatrix, double) - Method in class edu.cmu.tetrad.sem.Ricf: Same as above but takes a Graph instead of a SemGraph
RicfEjml - Class in edu.cmu.tetrad.sem: Residual Iterative Conditional Fitting (RICF) for Gaussian ADMG / MAG models (directed and bidirected edges only; no undirected/selection block).
RicfEjml() - Constructor for class edu.cmu.tetrad.sem.RicfEjml: Constructs an instance of RicfEjml.
RicfEjml.RicfResult - Class in edu.cmu.tetrad.sem: Result of RICF: the implied covariance, the error covariance, both coefficient matrices (Beta and I - Beta), the sweep count, the final convergence metric, and the Gaussian log-likelihood of the fitted model.
RicfResult(DoubleMatrix2D, DoubleMatrix2D, DoubleMatrix2D, DoubleMatrix2D, int, double, ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.sem.Ricf.RicfResult: The result.
RicfResult(DMatrixRMaj, DMatrixRMaj, DMatrixRMaj, DMatrixRMaj, int, double, double) - Constructor for class edu.cmu.tetrad.sem.RicfEjml.RicfResult: Constructs a result holder.
RICHARDS - Enum constant in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: Richard's layout; see LayoutUtil.richardsLayout(Graph, NodeSize).
richardsLayout(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Arranges the graph the way Richard lays out a graph for presentation: layered by causal depth, with each layer shifted a bit further right than the one above it, so that the flow reads down and to the right, and with within-layer orderings chosen by barycenter sweeps to reduce edge crossings.
richardsLayout(Graph, LayoutUtil.NodeSize) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Richard's layout with the given node sizes and default spacing: a 30 px horizontal gap between node boxes, 90 px between layer centerlines, and a 50 px rightward shear per layer.
richardsLayout(Graph, LayoutUtil.NodeSize, double, double, double) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Richard's layout, fully parameterized.
richardsLayout(Graph, LayoutUtil.NodeSize, double, double, double, boolean) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Richard's layout, with the nudge pass switchable.
ridge - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Ridge stabilization parameter for OLS and IRLS algorithms.
ridge - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the ridge parameter for regularization in the covariance matrix estimation.
ridge - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Represents the ridge parameter used as a regularization term in covariance computation.
RIDGE - Enum constant in enum class edu.cmu.tetrad.search.test.IndTestFisherZ.ShrinkageMode: Represents the Ridge shrinkage mode.
RIDGE - Static variable in class edu.cmu.tetrad.util.RankTests: A small constant value added as a ridge term during regularization to improve numerical stability.
ridgeIfIndefinite(Matrix, List) - Static method in class edu.cmu.tetrad.sem.ResidualCovariance: Adds a ridge to the diagonal if the smallest eigenvalue is below MIN_EIGENVALUE_FRACTION of the mean diagonal, logging what was done and which variables the deficient direction loads on; otherwise returns the matrix itself.
ridgeLambda - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: A regularization parameter used in ridge regression to prevent overfitting by adding a penalty proportional to the square of the coefficients' magnitude.
RIGHT_JUSTIFIED - Static variable in class edu.cmu.tetrad.util.TextTable: Set justification to this if the columns should be right justified.
rint(double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the closest value to the argument, with ties rounding to the even neighbor.
Rlcd - Class in edu.cmu.tetrad.algcomparison.algorithm.other: Wrapper for the RLCD algorithm (Rank-based Latent Causal Discovery; Dong et al., ICLR 2024), which learns a graph over the observed variables and newly introduced latent variables from rank constraints on the covariance matrix.
Rlcd - Class in edu.cmu.tetrad.search: Rank-based Latent Causal Discovery (RLCD), from Dong, Huang, Ng, Song, Zheng, Jin, Legaspi, Spirtes, and Zhang, "A versatile causal discovery framework to allow causally-related hidden variables," ICLR 2024.
Rlcd() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd: Constructs a new instance of the algorithm.
Rlcd(DataSet) - Constructor for class edu.cmu.tetrad.search.Rlcd: Constructs the search over a continuous data set.
Rlcd(List) - Constructor for class edu.cmu.tetrad.search.Rlcd: Constructs ONE search over several imputations of a data set with missing values.
RLCD_ALLOW_NON_LEAF_X - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_ALLOW_NON_LEAF_X="rlcdAllowNonLeafX"
RLCD_CEILING_VALUE - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_CEILING_VALUE="rlcdCeilingValue" The ceiling for RLCD_CENSOR_CEILING, the same for every variable; 100 for percentage grades.
RLCD_CENSOR_CEILING - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_CENSOR_CEILING="rlcdCensorCeiling" Whether RLCD treats values at a common ceiling (see RLCD_CEILING_VALUE) as censored -- known only to be at least the ceiling -- and estimates its covariance matrix accordingly.
RLCD_CHECK_V - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_CHECK_V="rlcdCheckV"
RLCD_KNOWLEDGE_IN_STAGE1 - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_KNOWLEDGE_IN_STAGE1="rlcdKnowledgeInStage1" Whether RLCD gives background knowledge to stage 1 as well as stage 2.
RLCD_LATENT_GATE - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_LATENT_GATE="rlcdLatentGate"
RLCD_MAX_K - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_MAX_K="rlcdMaxK"
RLCD_OBSERVED_EXPLANATION_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_OBSERVED_EXPLANATION_ALPHA="rlcdObservedExplanationAlpha" The alpha of RLCD's observed-explanation test.
RLCD_OBSERVED_EXPLANATION_CHECK - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_OBSERVED_EXPLANATION_CHECK="rlcdObservedExplanationCheck" Whether RLCD accepts a rank deficiency as a cluster only if no equally small set of observed variables explains it.
RLCD_PARTITION_CLIQUE_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_PARTITION_CLIQUE_THRESHOLD="rlcdPartitionCliqueThreshold"
RLCD_QUALIFY_OBSERVED_CAUSES - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_QUALIFY_OBSERVED_CAUSES="rlcdQualifyObservedCauses" Whether RLCD keeps an arrow from an observed variable into a latent only when an observed parent of that variable identifies the direction.
RLCD_RESOLVE_TWO_INDICATOR_LATENTS - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_RESOLVE_TWO_INDICATOR_LATENTS="rlcdResolveTwoIndicatorLatents" Whether RLCD tries to replace a latent with two indicators by a direct edge between them, using non-Gaussianity.
RLCD_STABILITY_RUNS - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_STABILITY_RUNS="rlcdStabilityRuns" Number of bootstrap reruns RLCD makes after the main run to report co-clustering and adjacency frequencies; 0 (the default) makes none.
RLCD_STABILITY_TIME_LIMIT - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_STABILITY_TIME_LIMIT="rlcdStabilityTimeLimit" Wall-clock budget, in seconds, for the stability reruns; checked between runs.
RLCD_STAGE1_FGES - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_STAGE1_FGES="rlcdStage1Fges"
RLCD_STAGE1_NONE - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_STAGE1_NONE="rlcdStage1None"
RLCD_UNFOLD_COVERS - Static variable in class edu.cmu.tetrad.util.Params: Constant RLCD_UNFOLD_COVERS="rlcdUnfoldCovers"
Rlcd.Stage1Method - Enum Class in edu.cmu.tetrad.search: Stage-1 search methods.
RlcdAccuracyStudy - Class in edu.cmu.tetrad.search.harness: A watch-the-output accuracy study of RLCD on linear non-Gaussian data with latent variables, comparing the plain search with the two recent additions: Rlcd.setResolveTwoIndicatorLatents(boolean) and temporal-tier knowledge, which RLCD now reads as constraining ancestry through latents.
RlcdClusterSearch - Class in edu.cmu.tetrad.search.rlcd: Stage 2 of RLCD over one partition of observed variables: the rank-deficiency search for clusters, translating the module-level functions findClusters, findClusters_at_k, findClusters_at_k_by_nonsinks, structuralRankTest, and findClusters_finish of RLCD_alg.py in causal-learn.
RlcdClusterSearch(List, RankTester, IntToDoubleFunction, int, boolean, boolean, boolean) - Constructor for class edu.cmu.tetrad.search.rlcd.RlcdClusterSearch: Constructs the search.
RlcdStabilitySummary - Class in edu.cmu.tetrad.search.rlcd: Summarizes how stable an RLCD output is across repeated runs on resampled data.
RlcdStabilitySummary(Graph, List) - Constructor for class edu.cmu.tetrad.search.rlcd.RlcdStabilitySummary: Tallies co-clustering, observed adjacencies and latent counts over the runs.
RlcdTwoIndicatorStudy - Class in edu.cmu.tetrad.search.harness: A watch-the-output study of Rlcd.setResolveTwoIndicatorLatents(boolean): RLCD is run with the step off and on, on the same data, and the output is scored for how it represents the pair X1, X2.
rmsea() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Root Mean Square Error of Approximation (RMSEA)
RoadmapTest - Class in edu.cmu.tetrad.search.unmix: RoadmapTest: EM baseline vs Residual-clustering across scenarios.
RoadmapTest() - Constructor for class edu.cmu.tetrad.search.unmix.RoadmapTest: Constructor for the RoadmapTest class.
ROBUST_CLIPPED - Enum constant in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: Robust clipped, per trait column, on the log scale: a = clip((log(w) - median) / (2 * IQR), -1, 1) over the column's finite, strictly positive entries, with quartiles by linear interpolation (type 7).
robustScaleResiduals - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Determines whether to use robust scaling for residuals in computations.
robustScaleResiduals - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Determines whether robust scaling should be applied to the residuals during the EmUnmix algorithm process.
robustSkew(double[], double[], boolean) - Static method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Calculates a left-right judgment using the robust skewness between two arrays of double values.
robustStandardizeInPlace(double[][]) - Static method in class edu.cmu.tetrad.search.unmix.ResidualUtils: Standardizes the input residual matrix in place using a robust method based on the median and median absolute deviation (MAD).
RocCalculator - Class in edu.cmu.tetrad.util: Calculates a ROC curve and AUC (area under curve) for a list of scored cases whose inclusion in category C is known.
RocCalculator(double[], boolean[], int) - Constructor for class edu.cmu.tetrad.util.RocCalculator: Constructs a calculator using the parameter information below.
Role mapping - Section in class edu.cmu.tetrad.search.test.IndTestCord
Role mapping - Section in class edu.cmu.tetrad.search.test.IndTestCordEric
round(double) - Static method in class edu.cmu.tetrad.util.TMath: Rounds the specified floating-point value to the nearest long value.
round(float) - Static method in class edu.cmu.tetrad.util.TMath: Rounds the given floating-point value to the nearest integer.
row(int) - Method in class edu.cmu.tetrad.util.Matrix: getRow.
Row(double, double, double, double, double, double) - Constructor for record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Creates an instance of a Row record class.
Row(double, double, double, double, double, double) - Constructor for record class edu.cmu.tetrad.search.test.AlphaReport.Row: Creates an instance of a Row record class.
Row alignment - Section in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest
rowAt(double) - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: Everything the report knows about one penalty discount, for the inverse direction and for the sweep.
rowAt(double) - Method in class edu.cmu.tetrad.search.test.AlphaReport: Everything the report knows about one level.
RowCorrelationEffN - Class in edu.cmu.tetrad.util: Utility class for estimating the average pairwise row correlation of a data matrix and computing the effective sample size (Neff) based on the correlations.
RowCorrelationEffN() - Constructor for class edu.cmu.tetrad.util.RowCorrelationEffN: Constructs a new instance of the RowCorrelationEffN class.
RowCorrelationEffN.Result - Class in edu.cmu.tetrad.util: Represents the result of an average pairwise row correlation estimation, containing the adjusted average row correlation value, the effective sample size, and the number of row pairs used in the computation.
rowCount() - Method in interface edu.pitt.isp.sverchkov.data.DataTable: rowCount.
rowCount() - Method in class edu.pitt.isp.sverchkov.data.DataTableImpl: rowCount.
rowIndexForCase(int, double[]) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Convenience overload when the child has NO discrete parents.
rowIndexForCase(int, int[], double[]) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Compute the local-table row index for a single data case.
rowIndexForCase(int, DataSet, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Convenience overload that reads parent states/values from a DataSet row.
rowIndexForCase(HybridCgModel.HybridCgPm, int, DataSet, int, int[]) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Computes the row index for a given data case in the hybrid causal graph model.
rowPropensity() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Spec: Returns the value of the rowPropensity record component.
rows() - Method in record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Returns the value of the rows record component.
rows() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Returns the value of the rows record component.
rowsExceedWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Number of simulated rows in which at least one node's continuous parents exceeded TrainedDagSimulatorGNM.SimResult.zWarn1 in training z-units, i.e.
RowsSettable - Interface in edu.cmu.tetrad.search.test: Interface for tests that can have their rows set on the fly.
rowsSignature(List) - Static method in class edu.cmu.tetrad.search.utils.MinimaxGroupCache: Computes a signature for a list of integers based on the FNV-1a hashing algorithm.
RowSummingExactUpdater - Class in edu.cmu.tetrad.bayes: Performs updating operations on a BayesIm by summing over cells in the joint probability table for the BayesIm.
RowSummingExactUpdater(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.RowSummingExactUpdater: Constructs a new updater for the given Bayes net.
RowSummingExactUpdater(BayesIm, Evidence) - Constructor for class edu.cmu.tetrad.bayes.RowSummingExactUpdater: Constructs a new updater for the given Bayes net.
RPAREN - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Right parenthesis.
Rskew - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: RSkew.
Rskew() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Constructor for Rskew.
Rskew(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Constructor for Rskew.
RSkew - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The RSkew rule.
RSKEW - Enum constant in enum class edu.cmu.tetrad.search.Fask.LeftRight: Use RSkew.
RSKEW - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

The robust skew rule from the Hyvarinen and Smith paper.
RskewE - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: RSkewE.
RskewE() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Constructor for RskewE.
RskewE(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Constructor for RskewE.
RSkewE - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The RSkewE rule.
RubinsRules - Class in edu.cmu.tetrad.data.missing: Rubin's rules for pooling a scalar estimate across the m completed data sets of a multiple imputation.
RubinsRules.Pooled - Record Class in edu.cmu.tetrad.data.missing: A pooled parameter.
ruleR0(Graph, Set, boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Orients unshielded colliders in the graph.
ruleR1(Node, Node, Node, Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R1: If α *→ β o––* γ, and α and γ are not adjacent, then orient the triple as α *→ β → γ.
ruleR10(Node, Node, Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R10 (Zhang 2008 FCI orientation rule).
ruleR2(Node, Node, Node, Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R2: If α → β ∘→ γ or α ∘→ β → γ, and α ∘–o γ, then orient α ∘–o γ as α ∘→ γ.
ruleR3(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R3: If α *→ β ←* γ, α *–o θ o–* γ, α and γ are not adjacent, and θ *–o β, then orient θ *–o β as θ *→ β.
ruleR4(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R4: If u = ⟨θ, …, α, β, γ⟩ is a discriminating path between θ and γ for β, and β o−∗ γ, then: If β ∈ Sepset(θ, γ), orient β o−∗ γ as β → γ; Otherwise, orient the triple ⟨α, β, γ⟩ as α ↔ β ↔ γ.
ruleR5(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R5: For every remaining α o−o β, if there exists an uncovered circle path p = ⟨α, γ, …, θ, β⟩ between α and β such that α and θ are not adjacent and β and γ are not adjacent, then orient α o−o β and every edge on p as undirected (−−).
ruleR6(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R6: If α — β o−∗ γ (where α and γ may or may not be adjacent), then orient β o−∗ γ as β −∗ γ.
ruleR7(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R7: If α −∘ β o−∗ γ, and α and γ are not adjacent, then orient β o−∗ γ as β −∗ γ.
ruleR8(Node, Node, Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R8: If α → β → γ or α −∘ β → γ, and α ∘→ γ, then orient α ∘→ γ as α → γ.
ruleR9(Node, Node, Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: R9: Suppose α ∘→ γ, and let p = ⟨α, β, θ, …, γ⟩ be an uncovered, potentially-directed path from α to γ such that γ and β are not adjacent.
rulesR1R2cycle(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Apply rules R1 and R2 in cycles for a given graph.
rulesR5R6R7(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Applies transformation rules R5, R6, and R7 to the provided graph.
rulesR8R9R10(Graph) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Implements Zhang's rules R8, R9, R10, applies them over the graph once.
run() - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPag: Executes the pipeline and returns the resulting PAG.
run() - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Runs the orientation pass and returns the resulting PAG.
run() - Method in class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi: Runs the study and prints results to standard output.
run() - Method in class edu.cmu.tetrad.search.harness.VertexRepairSimulation: Executes the VertexRepairSearch simulation process for a specified number of runs.
run() - Method in class edu.cmu.tetrad.search.RecursiveBlockingMemoEquivalenceHarness: Executes the equivalence testing harness for the RecursiveBlocking algorithm.
run(int) - Method in class edu.cmu.tetrad.simulation.HsimAutoC: run.
run(int) - Method in class edu.cmu.tetrad.simulation.HsimAutoRun: run.
run(int, double, int, double, int, int, boolean) - Static method in class edu.cmu.tetrad.simulation.HsimRobustCompare: run.
run(int, int) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAC: run.
run(int, int) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: run.
run(DataSet) - Method in interface edu.cmu.tetrad.search.harness.tsc.AlgorithmRunner: Runs the algorithm on dataSet and returns the recovered clusters.
run(DataSet) - Method in class edu.cmu.tetrad.search.harness.tsc.BpcRunner
run(DataSet) - Method in class edu.cmu.tetrad.search.harness.tsc.FofcRunner
run(DataSet) - Method in class edu.cmu.tetrad.search.harness.tsc.FtfcRunner
run(DataSet) - Method in class edu.cmu.tetrad.search.harness.tsc.TscRunner
run(DataSet, Knowledge, List, List, Parameters) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimicRunner: Deprecated.

Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
run(DataSet, Knowledge, List, List, Parameters) - Method in class edu.cmu.tetrad.search.mimic.DmRunner: Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
run(DataSet, Knowledge, List, List, Parameters) - Method in interface edu.cmu.tetrad.search.mimic.MimicSearchRunner: Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
run(DataSet, Knowledge, List, List, Parameters) - Method in class edu.cmu.tetrad.search.mimic.TrekMimicRunner: Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
run(DataSet, EmUnmix.Config, ResidualRegressor) - Static method in class edu.cmu.tetrad.search.unmix.EmUnmix: Runs the unmixing process on the provided dataset using the specified configuration and regressor.
run(DataSet, EmUnmix.Config, ResidualRegressor, Function, Function) - Static method in class edu.cmu.tetrad.search.unmix.EmUnmix: Executes the unmixing process on the given dataset using the specified configuration, residual regressor, and optional graph search functions.
run(DataSet, List, List) - Method in class edu.cmu.tetrad.search.harness.tsbi.TsbiRunner: Runs PC with TSBI given a dataset and a pre-determined clustering.
run(Graph, DataSet, List, Parameters, CiTestHarness.Config) - Method in class edu.cmu.tetrad.search.harness.CiTestHarness: Executes a series of independence tests on a dataset using a given true graph and configuration, and evaluates the results against predefined criteria.
run(Graph, IndependenceTest, ConditioningSetType, GraphoidClosureHarness.ClosureType) - Method in class edu.cmu.tetrad.search.harness.GraphoidClosureHarness: Runs the harness.
run(String, String, char, String[], boolean) - Static method in class edu.cmu.tetrad.simulation.HsimRun: run.
run(List) - Method in class edu.cmu.tetrad.util.TaskRunner: Executes a list of tasks that implement Callable in parallel using multiple threads.
runAll() - Method in class edu.cmu.tetrad.search.harness.tsc.SimulationStudy: Runs both studies and prints results to standard output.
RunConfig - Class in edu.cmu.tetrad.algcomparison.examples: An example script to save out data files and graphs from a simulation.
RunConfig() - Constructor for class edu.cmu.tetrad.algcomparison.examples.RunConfig: Constructs a new instance of the Save.
runDemo(DataSet, Cca, int, int, double) - Static method in class edu.cmu.tetrad.search.NtadExplorerHarness: Run the ntad explorer on the given dataset and CCA test, using ALL variables in the dataset as the candidate list.
runDemo(DataSet, List, Cca, int, int, double) - Static method in class edu.cmu.tetrad.search.NtadExplorerHarness: Runs the NTAD Explorer demo using the specified dataset, variable list, CCA test, and parameters.
runEngine(double[][], String[], int, long, WildBootstrapMarkovCheck.Multiplier) - Static method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Run the joint check directly on a precomputed product matrix.
runEngineBlocks(double[][], String[], int, long, WildBootstrapMarkovCheck.Multiplier, int[]) - Static method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Wild CLUSTER bootstrap-t engine for row dependence with a block (cluster) structure.
runEngineKernel(double[][], String[], int, long, double[][], double) - Static method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Dependent wild bootstrap-t engine (Shao 2010, self-normalized) for row dependence indexed by coordinates (spatial locations; a single column of time indices for serial data).
RunEvaluation - Class in edu.cmu.tetrad.search.mimic: The RunEvaluation class is responsible for configuring parameters, executing mimic benchmark trials, and generating a report based on the results.
RunEvaluation() - Constructor for class edu.cmu.tetrad.search.mimic.RunEvaluation: Default constructor for the RunEvaluation class.
runFullBattery() - Method in class edu.cmu.tetrad.search.harness.VertexRepairBattery: Executes a comprehensive battery of tests on the VertexRepair algorithm using a variety of different graph scenarios, average degrees, sample sizes, and repetition runs.
runFullBattery() - Method in class edu.cmu.tetrad.search.harness.VertexRepairBattery2: Executes a comprehensive battery of tests on the VertexRepair algorithm using a variety of configurations and parameters.
RunKemmeren - Class in edu.cmu.tetrad.algcomparison.examples: RunKemmeren class.
RunKemmeren() - Constructor for class edu.cmu.tetrad.algcomparison.examples.RunKemmeren: Constructor for RunKemmeren.
runOnce(List, CovarianceMatrix, double, int, int) - Static method in class edu.cmu.tetrad.search.TscHarnessTest: Executes the time series clustering (TSC) algorithm once with the given parameters and returns canonicalized clusters as a set of sets of integers.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Dagma: Runs the DAGMA algorithm to search for a directed acyclic graph (DAG) in the given data model with the specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Runs the Direct LiNGAM search algorithm on the given data model with the specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Runs the Fask search algorithm on the given data model with the specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Runs the Fask search algorithm on the given data model with the specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingam: Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.IcaLingD: Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Lingam: Searches for a graph structure based on the given data set and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.LingD: Runs a search on the provided data set using the given parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Runs the BOSS-CCD search algorithm on the given data set using the specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Runs the CCD (Cyclic Causal Discovery) search algorithm on the given data set using the specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Executes the search algorithm on a given data model and set of parameters, producing a partial ancestral graph (PAG) that represents the causal structure inferred from the data.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Runs a search algorithm to find a graph based on the given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Executes the search algorithm on a given data model and set of parameters, producing a partially directed acyclic graph (PAG) that represents the causal structure inferred from the data.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Runs the search.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Runs a search algorithm to find a graph structure based on a given data set and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Runs the ICD search and returns the resulting PAG.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvBossExperimental: Runs LV-BOSS on the given data model.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Runs the search algorithm to find a graph structure based on a given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Runs the search algorithm using the given data set and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Runs the search algorithm on the given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Runs a search algorithm using a given dataset and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Executes a search algorithm to infer the causal graph structure from a given data model
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis: Executes a factor analysis search on the given data model using the provided parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Glasso: Runs a search algorithm to create a graph representation of the data.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Runs the search algorithm using the given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Runs the search algorithm on the given data model with the provided parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Runs the search algorithm using the provided data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Runs the search algorithm to orient edges in the input graph using the provided data.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Runs the search algorithm using the provided data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Runs a search algorithm to find the orientation of edges in a graph using the given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Runs the search algorithm to orient edges in the input graph using the given data model and parameters.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Executes the SkewE search algorithm.
runSearch(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Runs the search algorithm using the given data model and parameters.
runStudy1() - Method in class edu.cmu.tetrad.search.harness.tsc.SimulationStudy: Runs Study 1 (rank-1 MIM) and returns its results.
runStudy2() - Method in class edu.cmu.tetrad.search.harness.tsc.SimulationStudy: Runs Study 2 (rank-2 MIM) and returns its results.
runTrial(Parameters) - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmark: Runs one benchmark trial.
runTrials(Parameters, int) - Method in class edu.cmu.tetrad.search.mimic.MimicBenchmark: Runs multiple benchmark trials.
runWithTimeout(Callable, long, TimeUnit) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Runs the given task with the given timeout.

S

S() - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Returns the value of the S record component.
SADDLEPOINT - Enum constant in enum class edu.cmu.tetrad.search.test.FfCiContinuous.Approx: Represents the saddlepoint approximation method.
SADDLEPOINT - Enum constant in enum class edu.cmu.tetrad.search.test.Rcit.Approx: Represents the saddlepoint
saddlepointLugannaniRiceP(double, double[]) - Static method in class edu.cmu.tetrad.search.test.QuadraticFormPValues: Saddlepoint approximation (Lugannani–Rice) for Q = sum lambda_i * chi^2_1.
safeGetInt(DataSet, int, int) - Static method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Safely retrieves an integer value from a DataSet, handling missing or non-integer values gracefully.
safeGetInt(DataSet, int, int) - Static method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Safely retrieves an integer value from the specified row and column of a DataSet.
safeGetInt(DataSet, int, int) - Static method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Safely retrieves an integer value from the specified DataSet at the given row and column.
safeGetInt(DataSet, int, int) - Static method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Safely retrieves an integer value from the specified DataSet at the given row and column.
safeOffsetFor(double[]) - Static method in record class edu.cmu.tetrad.data.LogTransformSpec: Returns the smallest offset that keeps every value of the given column strictly positive under a log transform, or 0.0 if the column is already strictly positive.
SAG - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: SAG test, for FTFC.
sameEdgeFlippedDirection() - Method in class edu.cmu.tetrad.graph.Edge: Flips this edge, so that if it's X->Y it's now Y<-X, or vice-versa.
sample() - Method in class edu.cmu.tetrad.sem.DistributionSampler: Generates a single random sample from the probability distribution encapsulated within the Sampler instance.
sample() - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns a sample from the empirical cumulative distribution function (CDF).
sample() - Method in class edu.cmu.tetrad.sem.ExpressionSampler: Evaluates the stored mathematical expression using an empty context and returns the result.
sample() - Method in interface edu.cmu.tetrad.sem.Sampler: Generates a sample value based on the implementation's defined sampling strategy.
sample(int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Simulate n rows from this IM.
sample(int) - Method in class edu.cmu.tetrad.sem.EmpiricalCdf: Returns a sample from the empirical cumulative distribution function (CDF).
sample(DataSet, int) - Method in class edu.cmu.tetrad.data.BootstrapSampler: This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
sample(DataSet, int) - Static method in class edu.cmu.tetrad.data.RandomSampler: This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
Sample(double[][], int[][], int) - Constructor for record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Creates an instance of a Sample record class.
SAMPLE_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant SAMPLE_SIZE="sampleSize"
SAMPLE_SIZE_FORCES_SINGULARITY - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: The stated sample size n of a supplied covariance matrix satisfies n - 1 < p, where p is the number of (positive-variance) variables, so any ordinary sample covariance matrix computed from n rows is singular by arithmetic.
SAMPLE_STYLE - Static variable in class edu.cmu.tetrad.util.Params: Constant SAMPLE_STYLE="sampleStyle"
SAMPLE_UNIFORM - Enum constant in enum class edu.cmu.tetrad.search.score.ImagesScore.WeightingScheme: Plain (equally weighted) average of the component scores.
sampleGaussianProcess(double[], double, double, boolean) - Static method in class edu.cmu.tetrad.search.utils.RandomFunctions: Samples values from a Gaussian Process defined by an RBF kernel with the given parameters.
Sampler - Interface in edu.cmu.tetrad.sem: Represents a generic sampling interface that provides a method to obtain a sampled value.
sampleSize() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the sampleSize record component.
sampleSize() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.ConditionResult: Returns the value of the sampleSize record component.
sampleSize() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the sampleSize record component.
sampleSize() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the sampleSize record component.
sampleSize() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Returns the value of the sampleSize record component.
sampleSize(int[], int[]) - Method in class edu.cmu.tetrad.search.rlcd.EmCovarianceRankTest: The sample size this test uses for a query, by the configured mode over the pairs of distinct columns the query mentions.
SampleVcpc - Class in edu.cmu.tetrad.search.work_in_progress: Implements a conservative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
SampleVcpc(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Constructs a CPC algorithm that uses the given independence test as oracle.
SampleVcpcFast - Class in edu.cmu.tetrad.search.work_in_progress: Implements a conservative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
SampleVcpcFast(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Constructs a CPC algorithm that uses the given independence test as oracle.
save(HybridCgModel.HybridCgIm, File) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgIo: Saves the given model to a file as JSON (UTF-8).
Save - Class in edu.cmu.tetrad.algcomparison.examples: An example script to save out data files and graphs from a simulation.
Save() - Constructor for class edu.cmu.tetrad.algcomparison.examples.Save: Constructs a new instance of the Save.
SAVE_BOOTSTRAP_GRAPHS - Static variable in class edu.cmu.tetrad.util.Params: Constant SAVE_BOOTSTRAP_GRAPHS="saveBootstrapGraphs"
SAVE_LATENT_VARS - Static variable in class edu.cmu.tetrad.util.Params: Constant SAVE_LATENT_VARS="saveLatentVars"
saveCSV(DataTable, File, boolean) - Static method in class edu.pitt.isp.sverchkov.data.DataTools: Saves a data table to a file.
SaveDGSimulations - Class in edu.cmu.tetrad.algcomparison.examples: An example script to save out data files and graphs from a simulation.
SaveDGSimulations() - Constructor for class edu.cmu.tetrad.algcomparison.examples.SaveDGSimulations: Constructs a new instance of the SaveDGSimulations.
saveGraph(Graph, File, boolean) - Static method in class edu.cmu.tetrad.graph.GraphSaveLoadUtils: saveGraph.
saveKnowledge(Knowledge, Writer) - Static method in class edu.cmu.tetrad.data.DataWriter: Saves knowledge in the tetrad2 text format that SimpleDataLoader.loadKnowledge reads.
saveKnowledge(Knowledge, Writer, boolean) - Static method in class edu.cmu.tetrad.data.DataWriter: Saves knowledge in the tetrad2 text format, choosing the delimiter explicitly.
saveToFiles(String, Simulation, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Saves the simulation data to file in the specified data path.
saveToFiles(String, Simulation, Parameters) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Saves simulationWrapper data.
saveToFilesSingleSimulation(String, Simulation, Parameters) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Saves the results of a single simulation to files.
scalarMult(double) - Method in class edu.cmu.tetrad.util.Matrix: scalarMult.
scalarMult(double) - Method in class edu.cmu.tetrad.util.Vector: scalarMult.
scalarPlus(double) - Method in class edu.cmu.tetrad.util.Matrix: Adds the specified scalar value to each element of the matrix and returns the resulting matrix.
scalarProduct(double, double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: scalarProduct.
scalarProduct(double, double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Multiplies the given matrix through by the given scalar.
scalb(double, int) - Static method in class edu.cmu.tetrad.util.TMath: Returns a floating-point value obtained by scaling the specified floating-point value `d` by an integer power of two specified by `scaleFactor`.
scale(double, double, double, double, double) - Static method in class edu.cmu.tetrad.data.DataTransforms: Scales a value from one range to another.
scale(DataSet, double[]) - Static method in class edu.cmu.tetrad.data.DataTransforms: Scales the columns of the provided dataset based on the given scale factors.
scale(DataSet, double, double) - Static method in class edu.cmu.tetrad.data.DataTransforms: Scales the continuous variables in the given DataSet to have values in the range [-1, 1].
scale(DataSet, double, double, Node) - Static method in class edu.cmu.tetrad.data.DataTransforms: Scales the values of a specified node in the given dataset to a specified range [scaleMin, scaleMax].
scale(Matrix) - Static method in class edu.cmu.tetrad.search.LingD: Scales a given square matrix such that each row is normalized by its diagonal element.
SCALE_FREE_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant SCALE_FREE_ALPHA="scaleFreeAlpha"
SCALE_FREE_BETA - Static variable in class edu.cmu.tetrad.util.Params: Constant SCALE_FREE_BETA="scaleFreeBeta"
SCALE_FREE_DAG - Static variable in class edu.cmu.tetrad.algcomparison.graph.GraphTypes: Constant SCALE_FREE_DAG="Scale Free DAG (Bollobas et al.)"
SCALE_FREE_DELTA_IN - Static variable in class edu.cmu.tetrad.util.Params: Constant SCALE_FREE_DELTA_IN="scaleFreeDeltaIn"
SCALE_FREE_DELTA_OUT - Static variable in class edu.cmu.tetrad.util.Params: Constant SCALE_FREE_DELTA_OUT="scaleFreeDeltaOut"
ScaleFree - Class in edu.cmu.tetrad.algcomparison.graph: Returns a scale free graph.
ScaleFree() - Constructor for class edu.cmu.tetrad.algcomparison.graph.ScaleFree: Constructs a new instance of the ScaleFree.
scaleInternalEdges(SemIm, Graph, List, double) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Scale all internal edges of an SCC by a factor.
SCALING_FACTOR - Static variable in class edu.cmu.tetrad.util.Params: Constant SCALING_FACTOR="scalingFactor"
scenario - Static variable in class edu.cmu.tetrad.search.harness.VertexRepairSimulation: Which starting-graph scenario to use.
score() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: score.
score(DataSet, Graph) - Method in class edu.cmu.tetrad.search.CyclicSemScorer: Computes the score of a given data set and graph by evaluating the Bayesian Information Criterion (BIC), stability of the system, and the number of parameters used.
score(Graph) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Scores the given MAG/ADMG: 2 * logLik - penaltyDiscount * numEdges * log(n), higher is better.
score(Graph) - Method in class edu.cmu.tetrad.sem.DagScorer: Scores the given DAG using the implemented algorithm.
score(Graph) - Method in interface edu.cmu.tetrad.sem.Scorer: score.
score(Graph, Graph) - Static method in class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer: Scores the recovered structural graph against the true structural graph.
score(Score) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the Score to be used by the BOSS backbone.
score(Collection, Collection) - Static method in class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer: Computes the best-Jaccard precision and recall.
score(List) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Scores the given permutation.
Score - Interface in edu.cmu.tetrad.search.score: Interface for a score.
Score - Annotation Interface in edu.cmu.tetrad.annotation: Sep 1, 2017 10:51:13 AM
Score(Scorer) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Score: Constructor for Score.
Score(Scorer) - Constructor for class edu.cmu.tetrad.search.work_in_progress.HbsmsGes.Score: Constructor for Score.
SCORE_CHECK_PENALTY_DISCOUNT - Static variable in class edu.cmu.tetrad.util.Params: Constant SCORE_CHECK_PENALTY_DISCOUNT="scoreCheckPenaltyDiscount"
SCORE_EPSILON - Static variable in class edu.cmu.tetrad.search.utils.CorrectnessGates: Score differences below this are ties.
SCORE_TEST_CALIBRATED_P_VALUES - Static variable in class edu.cmu.tetrad.util.Params: Constant SCORE_TEST_CALIBRATED_P_VALUES="scoreTestCalibratedPValues"
ScoreAnnotations - Class in edu.cmu.tetrad.annotation: Sep 26, 2017 1:14:01 AM
scoreDag(Graph) - Method in class edu.cmu.tetrad.search.Fges: Scores a Directed Acyclic Graph (DAG) based on its structure.
scoreDag(Graph) - Method in class edu.cmu.tetrad.search.FgesMb: Scores the given directed acyclic graph (DAG).
scoreDag(Graph) - Method in interface edu.cmu.tetrad.search.utils.DagScorer: scoreDag.
scoreDag(Graph) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: scoreDag.
scoreDag(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: scoreDag.
scoreDag(Graph, DataModel, boolean) - Static method in class edu.cmu.tetrad.search.score.SemBicScorer: Scores the given DAG using the given data model, usimg a BIC score.
scoreDag(Graph, DataModel, double, boolean) - Static method in class edu.cmu.tetrad.search.score.SemBicScorer: Scores the given DAG using the given data model, usimg a BIC score.
ScoredCandidate(VertexRepairSearch.CandidateEdit, int, int, double, double, double, int, boolean, double) - Constructor for record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Creates an instance of a ScoredCandidate record class.
ScoredClusterFinder - Class in edu.cmu.tetrad.search: ScoredClusterFinder ------------------- Given a DataSet and a subset of candidate variables Vsub (by column index), enumerate all clusters C â Vsub of a fixed size s and keep those for which a BIC-style RCCA score is maximized exactly at rank k when scored against D = Vsub \ C.
ScoredClusterFinder(DataSet, Collection) - Constructor for class edu.cmu.tetrad.search.ScoredClusterFinder: Constructs a ScoredClusterFinder instance using the provided dataset and a collection of candidate variable indices.
ScoredClusterFinder.ClusterHit - Class in edu.cmu.tetrad.search: Result holder for one accepted cluster.
ScoreDescriptions - Class in edu.cmu.tetrad.util: May 14, 2019 11:23:54 AM
ScoredGraph - Class in edu.cmu.tetrad.search.score: Stores a graph with a score for the graph.
ScoredGraph(Graph, Double) - Constructor for class edu.cmu.tetrad.search.score.ScoredGraph: Constructs a scored graph.
Score equivalence - Section in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore
scoreGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: scoreGraph.
scoreGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: scoreGraph.
ScoreIndTest - Class in edu.cmu.tetrad.search.test: Gives a way of interpreting a score as an independence test.
ScoreIndTest(Score) - Constructor for class edu.cmu.tetrad.search.test.ScoreIndTest: Constructor for ScoreIndTest.
ScoreIndTest(Score, DataModel) - Constructor for class edu.cmu.tetrad.search.test.ScoreIndTest: Constructor for ScoreIndTest.
scoreKm1 - Variable in class edu.cmu.tetrad.search.ScoredClusterFinder.ClusterHit: score at k-1 (NaN if k-1 < 0)
scoreKp1 - Variable in class edu.cmu.tetrad.search.ScoredClusterFinder.ClusterHit: score at k+1 (NaN if k+1 > m)
scoreLnGam(Node, Set, BayesPm, BayesIm) - Method in class edu.cmu.tetrad.bayes.BdeMetricCache: Computes the BDe score, using the logarithm of the gamma function, relative to the data, of the factor determined by a node and its parents.
Scorer - Interface in edu.cmu.tetrad.sem: Interface for a class that represents a scoring of a SEM model.
ScoreResult(double, boolean, int) - Constructor for record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Creates an instance of a ScoreResult record class.
scoreRow(int, Matrix, List, List) - Method in class edu.cmu.tetrad.search.Pairwise: Calculates the score for a given row in a matrix using the specified parameters.
scoreTest() - Method in class edu.cmu.tetrad.bayes.FactoredBayesStructuralEM: scoreTest.
scoreType - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: Specifies the type of correlation score to be used for statistical calculations in the screening process.
ScoreType - Enum Class in edu.cmu.tetrad.sem: Author : Jeremy Espino MD Created 1/12/18 2:05 PM
ScoreWrapper - Interface in edu.cmu.tetrad.algcomparison.score: Interface that algorithm must implement.
sd(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sd.
sd(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: sd.
sd(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sd.
sd(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: sd.
sdARI - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.StabilityResult: Represents the standard deviation of the Adjusted Rand Index (ARI) across multiple pairwise comparisons of cluster labelings in a clustering stability analysis.
sdIfIndependent() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns the value of the sdIfIndependent record component.
sdRowMissing() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns the value of the sdRowMissing record component.
search() - Method in class edu.cmu.tetrad.search.BfciBryan: Runs the search and returns the PAG.
search() - Method in class edu.cmu.tetrad.search.BfciBryanScore: Runs the search and returns the PAG.
search() - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Runs the search and returns the PAG.
search() - Method in class edu.cmu.tetrad.search.BossCcd: Runs BOSS to obtain a CPDAG, extracts its skeleton, and runs CCD with that skeleton as an adjacency superstructure.
search() - Method in class edu.cmu.tetrad.search.Cam: Executes the CAM (Causal Additive Model) algorithm to search for an optimal directed acyclic graph (DAG) structure that best fits the data.
search() - Method in class edu.cmu.tetrad.search.Ccd: Executes the CCD search algorithm to infer a causal graph based on statistical independence tests.
search() - Method in class edu.cmu.tetrad.search.Cdnod
search() - Method in class edu.cmu.tetrad.search.CdnodBoss
search() - Method in class edu.cmu.tetrad.search.Dagma: Performs a search algorithm to find a graph representation.
search() - Method in class edu.cmu.tetrad.search.DirectLingam: Executes DirectLiNGAM and returns the learned graph.
search() - Method in class edu.cmu.tetrad.search.Fas: Performs a conditional independence graph search using the default set of variables from the initialized independence test.
search() - Method in class edu.cmu.tetrad.search.Fasd: Discovers all adjacencies in data.
search() - Method in class edu.cmu.tetrad.search.Fask: Executes the FASK (Fast Adjacency Skewness) algorithm to search for a causal graph based on the provided dataset, knowledge, and configurations.
search() - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
search() - Method in class edu.cmu.tetrad.search.Fci: Searches and retrieves a graph using the specified algorithm.
search() - Method in class edu.cmu.tetrad.search.Fcit: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.Fcit2: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitMarkov: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitSl: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitSl2: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitZm: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitZm2: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Run the search and return a PAG.
search() - Method in class edu.cmu.tetrad.search.Fges: Greedy equivalence search: Start from the empty graph, add edges until the model is significant.
search() - Method in class edu.cmu.tetrad.search.Flop: Runs the search and returns the estimated CPDAG.
search() - Method in class edu.cmu.tetrad.search.GfciOld: Runs the graph and returns the search PAG.
search() - Method in class edu.cmu.tetrad.search.Gmas: Runs the search.
search() - Method in class edu.cmu.tetrad.search.Icd: Runs ICD to completion and returns the resulting PAG.
search() - Method in interface edu.cmu.tetrad.search.IFas: Run adjacency search and return the skeleton graph.
search() - Method in interface edu.cmu.tetrad.search.IGraphSearch: Runs the search and returns a graph.
search() - Method in class edu.cmu.tetrad.search.Ion: Runs the ION search and returns a list of compatible graphs.
search() - Method in class edu.cmu.tetrad.search.is.IsGFci: Executes IS-FGES, targeted-pruning, and FCI orientations to produce an instance-specific PAG.
search() - Method in class edu.cmu.tetrad.search.LatentParentRecovery: Runs latent-parent recovery and returns the augmented graph.
search() - Method in class edu.cmu.tetrad.search.LvHeuristic: Run the search and return s a PAG.
search() - Method in class edu.cmu.tetrad.search.MimbuildBollen: Executes a search operation to build and optimize a graphical structure based on the covariance of latent and measured variables.
search() - Method in class edu.cmu.tetrad.search.MimbuildPca: Executes a search algorithm to identify a structural dependency graph based on block-specific data and principal components analysis (PCA).
search() - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Runs the full Boss-Trek-MIMIC search.
search() - Method in class edu.cmu.tetrad.search.mimic.Dm: Runs the DM search and returns the resulting graph.
search() - Method in class edu.cmu.tetrad.search.mimic.DmPcHybrid
search() - Method in class edu.cmu.tetrad.search.mimic.DmPcRobust
search() - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Runs the full Trek-MIMIC search.
search() - Method in class edu.cmu.tetrad.search.Pc: Performs a search operation based on the test variables associated with the instance.
search() - Method in class edu.cmu.tetrad.search.PcAR: Performs a search operation based on the test variables associated with the instance.
search() - Method in class edu.cmu.tetrad.search.Pcd: Runs PC starting with a complete graph over all nodes of the given conditional independence test, using the given independence test and knowledge and returns the resultant graph.
search() - Method in class edu.cmu.tetrad.search.PcMb: Searches for the Markov blanket CPDAG for the given targets.
search() - Method in class edu.cmu.tetrad.search.Pcmci: Executes the PCMCI (Peter and Clark Momentary Conditional Independence) algorithm to discover causal relationships in time series data.
search() - Method in class edu.cmu.tetrad.search.PermutationSearch: Performs a search for a graph using the default options.
search() - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Performs a search for a graph using the default options.
search() - Method in class edu.cmu.tetrad.search.Rfci: Runs the search and returns the RFCI PAG.
search() - Method in class edu.cmu.tetrad.search.Rlcd: Runs the search.
search() - Method in class edu.cmu.tetrad.search.StarFci: Runs the search and returns the PAG.
search() - Method in class edu.cmu.tetrad.search.StarFciB: Runs the graph and returns the search PAG.
search() - Method in class edu.cmu.tetrad.search.StarFciC: Runs the graph and returns the search PAG.
search() - Method in class edu.cmu.tetrad.search.StarFciD: Runs the graph and returns the search PAG.
search() - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Runs the graph and returns the search PAG.
search() - Method in class edu.cmu.tetrad.search.StarFciOriginal: Runs the graph and returns the search PAG.
search() - Method in class edu.cmu.tetrad.search.Tgin: Deprecated.
search() - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Greedy equivalence search: Start from the empty graph, add edges till model is significant.
search() - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Performs pairwise comparisons of each variable in the graph with the variables that have not already been checked.
search() - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: search.
search() - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Runs the full repair sweep (using the configured strategy) and returns the repaired graph.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.BpcTetradPurifyWashdown: Runs the search and returns a graph.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.Dci: Begins the DCI search procedure, described at each step
search() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.FasDci: Discovers all adjacencies in data.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: search.
search() - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.InverseCorrelation: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Runs PC starting with a complete graph over all nodes of the given conditional independence test, using the given independence test and knowledge and returns the resultant graph.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: Runs the search and returns the best graph found.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: Runs PC starting with a fully connected graph over all of the variables in the domain of the independence test.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Discovers all adjacencies in data.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: search.
search() - Method in class edu.cmu.tetrad.search.work_in_progress.Washdown: Runs the Washdown algorithm and return a graph.
search() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Runs the search and returns a graph.
search() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Runs the search and returns a graph.
search(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearch: Performe the search and return a CPDAG.
search(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Performe the search and return a CPDAG (the top model).
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.AbstractBootstrapAlgorithm: Runs the search.
search(DataModel, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.algorithm.Algorithm: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.FirstInflection: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Executes the PCMCI (Peter and Clark Momentary Conditional Independence) causal discovery algorithm on a given data model using specified parameters.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Executes a factor analysis search on the given data model using the provided parameters.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Executes a factor analysis search on the given data model using the provided parameters.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.PythonGin: Deprecated.

Runs GIN on the supplied dataset and returns the resulting causal graph.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd: Runs RLCD on a continuous data set.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.StabilitySelection: Runs the search.
search(DataModel, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.StARS: Runs the search.
search(DataSet) - Method in interface edu.cmu.tetrad.search.cdnod_pag.CdnodPag.PagBuilder: Builds the baseline PAG from the given dataset.
search(DataSet) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Performs a search operation over the given dataset to analyze dependencies, refine coefficients, and build a directed graph representation.
search(DataSet) - Method in class edu.pitt.csb.stability.DataGraphSearch: search.
search(DataSet, Algorithm, Parameters, MultipleImputer, MissingDataSpec) - Static method in class edu.cmu.tetrad.data.missing.ImputationSearch: Runs the multiple-imputation search.
search(Fas, List) - Method in class edu.cmu.tetrad.search.Pcd: Searches for a graph using the given IFas instance and list of nodes.
search(Fas, List) - Method in class edu.cmu.tetrad.search.Rfci: Runs the search and returns the RFCI PAG.
search(IFas) - Method in class edu.cmu.tetrad.search.Fci: Executes the search process using the provided `IFas` implementation and performs various graph orientation and refinement steps based on the FCI algorithm.
search(Parameters) - Method in class edu.cmu.tetrad.search.FaskPool: Runs the search and returns the composite graph.
search(Parameters) - Method in class edu.cmu.tetrad.search.FaskVote: Runs the search and returns the composite graph.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.AbstractMultiBootstrapAlgorithm: Runs the multi-dataset search, bootstrapping if numberResampling is greater than zero.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Runs the search.
search(List, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm: Runs the search.
search(List, Parameters) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Estimates the common adjacency structure over the given standardized datasets, discarding any orientations the method produces.
search(List) - Method in class edu.cmu.tetrad.search.Fas: Searches for conditional independence relationships in a graph constructed from the given list of nodes.
search(List) - Method in class edu.cmu.tetrad.search.FgesMb: Greedy equivalence search: Start from the empty graph, add edges till the model is significant.
search(List) - Method in class edu.cmu.tetrad.search.Pc: Performs a search to generate a graph structure based on the provided list of nodes.
search(List) - Method in class edu.cmu.tetrad.search.PcAR: Performs the search over the given nodes.
search(List) - Method in class edu.cmu.tetrad.search.Pcd: Runs PC starting with a complete graph over the given list of nodes, using the given independence test and knowledge and returns the resultant graph.
search(List) - Method in class edu.cmu.tetrad.search.PcMb: Searches for the MB CPDAG for the given targets.
search(List) - Method in class edu.cmu.tetrad.search.Rfci: Searches of a specific sublist of nodes.
search(List) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Runs PC starting with a commplete graph over the given list of nodes, using the given independence test and knowledge and returns the resultant graph.
search(List, List, ICovarianceMatrix) - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

Does the search and returns the graph.
SEARCH - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.NoAmenablePolicy: Represents a policy option within the NoAmenablePolicy enumeration that is used to actively search for alternative solutions or options when a certain condition or requirement is not amenable.
search_for_Markov_blankets - Enum constant in enum class edu.cmu.tetrad.annotation.AlgType: If an algorithm searches for Markov blanekts.
search_for_structure_over_latents - Enum constant in enum class edu.cmu.tetrad.annotation.AlgType: If an algorithm searches for structure over latents.
searchDemixedData() - Method in class edu.cmu.tetrad.search.work_in_progress.MixtureModel: Perform an FGES search on each of the demixed data sets.
searchForNode(Node) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Computes and returns the ranked list of scored candidate edits for the given node in the current working graph.
searchImputations(List, Parameters) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd: Runs ONE RLCD search over several imputations of a data set with missing values; see Rlcd(List).
searchImputations(List, Parameters) - Method in interface edu.cmu.tetrad.algcomparison.utils.PoolsImputations: Runs one search over the imputations.
searchOnResamples(Algorithm, Parameters, List, List, boolean) - Static method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Runs the algorithm at the given parameters on each resample and returns the graphs, with nodes replaced by the given variable list so graphs are comparable.
searchParams - Variable in class edu.pitt.csb.stability.DataGraphSearch: searchParams.
searchSuborder(List, List, Map) - Method in class edu.cmu.tetrad.search.Boss: Searches a suborder of the variables.
searchSuborder(List, List, Map) - Method in class edu.cmu.tetrad.search.BossTopK: Searches a suborder of the variables.
searchSuborder(List, List, Map) - Method in class edu.cmu.tetrad.search.Sp: Searches for the best suborder of nodes given a prefix and a suborder.
searchSuborder(List, List, Map) - Method in interface edu.cmu.tetrad.search.SuborderSearch: Searches the suborder.
searchSuborder(List, List, Map) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Searches the suborder.
searchWithOrientations(List, Parameters) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Estimates the common adjacency structure over the given standardized datasets, retaining whatever orientations the method produces.
section(String, String...) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a section header with optional key-value pairs.
seDrBoot - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: The standard error of the Doubly Robust (DR) estimation for the effect size, computed using bootstrap resampling.
seed - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents a seed value used for generating random numbers and controlling deterministic behavior in computations or data-processing tasks.
seed - Variable in class edu.cmu.tetrad.search.CordEngine2: Seed value used for random operations in the CordEngine2 class.
seed - Variable in class edu.cmu.tetrad.search.CordEngine3: Represents the seed value used in various randomized operations within the CordEngine3 framework.
seed - Variable in class edu.cmu.tetrad.search.CordEngine4: Represents the base seed used for various randomization purposes within the CordEngine4 class.
seed - Variable in class edu.cmu.tetrad.search.CordEngine5: The base seed value used as a foundation for random number generation or other seed-based operations.
seed - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Represents the seed value used for initializing random number generation or other operations that require a deterministic starting point.
seed - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: The seed value used for random number generation within the configuration.
seed - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: The seed variable is used to initialize the random number generator, ensuring reproducibility in randomized operations within the configuration or associated processes.
seed - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: Random seed for the GNM simulator.
seed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Seed for the random number generator, used to ensure reproducibility of results.
seed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: The initial seed value used for random number generation or other computational purposes within the application.
seed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Random seed for the simulator.
seed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Random seed for the simulator.
seed() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns the value of the seed record component.
seed(long) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the random seed passed to the permutation search; -1 (the default) means no fixed seed.
SEED - Static variable in class edu.cmu.tetrad.util.Params: Constant SEED="seed"
SEED_ATTRIBUTE - Static variable in class edu.cmu.tetrad.search.MarkovAuditUtils: The graph attribute under which a seed vertex set travels between boxes, as a comma-separated list of node names.
seedIsMarkov() - Method in class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: Determines whether the seed graph satisfies the properties of a Markov structure.
SeedMarkovCheck - Class in edu.cmu.tetrad.search.utils: Harness drop-in: checks whether the graph FCIT starts from (the PAG of the BOSS/GRaSP DAG, i.e.
SeedMarkovCheck.Result - Class in edu.cmu.tetrad.search.utils: Encapsulates the results of evaluating the consistency of a seed graph with the true graph in terms of Markov properties and related graph discrepancies.
seImprovement - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: Standard error of the improvement across folds.
select(Node) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Selects a specified variable in the covariance matrix.
select(Node) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Selects a specified variable in the covariance matrix.
select(Node) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Selects a specified variable in the covariance matrix.
select(Node) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Selects a specified variable in the covariance matrix.
selectByInstability(double) - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Selects by the StARS rule as implemented historically in Tetrad's StARS class: among settings whose adjacency instability is strictly below the cutoff, returns the one with the largest instability; returns null if no setting qualifies.
selectByMarkovAdequacy() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Selects by Markov adequacy: the setting whose Anderson-Darling p-value for the implied independencies is largest (least evidence of asserting independencies the data reject), breaking ties by the larger fraction of implied dependencies detected.
SELECTION - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant SELECTION
SELECTION_MIN_EFFECT - Static variable in class edu.cmu.tetrad.util.Params: Constant SELECTION_MIN_EFFECT="selectionMinEffect"
SelectionGenerator - Class in edu.cmu.tetrad.util: Generates (nonrecursively) all of the selections from a items, where a is a nonnegative integer.
SelectionGenerator(int) - Constructor for class edu.cmu.tetrad.util.SelectionGenerator: Constructs a new selection generator for a items.
selectK(DataSet, int, int, ResidualRegressor, EmUnmix.Config) - Static method in class edu.cmu.tetrad.search.unmix.EmUnmix: Selects the optimal number of clusters (K) for unmixing the provided dataset within the specified range.
selectK(DataSet, int, int, ResidualRegressor, Function, Function, EmUnmix.Config) - Static method in class edu.cmu.tetrad.search.unmix.EmUnmix: Selects the optimal number of clusters (K) for unmixing a dataset within the specified range [Kmin, Kmax].
selectMedianEnsembleGraph(List) - Static method in class edu.cmu.tetrad.util.GraphSampling: Selects, from among the given member graphs (the actual outputs of the bootstrap searches), the single graph whose edges best agree with the ensemble edge-type frequencies, and returns a copy of it annotated with those frequencies.
selectMostStable() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Selects the setting with the smallest adjacency instability, or null if no setting has a computed instability.
SELF_LOOP_COEF - Static variable in class edu.cmu.tetrad.util.Params: Constant SELF_LOOP_COEF="selfLoopCoef"
selfMasking() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns the value of the selfMasking record component.
SEM_BIC_RULE - Static variable in class edu.cmu.tetrad.util.Params: Constant SEM_BIC_RULE="semBicRule"
SEM_BIC_STRUCTURE_PRIOR - Static variable in class edu.cmu.tetrad.util.Params: Constant SEM_BIC_STRUCTURE_PRIOR="semBicStructurePrior"
SEM_GIC_RULE - Static variable in class edu.cmu.tetrad.util.Params: Constant SEM_GIC_RULE="semGicRule"
semBic() - Static method in class edu.cmu.tetrad.sem.DagMetrics: Computes the Linear Gaussian BIC score for a given dataset and DAG.
SemBic - Enum constant in enum class edu.cmu.tetrad.sem.ScoreType: The BIC score
semBicLinearGaussian(DataSet, Graph) - Static method in class edu.cmu.tetrad.sem.DagScores: Computes the structural equation model Bayesian Information Criterion (SEM-BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of linear Gaussian relationships.
semBicLinearGaussian(DataSet, Graph, double) - Static method in class edu.cmu.tetrad.sem.DagScores: Computes the structural equation model Bayesian Information Criterion (SEM-BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of linear Gaussian relationships, with a specified penalty discount factor.
SemBicScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for linear, Gaussian SEM BIC score.
SemBicScore - Class in edu.cmu.tetrad.search.score: Implements the linear, Gaussian BIC score, with a 'penalty discount' multiplier on the BIC penalty.
SemBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.SemBicScore: Constructs a new instance of the SemBicScore.
SemBicScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.score.SemBicScore: Constructs the score using a covariance matrix.
SemBicScore(DataSet, boolean, MissingDataSpec) - Constructor for class edu.cmu.tetrad.search.score.SemBicScore: Constructs the score from a dataset with an explicit missing-data specification.
SemBicScore(DataSet, double, boolean) - Constructor for class edu.cmu.tetrad.search.score.SemBicScore: Constructs the score using a covariance matrix.
SemBicScore(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.SemBicScore: Constructs the score using a covariance matrix.
SemBicScore(ICovarianceMatrix, double) - Constructor for class edu.cmu.tetrad.search.score.SemBicScore: Constructs the score using a covariance matrix.
SemBicScore.CovAndCoefs - Record Class in edu.cmu.tetrad.search.score: Represents a covariance matrix and regression coefficients.
SemBicScore.LikelihoodResult - Record Class in edu.cmu.tetrad.search.score: A record that encapsulates the result of a likelihood computation.
SemBicScore.RuleType - Enum Class in edu.cmu.tetrad.search.score: Gives two options for calculating the BIC score, one describe by Chickering and the other due to Nandy et al.
SemBicScoreDeterministic - Class in edu.cmu.tetrad.search.work_in_progress: Implements the continuous BIC score for FGES.
SemBicScoreDeterministic(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Constructs the score using a covariance matrix.
SemBicScorer - Class in edu.cmu.tetrad.search.score: Scores an entire DAG using the SemBicScore.
SemBicTest - Class in edu.cmu.tetrad.algcomparison.independence: The SemBicTest class implements the IndependenceWrapper interface and represents a test for independence based on SEM BIC algorithm.
SemBicTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.SemBicTest: Constructs a new instance of the SEM BIC test.
SemEstimator - Class in edu.cmu.tetrad.sem: Estimates a SemIm given a CovarianceMatrix and a SemPm.
SemEstimator(DataSet, SemPm) - Constructor for class edu.cmu.tetrad.sem.SemEstimator: Constructs a Sem Estimator that does default estimation.
SemEstimator(DataSet, SemPm, SemOptimizer) - Constructor for class edu.cmu.tetrad.sem.SemEstimator: Constructs a new SemEstimator that uses the specified optimizer.
SemEstimator(ICovarianceMatrix, SemPm) - Constructor for class edu.cmu.tetrad.sem.SemEstimator: Constructs a SEM estimator that does default estimation.
SemEstimator(ICovarianceMatrix, SemPm, SemOptimizer) - Constructor for class edu.cmu.tetrad.sem.SemEstimator: Constructs a new SemEstimator that uses the specified optimizer.
SemEstimatorGibbs - Class in edu.cmu.tetrad.sem: Implements the Gibbs sampler apporach to obtain samples of arbitrary size from the posterior distribution over the freeParameters of a SEM given a continuous dataset and a SemPm.
SemEstimatorGibbs(int, double, double, double, double, SemPm, SemIm, boolean) - Constructor for class edu.cmu.tetrad.sem.SemEstimatorGibbs: Constructor for SemEstimatorGibbs.
SemEstimatorGibbs(SemPm, SemIm, double[][], boolean, double, int) - Constructor for class edu.cmu.tetrad.sem.SemEstimatorGibbs: Constructor for SemEstimatorGibbs.
SemEstimatorGibbsParams - Class in edu.cmu.tetrad.sem: Stores the freeParameters for an instance of a SemEstimatorGibbs.
SemEvidence - Class in edu.cmu.tetrad.sem: Stores information for a SemIm about evidence we have for each variable as well as whether each variable has been manipulated.
SemEvidence(SemEvidence) - Constructor for class edu.cmu.tetrad.sem.SemEvidence: Constructor for SemEvidence.
SemEvidence(SemIm) - Constructor for class edu.cmu.tetrad.sem.SemEvidence: Constructs a container for evidence for the given Bayes IM.
SemGraph - Class in edu.cmu.tetrad.graph: Represents the graphical structure of a structural equation model.
SemGraph() - Constructor for class edu.cmu.tetrad.graph.SemGraph: Constructs a new, empty SemGraph.
SemGraph(Graph) - Constructor for class edu.cmu.tetrad.graph.SemGraph: Constructs a new SemGraph from the nodes and edges of the given graph.
SemGraph(SemGraph) - Constructor for class edu.cmu.tetrad.graph.SemGraph: Copy constructor.
SemidirectedPathF1 - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the F1 statistic for adjacencies.
SemidirectedPathF1() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPathF1: Constructs the statistic.
SemidirectedPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
SemidirectedPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.SemidirectedPrecision: Constructs the statistic.
SemidirectedRecall - Class in edu.cmu.tetrad.algcomparison.statistic: A class implementing the Semidirected-Rec statistic.
SemidirectedRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.SemidirectedRecall: Constructs the statistic.
semigraphoid() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checked whether the IM is a semigraphoid.
SEMIGRAPHOID - Enum constant in enum class edu.cmu.tetrad.search.harness.GraphoidClosureHarness.ClosureType: Semigraphoid closure.
semIm() - Method in record class edu.cmu.tetrad.sem.SemIm.CyclicSimResult: Returns the value of the semIm record component.
SemIm - Class in edu.cmu.tetrad.sem: Stores an instantiated structural equation model (SEM), with error covariance terms, possibly cyclic, suitable for estimation and simulation.
SemIm(SemIm) - Constructor for class edu.cmu.tetrad.sem.SemIm: Copy constructor.
SemIm(SemPm) - Constructor for class edu.cmu.tetrad.sem.SemIm: Constructs a new SEM IM from a SEM PM.
SemIm(SemPm, ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.sem.SemIm: Constructs a SEM model using the given SEM PM and sample covariance matrix.
SemIm(SemPm, SemIm, Parameters) - Constructor for class edu.cmu.tetrad.sem.SemIm: Constructs a new SEM IM from the given SEM PM, using the old SEM IM and params object to guide the choice of parameter values.
SemIm(SemPm, Parameters) - Constructor for class edu.cmu.tetrad.sem.SemIm: Constructs a new SEM IM from the given SEM PM, using the given params object to guide the choice of parameter values.
SemIm(SemPm, List, List, Matrix, double[]) - Constructor for class edu.cmu.tetrad.sem.SemIm: Constructor for SemIm.
SemIm.CyclicSimResult - Record Class in edu.cmu.tetrad.sem: Represents the result of a cyclic simulation.
SemIm.Result - Record Class in edu.cmu.tetrad.sem: Represents the result of a computation or analysis, encapsulating the following: - The shrinkage mode used in an independent test.
semImToLavaan(SemIm) - Static method in class edu.cmu.tetrad.sem.SemLavaanRenderer: Converts a SemIm (parameterized SEM) into lavaan syntax (.lav) using default configurations.
semImToLavaan(SemIm, boolean, boolean, boolean, boolean) - Static method in class edu.cmu.tetrad.sem.SemLavaanRenderer: Converts a SemIm (parameterized Structural Equation Model) into lavaan syntax (.lav) with advanced configuration options.
SemLavaanRenderer - Class in edu.cmu.tetrad.sem: This class provides functionality for rendering a parameterized structural equation model (SEM) into lavaan syntax.
SemManipulation - Class in edu.cmu.tetrad.sem: Stores information for a BayesIm about evidence we have for each variable as well as whether each variable has been manipulated.
SemManipulation(SemIm) - Constructor for class edu.cmu.tetrad.sem.SemManipulation: Constructs a container for evidence for the given Bayes IM.
SemManipulation(SemManipulation) - Constructor for class edu.cmu.tetrad.sem.SemManipulation: Constructor for SemManipulation.
SemOptimizer - Interface in edu.cmu.tetrad.sem: Interface for algorithm that optimize the fitting function of a SemIm model by adjusting its freeParameters in search of a global maximum.
SemOptimizerEm - Class in edu.cmu.tetrad.sem: Optimizes a DAG SEM with hidden variables using expectation-maximization.
SemOptimizerEm() - Constructor for class edu.cmu.tetrad.sem.SemOptimizerEm: Constructor for SemOptimizerEm.
SemOptimizerPowell - Class in edu.cmu.tetrad.sem: Optimizes a SEM using Powell's method from the Apache library.
SemOptimizerPowell() - Constructor for class edu.cmu.tetrad.sem.SemOptimizerPowell: Blank constructor.
SemOptimizerRegression - Class in edu.cmu.tetrad.sem: Optimizes a DAG SEM by regressing each varaible onto its parents using a linear regression.
SemOptimizerRegression() - Constructor for class edu.cmu.tetrad.sem.SemOptimizerRegression: Blank constructor.
SemOptimizerRicf - Class in edu.cmu.tetrad.sem: Optimizes a SEM using RICF for ADMGs (no selection bias).
SemOptimizerRicf() - Constructor for class edu.cmu.tetrad.sem.SemOptimizerRicf: Default constructor for the SemOptimizerRicf class.
SemOptimizerScattershot - Class in edu.cmu.tetrad.sem: Optimizes a SEM by randomly selecting points in cubes of decreasing size about a given point.
SemOptimizerScattershot() - Constructor for class edu.cmu.tetrad.sem.SemOptimizerScattershot: Blank constructor.
SemParameterizer - Class in edu.cmu.tetrad.search.harness.tsc: Converts a graph produced by RandomMim into a parameterised linear Gaussian SEM (SemIm) and simulates data from it.
SemPm - Class in edu.cmu.tetrad.sem: Parametric model for Structural Equation Models.
SemPm(Graph) - Constructor for class edu.cmu.tetrad.sem.SemPm: Constructs a BayesPm from the given Graph, which must be convertible first into a ProtoSemGraph and then into a SemGraph.
SemPm(SemGraph) - Constructor for class edu.cmu.tetrad.sem.SemPm: Constructs a new SemPm from the given SemGraph.
SemPm(SemPm) - Constructor for class edu.cmu.tetrad.sem.SemPm: Copy constructor.
SemProposition - Class in edu.cmu.tetrad.sem: Represents propositions over the variables of a particular BayesIm describing and event of a fairly general sort--namely, conjunctions of propositions that particular variables take on values from a particular disjunctive list of categories.
SemProposition(SemIm) - Constructor for class edu.cmu.tetrad.sem.SemProposition: Creates a new Proposition which allows all values.
SemProposition(SemProposition) - Constructor for class edu.cmu.tetrad.sem.SemProposition: Creates a new SemProposition object by copying the values and semIm from the given proposition.
SemSandwichEstimator - Class in edu.cmu.tetrad.sem: Computes Satorra-Bentler robust ("sandwich") standard errors and the Satorra-Bentler scaled chi-square for an estimated SEM, correcting normal-theory inference for non-normal (e.g., heavy-tailed) data.
SemSandwichEstimator(ISemIm, DataSet) - Constructor for class edu.cmu.tetrad.sem.SemSandwichEstimator: Computes robust standard errors and the scaled chi-square for the given estimated SEM, using the raw data for fourth moments.
SemSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: This class represents a Simulation using Structural Equation Modeling (SEM).
SemSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Constructs a SemSimulation object with the given RandomGraph object.
SemSimulation(SemIm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Constructs a GeneralSemSimulation object with the given SemIm object.
SemSimulation(SemPm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.SemSimulation: Initializes a SemSimulation with the given SemPm object.
SemStartValues - Class in edu.cmu.tetrad.sem: Random starting values for a SEM's free parameters that respect the scale of the sample covariance.
SemStdErrorEstimator - Class in edu.cmu.tetrad.sem: Includes methods for estimating the standard errors of the freeParameters of an estimated SEM.
SemStdErrorEstimator() - Constructor for class edu.cmu.tetrad.sem.SemStdErrorEstimator: Blank constructor.
SemThenDiscretize - Class in edu.cmu.tetrad.algcomparison.simulation: SEM the discretize.
SemThenDiscretize(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Constructor for SemThenDiscretize.
SemThenDiscretize(RandomGraph, DataType) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.SemThenDiscretize: Constructor for SemThenDiscretize.
SemUpdater - Class in edu.cmu.tetrad.sem: Calculates updated structural equation models given evidence of the form X1=x1',...,The main task of such and algorithm is to calculate P(X = x' | evidence), where evidence takes the form of a Proposition over the variables in the Bayes net, possibly with additional information about which variables in the Bayes net have been manipulated.
SemUpdater(SemIm) - Constructor for class edu.cmu.tetrad.sem.SemUpdater: Constructor for SemUpdater.
SemXmlParser - Class in edu.cmu.tetrad.sem: This class takes an xml element representing a SEM semIm and converts it to a SemIM
SemXmlParser() - Constructor for class edu.cmu.tetrad.sem.SemXmlParser: This class represents a parser for SEM XML representation.
SemXmlRenderer - Class in edu.cmu.tetrad.sem: This class converts a SemIm into xml.
SENTINEL_VALUE - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable has a repeated point mass at one extreme of its observed range, separated from the rest of the distribution by a gap far larger than the typical spacing between adjacent observed values elsewhere in the column.
SENTINEL_VALUE_NOTE - Static variable in class edu.cmu.tetrad.data.audit.DataAudit: Cross-reference emitted when SENTINEL_VALUE fires: the check judges the shape of the observed distribution, and only the data's provenance settles whether the flagged value is a code or a measurement.
seOrBoot - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.EffectEstimateResultV1: Represents the bootstrap standard error of the average treatment effect (ATE) estimated using the outcome regression (OR) method.
sepset() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the sepset record component.
sepset() - Method in record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Returns the value of the sepset record component.
sepset() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns the value of the sepset record component.
sepset() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetResult: Returns the value of the sepset record component.
SEPSET_FINDER_METHOD - Static variable in class edu.cmu.tetrad.util.Params: Constant SEPSET_FINDER_METHOD="sepsetFinderMethod"
SepsetComparisonHarness - Class in edu.cmu.tetrad.search.harness: Harness comparing the number of independence tests (d-separation oracle queries) required by two methods to find a separating set for each non-adjacent pair in a random DAG: Exhaustive enumeration (PC-style): subsets of Adj(x) and Adj(y) in greedy depth order. Iterative-deepening recursive blocking (RB): calls blockPathsRecursivelyFull with increasing path-length caps.
SepsetComparisonHarness() - Constructor for class edu.cmu.tetrad.search.harness.SepsetComparisonHarness: Default constructor for the SepsetComparisonHarness class.
SepsetComparisonPagHarness - Class in edu.cmu.tetrad.search.harness: Harness measuring the number of independence tests (m-separation oracle queries) required by iterative-deepening recursive blocking to find a separating set for each sampled non-adjacent pair in a random PAG.
SepsetComparisonPagHarness() - Constructor for class edu.cmu.tetrad.search.harness.SepsetComparisonPagHarness: Default constructor for the SepsetComparisonPagHarness class.
SepsetFinder - Class in edu.cmu.tetrad.search: This class provides methods for finding sepsets in a given graph.
SepsetFinder() - Constructor for class edu.cmu.tetrad.search.SepsetFinder: Private constructor to prevent instantiation.
SepsetMap - Class in edu.cmu.tetrad.search.utils: Stores a map from pairs of nodes to separating sets--that is, for each unordered pair of nodes {node1, node2} in a graph, stores a set of nodes conditional on which node1 and node2 are independent (where the nodes are considered as variables) or stores null if the pair was not judged to be independent.
SepsetMap() - Constructor for class edu.cmu.tetrad.search.utils.SepsetMap: Constructor.
SepsetMap(SepsetMap) - Constructor for class edu.cmu.tetrad.search.utils.SepsetMap: Copy constructor.
SepsetMapDci - Class in edu.cmu.tetrad.search.work_in_progress: This is the same as the usual SepsetMap described below, but also keeps up with the individual sets of conditioning nodes for d-separation relations for use with the Distributed Causal Inference (DCI) algorithm.
SepsetMapDci() - Constructor for class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: Constructor for SepsetMapDci.
SepsetMapDci(SepsetMapDci) - Constructor for class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: Constructor for SepsetMapDci.
SepsetProducer - Interface in edu.cmu.tetrad.search.utils: Provides a covenience interface for classes that can generate and keep track of sepsets.
SepsetResult(Set, Double) - Constructor for record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetResult: Creates an instance of a SepsetResult record class.
SEPSETS - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.ColliderStyle: Represents the Sepsets collider style in the graph learning process.
SEPSETS - Enum constant in enum class edu.cmu.tetrad.search.Cdnod.ColliderOrientationStyle: Represents a collider orientation strategy in causal discovery where separation sets (also known as sepsets) are used to identify and orient colliders in a causal graph.
SEPSETS - Enum constant in enum class edu.cmu.tetrad.search.CdnodBoss.ColliderOrientationStyle: Orient based on separating sets derived from the data.
SEPSETS - Enum constant in enum class edu.cmu.tetrad.search.Fci.ColliderRule: Indicates that separation sets (or conditional independence sets) are to be used for handling colliders.
SEPSETS - Enum constant in enum class edu.cmu.tetrad.search.Pc.ColliderOrientationStyle: Represents the collider orientation style that utilizes separating sets (sepsets).
SEPSETS - Enum constant in enum class edu.cmu.tetrad.search.PcAR.ColliderOrientationStyle: Original PC: orient x -> z <- y iff z is not in the separating set FAS recorded for (x, y).
SepsetSearch(FcitSepsets.SepsetResult, boolean) - Constructor for record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: Creates an instance of a SepsetSearch record class.
SepsetsGreedy - Class in edu.cmu.tetrad.search.utils: Provides a SepsetProducer that selects the first sepset it comes to from among the extra sepsets or the adjacents of i or k, or null if none is found.
SepsetsGreedy(Graph, IndependenceTest, int) - Constructor for class edu.cmu.tetrad.search.utils.SepsetsGreedy: Constructor for Sepsets.
SepsetsGreedyMb - Class in edu.cmu.tetrad.search.utils: Provides a SepsetProducer that selects the first sepset it comes to from among the extra sepsets or the adjacents of i or k, or null if none is found.
SepsetsGreedyMb(Graph, Graph, IndependenceTest, int) - Constructor for class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Constructor for Sepsets.
SepsetsMaxP - Class in edu.cmu.tetrad.search.utils: The class SepsetsMaxP implements the SepsetProducer interface and provides methods for generating sepsets based on a given graph and an independence test.
SepsetsMaxP(Graph, IndependenceTest, int) - Constructor for class edu.cmu.tetrad.search.utils.SepsetsMaxP: Constructs a SepsetsMaxP object with the given graph, independence test, and depth.
SepsetsMinP - Class in edu.cmu.tetrad.search.utils: The SepsetsMinP class is a concrete implementation of the SepsetProducer interface.
SepsetsMinP(Graph, IndependenceTest, int) - Constructor for class edu.cmu.tetrad.search.utils.SepsetsMinP: Initializes a new instance of the SepsetsMinP class.
SepsetsPossibleDsep - Class in edu.cmu.tetrad.search.utils: Provides a sepset producer using conditional independence tests to generate the Sepset map, for the case where possible dsep sets are required.
SepsetsPossibleDsep(Graph, IndependenceTest, Knowledge, int, int) - Constructor for class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Constructor for SepsetsPossibleDsep.
SepsetsSet - Class in edu.cmu.tetrad.search.utils: Provides a sepset producer using conditional independence tests to generate the Sepset map.
SepsetsSet(SepsetMap, IndependenceTest) - Constructor for class edu.cmu.tetrad.search.utils.SepsetsSet: Constructor for SepsetsSet.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
sepsetSubsetOfAdjxOrAdjy(Graph, Node, Node, Set, IndependenceTest, int, List, boolean) - Static method in class edu.cmu.tetrad.search.GfciOld: Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
SERIAL_DEPENDENCE - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A continuous variable is serially dependent in file order (autocorrelated across consecutive rows), so rows are not exchangeable as given and independence tests that assume i.i.d.
SERIAL_DEPENDENCE_NOTE - Static variable in class edu.cmu.tetrad.data.audit.DataAudit: The note attached by DataAudit.notes() when at least one FindingCode.SERIAL_DEPENDENCE finding fired and the check was computed pooled, without a serial grouping variable.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Returns a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.BayesImProbs: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.BayesPm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.Evidence: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.Identifiability: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Creates and returns a serializable instance of JunctionTreeUpdater.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.Manipulation: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.MlBayesIm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.MlBayesImObs: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.Proposition: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.StoredCellProbs: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Creates and returns a serializable instance of VariableElimiinationUpdater.
serializableInstance() - Static method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: serializableInstance.
serializableInstance() - Static method in class edu.cmu.tetrad.calculator.expression.EvaluationExpression: serializableInstance.
serializableInstance() - Static method in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: serializableInstance.
serializableInstance() - Static method in class edu.cmu.tetrad.calculator.expression.VariableExpression: serializableInstance.
serializableInstance() - Static method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.BoxDataSet: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.ByteDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.ContinuousVariable: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.CorrelationMatrix: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.CovarianceMatrix: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.DataModelList: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.DelimiterType: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.DiscreteDiscretizationSpec: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.DiscreteVariable: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.DiscreteVariableType: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.DoubleDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.FloatDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.IndependenceFacts: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.IntDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.Knowledge: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.KnowledgeEdge: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.KnowledgeGroup: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.LongDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.MixedDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.NumberObjectDataSet: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.ShortDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.SplitCasesSpec: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.TimeSeriesData: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.data.VerticalIntDataBox: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.Dag: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.Edge: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.EdgeListGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.GraphNode: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.IndependenceFact: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.LagGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.SemGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.TimeLagGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.graph.Triple: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.regression.LogisticRegression.Result: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.regression.LogisticRegression: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.regression.RegressionResult: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.score.ScoredGraph: Returns a serializable instance of this class.
serializableInstance() - Static method in class edu.cmu.tetrad.search.test.IndependenceResult: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: Returns the serializable instance of the algorithm type.
serializableInstance() - Static method in enum class edu.cmu.tetrad.search.utils.BpcTestType: Returns the serializable instance of the test type.
serializableInstance() - Static method in class edu.cmu.tetrad.search.utils.DeltaSextadTest: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.utils.DeltaTetradTest2: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.utils.SepsetMap: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.utils.Sextad: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.utils.Tetrad: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.search.work_in_progress.Sextad: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.DagScorer: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.Mapping: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.ParamConstraint: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.Parameter: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.ParameterPair: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemEstimator: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemEvidence: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemIm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemManipulation: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemOptimizerEm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemOptimizerPowell: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemOptimizerRegression: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Creates and returns a new instance of the SemOptimizerRicf class that is serializable.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemOptimizerScattershot: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemPm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemProposition: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.SemUpdater: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.sem.StandardizedSemIm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: serializableInstance.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraphParams: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.StoredLagGraphParams: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasalInitializer: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedParent: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedEdge: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig.DefaultEvent: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Beta: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.ChiSquare: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Discrete: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Exponential: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Gamma: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.GaussianPower: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Indicator: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.LogNormal: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Normal: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Poisson: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.SingleValue: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Split: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.TruncatedNormal: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.dist.Uniform: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.Matrix: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.Parameters: serializableInstance.
serializableInstance() - Static method in class edu.cmu.tetrad.util.PartialCorrelationPdf: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.PointXy: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.Vector: Generates a simple exemplar of this class to test serialization.
serializableInstance() - Static method in class edu.cmu.tetrad.util.Version: Generates a simple exemplar of this class to test serialization.
serializeCurrentDirectory() - Method in class edu.cmu.tetrad.util.TetradSerializableUtils: Finds all classes inside the stated scope that implement TetradSerializable and serializes them out to the getCurrentDirectory() directory.
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.graph.RandomGraph: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.independence.BlockIndependenceWrapper: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.independence.IndependenceWrapper: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.score.ScoreWrapper: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.simulation.Simulation: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.calculator.expression.Expression: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.calculator.expression.ExpressionDescriptor: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.calculator.expression.ExpressionSignature: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.Covariances: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.DataBox: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.DataModel: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.DataSet: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.KnowledgeTransferable: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.Simulator: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.data.Variable: Required serial version UID for serialization.
serialVersionUID - Static variable in interface edu.cmu.tetrad.graph.Graph: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.graph.Node: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.search.ModelObserver: The serial version UID.
serialVersionUID - Static variable in interface edu.cmu.tetrad.sem.ISemIm: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.sem.SemOptimizer: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.GraphRandomizer: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.Initializer: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.RandomDistribution: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.util.dist.Distribution: Constant serialVersionUID=23L
serialVersionUID - Static variable in interface edu.cmu.tetrad.util.Im: Constant serialVersionUID=23L
Server script resolution - Section in class edu.cmu.tetrad.algcomparison.independence.ClFisherZPython
Server script resolution - Section in class edu.cmu.tetrad.algcomparison.independence.ClKciPython
Server script resolution - Section in class edu.cmu.tetrad.algcomparison.independence.ClRcitPython
SESSION - Enum constant in enum class edu.cmu.tetrad.graph.NodeType: Constant SESSION
set(double) - Method in class edu.cmu.tetrad.util.MView: Sets all elements in the specified rows and columns of the matrix view to the given value.
set(double[]) - Method in class edu.cmu.tetrad.util.MView: Sets all elements in the current matrix view to the corresponding values from the given matrix.
set(double[][]) - Method in class edu.cmu.tetrad.util.MView: Updates the matrix view with the values from the specified 2D array.
set(int, double) - Method in class edu.cmu.tetrad.util.Vector: set.
set(int, int, double) - Method in class edu.cmu.tetrad.bayes.CptMapProbs: Sets the probability of the node taking on the value specified by the given row and column to the given value.
set(int, int, double) - Method in class edu.cmu.tetrad.util.Matrix: set.
set(int, int, double) - Method in class edu.cmu.tetrad.util.MView: Updates the value at a specified position in the matrix view.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.ByteDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in interface edu.cmu.tetrad.data.DataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.DoubleDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.FloatDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.IntDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.LongDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.MixedDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.ShortDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: Sets the value at the given row and column to the given Number.
set(int, int, Number) - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: Sets the value at the given row and column to the given Number.
set(Node, Node, Set) - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Sets the sepset for {x, y} to be z.
set(Node, Node, Set) - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: Sets the sepset for {x, y} to be z.
set(Node, LinkedHashSet) - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Sets the parents of x to the (ordered) set z.
set(Node, LinkedHashSet) - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: set.
set(Matrix) - Method in class edu.cmu.tetrad.util.MView: Sets the values of the current matrix view to match the provided matrix.
set(MView) - Method in class edu.cmu.tetrad.util.MView: Sets all elements in the current matrix view to the corresponding values from the given matrix view.
set(String, Object) - Method in class edu.cmu.tetrad.util.Parameters: Sets the given parameter to the given value.
set(String, Object...) - Method in class edu.cmu.tetrad.util.Parameters: Sets the value(s) of the given parameter to a list of strings.
set(String, String) - Method in class edu.cmu.tetrad.util.Parameters: Sets the given parameter to the given value.
set(String, String...) - Method in class edu.cmu.tetrad.util.Parameters: Sets the value(s) of the given parameter to a list of values.
setAcceptTies(boolean) - Method in class edu.cmu.tetrad.search.Flop: Sets whether the grow and shrink phases accept a candidate whose local score ties the incumbent, rather than requiring strict improvement.
setActivationFunction(Function) - Method in class edu.cmu.tetrad.sem.GeneralAdditiveModel: Sets the activation function for the generalized additive model.
setActivationFunction(Function) - Method in class edu.cmu.tetrad.sem.PostnonlinearSem: Sets the activation function used in the model.
setActiveNonSinkSet(Set) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Replaces the active non-sink set.
setActiveSet(Set) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Replaces the active set (used by the unfolding step of the cluster search).
setAdaptiveGamma(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the value of the adaptive gamma parameter used in the two-step regression process.
setAdjacencies(Graph) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets the set of preset adjacenies for the algorithm; edges not in this adjacencies graph will not be added.
setAdjacencyMethod(FaskOrig.AdjacencyMethod) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the adjacency method used.
setAdjacencyMethod(PooledAdjacencySearch.Method) - Method in class edu.cmu.tetrad.search.FaskPool: Sets how the common adjacency structure is obtained when no external graph is set.
setAdjacencyMethod(PooledAdjacencySearch.Method) - Method in class edu.cmu.tetrad.search.FaskVote: Sets how the common adjacency structure is obtained.
setAdjacencyTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Ccd: Sets a separate independence test for the adjacency phase (Step A), leaving the orientation steps on the main test.
setAffectedOnlyInvalidation(boolean) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: When true, VertexRepairSearch.RepairStrategy.GLOBAL_QUEUE recomputes candidate lists after each applied edit only for the vertices plausibly affected by that edit (endpoints of every edge changed between the before/after graphs — including changes propagated by CPDAG canonicalization — closed under one hop of adjacency, plus the edited node), rather than for every vertex in the graph.
setAggregation(String, LongToWide.Aggregation) - Method in class edu.cmu.tetrad.data.LongToWide: Sets the aggregation used for a value variable whose (unit, level) combinations are duplicated.
setAlgorithm(ComparisonParameters.Algorithm) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field algorithm.
setAlgorithmType(int) - Method in class edu.cmu.tetrad.search.FastIca: Sets the algorithm type to be used in the FastICA computation.
setAll(HybridCgModel.HybridCgPm, DataSet, int, HybridCgCutpoints.Method) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgCutpoints: For every DISCRETE child Y with ≥1 continuous parent, computes and installs cutpoints for each continuous parent using the chosen method and desired number of bins.
setAllowAdditions(boolean) - Method in class edu.cmu.tetrad.search.Gmas: Sets whether addition moves are considered.
setAllowBidirected(Pc.AllowBidirected) - Method in class edu.cmu.tetrad.search.Pc: Sets the allowance for bidirected edges in the structure being analyzed.
setAllowBidirected(PcAR.AllowBidirected) - Method in class edu.cmu.tetrad.search.PcAR: Sets the allowance for bidirected edges in the structure being analyzed.
setAllowClassEscape(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether pass 3 (certified out-of-class seeds) may run; see FcitSl.seedMags(Graph, Node, Node, Set, long, boolean).
setAllowClassEscape(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether pass 3 (certified out-of-class seeds) may run; see FcitSl2.seedMags(Graph, Node, Node, Set, long, boolean).
setAllowClassEscape(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether pass 3 (certified out-of-class seeds) may run; see FcitSlKeepKnowledgeOrientations.seedMags(Graph, Node, Node, Set, long, boolean).
setAllowClassEscape(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether pass 3 (certified out-of-class seeds) may run; see FcitSlScoreCheck.seedMags(Graph, Node, Node, Set, long, boolean).
setAllowedCategories(Proposition) - Method in class edu.cmu.tetrad.bayes.JunctionTreeInference: Sets the allowed categories for variables in the Bayesian network by applying the given Proposition.
setAllowedCategories(Proposition) - Method in class edu.cmu.tetrad.bayes.VariableEliminationInference: Soft evidence: set allowed categories for each variable.
setAllowedValues(List) - Method in class edu.cmu.tetrad.util.ParamDescription: Sets the legal values for an enumerated String parameter.
setAllowInternalRandomness(boolean) - Method in class edu.cmu.tetrad.search.Grasp: Sets whether to allow internal randomness in the algorithm.
setAllowMultipleLatentParents(boolean) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets whether one measured input may attach to multiple latents.
setAllowNonLeafX(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Whether observed variables may act as non-sinks (parents conditioned on in the rank tests).
setAllowOneToMany(boolean) - Method in class edu.cmu.tetrad.data.JoinDatasets: Sets whether a key value may occur in more than one right row (default false).
setAllowRandomnessInsideAlgorithm(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field allowRandomnessInsideAlgorithm.
setAllValuesToZero() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Assign zero to all elements in the matrix
setAllValuesToZero() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Assign zero to all elements in the matrix
setAllValuesToZero() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Assign zero to all elements in the matrix
setAllValuesToZero() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Assign zero to all elements in the matrix
setAllValuesToZero() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Assign zero to all elements in the matrix
setAlpha(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: Setter for the field alpha.
setAlpha(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: Setter for the field alpha.
setAlpha(double) - Method in class edu.cmu.tetrad.regression.LogisticRegression: Sets the alpha level.
setAlpha(double) - Method in interface edu.cmu.tetrad.regression.Regression: Sets the significance level at which coefficients are judged to be significant.
setAlpha(double) - Method in class edu.cmu.tetrad.regression.RegressionCovariance: Sets the significance level at which coefficients are judged to be significant.
setAlpha(double) - Method in class edu.cmu.tetrad.regression.RegressionDataset: Sets the significance level at which coefficients are judged to be significant.
setAlpha(double) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Deprecated.
Use Cdnod.Builder.alpha(double) instead.
setAlpha(double) - Method in class edu.cmu.tetrad.search.FastIca: Sets the alpha parameter for the FastICA algorithm.
setAlpha(double) - Method in class edu.cmu.tetrad.search.IndTestIod: Sets the alpha level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Sets the alpha value used for threshold-based decisions in this object.
setAlpha(double) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Sets the alpha value used in the model computation.
setAlpha(double) - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

The alpha to use.
setAlpha(double) - Method in class edu.cmu.tetrad.search.Pairwise: Sets the alpha to use, where applicable.
setAlpha(double) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the significance level used for all rank tests (default 0.01, as in causal-learn).
setAlpha(double) - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Sets the alpha value for this instance.
setAlpha(double) - Method in interface edu.cmu.tetrad.search.score.InstanceSpecificScore: Sets the instance-specific weight alpha.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Sets the alpha of the base test and clears the cache, since cached independence judgments were made under the previous alpha and would otherwise be returned inconsistently.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: Sets the significance level to be used for tests.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Not supported.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the cutoff for judging independence.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the alpha value to control transparency or blending levels.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the significance level (alpha) used for statistical testing.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.Gcm
setAlpha(double) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Sets the alpha value used as a threshold or parameter for statistical tests or computations.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Sets the significance level at which independence judgments should be made.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Sets the significance level for the test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the significance level, validated to lie in [0, 1].
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the significance level, validated to lie in [0, 1].
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Sets the significance level (alpha) for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Sets the one-sided level of each of the two tests.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Sets the alpha significance cutoff value.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Sets the alpha significance cutoff value.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestGin: Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Sets the significance level at which independence judgments should be made.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Sets the alpha value used for checking independence.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Sets the alpha level on every component test and clears the cache.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Sets the significance level of the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Sets the alpha parameter for the probabilistic test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets the significance level (alpha) for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the value of the alpha threshold, which is typically used for statistical testing to determine the significance level or rejection criteria.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Sets the significance level (alpha) used by the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the significance level (alpha) for statistical testing in this object.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.MsepTest: Sets the alpha level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Sets the significance level (alpha) used in conditional independence tests.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Sets the significance level (alpha) used in conditional independence tests.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the value of alpha, which must be within the range (0, 1).
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Sets the significance level (alpha) for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Sets the significance level (alpha) used for determining independence.
setAlpha(double) - Method in class edu.cmu.tetrad.search.Tsc: Sets the significance level alpha used in statistical computations.
setAlpha(double) - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: setAlpha.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: setAlpha.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: setAlpha.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Sets the significance level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Set the alpha level for the significance of the test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Sets the alpha level for the independence test.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Sets the significance level at which independence judgments should be made.
setAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Sets the significance level.
setAlpha(double) - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
setAlpha(double) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field alpha.
setAlpha(double) - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Sets the significance level of the independence test.
setAlphaByRank(double[]) - Method in class edu.cmu.tetrad.search.Rlcd: Optionally sets a different significance level for each hypothesized rank: entry k is used when testing rank ≤ k.
setAlphaMu(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Sets the prior precision on the mean; must be positive.
setAlphaMu(double) - Method in class edu.cmu.tetrad.search.score.BgeScore: Sets the prior precision on the mean; must be positive.
setAlphaPC(double) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field alphaPC.
setAlphaSober(double) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field alphaSober.
setAlphaWOffset(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Sets the Wishart degrees-of-freedom offset; must exceed 1.
setAlphaWOffset(double) - Method in class edu.cmu.tetrad.search.score.BgeScore: Sets the Wishart degrees of freedom offset above the number of variables; must exceed 1 so that the prior scale t = alpha_mu (alpha_w - p - 1) / (alpha_mu + 1) is positive.
setAmbiguousTriples(Set) - Method in class edu.cmu.tetrad.graph.Dag: Sets the ambiguous triples for the object.
setAmbiguousTriples(Set) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: setAmbiguousTriples.
setAmbiguousTriples(Set) - Method in interface edu.cmu.tetrad.graph.Graph: setAmbiguousTriples.
setAmbiguousTriples(Set) - Method in class edu.cmu.tetrad.graph.LagGraph: setAmbiguousTriples.
setAmbiguousTriples(Set) - Method in class edu.cmu.tetrad.graph.SemGraph: setAmbiguousTriples.
setAmbiguousTriples(Set) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Sets the set of ambiguous triples.
setAmbiguousTriples(Set) - Method in class edu.cmu.tetrad.graph.Underlines: Setter for the field ambiguousTriples.
setAncillaryGraph(String, Graph) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Sets an ancillary graph with the given name.
setAnnealStartT(double) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the starting temperature for annealing.
setAnnealSteps(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the number of annealing steps.
setAnnotation(String) - Method in class edu.cmu.tetrad.graph.Edge: Sets an annotation to be shown in the workbench tooltip for this edge, or null for none.
setAntilogCalculated(boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets whether the antilog of each expression level should be calculated.
setAntilogCalculated(boolean) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setAntilogCalculated.
setApplyR1(boolean) - Method in class edu.cmu.tetrad.search.BossCcd: Sets whether CCD's R1 push-away rule should be applied.
setApplyR1(boolean) - Method in class edu.cmu.tetrad.search.Ccd: Sets whether the R1 orientation rule should be applied during the search process.
setApprox(FfCiContinuous.Approx) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the approximation object and ensures it is not null.
setApproximate(boolean) - Method in class edu.cmu.tetrad.search.test.Kci: Sets whether the method should use an approximate approach or a permutation test.
setApproximation(FfCiContinuous.Approx) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the approximation method to be used in statistical computations within the mixed independence test.
setApproximation(FfCiContinuous.Approx) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the approximation method to be used in statistical computations within the mixed independence test.
setApproximation(Rcit.Approx) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the approximation object to be used.
setAtomic(boolean) - Method in class edu.cmu.tetrad.search.rlcd.Cover: Sets the atomic flag.
setAuditLegalityMemo(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Turns the legality memo into an audit: hits are recomputed and mismatches logged, verifying that reorientation is deterministic given (skeleton, sepsets).
setAuditLegalityMemo(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Turns the legality memo into an audit: hits are recomputed and mismatches logged, verifying that reorientation is deterministic given (skeleton, sepsets).
setAuditLegalityMemo(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Turns the legality memo into an audit: hits are recomputed and mismatches logged, verifying that reorientation is deterministic given (skeleton, sepsets).
setAutomaticLogDisplayEnabled(boolean) - Method in class edu.cmu.tetrad.util.TetradLogger: States whether log displays should be automatically displayed or not.
setAutoSelectK(boolean) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets whether to enable or disable the automatic selection of the best value of K.
setBackend(IndTestGin.UncondIndTest) - Method in class edu.cmu.tetrad.search.test.IndTestGin: Sets the backend to be used for the independence test.
setBandwidthMode(KcvBicScore.BandwidthMode) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the bandwidth estimation mode.
setBandwidthMode(RffBicScore.BandwidthMode) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the bandwidth mode used for computations.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the value of the bandwidth multiplier parameter in the current instance.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the value of the bandwidth multiplier parameter in the current instance.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Sets the value of the bandwidth multiplier parameter in the current instance.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Sets the multiplier used to adjust the bandwidth in calculations.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Sets the bandwidth multiplier and forwards it to the continuous engine.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Sets the bandwidth multiplier parameter for the Kernel Marginal Likelihood Score.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.test.FfCi: Updates the bandwidth multiplier used for computations.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.test.FfCi1: Updates the bandwidth multiplier used for computations.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the bandwidth multiplier that is used to adjust the bandwidth during calculations.
setBandwidthMultiplier(double) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the bandwidth multiplier, which is used to scale the bandwidth allocation.
setBasalExpression(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Sets the basalExpression.
setBaseSeed(long) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the base seed value for the random generator or algorithm and resets any associated cached data.
setBatteryZMax(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the maximum conditioning-set size for the deleted-pair battery.
setBatteryZMax(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the maximum conditioning-set size for the deleted-pair battery.
setBatteryZMax(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the maximum conditioning-set size for the deleted-pair battery.
setBatteryZMax(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the maximum conditioning-set size for the deleted-pair battery.
setBeamWidth(int) - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: setBeamWidth.
setBeamWidth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: setBeamWidth.
setBeamWidth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: setBeamWidth.
setBeta(double) - Method in class edu.cmu.tetrad.algcomparison.statistic.FBetaAdj: Setter for the field beta.
setBetaHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field betaHigh.
setBetaLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field betaLow.
setBinsPerContXY(int) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the number of bins per container along the X and Y axes.
setBinsPerContZ(int) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the number of quantile bins per continuous Z variable, used only when adaptive Z binning is disabled (see MinimaxCITest.setUseAdaptiveZBins(boolean)).
setBlockingType(R0R4StrategyTestBased.BlockingType) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the blocking type for the strategy.
setBlocks(int[]) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Dependent wild bootstrap, cluster form: switches the check to the wild cluster bootstrap-t of WildBootstrapMarkovCheck.runEngineBlocks(double[][], String[], int, long, WildBootstrapMarkovCheck.Multiplier, int[]).
setBlockSpec(BlockSpec) - Method in interface edu.cmu.tetrad.algcomparison.algorithm.ExtraLatentStructureAlgorithm: Sets the block specification for configuring the algorithm.
setBlockSpec(BlockSpec) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildBollen: Sets the block specification to be used by the MimbuildBollen algorithm.
setBlockSpec(BlockSpec) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.MimbuildPca: Sets the block specification for the current instance.
setBlockSpec(BlockSpec) - Method in interface edu.cmu.tetrad.algcomparison.independence.BlockIndependenceWrapper: Configures the block specification for the independence test.
setBlockSpec(BlockSpec) - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTest: Deprecated.

Sets the BlockSpec for this instance.
setBlockSpec(BlockSpec) - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestLemma10: Deprecated.

Sets the specification for block structures required in the "Blocks Test Lemma 10" independence test implementation.
setBlockSpec(BlockSpec) - Method in class edu.cmu.tetrad.algcomparison.independence.BlocksIndTestTs: Sets the specification for block structures required in the "Blocks Test Lemma 10" independence test implementation.
setBlockSpec(BlockSpec) - Method in class edu.cmu.tetrad.algcomparison.score.BlocksBicScore: Sets the block specification to define the structure for organizing variables into predefined blocks for analysis.
setBlockSpec(BlockSpec) - Method in interface edu.cmu.tetrad.algcomparison.score.BlockScoreWrapper: Sets the block specification to be used by this implementation.
setBollenTest(DeltaTetradTest2) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: setBollenTest.
setBooleanInfluenceRate(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Sets the rate at which the output of the Glass function influences the change in expression level of a gene.
setBossPenaltyDiscount(double) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the BOSS penalty discount value for this instance of UnmixSpec.
setBossUseBes(boolean) - Method in class edu.cmu.tetrad.search.Bfci: Sets whether the BES should be used.
setBossUseBes(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether BOSS should use BES.
setBossUseBes(boolean) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets whether BOSS should use BES.
setBossUseBes(boolean) - Method in class edu.cmu.tetrad.search.BfciC: Sets whether the BES should be used.
setBossUseBes(boolean) - Method in class edu.cmu.tetrad.search.BfciD: Sets whether the BES should be used.
setBossUseBes(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether the BES should be used in BOSS.
setBoundGraph(Graph) - Method in class edu.cmu.tetrad.search.Fges: If non-null, edges not adjacent in this graph will not be added.
setBoundGraph(Graph) - Method in class edu.cmu.tetrad.search.FgesMb: If non-null, edges not adjacent in this graph will not be added.
setBoundsEnforced(boolean) - Method in class edu.cmu.tetrad.bayes.DirichletDataSetProbs: True iff bounds checking is performed on variable values indices.
setBreakTwoCyclesEnabled(boolean) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Enables or disables the breaking of two-cycle constraints in the processing.
setBThreshold(double) - Method in class edu.cmu.tetrad.search.Lingam: Threshold for |B_ij| -> 0 before trimming.
setBThreshold(double) - Method in class edu.cmu.tetrad.search.LingD: Sets the threshold value for the `bThreshold` field.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the maximum number of rows to consider for bandwidth estimation in the current instance.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the maximum number of rows to consider for bandwidth estimation in the current instance.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the maximum number of rows allowed for a given operation, ensuring a minimum value of 50.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Sets the maximum number of rows to consider for bandwidth estimation in the current instance.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Sets the maximum number of rows for bandwidth estimation (min 50) and forwards it.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Sets the maximum number of rows to be used in bandwidth calculations.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the maximum number of rows to be considered during bandwidth computations.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the maximum number of rows to be considered during bandwidth computations.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the maximum number of rows for the bandwidth resource.
setBwMaxRows(int) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the maximum number of rows for the bandwidth calculations.
setC(int) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field c.
setCacheDepthLimit(int) - Method in class edu.cmu.tetrad.search.utils.AdTree: Sets the cache depth limit.
setCacheDepthLimit(int) - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Sets the cache depth limit.
setCacheDepthLimit(int) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Sets the maximum depth of the cache.
setCalibratePValues(boolean) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Sets whether this test reports a calibrated p-value in the p-value slot of its results, in place of the raw local score difference.
setCanceled(boolean) - Method in class edu.cmu.tetrad.util.TaskManager: Setter for the field canceled.
setCategories(Node, List) - Method in class edu.cmu.tetrad.bayes.BayesPm: Sets the number of values for the given node to the given number.
setCategory(int, int) - Method in class edu.cmu.tetrad.bayes.Proposition: Sets the given value to true and all other values to false for the given variable.
setCategoryNamesDisplayed(boolean) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Sets whether categories for this variable should be displayed.
setCatRho(double) - Method in class edu.cmu.tetrad.search.score.FfMl: Set categorical similarity rho in [0,1).
setCatRho(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Sets the categorical similarity rho in [0, 1).
setCatRho(double) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the categorical feature correlation coefficient (catRho) for the mixed independence test.
setCatRho(double) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the categorical feature correlation coefficient (catRho) for the mixed independence test.
setCellProbability(int[], double) - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: setCellProbability.
setCellTableType(ChiSquareTest.CellTableType) - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: Sets the type of cell table to use.
setCellTableType(ChiSquareTest.CellTableType) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Sets the cell table type.
setCellTableType(ChiSquareTest.CellTableType) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Sets the cell table type.
setCensoredCovariance(CensoredMvnCovarianceEstimator, MissingDataSpec.EffectiveSampleSizeMode) - Method in class edu.cmu.tetrad.search.Rlcd: Runs both stages on a covariance matrix estimated with censored values treated as censored -- a grade at the top of its scale is at least that, not missing and not exactly that -- by CensoredMvnCovarianceEstimator.
setCenter(double) - Method in class edu.cmu.tetrad.util.TaylorSeries: Sets the center point of the Taylor series.
setCenter(int, int) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Sets the (x, y) coordinates of the center of this node.
setCenter(int, int) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Sets the (x, y) coordinates of the center of this node.
setCenter(int, int) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets the (x, y) coordinates of the center of this node.
setCenter(int, int) - Method in interface edu.cmu.tetrad.graph.Node: Sets the (x, y) coordinates of the center of this node.
setCenterData(boolean) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the centerData flag to determine if data should be centered, and clears the cache.
setCenterFeatures(boolean) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets whether feature centering is enabled.
setCenterFeatures(boolean) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets whether the features should be centered.
setCenterFeatures(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets whether the features should be centered for certain calculations.
setCenteringComp(JComponent) - Static method in class edu.cmu.tetrad.util.JOptionUtils: Sets the centering component used throughout.
setCenterX(int) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Sets the x coordinate of the center of this node.
setCenterX(int) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Sets the x coordinate of the center of this node.
setCenterX(int) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets the x coordinate of the center of this node.
setCenterX(int) - Method in interface edu.cmu.tetrad.graph.Node: Sets the x coordinate of the center of this node.
setCenterY(int) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Sets the y coordinate of the center of this node.
setCenterY(int) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Sets the y coordinate of the center of this node.
setCenterY(int) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets the y coordinate of the center of this node.
setCenterY(int) - Method in interface edu.cmu.tetrad.graph.Node: Sets the y coordinate of the center of this node.
setChannels(FfCordContinuous.Channel...) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the response channels summed into each local score.
setChannels(FfDmlContinuous.Channel...) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the response channels summed into each local score.
setChannelWeights(double...) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the per-channel weights.
setChannelWeights(double...) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the per-channel weights.
setCheckType(ClusterSignificance.CheckType) - Method in class edu.cmu.tetrad.search.utils.ClusterSignificance: Sets the type of check to perform.
setCheckV(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Whether a rank-deficient set is discarded when one of its proper subsets is already rank deficient at the corresponding lower rank.
setChipChipVariability(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the chip to chip variability.
setChipChipVariability(double) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setChipChipVariability.
setCholJitter(double) - Method in class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Getter for cholJitter.
setCholRetries(int) - Method in class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Sets the maximum number of retries allowed for the Cholesky decomposition process.
setClassWalkBestFirst(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the class walk's ordering: true (default) for best-first on stamp deficit, false for plain breadth-first.
setClassWalkBestFirst(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the class walk's ordering: true (default) for best-first on stamp deficit, false for plain breadth-first.
setClassWalkBestFirst(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the class walk's ordering: true (default) for best-first on stamp deficit, false for plain breadth-first.
setClassWalkBestFirst(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the class walk's ordering: true (default) for best-first on stamp deficit, false for plain breadth-first.
setClassWalkMaxCandidates(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the per-deletion cap on class members the walk may emit; negative means uncapped.
setClassWalkMaxCandidates(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the per-deletion cap on class members the walk may emit; negative means uncapped.
setClassWalkMaxCandidates(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the per-deletion cap on class members the walk may emit; negative means uncapped.
setClassWalkMaxCandidates(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the per-deletion cap on class members the walk may emit; negative means uncapped.
setClosureAutoBudgetCap(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the ceiling on the AUTO move budget; see FcitSl.maxCoverMoves.
setClosureAutoBudgetCap(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the ceiling on the AUTO move budget; see FcitSl2.maxCoverMoves.
setClosureAutoBudgetCap(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the ceiling on the AUTO move budget; see FcitSlKeepKnowledgeOrientations.maxCoverMoves.
setClosureAutoBudgetCap(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the ceiling on the AUTO move budget; see FcitSlScoreCheck.maxCoverMoves.
setClosureFallbackToStaged(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether a pair the closure-cover search cannot host is handed to the staged generator; see FcitSl.closureFallbackToStaged.
setClosureFallbackToStaged(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether a pair the closure-cover search cannot host is handed to the staged generator; see FcitSl2.closureFallbackToStaged.
setClosureFallbackToStaged(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether a pair the closure-cover search cannot host is handed to the staged generator; see FcitSlKeepKnowledgeOrientations.closureFallbackToStaged.
setClosureFallbackToStaged(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether a pair the closure-cover search cannot host is handed to the staged generator; see FcitSlScoreCheck.closureFallbackToStaged.
setClosureMaxPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the bound (in nodes) on the skeleton paths the closure-cover search enumerates.
setClosureMaxPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the bound (in nodes) on the skeleton paths the closure-cover search enumerates.
setClosureMaxPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the bound (in nodes) on the skeleton paths the closure-cover search enumerates.
setClosureMaxPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the bound (in nodes) on the skeleton paths the closure-cover search enumerates.
setClosureMaxPaths(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the bound on the number of skeleton paths the closure-cover search enumerates.
setClosureMaxPaths(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the bound on the number of skeleton paths the closure-cover search enumerates.
setClosureMaxPaths(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the bound on the number of skeleton paths the closure-cover search enumerates.
setClosureMaxPaths(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the bound on the number of skeleton paths the closure-cover search enumerates.
setClusterCoherenceThreshold(double) - Method in class edu.cmu.tetrad.search.mimic.DmPcRobust: Sets the threshold value for cluster coherence.
setCoefficient(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Sets the coefficient.
setCoefficient(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Sets the coefficient for the given parent of the given factor.
setCoefficient(int, int, int, double) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Sets the coefficient value for a continuous child node.
setCoefficient(String, LaggedFactor, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Sets the intercept for the given factor.
setCoefficientDraws(boolean) - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: Sets whether each column update draws its regression coefficients from their approximate posterior before computing the fitted values of the MISSING rows, as R's mice does ("type-1" matching in the taxonomy of Morris et al.
setCoefHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field coefHigh.
setCoefLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field coefLow.
setCoefRange(double, double) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: setCoefRange.
setCoefSymmetric(boolean) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field coefSymmetric.
setCoefThreshold(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the coefficient threshold value for the regression process.
setColliderOrientationStyle(Pc.ColliderOrientationStyle) - Method in class edu.cmu.tetrad.search.Pc: Sets the orientation style for handling colliders in the graph.
setColliderOrientationStyle(PcAR.ColliderOrientationStyle) - Method in class edu.cmu.tetrad.search.PcAR: Sets the orientation style for handling colliders in the graph.
setColliderPolicy(RecursiveAdjustment.ColliderPolicy) - Method in class edu.cmu.tetrad.search.RecursiveAdjustment: Sets the collider policy for handling colliders during adjustment set definition.
setColliderPolicy(RecursiveAdjustment.ColliderPolicy) - Method in class edu.cmu.tetrad.search.RecursiveAdjustmentMultiple: Sets the collider policy for the AdjustmentMultiple instance.
setColliderRule(Ccd.ColliderRule) - Method in class edu.cmu.tetrad.search.Ccd: Sets Step B's collider rule.
setColumn(int, double[]) - Method in class edu.cmu.tetrad.util.MView: Sets the values of a specific column in the matrix view using the provided array of values.
setColumn(int, Vector) - Method in class edu.cmu.tetrad.util.MView: Sets the values of a specific column in the matrix view using the provided vector.
setColumnSpacing(int) - Method in class edu.cmu.tetrad.util.TextTable: Sets the number of spaces between columns, to some number >= 0.
setCommitGate(FcitSl.CommitGate) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the commit gate; see FcitSl.CommitGate.
setCommitGate(FcitSl2.CommitGate) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the commit gate; see FcitSl2.CommitGate.
setCommitGate(FcitSlKeepKnowledgeOrientations.CommitGate) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the commit gate; see FcitSlKeepKnowledgeOrientations.CommitGate.
setCommitGate(FcitSlScoreCheck.CommitGate) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the commit gate; see FcitSlScoreCheck.CommitGate.
setCommitRoute(FcitZm.COMMIT_ROUTE) - Method in class edu.cmu.tetrad.search.FcitZm: Sets how an accepted deletion is carried forward; the MAG legality gate applies to both routes.
setCommitRoute(FcitZm2.COMMIT_ROUTE) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets how an accepted deletion is carried forward; the MAG legality gate applies to both routes.
setCommitRoute(FcitZmKeepKnowledgeOrientations.COMMIT_ROUTE) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets how an accepted deletion is carried forward; the MAG legality gate applies to both routes.
setCompareToTrue(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Setter for the field compareToTrue.
setComparisonGraph(Comparison.ComparisonGraph) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the comparison graph.
setComparisonGraph(TimeoutComparison.ComparisonGraph) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: The type of graph the results are compared to.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.Fci: Configures whether the complete rule set is used in the FCI algorithm during the causal discovery process.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the Zhang complete rule set should be used; false if only R1-R4 (the rule set of the original FCI) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.Icd: Sets whether the complete rule set for orientation is used during the algorithm execution.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets whether the complete rule set is used in the instance.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.LvHeuristic: True if the complete final FCI rule set (Zhang 2008) should be used, false if the Spirtes ruleset from Causation, Prediction and Search (2000) should be used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.SpFci: Sets whether Zhang's complete rule set is used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets whether Zhang's complete rules are used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets the flag indicating if the complete rule set is being used.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.utils.MagToPag: Setter for the field completeRuleSetUsed.
setCompleteRuleSetUsed(boolean) - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Setter for the field completeRuleSetUsed.
setConditioningSetType(ConditioningSetType) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets the conditioning set type for the Markov check.
setCondWarnThreshold(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the condition warning threshold value used in the processing.
setConfigForClass(Class) - Method in class edu.cmu.tetrad.util.TetradLogger: If there is a pre-defined configuration for the given model it is set, otherwise an exception is thrown.
setContext(Context) - Method in class edu.cmu.tetrad.sem.ExpressionSampler: Updates the current Context instance associated with this ExpressionSampler.
setContinuousBounds(double, double, boolean) - Method in class edu.cmu.tetrad.data.Histogram: Sets the continuous bounds for the target variable along with an option to ignore data outside these bounds.
setContParentCutpointsForDiscreteChild(Node, Map) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgPm: Sets the continuous parent cutpoints for a specified discrete child node.
setControlFunction(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Sets whether each side's regression design is augmented with a univariate basis in that side's first-stage fitted mean (a control function), which captures the interaction and composition terms the additive Z-sieve cannot represent.
setCopyData(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field copyData.
setCordEngine(int) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the cord engine version.
setCorrectnessOption(int, double) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Applies one of the three correctness options shared with FCIT and the *-FCI family: 1 = Guarantee I-map (I-map gate on, saturating pass off), 2 = Score step (score step on, saturating pass off), 3 = Saturating step (both gates off, saturating pass on).
setCorrectnessOption(int, Score, double) - Method in class edu.cmu.tetrad.search.StarFci: Applies one of the three correctness options shared with FCIT's fcitCorrectness: 1 = Guarantee I-map (I-map gate on, saturating step off), 2 = Score step (score step on with the given score, saturating step off), 3 = Saturating step (both gates off, saturating step on).
setCorrectResult(Graph) - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Setter for the field correctResult.
setCorrelationThreshold(double) - Method in class edu.cmu.tetrad.search.LatentParentRecovery: Sets the absolute correlation threshold used to select measured parents.
setCountMap(int, CptMapCounts) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Sets the count map for a specific node index in the Bayesian network.
setCoupleFeaturesByTarget(boolean) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the state of the coupleFeaturesByTarget property, which determines whether features are coupled based on the target.
setCovariance(int, int, double) - Method in interface edu.cmu.tetrad.data.Covariances: Sets the covariance at (i, j) to a particular value.
setCovarianceMatrix(ICovarianceMatrix) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the covariance matrix used by the RICF score check.
setCovarianceMatrix(ICovarianceMatrix) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the covariance matrix used by the MAG score check explicitly.
setCovarianceMatrix(ICovarianceMatrix) - Method in class edu.cmu.tetrad.search.Gmas: Sets the covariance matrix used by the MAG score explicitly.
setCovarianceMatrix(ICovarianceMatrix) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets the covariance matrix for the instance.
setCovarianceMode(UnmixSpec.CovarianceMode) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the covariance mode.
setCovMatrix(ICovarianceMatrix) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: Setter for the field covMatrix.
setCovMatrix(ICovarianceMatrix) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the sample covariance matrix for this Sem as a submatrix of the given matrix.
setCpdag(boolean) - Method in class edu.cmu.tetrad.search.Dagma: Sets the value of the cpdag field.
setCpdag(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearch: Sets the flag indicating whether a CPDAG (partially directed acyclic graph) is wanted or not.
setCpdag(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets the flag indicating whether a CPDAG (partially directed acyclic graph) is wanted or not.
setCpdagAlgorithm(Cstar.CpdagAlgorithm) - Method in class edu.cmu.tetrad.search.Cstar: The CSTaR algorithm can use any CPDAG algorithm; here you can set it.
setCrossFit(boolean) - Method in class edu.cmu.tetrad.search.test.Gcm: Enables or disables cross-fitting in the model.
setCrossFitFolds(int) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the number of folds to be used in the cross-fitting process.
setCrossFitSeed(long) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the seed for the cross-fitting process used in the model.
setCutoff(double) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Sets the cutoff value.
setCutoff(double) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Sets the cutoff value used in the model.
setCutoff(double) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Sets the cutoff for the independence test.
setCutoff(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the cutoff.
setCutoffConstrainSearch(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Setter for the field cutoffConstrainSearch.
setCutoffDataSearch(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Setter for the field cutoffDataSearch.
setData(DataSet) - Method in class edu.cmu.tetrad.search.Cdnod: Sets the dataset to be used in this instance.
setData(DataSet) - Method in class edu.cmu.tetrad.search.CdnodBoss: Sets the dataset to be used in this instance.
setData(DataSet) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field data.
setDataAndIndex(DataSet, double[], String) - Method in class edu.cmu.tetrad.search.Cdnod: Sets the dataset with an additional continuous change-index column added as the last column.
setDataAndIndex(DataSet, double[], String) - Method in class edu.cmu.tetrad.search.CdnodBoss: Updates the internal dataset by appending a change index as the last column.
setDataFile(String) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field dataFile.
setDataFromFile(boolean) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field dataFromFile.
setDataSet(DataSet) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets the data set.
setDataSet(DataSet) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets the data set.
setDataSet(DataSet) - Method in class edu.cmu.tetrad.search.Tgin: Deprecated.

Sets the data set to be used for the analysis.
setDataSet(DataSet) - Method in class edu.cmu.tetrad.sem.SemIm: Calculates the covariance matrix of the given DataSet and sets the sample covariance matrix for this model to a subset of it.
setDataType(ComparisonParameters.DataType) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field dataType.
setDataWithC(DataSet) - Method in class edu.cmu.tetrad.search.CdnodBoss: Deprecated.
Contexts are now determined from Knowledge tier 0, not from column position. Use CdnodBoss.setData(DataSet) instead.
setDdpMaxP(boolean) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the PreserveMarkov object used by the R0R4StrategyTestBased.
setDecayRate(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Sets the rate at which expression levels tend to return to equilibrium.
setDedupByCpdag(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: Sets whether top-k retention de-duplicates by Markov equivalence class rather than by permutation.
setDedupByCpdag(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets whether top-k retention de-duplicates by Markov equivalence class (canonical CPDAG) rather than by permutation.
setDedupByCpdag(boolean) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Sets whether top-k retention de-duplicates by Markov equivalence class rather than by permutation.
setDeepeningStart(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the first conditioning-set cap in the deepening schedule (default 0).
setDeepeningStart(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the first conditioning-set cap in the deepening schedule (default 0).
setDefaultAggregation(LongToWide.Aggregation) - Method in class edu.cmu.tetrad.data.LongToWide: Sets the aggregation used for value variables not given one by LongToWide.setAggregation(String, Aggregation).
setDefaultBw(DataSet, Node) - Method in interface edu.cmu.tetrad.search.utils.Kernel: Sets bandwidth from data using default method
setDefaultBw(DataSet, Node) - Method in class edu.cmu.tetrad.search.utils.KernelGaussian: Sets bandwidth from data using default method
setDefaultLayout(LayoutUtil.DefaultLayout) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: Sets the layout used for graphs that have not been laid out, for this process.
setDefaultToKnowledgeLayout(boolean) - Method in class edu.cmu.tetrad.data.Knowledge: Setter for the field defaultToKnowledgeLayout.
setDefaultValue(Serializable) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field defaultValue.
setDelimiter(char) - Method in class edu.cmu.tetrad.simulation.HsimAutoC: Setter for the field delimiter.
setDelimiter(char) - Method in class edu.cmu.tetrad.simulation.HsimAutoRun: Setter for the field delimiter.
setDelimiter(char) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAC: Setter for the field delimiter.
setDelimiter(char) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: Setter for the field delimiter.
setDelimiter(TextTable.Delimiter) - Method in class edu.cmu.tetrad.util.TextTable: Setter for the field delimiter.
setDelta(double) - Method in class edu.cmu.tetrad.search.BossTopK: Sets the "split" threshold delta (≥ 0).
setDelta(double) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the delta to use.
setDelta(double) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Sets the "split" threshold delta (≥ 0).
setDepth(int) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the depth of the sepset search.
setDepth(int) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the depth of the sepset search.
setDepth(int) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.BossCcd: Sets the maximum conditioning depth for the CCD phase; -1 means unlimited.
setDepth(int) - Method in class edu.cmu.tetrad.search.Ccd: Sets the depth parameter, which may affect the algorithm's behavior during its operation.
setDepth(int) - Method in class edu.cmu.tetrad.search.Fas: Sets the depth for the search process.
setDepth(int) - Method in class edu.cmu.tetrad.search.Fasd: Sets the depth of the search.
setDepth(int) - Method in class edu.cmu.tetrad.search.Fask: Sets the depth of the search in the FASK algorithm.
setDepth(int) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.Fci: Sets the maximum depth for the algorithm's search process.
setDepth(int) - Method in class edu.cmu.tetrad.search.Fcit: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the depth of search, which is the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.Grasp: Sets the maximum depth of the depth-first search that GRaSP performs while searching for a weakly increasing tuck sequence that improves the score.
setDepth(int) - Method in class edu.cmu.tetrad.search.GraspFci: Sets the depth for the search algorithm.
setDepth(int) - Method in interface edu.cmu.tetrad.search.IFas: Sets the depth limit for the search process.
setDepth(int) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets the PC depth used in measurement-model construction.
setDepth(int) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Sets the PC depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets the PC depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.Pc: Sets the depth parameter for the instance.
setDepth(int) - Method in class edu.cmu.tetrad.search.PcAR: Sets the depth parameter for the instance.
setDepth(int) - Method in class edu.cmu.tetrad.search.Pcd: Sets the depth of the search--that is, the maximum number of conditioning nodes for any conditional independence checked.
setDepth(int) - Method in class edu.cmu.tetrad.search.PcMb: Sets the maximum number of conditioning variables for any conditional independence test.
setDepth(int) - Method in class edu.cmu.tetrad.search.Rfci: Sets the maximum number of variables conditioned on in any test.
setDepth(int) - Method in class edu.cmu.tetrad.search.StarFci: Sets the depth of the sepset search.
setDepth(int) - Method in class edu.cmu.tetrad.search.StarFciB: Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.StarFciC: Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.StarFciD: Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets the depth of the search for the possible m-sep search.
setDepth(int) - Method in class edu.cmu.tetrad.search.utils.Bes: Sets the depth for the search, which is the maximum number of variables conditioned on.
setDepth(int) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: -1 for unlimited depth, otherwise a number >= 0.
setDepth(int) - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the depth for the FciOrientDataExaminationStrategy object.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.Dci: Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.FasDci: Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Sets the depth of the search--that is, the maximum number of conditioning nodes for any conditional independence checked.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Sets the maximum number of variables conditioned on in any conditional independence test.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Sets the maximum number of variables conditioned on in any conditional independence test.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Setter for the field depth.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Sets the maximum number of variables conditioned on in any conditional independence test.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Sets the maximum number of variables conditioned on in any conditional independence test.
setDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Sets the maximum number of variables conditioned on in any conditional independence test.
setDepth(int) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the depth.
setDetectionEnabled(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether the final discriminating-path detection scan runs.
setDetectionEnabled(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets whether the final discriminating-path detection scan runs.
setDetectionEnabled(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether the final discriminating-path detection scan runs.
setDeterminationP(double) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Sets the threshold for making judgments of determination.
setDeterminationP(double) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Sets the threshold for making judgments of determination.
setDeterminismGuard(Fas.DeterminismGuard) - Method in class edu.cmu.tetrad.search.Fas: Wires a determinism guard gating which separating sets FAS is willing to accept.
setDeterminismGuard(Fas.DeterminismGuard) - Method in class edu.cmu.tetrad.search.PcAR: Supplies the Var(v|S)=0 determinism check.
setDeterminismThreshold(double) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Setter for the field determinismThreshold.
setDifference(Collection, Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: Set difference that also drops any cover in as that shares a variable with some cover in bs (Python setDifference).
setDiscount(double) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: setDiscount.
setDiscoveryAlpha(double) - Method in class edu.cmu.tetrad.search.Tsc: Sets an explicit discovery alpha, overriding the adaptive default.
setDiscreteInteractionOrder(int) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Sets the discrete interaction order: 1 for the additive parent design (the default, score-equivalent), 2 for pairwise interactions among discrete parents, and so on; an order at least the number of discrete parents of a family gives the saturated design.
setDiscreteScore(DiscreteScore) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets the discrete scoring function to use.
setDiscretize(boolean) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Sets whether to discretize child variables to avoid integration.
setDiscretize(boolean) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Sets whether to discretize child variables for shadow discretization.
setDishBumpStDev(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Setter for the field dishBumpStDev.
setDishDishVariability(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the standard deviation sd% (in percent) of the distribution N(100.0, sd%), from which errors will be drawn for the dish model.
setDishDishVariability(double) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setDishDishVariability.
setDishModel(DishModel) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Sets the dish model.
setDishNumber(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.DishModel: Sets the number of the getModel dish.
setDisplayLogEnabled(boolean) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets whether the display log should be used or not.
setDistortionType(NonlinearFunctionOfLinear.DistortionType) - Method in class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear: Sets the type of distortion to be applied in the nonlinear additive causal model.
setDistribution(Distribution) - Method in class edu.cmu.tetrad.sem.Parameter: Sets the distribution that initial values should be drawn from for this parameter.
setDmStructure(DMSearch.LatentStructure) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field dmStructure.
setDoAdjacencySearch(boolean) - Method in class edu.cmu.tetrad.search.Ion: Sets adjacency search on or off
setDoHigherRankExpansion(boolean) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets whether to run the higher-rank expansion phase.
setDoLegalityGating(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether each candidate removal is gated on per-step PAG legality (true, the default) or handled by the original greedy *-FCI machinery with a post-hoc repair (false).
setDoLegalityGating(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: When true, the extra-edge-removal step is gated: each candidate removal is committed only if the resulting graph (after re-running the full *-FCI orientation from scratch) passes the modulo-knowledge legality certificate, with sepset re-selection past gate rejections; otherwise the next separating set is tried.
setDoLegalityGating(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: When true, the extra-edge-removal step mimics FCIT: each candidate removal is committed only if the resulting graph (after re-running the full *-FCI orientation) passes the modulo-knowledge legality certificate; otherwise it is reverted.
setDoLegalityGating(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: When true, the extra-edge-removal step mimics FCIT: each candidate removal is committed only if the resulting graph (after re-running the full *-FCI orientation) passes the modulo-knowledge legality certificate; otherwise it is reverted.
setDoLegalityGating(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

When true, the extra-edge-removal step mimics FCIT: each candidate removal is committed only if the resulting graph (after re-running the full *-FCI orientation) passes the modulo-knowledge legality certificate; otherwise it is reverted.
setDoLegalityGating(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: When true, the extra-edge-removal step mimics FCIT: each candidate removal is committed only if the resulting graph (after re-running the full *-FCI orientation) passes the modulo-knowledge legality certificate; otherwise it is reverted.
setDoOneEquationOnly(boolean) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: When calculation the score for X = <X1 = X, X2, X3,..., Xp> use the equation for X1 only, if true; otherwise, use equations for all of X1, X2,...,Xp.
setDoOneEquationOnly(boolean) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: When calculation the score for X = <X1 = X, X2, X3,..., Xp> use the equation for X1 only, if true; otherwise, use equations for all of X1, X2,...,Xp.
setDoOneEquationOnly(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

When calculation the score for X = <X1 = X, X2, X3,..., Xp> use the equation for X1 only, if true; otherwise, use equations for all of X1, X2,...,Xp.
setDoOneEquationOnly(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

When calculation the score for X = <X1 = X, X2, X3,..., Xp> use the equation for X1 only, if true; otherwise, use equations for all of X1, X2,...,Xp.
setDoPathLengthSearch(boolean) - Method in class edu.cmu.tetrad.search.Ion: Sets path length search on or off.
setDoPossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.Fci: Configures whether the possible-DSEP (Definite Separation) step is performed as part of the FCI algorithm during the causal discovery process.
setDoRcit(boolean) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the value indicating whether the RCIT (Randomized Conditional Independence Test) functionality should be enabled or disabled.
setDottedUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.Dag: Sets the dotted underline triples for the given set of Triples.
setDottedUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: setDottedUnderLineTriples.
setDottedUnderLineTriples(Set) - Method in interface edu.cmu.tetrad.graph.Graph: setDottedUnderLineTriples.
setDottedUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.LagGraph: setDottedUnderLineTriples.
setDottedUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.SemGraph: setDottedUnderLineTriples.
setDottedUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Sets the dotted underline triples.
setDottedUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.Underlines: Setter for the field dottedUnderLineTriples.
setDouble(int, int, double) - Method in class edu.cmu.tetrad.data.BoxDataSet: Sets the value at the given (row, column) to the given double value, assuming the variable for the column is continuous.
setDouble(int, int, double) - Method in interface edu.cmu.tetrad.data.DataSet: Sets the value at the given (row, column) to the given double value, assuming the variable for the column is continuous.
setDouble(int, int, double) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Sets the value at the given (row, column) to the given double value, assuming the variable for the column is continuous.
setDoubleValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Assigns double value x in matrix element (r, c).
setDoubleValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Assigns double value x in matrix element (r, c).
setDoubleValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Assigns double value x in matrix element (r, c).
setDoubleValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Assigns double value x in matrix element (r, c).
setDoubleValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Assigns double value x in matrix element (r, c).
setEbicGamma(double) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets the penalty coefficient (gamma) used in the Extended Bayesian Information Criterion (EBIC) calculation.
setEbicGamma(double) - Method in class edu.cmu.tetrad.search.ScoredClusterFinder: Sets the EBIC gamma parameter to the specified value.
setEdge(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.Digraph: Sets a value of edge between nodes i and j
setEdge(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Sets a value of edge between nodes i and j
setEdgeCoef(Node, Node, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setEdgeCoef.
setEdgeCoef(Node, Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the coefficient value for the edge between two nodes in the graph.
setEdgeCoefficient(Node, Node, double) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Sets the coefficient for the a->b edge to the given coefficient, if within range.
setEdgeType(EdgeTypeProbability.EdgeType) - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: Setter for the field edgeType.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.MarkovCheck
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: Sets the effective sample size; a value less than 1 restores the actual sample size.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.BgeScore: Sets the effective sample size used in the score; a value less than 1 restores the actual sample size.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Sets the effective sample size used in the score calculation.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets the effective sample size to be used in computations.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Sets the effective sample size, or -1 (any negative value) to use the actual number of rows.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.EbicScore
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the effective sample size to be used in calculations.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the effective sample size to be used in calculations.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the effective sample size for computations.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Sets the effective sample size to be used in calculations.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.GicScores
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the effective sample size used in the statistical model.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Sets the effective sample size for score calculations.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the effective sample size for the model.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Sets the effective sample size used for scoring.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the effective sample size.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the effective sample size for the score calculation.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.SemBicScore
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the effective sample size for the current instance.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.score.ZsbScore
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Sets the effective sample size.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Sets the sample size to use for the independence test, which may be different from the sample size of the data set or covariance matrix.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Sets the effective sample size, interpreted relative to the full dataset, or -1 (any negative value) to use the actual number of rows.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Sets the effective sample size for the independence test.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Sets the sample size to use for the independence test, which may be different from the sample size of the data set or covariance matrix.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the effective sample size.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Sets the effective sample size for the test.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Sets the effective sample size for the independence test.
setEffectiveSampleSize(int) - Method in class edu.cmu.tetrad.search.Tsc: Sets the expected sample size used in calculations.
setEffectiveSampleSize(int) - Method in interface edu.cmu.tetrad.util.EffectiveSampleSizeSettable: Sets the effective sample size, or -1 if the actual sample size should be used.
setEffects(LinkedList) - Method in class edu.cmu.tetrad.search.Ida.NodeEffects: Sets the effects.
setEffects(LinkedList) - Method in class edu.cmu.tetrad.search.PdagPagIda.NodeEffects: Sets the list of effect values associated with the nodes.
setElapsed(long) - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Setter for the field elapsed.
setEmMaxIters(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the maximum number of iterations for the EM algorithm.
setEmpirical(boolean) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets whether the empirical option is selected.
setEndpoint(Graph, Node, Node, Endpoint) - Method in class edu.cmu.tetrad.search.utils.DefaultSetEndpointStrategy: Sets the endpoint of a graph given the two nodes and the desired endpoint.
setEndpoint(Graph, Node, Node, Endpoint) - Method in interface edu.cmu.tetrad.search.utils.SetEndpointStrategy: Sets the endpoint of a graph given the two nodes and the desired endpoint.
setEndpoint(Node, Node, Endpoint) - Method in class edu.cmu.tetrad.graph.Dag: Sets the endpoint of a directed edge between two nodes in a graph.
setEndpoint(Node, Node, Endpoint) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.
setEndpoint(Node, Node, Endpoint) - Method in interface edu.cmu.tetrad.graph.Graph: Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.
setEndpoint(Node, Node, Endpoint) - Method in class edu.cmu.tetrad.graph.LagGraph: Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.
setEndpoint(Node, Node, Endpoint) - Method in class edu.cmu.tetrad.graph.SemGraph: Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.
setEndpoint(Node, Node, Endpoint) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Sets the endpoint of an edge between two nodes in the graph.
setEndpoint_mirrorsOrientationToAllSlices() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Orienting one slice of a contemporaneous undirected edge should orient all slices the same way.
setEndpoint1(Endpoint) - Method in class edu.cmu.tetrad.graph.Edge: Setter for the field endpoint1.
setEndpoint2(Endpoint) - Method in class edu.cmu.tetrad.graph.Edge: Setter for the field endpoint2.
setEndpointStrategy(SetEndpointStrategy) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets the endpoint strategy for this object.
SetEndpointStrategy - Interface in edu.cmu.tetrad.search.utils: The SetEndpointStrategy interface provides a strategy for setting the endpoint of an edge in a graph.
setEnterThreshold(double) - Method in class edu.cmu.tetrad.search.LatentParentRecovery: Sets the enter threshold used to determine whether a candidate measured parent is included in the selection process based on its correlation with the latent residual.
setEpsilon(double) - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

Sets the parameter convergence threshold.
setEpsilon(double) - Method in class edu.cmu.tetrad.search.Pairwise: Sets the epsilon value.
setEpsilon(double) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the epsilon value.
setErrCovar(Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Deprecated.
Use setErrVar(x, value) for variances, or setErrCovar(x, y, value) for covariances.
setErrCovar(Node, Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Setter for the field errCovar.
setErrorCovariance(Node, Node, double) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Sets the covariance for the a<->b edge to the given covariance, if within range.
setErrorDistribution(int, Distribution) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Sets the error distribution for the factor'th factor.
setErrorDistribution(int, Distribution) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Method setIntenalNoiseModel
setErrorDistribution(int, Distribution) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Method setIntenalNoiseModel
setErrorPolicy(CachedIndependenceQueries.ErrorPolicy) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Sets the error-handling policy for this instance.
setErrorsNormal(boolean) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Setter for the field errorsNormal.
setErrorsTemplate(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the error template for the software.
setErrVar(Node, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setErrVar.
setErrVar(Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the error variance value for a specific node in the model's structural equation.
setEssBlockColumns(List) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the columns whose distinct joint values define the blocks for per-fact effective sample sizes (e.g., design or grouping variables identified by the data audit).
setEssBlockIds(int[]) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the row-to-block assignment for per-fact effective sample sizes directly.
setEstimated(boolean) - Method in class edu.cmu.tetrad.sem.SemIm: Setter for the field estimated.
setEstimator(EmBayesProperties.Estimator) - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Setter for the field estimator.
setEventActive(String, boolean) - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: Sets whether the event associated with the given id is active or not.
setEventActive(String, boolean) - Method in class edu.cmu.tetrad.util.TetradLogger.EmptyConfig: setEventActive.
setEventActive(String, boolean) - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig: Sets whether the event associated with the given id is active or not.
setEvidence(int, int) - Method in class edu.cmu.tetrad.bayes.VariableEliminationInference: Hard evidence: set node = category.
setEvidence(Evidence) - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Sets new evidence for the updater.
setEvidence(Evidence) - Method in interface edu.cmu.tetrad.bayes.BayesUpdater: Sets new evidence for the updater.
setEvidence(Evidence) - Method in class edu.cmu.tetrad.bayes.Identifiability: Sets new evidence for the updater.
setEvidence(Evidence) - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Sets the evidence for the Bayesian network updater.
setEvidence(Evidence) - Method in interface edu.cmu.tetrad.bayes.ManipulatingBayesUpdater: Sets new evidence for the updater.
setEvidence(Evidence) - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater: Sets new evidence for the updater.
setEvidence(Evidence) - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Sets the evidence for the Bayesian network updater.
setEvidence(SemEvidence) - Method in class edu.cmu.tetrad.sem.SemUpdater: Sets new evidence for the updater.
setExactFfmlBaseline(boolean) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets whether to use FF-ML's fixed-sigma^2 no-parent baseline.
setExactFfmlBaseline(boolean) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets whether to use FF-ML's fixed-sigma^2 no-parent baseline.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Sets whether selection bias should be excluded in the comparison graph.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Sets whether selection bias should be excluded in the comparison graph.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether selection bias should be excluded.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets whether selection bias should be excluded.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.Fci: Sets whether selection bias is allowed.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.Gmas: Sets whether selection bias is excluded in the projections.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets whether to exclude selection bias during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.Rfci: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether selection bias should be excluded.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets whether selection bias should be excluded during the search process.
setExcludeSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets whether selection bias should be excluded during the search process.
setExternalAdjacencyFraction(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the fraction of the external graphs in which a pair must be adjacent for it to be an adjacency of the composite skeleton: the pair is kept when its count exceeds this fraction of the number of graphs.
setExternalAdjacencyFraction(double) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the fraction of the external graphs in which a pair must be adjacent to enter the composite skeleton.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Sets the external graph to be used by the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Sets the external graph to be used by the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: setExternalGraph.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: setExternalGraph.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.FaskPw: Sets the external graph to be used by the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R1: Sets the external graph for the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R2: Sets the external graph for the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.R3: Sets the external graph to be used by the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Rskew: Sets the external graph for this algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.RskewE: Sets the external graph for the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Sets the external graph to be used by the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Sets the external graph to be used by the algorithm.
setExternalGraph(Algorithm) - Method in class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Sets the external graph for the algorithm.
setExternalGraph(Algorithm) - Method in interface edu.cmu.tetrad.algcomparison.utils.TakesExternalGraph: setExternalGraph.
setExternalGraph(Graph) - Method in class edu.cmu.tetrad.search.Fasd: Sets the external graph.
setExternalGraph(Graph) - Method in class edu.cmu.tetrad.search.Fask: Sets the external graph for the FASK algorithm.
setExternalGraph(Graph) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the external graph to use.
setExternalGraph(Graph) - Method in class edu.cmu.tetrad.search.FaskPool: Sets a single external graph, applied to every dataset.
setExternalGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets the initial graph.
setExternalGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Setter for the field externalGraph.
setExternalGraphs(List) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the external graphs, one per dataset, in the order of the datasets.
setExternalGraphs(List) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the external graphs, one per dataset (or a single graph, when the external-graph algorithm pools the datasets internally).
setExternalMask(SimpleMatrix) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets an external mask for use in the current object.
setExtraEdgeThreshold(double) - Method in class edu.cmu.tetrad.search.Fask: Sets the threshold value for considering extra edges in the FASK algorithm.
setFactor(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the factor.
setFactor(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Sets the name of the lagged factor
setFacts() - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: setFacts.
setFacts(IndependenceFacts) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Setter for the field facts.
setFacts(IndependenceFacts) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Setter for the field facts.
setFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.search.Fges: Sets whether one-edge faithfulness should be assumed.
setFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.search.FgesMb: Sets whether one-edge faithfulness should be assumed.
setFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.search.Gfci: Sets whether one-edge faithfulness is assumed.
setFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets whether one-edge faithfulness is assumed.
setFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets whether faithfulness is assumed in the graph search procedure.
setFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Set to true if it is assumed that all path pairs with one length 1 path do not cancelAll.
setFasAlpha(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the alpha level for the FAS-style adjacency tests.
setFasAlpha(double) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the alpha level for the FAS-style adjacency tests.
setFasAlpha(double) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the alpha level for the FAS-style tests.
setFasDepth(int) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the depth of the FAS-style adjacency searches (-1 for unlimited; capped at 4 in MG_FAS).
setFasDepth(int) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the depth of the FAS-style adjacency searches (-1 for unlimited; capped at 4 in MG_FAS).
setFasDepth(int) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the depth of the FAS-style searches (-1 for unlimited; capped at 4 in MG_FAS).
setFasStable(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets whether the Fast Adjacency Search (FAS) algorithm will use the "stable" modification.
setFasStable(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Sets whether the Fast Adjacency Search (FAS) algorithm will use the "stable" modification.
setFastIcaA(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the FastICA tanh nonlinearity parameter for the LiNG adjacency stage.
setFastIcaA(double) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the FastICA tanh nonlinearity parameter for the LiNG adjacency stage.
setFastIcaA(double) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the FastICA tanh nonlinearity parameter for the LiNG stage.
setFastIcaAlgorithm(int) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the FastICA algorithm type to be used in the Independent Component Analysis (ICA) procedure.
setFastIcaAlpha(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the alpha parameter for the FastICA (Independent Component Analysis) process.
setFastIcaFunction(int) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the FastICA function type to be used within the Independent Component Analysis (ICA) process.
setFastIcaMaxIter(int) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the FastICA maximum iterations for the LiNG adjacency stage.
setFastIcaMaxIter(int) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the FastICA maximum iterations for the LiNG adjacency stage.
setFastIcaMaxIter(int) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the FastICA maximum iterations for the LiNG stage.
setFastIcaRowNorm(boolean) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets whether row normalization is applied in the FastICA (Independent Component Analysis) process.
setFastIcaTolerance(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the FastICA convergence tolerance for the LiNG adjacency stage.
setFastIcaTolerance(double) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the FastICA convergence tolerance for the LiNG adjacency stage.
setFastIcaTolerance(double) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the FastICA convergence tolerance for the LiNG stage.
setFdr(boolean) - Method in class edu.cmu.tetrad.search.Pcd: Sets whether this test will run with False Discovery Rate tests.
setFdr(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Sets the value of the fdr field.
setFeatureScale(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the scaling factor to be applied to features in computations.
setFeatureType(FfCordContinuous.FeatureType) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the feature type for the current instance based on the provided integer value.
setFeatureType(FfDmlContinuous.FeatureType) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the feature type for the current instance based on the provided integer value.
setFeatureType(FfMl.FeatureType) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the feature type for the current object.
setFeatureType(FfMlContinuous.FeatureType) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Sets the feature type for the current instance based on the provided integer value.
setFeatureType(FfCiContinuous.FeatureType) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the feature type to be used in the mixed independence test.
setFeatureType(FfCiContinuous.FeatureType) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the feature type to be used in the mixed independence test.
setFeatureType(FfCiContinuous.FeatureType) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the feature type for the current instance and invalidates the feature cache.
setFeatureType(Rcit.FeatureType) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the feature type for this instance and invalidates the feature cache.
setFileLoggingEnabled(boolean) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets whether "file logging" is enabled or not; that is whether calls to setNextOutputStream will be respected.
setFilename(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.StoredLagGraphParams: Sets the stored file.
setFilenameOut(String) - Method in class edu.cmu.tetrad.simulation.HsimAutoC: Setter for the field filenameOut.
setFilenameOut(String) - Method in class edu.cmu.tetrad.simulation.HsimAutoRun: Setter for the field filenameOut.
setFilenameOut(String) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAC: Setter for the field filenameOut.
setFilenameOut(String) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: Setter for the field filenameOut.
setFindMb(boolean) - Method in class edu.cmu.tetrad.search.PcMb: Setter for the field findMb.
setFindSmallestSubset(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the flag indicating whether a smallest subset of each conditioning set yielding independence should be reported.
setFirstStepStored(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the index of the first step to actually be stored out.
setFirstStepStored(int) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setFirstStepStored.
setFixed(boolean) - Method in class edu.cmu.tetrad.sem.Parameter: Sets whether this parameter should be held fixed during estimation.
setFixedParamValue(Parameter, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setFixedParamValue.
setFixedParamValue(Parameter, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the fixed value for a specified parameter in the model.
setFlatPrior(boolean) - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Setter for the field flatPrior.
setFocusPair(Node, Node) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the focus pair by combining the names of the given nodes in a specific order.
setFocusPair(Node, Node) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the focus pair by combining the names of the given nodes in a specific order.
setFocusPair(Node, Node) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the focus pair by combining the names of the given nodes in a specific order.
setFocusPair(Node, Node) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the focus pair by combining the names of the given nodes in a specific order.
setForbidden(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Marks the edge var1 --> var2 as forbid.
setForbidDirectedCycles(boolean) - Method in class edu.cmu.tetrad.search.Pc: Configures whether directed cycles are forbidden in the graph.
setForbidDirectedCycles(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Configures whether directed cycles are forbidden in the graph.
setForceChildrenVisibleInRA(boolean) - Method in class edu.cmu.tetrad.search.PdagPagIda: Sets whether the children nodes should be forcibly marked as visible during refinement adjustment (RA) processes.
setForceVisibility(Set) - Method in class edu.cmu.tetrad.search.GRegressionPag: Sets nodes b for which a directed edge X -> b is to be treated as visible, as background knowledge that X and b are not confounded.
setForwardSearch(boolean) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field forwardSearch.
setFractionResample(double) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the percentage of all samples to use when resampling for each conditional independence test.
setFreeParamValues(double[]) - Method in interface edu.cmu.tetrad.sem.ISemIm: setFreeParamValues.
setFreeParamValues(double[]) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the values of the free freeParameters (in the order in which they appear in getFreeParameters()) to the values contained in the given array.
setFullSigmaSafetyMargin(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the full sigma safety margin.
setFunction(int) - Method in class edu.cmu.tetrad.search.FastIca: Sets the function to be used for the ICA algorithm.
setGamma(double) - Method in class edu.cmu.tetrad.search.score.EbicScore: Sets the gamma parameter for EBIC.
setGammaHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field gammaHigh.
setGammaLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field gammaLow.
setGateScore(Score) - Method in class edu.cmu.tetrad.search.StarFci: Sets the score the score step uses for its covariance matrix and penalty discount.
setGateScore(Score) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Sets the score the score step takes its covariance matrix and penalty discount from.
setGesDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field gesDepth.
setGoldStandard(Graph) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Sets the gold standard graph for the independence test.
setGraph(Dag) - Method in class edu.cmu.tetrad.graph.LagGraph: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.independence.MSeparationTest: Setter for the field graph.
setGraph(Graph) - Method in interface edu.cmu.tetrad.algcomparison.independence.TakesGraph: setGraph.
setGraph(Graph) - Method in class edu.cmu.tetrad.algcomparison.score.MSepScore: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.bayes.EmBayesProperties: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.DagSepsets
setGraph(Graph) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Sets the graph for the SepsetProducer object.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Sets the graph for the Sepsets object.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Sets the graph for the Sepsets object.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Sets the graph for the SepsetsMaxP object.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Sets the graph for the Sepsets object.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep
setGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet
setGraph(Graph) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Replaces the working graph used for repair.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Setter for the field graph.
setGraph(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Setter for the field graph.
setGraphFile(String) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field graphFile.
setGraphLearner(UnmixSpec.GraphLearner) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the graph learning method for this instance of UnmixSpec.
setGraphName(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Sets the name of the graph
setGraphNum(int) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field graphNum.
setGraphType(VertexRepairSearch.AdjustmentGraphType) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets the graph type that governs which edits are legal.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGraspUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether GRaSP, when used as the starting algorithm, should use the Raskutti-Uhler test-based method instead of the score-based method.
setGuaranteeCpdag(boolean) - Method in class edu.cmu.tetrad.search.Pcd: Sets whether the algorithm should prevent cycles during the search.
setGuaranteeCpdag(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Sets the flag to determine whether to guarantee a CPDAG (Consensus Partially Directed Acyclic Graph) result in the search.
setGuaranteeIid(boolean) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Setter for the field guaranteeIid.
setGuaranteePag(boolean) - Method in class edu.cmu.tetrad.search.Fci: Sets the value of the guaranteePag flag.
setGuaranteePag(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the flag indicating whether to guarantee the output is a legal PAG.
setGuaranteePag(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether to map the final graph to a nearby legal PAG (GraphUtils.guaranteePag) when it fails the modulo-knowledge certificate.
setGuidedFallbackToLegacy(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether a pair the guided construction cannot host is handed to the configured legacy generator.
setGuidedMaxLeaves(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the per-deletion cap on completed leaf candidates for the guided construction; negative means uncapped.
setHiddenDimensions(int...) - Method in class edu.cmu.tetrad.sem.GeneralAdditiveModel: Sets the hidden dimensions for the model.
setHighlighted(boolean) - Method in class edu.cmu.tetrad.graph.Edge: Setter for the field highlighted.
setHighPValueAlpha(double) - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: setHighPValueAlpha.
setHighPValueAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: setHighPValueAlpha.
setHighPValueAlpha(double) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: setHighPValueAlpha.
setHistory(GeneHistory) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the history.
setHistory(GeneHistory) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setHistory.
setIa(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field ia.
setIcaMaxIter(int) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the maximum number of iterations for the ICA (Independent Component Analysis) process.
setIcaTol(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the tolerance value for the ICA (Independent Component Analysis) process.
setIdaType(Ida.IDA_TYPE) - Method in class edu.cmu.tetrad.search.Ida: Sets the IDA type for this instance.
setIdaType(Ida.IDA_TYPE) - Method in class edu.cmu.tetrad.search.IdaCheck: Sets the IDA (Intervention Distribution Approximation) type for the current instance.
setIdaType(PdagPagIda.IDA_TYPE) - Method in class edu.cmu.tetrad.search.PdagPagIda: Sets the IDA type for the instance.
setImapAlpha(double) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the significance level of the I-map gate's tests (default 0.001); see BfciBryan.imapAlpha.
setImapAlpha(double) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the significance level of the I-map gate's tests (default 0.001); see CorrectnessGates.setImapAlpha(double).
setImapAlpha(double) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the significance level of the I-map gate's tests.
setImapAlpha(double) - Method in class edu.cmu.tetrad.search.StarFci: Sets the significance level of the I-map gate's tests.
setImapAlpha(double) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Sets the significance level of the I-map gate's tests.
setImprovementEpsilon(double) - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: Sets the minimum score improvement for a relocation to be adopted.
setImprovementEpsilon(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Sets the minimum score improvement for a move to be accepted.
setIncludeDishAndChipColumns(boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: setIncludeDishAndChipColumns.
setIncludeDishAndChipVariables(boolean) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: Setter for the field includeDishAndChipVariables.
setIncludeInterceptWhenNotCentered(boolean) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the includeInterceptWhenNotCentered flag to determine if the intercept should be included when the data is not centered, and clears the cache.
setIncludeNegativeCoefs(boolean) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Setter for the field includeNegativeCoefs.
setIncludePositiveCoefs(boolean) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Setter for the field includePositiveCoefs.
setIncludeValueName(boolean) - Method in class edu.cmu.tetrad.data.LongToWide: If false, and exactly one value variable is spread, output columns are named by level alone (e.g., Final rather than score.Final).
setIncompleteCholesky(double) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Sets the precision for the Incomplete Choleksy factorization method for approximating Gram matrices.
setIndegree(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Setter for the field indegree.
setIndegreeType(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Setter for the field indegreeType.
setIndependenceTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the independence test used for the checks.
setIndependenceTest(ComparisonParameters.IndependenceTestType) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field independenceTest.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Sets the independence wrapper for the object.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Sets the independence wrapper of the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Sets the IndependenceWrapper test instance for this object.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Sets the independence wrapper for the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci: Sets the IndependenceWrapper instance to be used for the PCMCI algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Sets the IndependenceWrapper object for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Sets the IndependenceWrapper object for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Sets the IndependenceWrapper object for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Sets the IndependenceWrapper object for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Sets the IndependenceWrapper object for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Sets the IndependenceWrapper object for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Updates the independence wrapper for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Updates the independence wrapper for this algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Sets the IndependenceWrapper object associated with this method.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cpw: Sets the IndependenceWrapper instance used for conditional independence testing in the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Sets the IndependenceWrapper object associated with this method.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciCyclicPw: Sets the IndependenceWrapper instance used for conditional independence testing in the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Sets the IndependenceWrapper object associated with this method.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Sets the independence wrapper for the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Sets the independence wrapper for the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Sets the independence wrapper.
setIndependenceWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Sets the IndependenceWrapper object for the algorithm.
setIndependenceWrapper(IndependenceWrapper) - Method in interface edu.cmu.tetrad.algcomparison.utils.TakesIndependenceWrapper: Sets the independence wrapper.
setIndices(int[]) - Method in class edu.cmu.tetrad.util.IndexedMatrix: Sets the index array.
setIndTestWrapper(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Not used.
setInhibitExcite(int[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the inhibit/excite.
setInitialGraph(Graph) - Method in class edu.cmu.tetrad.search.Fges: Sets the initial graph for the application.
setInitialGraph(Graph) - Method in class edu.cmu.tetrad.search.FgesMb: Sets the initial graph for the software.
setInitializedRandomly(boolean) - Method in class edu.cmu.tetrad.sem.Parameter: Set to true iff this parameter should be initialized randomly by drawing an initial value from its preset random distribution.
setInitSync(boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Sets whether initialization should be synchronized.
setInitSync(boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets whether the expression levels of cells should be synchronized on initialization.
setInitSync(boolean) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setInitSync.
setInputClassMergeThreshold(double) - Method in class edu.cmu.tetrad.search.mimic.Dm: Sets the threshold value for merging input classes.
setInputNames(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Sets the input variable names directly.
setInputNames(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Sets the input variable names directly.
setInputNames(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Sets the input variable names directly.
setInputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets known input variable names.
setInputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss: Sets known input variable names.
setInputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Sets known input variable names.
setInputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets known input variable names.
setInputs(int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field inputs.
setInputs(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Sets the observed variables known to be inputs to the latent structure.
setInputs(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Sets the observed variables known to be inputs to the latent structure.
setInputs(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Sets the observed variables known to be inputs to the latent structure.
setInputScale(double) - Method in class edu.cmu.tetrad.sem.GeneralAdditiveModel: Sets the input scale for the model.
setInputStandardize(boolean) - Method in class edu.cmu.tetrad.sem.GeneralAdditiveModel: Sets whether the input data should be standardized for the model.
setInt(int, int, int) - Method in class edu.cmu.tetrad.data.BoxDataSet: Sets the value at the given (row, column) to the given int value, assuming the variable for the column is discrete.
setInt(int, int, int) - Method in interface edu.cmu.tetrad.data.DataSet: Sets the value at the given (row, column) to the given int value, assuming the variable for the column is discrete.
setInt(int, int, int) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Sets the value at the given (row, column) to the given int value, assuming the variable for the column is discrete.
setInteractionLambda(double) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the weight of the discrete-continuous interaction (product-kernel) term.
setInteractionMaxParents(int) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the maximum number of parent interactions allowed for an entity.
setIntercept(int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Sets the intercept for the given factor.
setIntercept(Node, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setIntercept.
setIntercept(Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the intercept for a specified node in the SEM model.
setIntercept(String, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Sets the intercept for the given factor.
setInterceptHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field interceptHigh.
setInterceptLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field interceptLow.
setIntersection(Collection, Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: Variable-level intersection of two sets of covers (Python setIntersection).
setInterval(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Returns the interval (int time steps) between time steps stored out.
setInterval(int) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setInterval.
setIpen(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field ipen.
setIrlsIters(int) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the number of iterations for the IRLS (Iteratively Reweighted Least Squares) process.
setIrlsIters(int) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the number of iterations for the IRLS (Iteratively Reweighted Least Squares) algorithm.
setIrlsTol(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the tolerance value used for the Iterative Reweighted Least Squares (IRLS) algorithm.
setIrlsTol(double) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the tolerance value for the IRLS (Iterative Reweighted Least Squares) algorithm.
setIs(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field is.
setIsAlpha(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Sets the value of the isAlpha property, which likely represents a parameter or constant used in the scoring or computation within the class.
setIterations(int, int) - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Sets the maximum number of iterations (default 60) and, for the stochastic form, the burn-in before averaging (default 20).
setIterativeDeepening(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets whether the main loop is run once at the full depth (false, the default and the historical behavior) or repeatedly at increasing conditioning-set caps up to the full depth (true).
setIterativeDeepening(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets whether the main loop is run once at the full depth (false, the default and the historical behavior) or repeatedly at increasing conditioning-set caps up to the full depth (true).
setItr(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field itr.
setJitter(double) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Sets the value of the jitter parameter and clears the local score cache.
setJitter(double) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the jitter value used in bandwidth estimation.
setJitter(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Sets the Cholesky jitter base and forwards it to the continuous engine.
setJitter(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Sets the jitter parameter for the Kernel Marginal Likelihood Score.
setJitter(double) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the jitter value used to introduce a small positive perturbation for numerical stability in certain computations, such as solving systems involving positive definite matrices.
setJoinType(JoinDatasets.JoinType) - Method in class edu.cmu.tetrad.data.JoinDatasets: Sets the join type (default JoinDatasets.JoinType.LEFT).
setJustification(int) - Method in class edu.cmu.tetrad.util.TextTable: Sets the justification, either LEFT_JUSTIFIED or RIGHT_JUSTIFIED.
setK(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the value of K for the current instance of UnmixSpec.
setKAddition(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Sets the value of the k_addition parameter.
setKAddition(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the K addition value, which is used to adjust the evaluation of certain configurations or parameters in scoring mechanisms within the class.
setKciParam(String, Object) - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Sets a KCI-specific parameter for the PythonKciIndependenceTest instance.
setKciParam(String, Object) - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Sets a RCIT-specific parameter for the PythonRcitIndependenceTest instance.
setKDeletion(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Sets the value of the k_deletion parameter.
setKDeletion(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the K deletion value, which is used in the scoring mechanisms within the class to adjust the evaluation of certain configurations or parameters.
setKeepInterimTrace(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets whether the full trajectory of committed PAGs is retained.
setKeepInterimTrace(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets whether the full trajectory of committed PAGs is retained.
setKeepInterimTrace(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the full trajectory of committed PAGs is retained.
setKeepRowKeys(boolean) - Method in class edu.cmu.tetrad.data.LongToWide: If true, the row key variable(s) are kept as the leading column(s) of the wide data set (as discrete variables, one category per observed value).
setKernel(double[][], double) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Dependent wild bootstrap, kernel form: switches the check to the dependent wild bootstrap-t of WildBootstrapMarkovCheck.runEngineKernel(double[][], String[], int, long, double[][], double) (Shao 2010, self-normalized), with multiplier covariance exp(-d^2/(2 h^2)) over the given row coordinates.
setKernelType(Kci.KernelType) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the kernel type.
setKmax(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the maximum allowed value of K (Kmax) for the current instance of UnmixSpec.
setKmeansRestarts(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the number of k-means restarts.
setKmin(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the minimum allowed value of K.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Sets the knowledge associated with this object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Sets the knowledge associated with this object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Sets the knowledge object for this instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cam
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cdnod: Sets the knowledge of the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag: Sets the knowledge object for the current instance of the Cdnod class.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cpc: Sets the knowledge associated with this object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fas: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.IsFges
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pc: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcAr: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcd: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMax: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.PcMb: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Pcmci
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Sets the knowledge object for the TwoStep algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Sets the knowledge associated with the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Sets the knowledge associated with the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Sets the knowledge associated with the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Sets the knowledge associated with the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Sets the knowledge associated with the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Sets the knowledge associated with the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Sets the knowledge object for this instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Ccd: Sets the knowledge object for this instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Cfci: Sets the knowledge object for this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Dm: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmPc: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fci: Sets the knowledge object for this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FciMax: Sets the knowledge object for this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Sets the Knowledge object associated with this instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Sets the Knowledge object associated with this instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Sets the knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Icd: Sets the knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.IsGfci: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Sets the knowledge object associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSampleRfci: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.PagSamplingRfci: Sets the knowledge associated with this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Rfci: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.RfciBsc: Sets the knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Sets the knowledge object associated with this algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.algorithm.other.Rlcd
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the knowledge for the current instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.score.InstanceAugmentedSemBicScoreWrapper: Sets the knowledge associated with this instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.score.IsBDeuScoreWrapper: Sets the knowledge used by this score wrapper.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.algcomparison.utils.AcceptsKnowledge: Sets a knowledge object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.BoxDataSet: Sets knowledge to a copy of the given object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Sets the knowledge associated with the ICovarianceMatrix.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Sets the knowledge associated with the ICovarianceMatrix.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Sets the knowledge associated with the ICovarianceMatrix.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.DataModelList: Sets knowledge to a copy of the given object.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Sets the knowledge associated with the ICovarianceMatrix.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.IndependenceFacts: Sets knowledge to a copy of the given object.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.data.KnowledgeTransferable: Sets knowledge to a copy of the given object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Sets knowledge to a copy of the given object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.data.TimeSeriesData: Sets knowledge to a copy of the given object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Boss: Sets the knowledge to be used for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.BossCcd: Set background knowledge (forbidden directed edges only, as in CCD).
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.BossTopK: Sets the knowledge to be used for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Ccd: Set background knowledge (forbidden edges).
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.DirectLingam: Sets background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fas: Sets the knowledge object used in the conditional independence search process.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fasd: Sets the knowledge for this object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fask: Sets the prior knowledge for the FASK algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FaskPool: Sets background knowledge for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FaskVote: Sets background knowledge for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fci: Sets the knowledge structure to be used or modified by the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fcit: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Fges: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.FgesMb: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Gmas: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Grasp: Sets the knowledge used in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Icd: Sets the knowledge object for the algorithm execution.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.search.IFas: Sets the background knowledge to be used during the search process.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Ion: Sets the knowledge to be used for this search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets the knowledge structure for the instance.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.LatentParentRecovery: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the knowledge object for the Markov checker.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

The knowledge to use in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets the knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.Dm: Sets optional background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.DmPcHybrid: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.DmPcRobust: Sets the knowledge constraints for the algorithm.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss: Sets background knowledge forwarded to BOSS.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Sets the knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets the knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Pairwise: Sets the knowledge for the object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Pc: Sets the knowledge object for this instance by creating a new instance based on the provided knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.PcAR: Sets the knowledge object for this instance by creating a new instance based on the provided knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Pcd: Sets the knowledge object used by this method.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.PcMb: Sets knowledge, to which the algorithm is in fact sensitive.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.PermutationSearch: Sets the knowledge to be used for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets the knowledge to be used for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets background knowledge, passed to the IMaGES and pooled-FAS stages.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Rfci: Sets the knowledge used in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Sets background knowledge over the observed variables; see LatentGroups.addCluster(Set, List, int, List).
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.rlcd.RlcdClusterSearch: Sets background knowledge over the observed variables.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Rlcd: Sets background knowledge over the observed variables.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.Sp: Sets the knowledge associated with this object.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.StarFci: Sets the background knowledge used by the initial search and by orientation.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.StarFciB: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.StarFciC: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.StarFciD: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets the knowledge to use in search.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.search.SuborderSearch: The knowledge being used.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: The knowledge being used.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.Bes: Sets the knowledge for the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.BesPermutation: Sets the knowledge that BES will use.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets the knowledge to use for the final orientation.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets the background knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.MagToPag: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.MeekRules: Sets the knowledge to be used in the orientation.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.search.utils.R0R4Strategy: Sets the knowledge object to be used by the strategy.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the knowledge object used by the FciOrientDataExaminationStrategy.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets background knowledge constraints.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.FasDci: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in interface edu.cmu.tetrad.search.work_in_progress.Hbsms: setKnowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam: setKnowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsGes: setKnowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Sets the knowledge specification to be used in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.Mmhc: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Sets the knowledge specification used in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Sets the knowledge specification used in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Setter for the field knowledge.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Sets the knowledge specification used in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Sets the knowledge specification used in the search.
setKnowledge(Knowledge) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Sets the knowledge specification used in the search.
setKnowledge(Knowledge) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the knowledge.
setKnowledge(List, List) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: setKnowledge.
setKnowledgeGroup(int, KnowledgeGroup) - Method in class edu.cmu.tetrad.data.Knowledge: Replaces the knowledge group at the given index.
setKnowledgeInStage1(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Sets whether background knowledge is given to stage 1 as well as stage 2 (default true).
setKReorientation(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Sets the value of the k_reorientation parameter.
setKReorientation(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the K reorientation value, which is used in the scoring mechanisms within the class to adjust the evaluation of certain configurations or parameters.
setKurtosisCap(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the kurtosis cap value.
setKurtosisGamma(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the kurtosis gamma value for the object.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.EbicScore: Sets the Singularity lambda.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the value of the lambda parameter in the current instance.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the value of the lambda parameter in the current instance.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the value of the lambda parameter.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Sets the value of the lambda parameter in the current instance.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.GicScores: Sets the lambda parameter.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Sets the value of the lambda parameter and clears the local score cache.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the regularization parameter lambda.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore: Sets the lambda (sigma^2) hyperparameter and forwards it to the continuous engine.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Sets the lambda hyperparameter for the Kernel Marginal Likelihood Score.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Sets the lambda parameter.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the value of the lambda variable.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns the covariance matrix of the regression of the ith variable on its parents and the regression coefficients.
setLambda(double) - Method in class edu.cmu.tetrad.search.score.ZsbScore: Sets the Singularity lambda.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the lambda value for the instance.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the lambda value for the instance.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the value of the lambda parameter and invalidates the feature cache.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Sets the value of the lambda parameter.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Sets the lambda parameter for the independence test.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Sets the regularization parameter lambda used in statistical computations or tests.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the value of the lambda parameter for this instance.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets the value of the lambda parameter, ensuring it is constrained to a minimum value of 1e-12.
setLambda(double) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the value of the lambda parameter and invalidates the feature cache.
setLambda(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the value of the regularization parameter lambda.
setLambda1(double) - Method in class edu.cmu.tetrad.search.Dagma: Sets the value of lambda1.
setLatentGate(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Sets whether a partition is searched for latents only if its rank constraints call for one (default true).
setLeaf(Boolean) - Method in class edu.cmu.tetrad.search.rlcd.Cover: Sets the tri-state leaf flag.
setLearningRate(double) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the learning rate used for training nuisance models.
setLearningRate(double) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the nuisance learning rate for the testing process.
setLeftGetsSmallerHalfWhenOdd(boolean) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets whether the left portion in column splits gets the smaller half when the number of columns being split is odd.
setLeftGetsSmallerHalfWhenOdd(boolean) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: If true and |Zi| is odd, left gets floor(|Zi|/2); otherwise left gets ceil(|Zi|/2).
setLeftRight(Fask.LeftRight) - Method in class edu.cmu.tetrad.search.Fask: Sets the left-right scoring method used in the FASK algorithm.
setLeftRight(Fask.LeftRight) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the left-right rule used within each dataset.
setLeftRight(FaskOrig.LeftRight) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the left-right rule used
setLeftSuffix(String) - Method in class edu.cmu.tetrad.data.JoinDatasets: Sets the suffix appended to a left non-key column whose name collides with a right non-key column (default "_left").
setLegendreClip(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the Legendre clip value used to configure the computation or process.
setLegendreDegree(int) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the degree for the Legendre polynomial calculations.
setLegendreMapMode(String) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Configures the Legendre map mode for the system.
setLength(Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: The number of distinct variables covered (Python setLength, written ||Vs||).
setLevel(OutputStream, Level) - Method in class edu.cmu.tetrad.util.LogUtils: Sets the logging level for the given stream.
setLevelNames(Map) - Method in class edu.cmu.tetrad.data.LongToWide: Sets short names for levels of the column key: an output column for level L uses levelNames.get(L) in place of L when present.
setLinearHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field linearHigh.
setLinearLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field linearLow.
setLineColor(Color) - Method in class edu.cmu.tetrad.graph.Edge: Sets a special line color for this edge, or null to restore the workbench default.
setLingThreshold(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the threshold on the pooled absolute LiNG B-hat entries above which a moral pair is kept as an adjacency.
setLingThreshold(double) - Method in class edu.cmu.tetrad.search.FaskVote: Sets the threshold on the pooled absolute LiNG B-hat entries above which a moral pair is kept as an adjacency.
setLingThreshold(double) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the threshold on the pooled absolute LiNG B-hat entries.
setListwiseDeleteMissing(boolean) - Method in class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Setter for listwiseDeleteMissing.
setLocation(String, PointXy) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Sets the location.
setLocation(String, PointXy) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Sets the location.
setLocation(String, PointXy) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Sets the location.
setLocation(String, PointXy) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Sets the location.
setLocationMap(DataSet) - Method in class edu.cmu.tetrad.simulation.GdistanceRandom: Setter for the field locationMap.
setLog(boolean) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets whether log output should be produced.
setLog(Consumer) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Sets a log sink for the search trace.
setLog(Consumer) - Method in class edu.cmu.tetrad.search.rlcd.RlcdClusterSearch: Sets a log sink for the search trace.
setLogCiDetails(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether to log the individual conditional independence results: the independence test itself is put in verbose mode (every CI query printed), and removal/trial log lines include the sepset's p-value (one extra test call per line).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether the final FCI orientation engine logs its rule-by-rule output.
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets whether the final FCI orientation engine logs its rule-by-rule output.
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether the FCI orientation engine logs its rule-by-rule output (R1-R4 firings, discriminating-path stamps).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether the FCI orientation engine logs its rule-by-rule output (R1-R4 firings, discriminating-path stamps).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the FCI orientation engine logs its rule-by-rule output (R1-R4 firings, discriminating-path stamps).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether the final FCI orientation engine logs its rule-by-rule output.
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether the final FCI orientation engine logs its rule-by-rule output (R1-R4 firings, BK orientation banners, discriminating-path stamps).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets whether the final FCI orientation engine logs its rule-by-rule output (R1-R4 firings, BK orientation banners, discriminating-path stamps).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets whether the final FCI orientation engine logs its rule-by-rule output (R1-R4 firings, BK orientation banners, discriminating-path stamps).
setLogFinalOrientations(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets whether the final FCI orientation engine logs its rule-by-rule output (R1-R4 firings, BK orientation banners, discriminating-path stamps).
setLogging(boolean) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets whether the logger is on or not.
setLoggingDirectory(String) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets the logging directory, but first checks whether we can write to it, etc.
setLoggingFilePrefix(String) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets the logging prefix.
setLogIntermediate(boolean) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the logging state for intermediate steps in a process.
setLogIntermediate(boolean) - Method in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: Sets whether intermediate steps of the computation should be logged.
setLogMaxPTies(boolean) - Method in class edu.cmu.tetrad.search.Fci: Configures whether logging is enabled for ties in the MAX-P condition during the FCI algorithm's causal discovery process.
setLogMaxPTies(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets whether ties in the MAX-P conditional independence tests are logged during execution.
setLogMaxPTies(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Sets whether ties in the MAX-P conditional independence tests are logged during execution.
setLogStream(PrintStream) - Method in class edu.cmu.tetrad.search.Fci: Sets the log stream for capturing output messages of the algorithm's execution.
setLogStream(PrintStream) - Method in class edu.cmu.tetrad.search.Pc: Sets the output stream for logging messages.
setLogStream(PrintStream) - Method in class edu.cmu.tetrad.search.PcAR: Sets the output stream for logging messages.
setLongDescription(String) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field longDescription.
setLookaheadDepth(int) - Method in class edu.cmu.tetrad.search.Gmas: Sets how many moves deep the search looks when the single-move sweep stalls.
setLowerBound(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanGlassFunction: Sets the lower bound for expression levels.
setLowerBound(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets the lower bound.
setLowerBoundDouble(double) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field lowerBoundDouble.
setLowerBoundInt(int) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field lowerBoundInt.
setLowerBoundLong(long) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field lowerBoundLong.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether to return dagToPag of the initializer's DAG representative with no edge removal.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

A flag indicating whether the LV-Heuristic results should be returned.
setLvHeuristicOnly(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: A flag indicating whether the LV-Heuristic results should be returned.
setMag(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Sets the MAG to wrap.
setMag(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Sets the MAG to wrap.
setMag(Graph) - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Sets the MAG to wrap.
setMagPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.Gmas: Sets the penalty discount c for the MAG score.
setManipulated(int, boolean) - Method in class edu.cmu.tetrad.bayes.Evidence: setManipulated.
setManipulated(int, boolean) - Method in class edu.cmu.tetrad.bayes.Manipulation: Setter for the field manipulated.
setManipulated(int, boolean) - Method in class edu.cmu.tetrad.sem.SemEvidence: setManipulated.
setManipulated(int, boolean) - Method in class edu.cmu.tetrad.sem.SemManipulation: Setter for the field manipulated.
setMapQuantiles(double, double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the lower and upper quantile thresholds for mapping.
setMargins(double, double) - Method in class edu.cmu.tetrad.search.ScoredClusterFinder: Sets the margin values for the preceding and succeeding clusters.
setMarkovAuditor(PcAR.MarkovAuditor) - Method in class edu.cmu.tetrad.search.PcAR: Supplies a real Markov-audit implication pass over the finished graph.
setMarkovCheckTest(IndependenceWrapper) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets the test used to Markov-check each setting's point graph, or null to skip Markov checking.
setMaskThreshold(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the mask threshold value used in the two-step regression process.
setMatrix(Matrix) - Method in class edu.cmu.tetrad.data.CorrelationMatrix: Sets the covariance matrix.
setMatrix(Matrix) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Sets the covariance matrix.
setMatrix(Matrix) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Sets the covariance matrix.
setMatrix(Matrix) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Sets the covariance matrix.
setMatrix(Matrix) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Sets the covariance matrix.
setMaxBackfitIters(int) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Sets the maximum number of iterations for the backfitting process.
setMaxBandwidthRows(int) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the maximum number of rows to consider for bandwidth estimation.
setMaxBandwidthRows(int) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the maximum number of bandwidth rows to be considered during computations.
setMaxBlockingPathLength(int) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets the maximum length of discriminating paths to consider during the search.
setMaxBlockingSetAdditions(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the cap on common neighbours the sepset search may add to RB's blocking set; negative means unbounded.
setMaxBlockingSetAdditions(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the cap on common neighbours the sepset search may add to RB's blocking set; negative means unbounded.
setMaxBlockingSetAdditions(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the cap on common neighbours the sepset search may add to RB's blocking set; negative means unbounded.
setMaxBlockingSetAdditions(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the cap on common neighbours the sepset search may add to RB's blocking set; negative means unbounded.
setMaxBlockingSetRemovals(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the maximum number of nodes droppable from RB's blocking set during separator search.
setMaxBlockingSetRemovals(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the maximum number of nodes droppable from RB's blocking set during separator search.
setMaxBlockingSetRemovals(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the maximum number of nodes droppable from RB's blocking set during separator search.
setMaxBlockingSetRemovals(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the maximum number of nodes droppable from RB's blocking set during separator search.
setMaxCacheSize(int) - Method in class edu.cmu.tetrad.search.utils.AdTree: Sets the maximum cache size.
setMaxCacheSize(int) - Method in class edu.cmu.tetrad.search.utils.AdTreeCount: Sets the maximum cache size.
setMaxCacheSize(int) - Method in class edu.cmu.tetrad.search.utils.AdTreeStoreRows: Sets the maximum size of the cache.
setMaxCellsPerStratum(int) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the maximum number of cells allowed per stratum.
setMaxCoverMoves(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the per-candidate move budget of the closure-cover search; the analog of maxForkFlips, but counting path-directed assignments.
setMaxCoverMoves(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the per-candidate move budget of the closure-cover search; the analog of maxForkFlips, but counting path-directed assignments.
setMaxCoverMoves(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the per-candidate move budget of the closure-cover search; the analog of maxForkFlips, but counting path-directed assignments.
setMaxCoverMoves(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the per-candidate move budget of the closure-cover search; the analog of maxForkFlips, but counting path-directed assignments.
setMaxDegree(int) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Sets the maximum degree of any nodes in the graph.
setMaxDegree(int) - Method in class edu.cmu.tetrad.search.Fges: The maximum of parents any nodes can have in the output pattern.
setMaxDegree(int) - Method in class edu.cmu.tetrad.search.FgesMb: The maximum of parents any nodes can have in the output pattern.
setMaxDegree(int) - Method in class edu.cmu.tetrad.search.Gfci: Sets the maximum indegree of the output graph.
setMaxDegree(int) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the maximum indegree of the output graph.
setMaxDegree(int) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets the maximum degree for the graph search procedure.
setMaxDegree(int) - Method in class edu.cmu.tetrad.search.SpFci: Sets the max degree of the search.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.Fci: Sets the maximum discriminating path length for the algorithm or process.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.Fcit: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the maximum length of the discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.Rfci: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.SpFci: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.StarFci: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciB: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciC: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciD: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.utils.MagToPag: Sets the maximum length of discriminating paths to be considered during processing.
setMaxDiscriminatingPathLength(int) - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Sets the maximum length of any discriminating path.
setMaxDiscriminatingPathLength(int) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Sets the maximum length of any discriminating path.
setMaxEdges(int) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Sets the maximum number of edges in the graph.
setMaxForkFlips(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the maximum number of fork nodes converted to colliders when building an out-of-class seed.
setMaxForkFlips(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the maximum number of fork nodes converted to colliders when building an out-of-class seed.
setMaxForkFlips(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the maximum number of fork nodes converted to colliders when building an out-of-class seed.
setMaxForkFlips(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the maximum number of fork nodes converted to colliders when building an out-of-class seed.
setMaxForwardParents(int) - Method in class edu.cmu.tetrad.search.Cam: Sets the maximum number of forward parents that can be considered in the algorithm for the current Cam instance.
setMaxGateTrialsPerEdge(int) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the cap on legality-gate trials per edge per visit.
setMaxGateTrialsPerEdge(int) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the cap on legality-gate trials per edge per visit.
setMaxGateTrialsPerEdge(int) - Method in class edu.cmu.tetrad.search.StarFci: Sets the cap on legality-gate trials per edge per visit.
setMaxGateTrialsPerEdge(int) - Method in class edu.cmu.tetrad.search.StarFciB: Sets the cap on legality-gate trials (re-orientation + certificate) per edge per visit on the gated path.
setMaxInDegree(int) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Sets the maximum indegree of any node in the graph.
setMaxit(int) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field maxit.
setMaxIterations(int) - Method in class edu.cmu.tetrad.cluster.KMeans: Sets the maximum number of iterations, or -1 if the algorithm is allowed to run unconstrainted.
setMaxIterations(int) - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Sets the maximum number of EM iterations.
setMaxIterations(int) - Method in class edu.cmu.tetrad.search.FastIca: Sets the maximum number of iterations for the FastICA algorithm.
setMaxIters(int) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Sets the RICF maximum number of sweeps.
setMaxIters(int) - Method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Sets the maximum number of iterations for the optimizer.
setMaxK(int) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the largest cluster cardinality to search for (default 3).
setMaxLag(int) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Setter for the field maxLag.
setMaxLagAllowable(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ActiveLagGraph: Sets the maximum allowable lag.
setMaxLagAllowable(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: Sets the maximum allowable lag.
setMaxLagAllowable(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Sets the maximum allowable lag.
setMaxLagAllowable(int) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Sets the maximum allowable lag.
setMaxLatentSubsetSize(int) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets the maximum latent subset size for higher-rank expansion.
setMaxLeafNodes(int) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the maximum number of leaf nodes for the nuisance tree used in the test.
setMaxLeafNodes(int) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the maximum number of leaf nodes allowed for the decision trees used in the testing process.
setMaxLength(int) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the maximum path length for relevant paths.
setMaxLength(int) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the maximum length for relevant paths.
setMaxLengthAdjustment(int) - Method in class edu.cmu.tetrad.search.Ida: Sets the maximum length adjustment value.
setMaxLengthAdjustment(int) - Method in class edu.cmu.tetrad.search.PdagPagIda: Sets the maximum length adjustment value for internal computations.
setMaxLookaheadFirstMoves(int) - Method in class edu.cmu.tetrad.search.Gmas: Sets the cap on the number of first moves expanded per stall by the two-move escape.
setMaxLookaheadPairs(int) - Method in class edu.cmu.tetrad.search.Gmas: Deprecated.
Use Gmas.setMaxLookaheadFirstMoves(int).
setMaxMarkFlips(int) - Method in class edu.cmu.tetrad.search.Fcit: Caps the number of single-mark flip hypotheses tried per pair by the mark-flip escalation.
setMaxMarkFlips(int) - Method in class edu.cmu.tetrad.search.Fcit2: Caps the number of single-mark flip hypotheses tried per pair by the mark-flip escalation.
setMaxMarkFlips(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Caps the number of single-mark flip hypotheses tried per pair by the mark-flip escalation.
setMaxMarkFlips(int) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the maximum number of mark-flip hypotheses tried per pair; -1 for unlimited.
setMaxMarkFlips(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the maximum number of mark-flip hypotheses tried per pair; -1 for unlimited.
setMaxMarkFlips(int) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the maximum number of mark-flip hypotheses tried per pair; -1 for unlimited.
setMaxNumShifts(int) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field maxNumShifts.
setMaxObservedLevelsPerVar(int) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the maximum number of observed levels allowed per variable.
setMaxOutDegree(int) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Sets the maximum outdegree of any node in the graph.
setMaxPasses(int) - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: Sets the cap on passes over the variables per restart.
setMaxPathLength(int) - Method in class edu.cmu.tetrad.search.GRegressionPag: Sets the maximum path length for the amenability and adjustment searches.
setMaxPathLength(int) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets the maximum path length for the graph search procedure.
setMaxPDepthStratified(boolean) - Method in class edu.cmu.tetrad.search.Fci: Configures whether the depth-specific stratification of the MAX-P condition is enabled during the FCI algorithm's causal discovery process.
setMaxPDepthStratified(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets whether the MAX-P depth stratification procedure is enabled or disabled.
setMaxPDepthStratified(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Sets whether the MAX-P depth stratification procedure is enabled or disabled.
setMaxPGlobalOrder(boolean) - Method in class edu.cmu.tetrad.search.Fci: Configures whether the global MAX-P order is enabled to avoid order dependence during the causal discovery process in the FCI algorithm.
setMaxPGlobalOrder(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets the global order-independent MAX-P collider orientation option.
setMaxPGlobalOrder(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Sets the global order-independent MAX-P collider orientation option.
setMaxPlateauDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Sets the cap on the length of a plateau path (chain of neutral retypes) per escape attempt.
setMaxPlateauStates(int) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Sets the cap on distinct plateau states visited per escape attempt.
setMaxPMargin(double) - Method in class edu.cmu.tetrad.search.Fci: Sets the maximum probability margin (max-P margin) to be used in the FCI algorithm during the causal discovery process.
setMaxPolishMoves(int) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Sets the safety cap on accepted moves in the polish phase.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.StarFci: Sets the maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciB: Maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciC: Maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciD: Maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Maximum path length for possible d-sep.
setMaxPossibleDsepPathLength(int) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Maximum path length for possible d-sep.
setMaxReachablePathLength(int) - Method in class edu.cmu.tetrad.search.utils.PossibleDsepFci: Sets the maximum reachable path length for the search algorithm.
setMaxRescuePasses(int) - Method in class edu.cmu.tetrad.search.PcAR: Sets R, the maximum number of clash-detect-then-recover passes PcAR.search(List) will run (AUGMENTED-SEARCH's bounded iteration, Clark's note Sec.
setMaxRuns(int) - Method in class edu.cmu.tetrad.search.BossTopK: Sets a hard cap on the total number of hill-climb runs (initial restarts plus deferred continuations).
setMaxRuns(int) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets a hard cap on the total number of hill-climb runs performed by the underlying suborder search (initial restarts plus deferred continuations).
setMaxRuns(int) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Sets a hard cap on the total number of hill-climb runs (initial restarts plus deferred continuations).
setMaxShift(int) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field maxShift.
setMaxSweeps(int) - Method in class edu.cmu.tetrad.search.Gmas: Sets the maximum number of greedy sweeps.
setMaxTestsPerPair(long) - Method in class edu.cmu.tetrad.search.Fcit: Sets a deterministic per-pair budget on independence tests, across all views.
setMaxTestsPerPair(long) - Method in class edu.cmu.tetrad.search.Fcit2: Sets a deterministic per-pair budget on independence tests, across all views.
setMaxTestsPerPair(long) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets a deterministic per-pair budget on independence tests, across all views.
setMean(int, int, double) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Sets the mean value for a continuous child node.
setMean(Node, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setMean.
setMean(Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the mean value for a given node in the variableNodes list.
setMeanHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field meanHigh.
setMeanLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field meanLow.
setMeanRange(double, double) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: setMeanRange.
setMeanStandardDeviation(Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the standard deviation value for the specified node.
setMeasuredDataSaved(boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets whether measured data should be saved out for this simulation.
setMeasuredInputs(List) - Method in class edu.cmu.tetrad.search.mimic.Dm: Sets measured inputs explicitly.
setMeasuredOutputs(List) - Method in class edu.cmu.tetrad.search.mimic.Dm: Sets measured outputs explicitly.
setMedianBandwidth(DataSet, Node) - Method in class edu.cmu.tetrad.search.utils.KernelGaussian: Sets the bandwidth of the kernel to median distance between two points in the given vector
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.PcMb: Sets whether cycles should be prevented, using a cycle checker.
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.utils.MeekRules: Sets whether cycles should be prevented by cycle checking.
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Sets to true just in case edges will not be added if they would create cycles.
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Sets to true just in case the output is guaranteed to be compatible with a CPDAG--i.e., no bidirected edges and no actual or implied cycles due to the Meek rules.
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Sets to true just in case edges will not be added if they would create cycles.
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Sets to true just in case edges will not be added if they would create cycles.
setMeekPreventCycles(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Sets to true just in case edges will not be added if they would create cycles.
setMethod(int) - Method in class edu.cmu.tetrad.data.ContinuousDiscretizationSpec: Sets the method used for discretization.
setMethod(PooledAdjacencySearch.Method) - Method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Sets the adjacency method.
setMinClusterSize(int) - Method in class edu.cmu.tetrad.search.MimbuildTrek: Deprecated.

Sets the minimum cluster size.
setMinClusterSize(int) - Method in class edu.cmu.tetrad.search.mimic.DmPcHybrid: Sets the minimum cluster size.
setMinClusterSize(int) - Method in class edu.cmu.tetrad.search.mimic.DmPcRobust: Sets the minimum cluster size to be used during the clustering process.
setMinCorrelation(double) - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: Sets the minimum score a variable needs to be used as a predictor of a target (as quickpred does in the R mice package).
setMinCountPerCell(double) - Method in class edu.cmu.tetrad.search.test.ChiSquareTest: The minimum number of counts per conditional table for chi-square for that table and its degrees of freedom to be included in the overall chi-square and degrees of freedom.
setMinCountPerCell(double) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: The minimum number of counts per conditional table for chi-square for that table and its degrees of freedom to be included in the overall chi-square and degrees of freedom.
setMinCountPerCell(double) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: The minimum number of counts per conditional table for chi-square for that table and its degrees of freedom to be included in the overall chi-square and degrees of freedom.
setMinDiscount(int) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field minDiscount.
setMinIndicatorSupport(int) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets the minimum indicator support count.
setMinIndicatorSupportProportion(double) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets the minimum indicator support proportion.
setMinN(int) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the minimum value of 'minN' and ensures it is not less than 2.
setMinRedundancy(int) - Method in class edu.cmu.tetrad.search.Tsc: Sets the minimum redundancy value.
setMinSampleSize(int) - Method in class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: Sets the least number of complete rows a query must have to be answered; below it the query is reported as a rejection, so no rank deficiency is claimed.
setMinSampleSizePerCell(int) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Sets the minimum sample size per cell.
setMinSampleSizePerCell(int) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Sets the minimum sample size per cell for scoring calculations during the learning process.
setMinSampleSizePerCell(int) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Sets the minimum sample size per cell for the independence test.
setMinScale(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the minimum scale value for the component.
setMinStratumSize(int) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the minimum size for a stratum.
setMissingAsCensored(boolean) - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Sets how a NaN in a bounded column is read: as censored at that column's bound (true), or as an ordinary missing value (false, the default).
setMissingDataSpec(MissingDataSpec) - Method in class edu.cmu.tetrad.search.Rlcd: Tells the search how to treat missing values in a single data set, by Tetrad's standard MissingDataSpec.
setMlag(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Setter for the field mlag.
setMlag(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraphParams: Sets the maximum lag.
setMultiplier(WildBootstrapMarkovCheck.Multiplier) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Sets the multiplier distribution for the wild bootstrap.
setN(int) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field n.
setName(String) - Method in class edu.cmu.tetrad.data.AbstractVariable: Sets the name of this node.
setName(String) - Method in class edu.cmu.tetrad.data.BoxDataSet: Sets the name of the data model (may be null).
setName(String) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Sets the name of the covariance matrix.
setName(String) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Sets the name of the covariance matrix.
setName(String) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Sets the name of the covariance matrix.
setName(String) - Method in interface edu.cmu.tetrad.data.DataModel: Sets the name of the data model (may be null).
setName(String) - Method in class edu.cmu.tetrad.data.DataModelList: Sets the name of the data model (may be null).
setName(String) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Sets the name of the covariance matrix.
setName(String) - Method in class edu.cmu.tetrad.data.IndependenceFacts: Sets the name of the data model (may be null).
setName(String) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Sets the display name of this structure.
setName(String) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Sets the name of the data model (may be null).
setName(String) - Method in class edu.cmu.tetrad.data.TimeSeriesData: Sets the name of the data model (may be null).
setName(String) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets the name of this node.
setName(String) - Method in interface edu.cmu.tetrad.graph.Node: Sets the name of this node.
setName(String) - Method in class edu.cmu.tetrad.sem.Parameter: Sets the name for this parameter.
setName(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the name.
setName(String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Sets the name of this matrix
setNaturalEdgeLength(double) - Method in class edu.cmu.tetrad.graph.LayoutUtil.FruchtermanReingoldLayout: Sets the natural edge length, the distance at which an edge's pull and its endpoints' mutual push balance.
setNaturalEdgeLength(double) - Method in class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Sets the natural length of an edge.
setNEstimators(int) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the number of estimators (boosting iterations) used per nuisance model during training.
setNEstimators(int) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the number of boosting iterations (nEstimators) used per nuisance model.
setNextOutputStream() - Method in class edu.cmu.tetrad.util.TetradLogger: Sets the next output stream to use it for logging, call removeNextOutputStream to remove it.
setNLevels(int) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the number of threshold levels (nLevels) used in the computation.
setNLevels(int) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the number of Y-threshold levels (nLevels) used in the testing process.
setNoAmenablePolicy(RecursiveAdjustment.NoAmenablePolicy) - Method in class edu.cmu.tetrad.search.RecursiveAdjustment: Sets the policy for handling cases where the graph is not amenable relative to (X, Y).
setNoAmenablePolicy(RecursiveAdjustment.NoAmenablePolicy) - Method in class edu.cmu.tetrad.search.RecursiveAdjustmentMultiple: Sets the no amenable policy for the adjustment process.
setNoData(boolean) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field noData.
setNodeExpression(Node, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the expression for a given node.
setNodeName(int, String) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Sets the name of node i in this graph
setNodes(List) - Method in class edu.cmu.tetrad.data.IndependenceFacts: Setter for the field nodes.
setNodes(List) - Method in class edu.cmu.tetrad.graph.Dag: Set the nodes of the graph.
setNodes(List) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: setNodes.
setNodes(List) - Method in interface edu.cmu.tetrad.graph.Graph: setNodes.
setNodes(List) - Method in class edu.cmu.tetrad.graph.LagGraph: setNodes.
setNodes(List) - Method in class edu.cmu.tetrad.graph.SemGraph: setNodes.
setNodes(List) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Sets the nodes of the graph.
setNodes(List) - Method in class edu.cmu.tetrad.search.Ida.NodeEffects: Sets the nodes.
setNodes(List) - Method in class edu.cmu.tetrad.search.PdagPagIda.NodeEffects: Sets the list of nodes in this object.
setNodeType(NodeType) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Sets the node type for this node.
setNodeType(NodeType) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Sets the node type for this node.
setNodeType(NodeType) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets the node type for this node.
setNodeType(NodeType) - Method in interface edu.cmu.tetrad.graph.Node: Sets the node type for this node.
setNodeVariableType(NodeVariableType) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Sets the type (domain, interventional status, interventional value..) for this node variable
setNodeVariableType(NodeVariableType) - Method in class edu.cmu.tetrad.data.DiscreteVariable: Sets the type (domain, interventional status, interventional value..) for this node variable
setNodeVariableType(NodeVariableType) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets the type (domain, interventional status, interventional value..) for this node variable
setNodeVariableType(NodeVariableType) - Method in interface edu.cmu.tetrad.graph.Node: Sets the type (domain, interventional status, interventional value..) for this node variable
setNoiseModel(HeavyTailSemBicScore.NoiseModel) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the noise model used in the score calculation, and clears the cache.
setNonGaussianBonus(HeavyTailSemBicScore.NonGaussianBonus) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the non-Gaussian bonus for this instance.
setNonSingularDepth(int) - Method in class edu.cmu.tetrad.search.Grasp: Sets the maximum depth at which singular tucks can be performed within the depth-first search of GRaSP.
setNonSingularDepth(int) - Method in class edu.cmu.tetrad.search.GraspFci: Sets depth for non-singular tucks.
setNonSingularDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field nonSingularDepth.
setNormalizeLossByN(boolean) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

If true, Step-1 uses lambdaEff = lambda * (ln N)/2 (paper-style).
setNoveltyStrength(double) - Method in class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Setter for noveltyStrength.
setNparents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the number of parents.
setNu(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the value of nu.
setNu(double) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the value of nu, which must be a finite number greater than 2.
setNumBasis(int) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Sets the number of basis functions, ensuring a minimum value of 4.
setNumberFormat(NumberFormat) - Method in class edu.cmu.tetrad.data.BoxDataSet: The number formatter used to print out continuous values.
setNumberFormat(NumberFormat) - Method in interface edu.cmu.tetrad.data.DataSet: The number formatter used to print out continuous values.
setNumberFormat(NumberFormat) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: The number formatter used to print out continuous values.
setNumberFormat(NumberFormat) - Method in class edu.cmu.tetrad.util.NumberFormatUtil: Sets the number format, nf.
setNumBins(int) - Method in class edu.cmu.tetrad.data.Histogram: Sets the number of bins to be used in the histogram.
setNumBootstraps(int) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the number of bootstrap resamples per dataset used to estimate Var(lr_k).
setNumBootstraps(int) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Sets the number of bootstrap replicates B.
setNumBscBootstrapSamples(int) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets the number of bootstrap samples.
setNumCategories(Node, int) - Method in class edu.cmu.tetrad.bayes.BayesPm: Sets the number of values for the given node to the given number.
setNumCategoriesToDiscretize(int) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Sets the number of categories to use to discretize child variables to avoid integration
setNumCategoriesToDiscretize(int) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Sets the number of categories to be used for discretizing child variables in order to avoid integration.
setNumCategoriesToDiscretize(int) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Sets the number of categories used to discretize variables.
setNumCellsPerDish(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the number of cells per dish.
setNumCellsPerDish(int) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setNumCellsPerDish.
setNumDishes(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the number of dishes that are to be simulated.
setNumDishes(int) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setNumDishes.
setNumEdges(int) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field numEdges.
setNumEdges1(int) - Method in class edu.cmu.tetrad.simulation.GdistanceRandom: Setter for the field numEdges1.
setNumEdges2(int) - Method in class edu.cmu.tetrad.simulation.GdistanceRandom: Setter for the field numEdges2.
setNumExpansions(int) - Method in class edu.cmu.tetrad.search.FgesMb: Sets the number of expansions for a given object.
setNumFactors(int) - Method in class edu.cmu.tetrad.search.FactorAnalysis: Sets the number of factors to find.
setNumFeatures(int) - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Sets the number of features for computations in the current instance.
setNumFeatures(int) - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Sets the number of features for computations in the current instance.
setNumFeatures(int) - Method in class edu.cmu.tetrad.search.score.FfMl: Sets the number of features to be used and resets the internal cache.
setNumFeatures(int) - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Sets the number of features for computations in the current instance.
setNumFeaturesXY(int) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the number of features in the XY dimension.
setNumFeaturesXY(int) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the number of features in the XY dimension.
setNumFeaturesXY(int) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the number of features along the XY direction, ensuring it is at least 1.
setNumFeaturesXY(int) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets the number of features to be used for the XY component in the calculation.
setNumFeaturesXY(int) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the number of features corresponding to the XY interaction for this instance.
setNumFeaturesYAug(int) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the number of augmented Y features for this instance.
setNumFeaturesZ(int) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the number of features for the Z component.
setNumFeaturesZ(int) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the number of features for the Z component.
setNumFeaturesZ(int) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the number of features in the Z dimension.
setNumFeaturesZ(int) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets the number of features to be used for the Z component in the calculation.
setNumFeaturesZ(int) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the number of features corresponding to the Z interaction for this instance.
setNumFeatXY(int) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the number of features along the XY axes.
setNumFeatZ(int) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the number of features for the Z dimension.
setNumFeatZ(int) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the number of features for Z.
setNumFeatZ(int) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the value of the numFeatZ property.
setNumFunctions(int) - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Sets the number of functions used in the ConditionalCorrelationIndependence analysis.
setNumInitialLags(int) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Setter for the field numInitialLags.
setNumIterations(int) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Sets the number of iterations for the Markov chain process.
setNumIterations(int) - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Setter for the field numIterations.
setNumMultiplierSamples(int) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Sets the number of Rademacher multiplier bootstrap samples used for the max-statistic p-value.
setNumNodes(int) - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Sets the number of nodes and resets all of the other parameters to default values accordingly.
setNumPermutations(int) - Method in class edu.cmu.tetrad.search.test.IndTestGin: Sets the number of permutations to be used for the independence test.
setNumPermutations(int) - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Sets the number of permutations.
setNumPermutations(int) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the number of permutations to be used in permutation tests.
setNumRandomizedSearchModels(int) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the number of randomized search models.
setNumRandomizedSearchModels(int) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets the number of randomized search models.
setNumReplications(int) - Method in class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi: Sets the number of replications per sample-size condition.
setNumReplications(int) - Method in class edu.cmu.tetrad.search.harness.tsc.SimulationStudy: Sets the number of replications per condition (default 100).
setNumResamples(int) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets the number of resamples drawn per sweep and shared across settings.
setNumRestarts(int) - Method in class edu.cmu.tetrad.search.Flop: Sets the number of iterated local search (ILS) restarts.
setNumRestarts(int) - Method in class edu.cmu.tetrad.sem.SemEstimator: Setter for the field numRestarts.
setNumRestarts(int) - Method in interface edu.cmu.tetrad.sem.SemOptimizer: setNumRestarts.
setNumRestarts(int) - Method in class edu.cmu.tetrad.sem.SemOptimizerEm: setNumRestarts.
setNumRestarts(int) - Method in class edu.cmu.tetrad.sem.SemOptimizerPowell: setNumRestarts.
setNumRestarts(int) - Method in class edu.cmu.tetrad.sem.SemOptimizerRegression: setNumRestarts.
setNumRestarts(int) - Method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Sets the number of restarts for the optimizer.
setNumRestarts(int) - Method in class edu.cmu.tetrad.sem.SemOptimizerScattershot: setNumRestarts.
setNumSamplesPerDish(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the number of samples that will be generated in the measured data for each dish.
setNumSamplesPerDish(int) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setNumSamplesPerDish.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.Bfci: Sets the number of times to restart the search
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the number of BOSS restarts.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the number of BOSS restarts.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BfciC: Sets the number of times to restart the search
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BfciD: Sets the number of times to restart the search
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the number of times to restart the BOSS search.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.Boss: Sets the number of random starts to use.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BossCcd: Sets the number of random restarts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.BossTopK: Sets the number of random starts to use.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.Fcit: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.Gmas: Sets the number of BOSS starts.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.Grasp: Sets the number of times the best order algorithm should be rerun with different starting permutations in search of a best BIC scoring permutation.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.GraspFci: Sets the number of starts for GRaSP.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets the number of starts for BOSS.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field numStarts.
setNumStarts(int) - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: Sets the number of restarts; the first uses the variables as given, the rest seeded shuffles.
setNumSubsamples(int) - Method in class edu.cmu.tetrad.search.Cstar: Sets the number of subsamples.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.Bfci: Sets the number of threads to use.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets the number of threads BOSS uses.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets the number of threads BOSS uses.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.BfciC: Sets the number of threads to use.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.BfciD: Sets the number of threads to use.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the number of threads to use in BOSS.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.Boss: Sets the number of threads to use.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.BossTopK: Sets the number of threads to use.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.Fges: Sets the number of threads to use.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.Gfci: Sets the number of threads to use in the search.
setNumThreads(int) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the number of threads to use in the search.
setNumThreads(int) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the number of threads for parallel execution.
setNumTrials(int) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets the number of trials to be used for computations.
setNumTrials(int) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Sets the number of trials for the independence test.
setNumVars(int) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field numVars.
setObject(int, int, Object) - Method in class edu.cmu.tetrad.data.BoxDataSet: Sets the value at the given (row, column) to the given value.
setObject(int, int, Object) - Method in interface edu.cmu.tetrad.data.DataSet: Sets the value at the given (row, column) to the given value.
setObject(int, int, Object) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Sets the value at the given (row, column) to the given value.
setObservedExplanationCheck(boolean, double) - Method in class edu.cmu.tetrad.search.Rlcd: Sets whether a rank deficiency must survive an observed-explanation check before it becomes a cluster (default false).
setObservedExplanationCheck(boolean, double, int, int) - Method in class edu.cmu.tetrad.search.rlcd.RlcdClusterSearch: Sets whether a rank deficiency is checked for an observed explanation before it is accepted as a cluster (default false).
setOneEdgeFaithfulnessAssumed(boolean) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field oneEdgeFaithfulnessAssumed.
setOnlyCanCauseNextTier(int, boolean) - Method in class edu.cmu.tetrad.data.Knowledge: setOnlyCanCauseNextTier.
setOptimalAcrossBranches(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: Sets whether every ordering visited across all branches is offered to the top-k pool (including orderings that are suboptimal within their own branch), rather than only each branch's converged optimum.
setOptimalAcrossBranches(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets whether every ordering visited across all branches is offered to the top-k pool (including orderings that are suboptimal within their own branch), rather than only each branch's converged optimum.
setOptimalAcrossBranches(boolean) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Sets whether every ordering visited across all branches is offered to the top-k pool (including orderings that are suboptimal within their own branch), rather than only each branch's converged optimum.
setOrder(List) - Method in class edu.cmu.tetrad.search.PermutationSearch: Sets the order list for the search.
setOrder(List) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets the order list for the search.
setOrder(List) - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Sets the order.
setOrder(List) - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Sets the order.
setOrder(List) - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Sets the order.
setOrdered(boolean) - Method in class edu.cmu.tetrad.search.Grasp: True if GRasP0 should be performed before GRaSP1 and GRaSP1 before GRaSP2.
setOrdered(boolean) - Method in class edu.cmu.tetrad.search.GraspFci: Sets whether to use the ordered version of GRaSP.
setOrdered(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field ordered.
setOrderRefinement(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets whether the PC result should be refined by a permutation search over causal orders that uses the same independence test.
setOrientAndPrune(boolean) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets whether to orient latent-latent edges after pruning.
setOrientAndPrune(boolean) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets whether to orient latent-latent edges after pruning.
setOrientationAlpha(double) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the orientation alpha.
setOrientationAlpha(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the significance level for the orientation decision.
setOrientationBlindFallback(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether the sepset search retries with RB run against the skeleton after every oriented pass fails.
setOrientationBlindFallback(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether the sepset search retries with RB run against the skeleton after every oriented pass fails.
setOrientationBlindFallback(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether the sepset search retries with RB run against the skeleton after every oriented pass fails.
setOrientationBlindFallback(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether the sepset search retries with RB run against the skeleton after every oriented pass fails.
setOrientationProportion(double) - Method in class edu.cmu.tetrad.search.mimic.LatentGraphRefinement: Sets the orientation proportion which defines the spatial distribution ratio.
setOrientStrongerDirection(boolean) - Method in class edu.cmu.tetrad.search.Pairwise: Sets whether orientation should be done in the stronger direction, where applicable.
setOut(OutputStream) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field out.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.Fges: Sets the output stream that output (except for log output) should be sent to.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.FgesMb: Sets the output stream that output (except for log output) should be sent to.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.Gfci: Sets the print stream used for output, default System.out.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.GfciOld: Sets the print stream used for output, default System.out.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets the output stream for the instance.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.SpFci: Sets the output stream used to print.
setOut(PrintStream) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets the output stream that output (except for log output) should be sent to.
setOut(PrintStream) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Setter for the field out.
setOut(PrintStream) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets the output stream that output (except for log output) should be sent to.
setOut(String, int, int, int, int, String, int, double, double, String) - Method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: setOut.
setOutputDelimiter(Character) - Method in class edu.cmu.tetrad.data.BoxDataSet: The character used a delimiter when the dataset is output
setOutputDelimiter(Character) - Method in interface edu.cmu.tetrad.data.DataSet: The character used a delimiter when the dataset is output
setOutputDelimiter(Character) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: The character used a delimiter when the dataset is output
setOutputNames(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Sets the output variable names directly.
setOutputNames(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Sets the output variable names directly.
setOutputNames(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Sets the output variable names directly.
setOutputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets known output variable names.
setOutputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss: Sets known output variable names.
setOutputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Sets known output variable names.
setOutputNames(Collection) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets known output variable names.
setOutputRBD(boolean) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets whether the output should be RBD.
setOutputs(int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field outputs.
setOutputs(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTrekMimic: Deprecated.

Sets the observed variables known to be outputs from the latent structure.
setOutputs(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Sets the observed variables known to be outputs from the latent structure.
setOutputs(Collection) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Sets the observed variables known to be outputs from the latent structure.
setOverrideBlockLength(int) - Method in class edu.cmu.tetrad.search.Fask: Sets the block length for the override bootstrap.
setOverrideBootstraps(int) - Method in class edu.cmu.tetrad.search.Fask: Sets the number of bootstrap resamples gating an override of a compelled external orientation.
setPag(boolean) - Method in class edu.cmu.tetrad.search.test.MsepTest: True iff the graph is a PAG
setParallel(boolean) - Method in class edu.cmu.tetrad.search.Tsc: Sets the parallel processing mode.
setParallel(boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets whether the discriminating path orientation should be run in parallel.
setParallelism(int) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the number of threads to be used for parallel processing.
setParallelized(boolean) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets whether resample searches and the Markov check run in parallel.
setParallelized(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field parallelized.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the parallel processing mode.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.BossCcd: Sets whether CCD's Step D (dotted-underline discovery) is parallelized across processor cores.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.Ccd: Sets whether Step D (dotted-underline discovery) distributes its per-node searches across all available processor cores.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.Cstar: Sets whether the algorithm should be parallelized.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.FgesMb: Sets the parallelized flag of the object.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: True if the checks should be parallelized.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets the parallel processing mode for the StarFci class.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets the parallel processing mode for the StarFci class.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets the parallel processing mode for the StarFci class.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets the parallel processing mode for the StarFciKeepKnowledgeOrientations class.
setParallelized(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets the parallel processing mode for the StarFci class.
setParamComparison(Parameter, Parameter, ParamComparison) - Method in class edu.cmu.tetrad.sem.SemPm: Sets the comparison of parameter a to parameter b.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Beta: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.ChiSquare: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Discrete: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in interface edu.cmu.tetrad.util.dist.Distribution: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Exponential: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Gamma: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.GaussianPower: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Indicator: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.LogNormal: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Normal: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Poisson: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.SingleValue: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Split: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: Sets the index'th parameter to the given value.
setParameter(int, double) - Method in class edu.cmu.tetrad.util.dist.Uniform: Sets the index'th parameter to the given value.
setParameterBoundsEnforced(boolean) - Method in interface edu.cmu.tetrad.sem.ISemIm: setParameterBoundsEnforced.
setParameterBoundsEnforced(boolean) - Method in class edu.cmu.tetrad.sem.SemIm: setParameterBoundsEnforced.
setParameterEstimationInitializationExpression(String, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the parameter estimation initialization expression for the given parameter.
setParameterEstimationInitializationExpression(String, String, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the expression which should be evaluated when calculating new values for the given parameter.
setParameterExpression(String, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the expression which should be evaluated when calculating new values for the given parameter.
setParameterExpression(String, String, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the expression which should be evaluated when calculating new values for the given parameter.
setParameters(Parameters) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets the parameters.
setParameters(Parameters) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets the parameters.
setParameters(Parameters) - Method in class edu.cmu.tetrad.search.Tgin: Deprecated.

Sets the parameters to be used for the analysis.
setParametersEstimationInitializationTemplate(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the template for parameter estimation initialization.
setParametersTemplate(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the parameter template for the object.
setParameterValue(Edge, double) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: setParameterValue.
setParameterValue(String, double) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: setParameterValue.
setParamName(String) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field paramName.
setParams(Parameters) - Method in class edu.cmu.tetrad.sem.SemIm: Setter for the field params.
setParamValue(Node, Node, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setParamValue.
setParamValue(Node, Node, double) - Method in class edu.cmu.tetrad.sem.SemIm: setParamValue.
setParamValue(Parameter, double) - Method in interface edu.cmu.tetrad.sem.ISemIm: setParamValue.
setParamValue(Parameter, double) - Method in class edu.cmu.tetrad.sem.SemIm: Sets the value of a parameter in the model.
setParentAssignmentThreshold(double) - Method in class edu.cmu.tetrad.search.mimic.DmPcHybrid: Sets the minimum fraction of a latent cluster's outputs that must appear in OUT(X) for X to be assigned to that latent.
setParentFrequencyThreshold(double) - Method in class edu.cmu.tetrad.search.mimic.DmPcRobust: Sets the threshold value for parent frequency.
setParents(NbComponent[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the parents.
setPartitionCliqueThreshold(int) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the minimum size of a stage-1 clique to be considered for a partition (default 3).
setPath(String) - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: Setter for the field path.
setPathContainingFallback(boolean) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the opt-in R4 fallback for recorded separators that do not contain the collider path; see the pathContainingFallback field.
setPcAlpha(double) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the alpha value for the PC algorithm.
setPcColliderStyle(UnmixSpec.ColliderStyle) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the collider style to be used in the PC algorithm for identifying colliders during causal graph discovery.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.Cam: Sets the penalty discount value for the current Cam instance.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.Flop: Sets the penalty discount, the multiplier on the ln(n) * |parents| BIC penalty term (lambda in the FLOP paper).
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the penalty discount value, which is used in scoring mechanisms within the class to adjust the influence of complexity penalties.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.MimbuildBollen: Sets the penalty discount value associated with the MimbuildBollen class.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.MimbuildPca: Sets the penalty discount value used in the PCA analysis.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the SEM BIC penalty discount for the stage-1 search (default 1, the standard BIC penalty; causal-learn's default GES sparsity of 0.5 corresponds to this value).
setPenaltyDiscount(double) - Method in interface edu.cmu.tetrad.search.score.AdditiveLocalScorer: Sets the penalty discount value used in the additive scoring framework.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Sets the penalty discount value, which is used to adjust the penalty term in the BIC score calculation.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Sets the penalty discount value, which is used to adjust the penalty term in the BIC score calculation.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Sets the penalty discount value used in score calculation.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets the penalty discount factor for score computations in this instance.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Sets the penalty discount parameter for the P-spline model.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Updates the penalty discount value used in the scoring calculations.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: Sets the penalty discount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Sets the penalty discount, which is a multiplier on the penalty term of BIC.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Sets the penalty discount, which is a multiplier on the penalty term of BIC.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.GicScores: Sets the penalty discount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the penalty discount used for regularization and penalization in the statistical model.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Sets the multiplier for the penalty term in this score.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the penalty discount factor used in the score calculation.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the penalty discount value.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Sets the multiplier on the BIC penalty term.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the penalty discount factor.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Sets the multiplier on the penalty term for this score.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the penalty discount factor for the score function.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.ScoredClusterFinder: Sets the penalty discount value to the given value.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: For BIC score, a multiplier on the penalty term.
setPenaltyDiscount(double) - Method in interface edu.cmu.tetrad.search.utils.HasPenaltyDiscount: setPenaltyDiscount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: Seets the penalty discount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: Seets the penalty discount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: Seets the penalty discount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Setter for the field penaltyDiscount.
setPenaltyDiscount(double) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field penaltyDiscount.
setPenaltyOrder(int) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Sets the penalty order for the P-spline model.
setPercent(double) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Sets the percentage value.
setPercentResampleSize(double) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets the fraction of the sample size drawn per resample, in (0, 1].
setPercentUnregulated(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Setter for the field percentUnregulated.
setPerFactEss(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets whether each implied fact is tested at its own block-based effective sample size (see PerFactEss).
setPerms(int) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Set the number of bootstrap samples to use
setPerms(int) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Set the number of bootstrap samples to use
setPermSeed(long) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the permutation seed to the specified value.
setPermutations(int) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the number of permutations to be used in the mixed independence test.
setPermutations(int) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the number of permutations to be used in the mixed independence test.
setPermutations(int) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the number of permutations to be used or processed.
setPermutations(int) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the value of permutations based on the input parameter.
setPermutations(int) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the number of permutations.
setPinvTolerance(double) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the tolerance for the pseudoinverse computation.
setPixelDigitalization(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the pixel digitalization error.
setPixelDigitalization(double) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setPixelDigitalization.
setPlateauEscapesEnabled(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.MagOrderProjection: Turns the polish phase's plateau escapes on or off; see the field.
setPnsTopK(int) - Method in class edu.cmu.tetrad.search.Cam: Sets the maximum number of top candidates to be considered during the Parent-Node Selection (PNS) process.
setPolyCoef0(double) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the value of the polynomial coefficient at index 0.
setPolyDegree(int) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the degree of the polynomial.
setPolyGamma(double) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the polyGamma value.
setPolynomial(int, Polynomial) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialFunction: Sets the polynomial for the given factor.
setPopulationGraph(Graph) - Method in class edu.cmu.tetrad.search.is.IsFges: Sets the population graph by either assigning it directly or replacing its nodes with the corresponding search variables using the utility method.
setPopulationGraph(Graph) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets the population graph used in the instance.
setPower(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the power.
setPrecomputeCovariances(boolean) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiff: Returns the precompute covariances flag.
setPrecomputeCovariances(boolean) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicDiffPerRecord: Returns true if the covariances are precomputed.
setPrecomputeCovariances(boolean) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicEst: Returns the precompute covariances flag.
setPrecomputeCovariances(boolean) - Method in class edu.cmu.tetrad.algcomparison.statistic.BicTrue: Returns whether to precompute covariances.
setPrecomputeCovariances(boolean) - Method in class edu.cmu.tetrad.calibration.DataForCalibrationRfci: Setter for the field precomputeCovariances.
setPrecomputeCovariances(boolean) - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: Setter for the field precomputeCovariances.
setPredictorDegree(int) - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: Sets the highest power of a continuous predictor used in the regressions.
setPreferLinear(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Setter for the field preferLinear.
setPreserveMarkovHelper(PreserveMarkov) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the PreserveMarkov helper.
setPriorCount(double) - Method in class edu.cmu.tetrad.bayes.CptMapCounts: Sets the prior count for all cells in the CptMapCounts.
setPriorEqivalentSampleSize(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference: Sets the prior equivalent sample size.
setPriorEquivalentSampleSize(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore: Sets the prior equivalent sample size (ESS), which is used in Bayesian scoring to incorporate prior knowledge or assumptions about the data into the scoring function.
setPriorEquivalentSampleSize(double) - Method in class edu.cmu.tetrad.search.score.BdeScore: Sets the sample prior for the BDe score.
setPriorEquivalentSampleSize(double) - Method in class edu.cmu.tetrad.search.score.BDeuScore: Sets the sample prior for the BdeuScore object.
setPriorEquivalentSampleSize(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Sets the sample prior.
setPriorEquivalentSampleSize(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Sets the sample prior.
setPriorEquivalentSampleSize(double) - Method in interface edu.cmu.tetrad.search.score.DiscreteScore: Sets the sample prior.
setPriorEquivalentSampleSize(double) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Sets the prior equivalent sample size for the independence test.
setPriorEquivalentSampleSize(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the prior equivalent sample size.
setProbability(double) - Method in class edu.cmu.tetrad.graph.Edge: Setter for the field probability.
setProbability(double) - Method in class edu.cmu.tetrad.graph.EdgeTypeProbability: Setter for the field probability.
setProbability(int, double) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: setProbability.
setProbability(int, double[][]) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Sets the probability for the given node.
setProbability(int, double[][]) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Sets the probability for the given node.
setProbability(int, double[][]) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Sets the probability for the given node.
setProbability(int, double[][]) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Sets the probability for the given node.
setProbability(int, double[][]) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: setProbability.
setProbability(int, int, int, double) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Sets the probability for the given node at a given row and column in the table for that node.
setProbability(int, int, int, double) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Sets the probability for the given node at a given row and column in the table for that node.
setProbability(int, int, int, double) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Sets the probability value for a specific node, row, and column in the probability table.
setProbability(int, int, int, double) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Sets the probability for the given node at a given row and column in the table for that node.
setProbability(int, int, int, double) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Sets the probability for the given node at a given row and column in the table for that node.
setProbability(int, int, int, double) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Sets the probability of a specific category for a discrete child node.
setProgressListener(LayoutUtil.LayoutProgressListener) - Method in class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Sets the progress listener.
setProximalEndpoint(Node, Endpoint) - Method in class edu.cmu.tetrad.graph.Edge: Sets the endpoint nearest the given nodes to the given endpoint.
setPruneAlpha(double) - Method in class edu.cmu.tetrad.search.Lingam: Alpha for the per-coefficient t-test in the refit-and-prune step.
setPruneAlpha(double) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets the prune alpha value, which must be within the range [0, 1].
setPruneInheritedParents(boolean) - Method in class edu.cmu.tetrad.search.LatentParentRecovery: Sets whether inherited measured parents should be pruned structurally after selection.
setPruneLatentEdges(boolean) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets whether to prune latent-latent edges.
setPseudocount(int, int, int, double) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: setPseudocount.
setQMinimax(double) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the qMinimax value, ensuring it is clamped between 0.50 and 1.0.
setQualifyObservedCauses(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Sets whether a directed edge between an observed variable O and a latent L is kept only when the data identify its direction (default true).
setQuickSubsetDepth(int) - Method in class edu.cmu.tetrad.search.Fcit: Sets the cap for the small-subset shortcut pass; -1 disables it.
setQuickSubsetDepth(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the cap for the small-subset shortcut pass; -1 disables it.
setQuickSubsetDepth(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the cap for the small-subset shortcut pass; -1 disables it.
setQuoteSensitive(boolean) - Method in class edu.cmu.tetrad.data.RegexTokenizer: True iff the parser should be aware of quotation marks and remove them from returned strings.
setR0ColliderRule(Fci.ColliderRule) - Method in class edu.cmu.tetrad.search.Fci: Configures the R0 collider rule to be used in the FCI algorithm.
setR2Orient2Cycles(boolean) - Method in class edu.cmu.tetrad.search.Pairwise: Sets for R2 whether cycles should be oriented.
setRaMode(RecursiveAdjustment.RaMode) - Method in class edu.cmu.tetrad.search.RecursiveAdjustment: Sets the Recursive Adjustment (RA) mode.
setRandomizeGrowShrink(boolean) - Method in class edu.cmu.tetrad.search.Flop: Sets whether the grow and shrink phases of the local search visit candidates in a random order, as in the authors' reference implementation, rather than in the index order of the current permutation.
setRandomizeSplits(boolean, long) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets whether to randomize the splits of columns when computing scores and specifies the seed to be used by the randomization process.
setRandomizeSplits(boolean, long) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Sets whether to randomize the splits and specifies a seed for randomization.
setRandomlyInitialized(boolean) - Method in class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Sets whether the spring layout should start from a randomly initialized position or from the getModel positions of the nodes.
setRandomSeed(long) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the random seed for reproducibility in the algorithm's processes.
setRank(int) - Method in class edu.cmu.tetrad.data.ContinuousVariable: Sets the rank for this variable.
setRank(int) - Method in interface edu.cmu.tetrad.graph.Node: Sets the rank, or -1 if no rank is set.
setRankTester(RankTester) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the rank test.
setRawDataSaved(boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets whether the raw data that is generated should be saved beyond what's needed for the getModel cell being simulated.
setRawDataSaved(boolean) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setRawDataSaved.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.Fcit: Sets the radius for the RB (Recursive Blocking) conditioning pool.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the radius for the RB (Recursive Blocking) conditioning pool.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the radius for the RB (Recursive Blocking) conditioning pool.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the radius for RA (Recursive Association) algorithm.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the radius for RA (Recursive Association) algorithm.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the radius for the RB (Recursive Blocking) algorithm, which bounds the scope of the recursive path-blocking search.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the radius for the RB (Recursive Blocking) algorithm, which bounds the scope of the recursive path-blocking search.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the radius for the RB (Recursive Blocking) algorithm, which bounds the scope of the recursive path-blocking search.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the radius for the RB (Recursive Blocking) algorithm, which bounds the scope of the recursive path-blocking search.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the radius value for the rbRadius property.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the radius value for the rbRadius property.
setRbRadius(int) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the radius for RB (Recursive Blocking) algorithm.
setRecoveryOddsEstimator(PcAR.RecoveryOddsEstimator) - Method in class edu.cmu.tetrad.search.PcAR: Supplies the posterior-odds calculation (base rate x likelihood ratio, Sec.
setRecoveryOddsThreshold(double) - Method in class edu.cmu.tetrad.search.PcAR: Sets the posterior-odds threshold at or above which a clash triggers RECOVER rather than MARK.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.Fcit: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the depth level for recursive operations.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets the maximum allowed blocking path length.
setRecursiveDepth(int) - Method in class edu.cmu.tetrad.search.utils.MagToPag: Sets the maximum length of blocking paths to be considered during processing.
setRefinementAlpha(double) - Method in class edu.cmu.tetrad.search.Pc: Sets the alpha used by the independence test during order refinement.
setRefitAndPrune(boolean) - Method in class edu.cmu.tetrad.search.Lingam: Whether fit() follows the acyclicity trimming with the regression refit-and-prune step.
setRegressor(IndTestGin.Regressor) - Method in class edu.cmu.tetrad.search.test.IndTestGin: Sets the regressor to be used for the independence test.
setRegressorType(Gcm.RegressorType) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the type of regressor to be used in the model.
setRegularizer(double) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Sets the regularizer.
setReharvestSepsets(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Turns sepset re-harvesting on or off (default off); see BfciBryan.reharvestSepsets.
setRepairStrategy(VertexRepairSearch.RepairStrategy) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets the repair strategy.
setReplicating(boolean) - Method in class edu.cmu.tetrad.search.Fges: Sets the replicating status of the current object.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Fas: (Unused))
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Fasd
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Fci: Sets the state of the replicatingGraph flag.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the flag indicating whether the graph should be replicated during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Grasp: Sets the replicatingGraph flag to determine if graph replication is enabled.
setReplicatingGraph(boolean) - Method in interface edu.cmu.tetrad.search.IFas: Sets whether the adjacency search should use a replicating graph.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets the value that determines whether the graph is in a replicating state.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Sets the value that determines whether the graph is in a replicating state.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearch: Sets the flag indicating whether graph replication is enabled or not.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets the flag indicating whether graph replication is enabled or not.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.Rfci: Sets the state of whether the graph is set to replicate or not during the search process.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether graphs should replicate edits across time lags (time-series data).
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether the working PAG should be a replicating (time-lag repeating) graph: if set, the PAG is maintained as a ReplicatingGraph with a LagReplicationPolicy, so that edge additions, removals, and endpoint orientations are mirrored across homologous lagged variable pairs during the search, as in the other SVAR-capable algorithms.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets whether the working PAG should be a replicating (time-lag repeating) graph: if set, the PAG is maintained as a ReplicatingGraph with a LagReplicationPolicy, so that edge additions, removals, and endpoint orientations are mirrored across homologous lagged variable pairs during the search, as in the other SVAR-capable algorithms.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets whether the working PAG should be a replicating (time-lag repeating) graph: if set, the PAG is maintained as a ReplicatingGraph with a LagReplicationPolicy, so that edge additions, removals, and endpoint orientations are mirrored across homologous lagged variable pairs during the search, as in the other SVAR-capable algorithms.
setReplicatingGraph(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets whether the working PAG should be a replicating (time-lag repeating) graph: if set, the PAG is maintained as a ReplicatingGraph with a LagReplicationPolicy, so that edge additions, removals, and endpoint orientations are mirrored across homologous lagged variable pairs during the search, as in the other SVAR-capable algorithms.
setRequired(String, String) - Method in class edu.cmu.tetrad.data.Knowledge: Marks the edge var1 --> var2 as required.
setRequireImapStates(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Turns the experimental I-map gate on or off (default off); see BfciBryan.requireImapStates.
setRequireImapStates(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Turns the I-map gate on or off (default off); see CorrectnessGates.setRequireImapStates(boolean).
setRequireImapStates(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the I-map gate is on (option 1 of the three-way correctness choice).
setRequireImapStates(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether the I-map gate is on (see the class doc, CORRECTNESS WARRANT, option 1).
setRequireImapStates(boolean) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Turns the I-map gate on or off.
setRequireNoScoreDrop(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Turns the score step on or off (default off).
setResampleGroups(int[]) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets the row groups for block resampling, or null (the default) for row resampling.
setRescueAction(PcAR.RescueAction) - Method in class edu.cmu.tetrad.search.PcAR: Sets what to do with a tier-1 clash positive.
setResetAfterBM(boolean) - Method in class edu.cmu.tetrad.search.Boss: Sets whether the grow-shrink trees should be reset after each best-mutation step.
setResetAfterBM(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: Sets whether the grow-shrink trees should be reset after each best-mutation step.
setResetAfterRS(boolean) - Method in class edu.cmu.tetrad.search.Boss: Sets whether the grow-shrink trees should be reset after each restart.
setResetAfterRS(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: Sets whether the grow-shrink trees should be reset after each restart.
setResidualizer(WildBootstrapMarkovCheck.Residualizer) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Sets the residualization used to regress X and Y on the conditioning set.
setResolveTwoIndicatorLatents(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Sets whether to try to replace two-indicator latents by direct edges, using non-Gaussianity or, failing that, the temporal tiers of the background knowledge (default false).
setRestarts(int) - Method in class edu.cmu.tetrad.search.Cam: Sets the number of restart attempts for the CAM algorithm.
setResultGraph(Graph) - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Setter for the field resultGraph.
setResultType(ComparisonParameters.ResultType) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field resultType.
setRetrySepsetsOnRevert(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether, after a gated removal reverts, the remaining data-valid separators for the pair are tried in canonical order until one commits.
setRetrySepsetsOnRevert(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether, after a gated removal reverts, the remaining data-valid separators for the pair are tried in canonical order until one commits.
setRevertToUnshieldedColliders(boolean) - Method in class edu.cmu.tetrad.search.utils.MeekRules: Sets whether orientations in the graph should be reverted to its unshielded colliders before performing any Meek rule orientations.
setRffEnsemble(int) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the number of random Fourier feature ensembles (RFF ensembles) to be used in computations.
setRffFeatures(int) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the number of Random Fourier Features (RFF) to be used.
setRffFeatures(int) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the number of random Fourier features (RFF) to be used in the model.
setRffSeed(long) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the random Fourier feature (RFF) seed used for generating random projections.
setRffSeed(long) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the seed value for the random Fourier features (RFF) used in the model.
setRffSigma(double) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the value of the RFF (Random Fourier Features) sigma parameter.
setRffSigma(double) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the bandwidth parameter (sigma) for random Fourier features (RFF).
setRhoAllEqual(double) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: setRhoAllEqual.
setRidge(double) - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Sets a ridge added to the diagonal of the covariance estimate at each M-step, as a fraction of the mean diagonal entry.
setRidge(double) - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Sets the ridge added to the diagonal of each iteration's covariance before inversion (default 1e-6).
setRidge(double) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the ridge parameter.
setRidge(double) - Method in class edu.cmu.tetrad.search.Cam: Sets the ridge parameter for the current Cam instance.
setRidge(double) - Method in interface edu.cmu.tetrad.search.score.AdditiveLocalScorer: Sets the ridge regularization parameter to be used in the additive scoring framework.
setRidge(double) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Sets the ridge parameter used in the scoring process.
setRidge(double) - Method in class edu.cmu.tetrad.search.score.BlocksTrekBicScore: Sets the ridge regularization parameter and clears the associated caches.
setRidge(double) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Sets the ridge parameter for the P-spline model to control the amount of regularization.
setRidge(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the ridge parameter used in the score calculation, and clears the cache.
setRidge(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the ridge parameter used in the computation.
setRidge(double) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the ridge parameter used in the computation.
setRidge(double) - Method in class edu.cmu.tetrad.search.ScoredClusterFinder: Sets the ridge parameter to the given value.
setRidge(double) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the ridge parameter for regularization in the regression model.
setRidge(double) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the value of the ridge parameter for this instance.
setRidgeLambda(double) - Method in class edu.cmu.tetrad.search.unmix.LinearQRRegressor: Set L2 ridge strength (applied only when ill-conditioning is detected).
setRightSuffix(String) - Method in class edu.cmu.tetrad.data.JoinDatasets: Sets the suffix appended to a right non-key column whose name collides with a left column (default "_right").
setRiskBound(double) - Method in class edu.cmu.tetrad.search.score.ZsbScore: Sets the risk bound for the Zhang Shen Bound score.
setRmax(int) - Method in class edu.cmu.tetrad.search.Tsc: The algorithm will consider ranks from rMin up to this value, rMax.
setRmin(int) - Method in class edu.cmu.tetrad.search.Tsc: The algorithm will consider ranks from this value up to rMax.
setRobustScaleResiduals(boolean) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets whether robust scaling of residuals is enabled.
setRobustStandardErrors(double[]) - Method in class edu.cmu.tetrad.sem.SemIm: Sets robust (sandwich) standard errors for the free parameters, e.g., Satorra-Bentler standard errors computed by SemSandwichEstimator.
setRow(int, double[]) - Method in class edu.cmu.tetrad.util.MView: Sets the values of a specific row in the matrix view.
setRow(int, Vector) - Method in class edu.cmu.tetrad.util.MView: Sets the values of a specific row in the matrix view using the provided vector.
setRowCaseCount(int, int, int) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Records the number of complete cases behind a local-table row.
setRowNorm(boolean) - Method in class edu.cmu.tetrad.search.FastIca: Sets whether row normalization should be applied.
setRows(int[]) - Method in class edu.cmu.tetrad.regression.LogisticRegression: Setter for the field rows.
setRows(int[]) - Method in class edu.cmu.tetrad.regression.RegressionDataset: Setter for the field rows.
setRows(List) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood: Sets the rows to be used in the table.
setRows(List) - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Sets the list of row indices
setRows(List) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the rows to be used in computations.
setRows(List) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the rows to be used in computations.
setRows(List) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Sets the subset of row indices to be used in computations or analyses.
setRows(List) - Method in class edu.cmu.tetrad.search.test.Gcm: Sets the list of row indices and clears the associated cache to ensure consistency.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Sets the row subset to test on (null = all rows).
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Allows the user to set which rows are used in the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Sets the rows to use for the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Sets the rows to use for the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Allows the user to set which rows are used in the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Restricts the test to a subset of rows (for subsampling / bootstrap).
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Restricts the test to a subset of rows (for subsampling / bootstrap).
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Sets the rows to use for the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Allows the user to set which rows are used in the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the list of row indices for the instance.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Sets the list of rows to use for the test.
setRows(List) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes
setRows(List) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest
setRows(List) - Method in class edu.cmu.tetrad.search.test.Rcit
setRows(List) - Method in interface edu.cmu.tetrad.search.test.RowsSettable: Sets the rows to use for the test.
setRowsPerPredictor(int) - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: Sets the fewest observed rows of a target allowed per predictor term (a continuous or two-category variable is one term; a variable with K categories is K - 1).
setRule(Pairwise.Rule) - Method in class edu.cmu.tetrad.search.Pairwise: Sets the rule to use to do the orientation.
setRuleType(GicScores.RuleType) - Method in class edu.cmu.tetrad.search.score.GicScores: Sets the rule type.
setRuleType(SemBicScore.RuleType) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Sets the rule type to use.
setSampleprior(double) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Sets the sample prior value.
setSamplePrior(double) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Sets the sample prior value for the BDeu scoring metric.
setSamplePrior(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Sets the prior probability assigned to the sample during computations or analysis.
setSamplePrior(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the sample prior value.
setSamplePrior(double) - Method in interface edu.cmu.tetrad.search.is.IsScore: Sets the sample prior or equivalent sample size used in the scoring process.
setSamplePrior(double) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: For BDeu score for discrete search; see Chickering (2002).
setSamplePrior(double) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field samplePrior.
setSampleSampleVariability(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the sample to sample variability.
setSampleSampleVariability(double) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setSampleSampleVariability.
setSampleSize(int) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Sets the sample size used in the covariance matrix.
setSampleSize(int) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Sets the sample size used in the covariance matrix.
setSampleSize(int) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Sets the sample size used in the covariance matrix.
setSampleSize(int) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Sets the sample size used in the covariance matrix.
setSampleSize(int) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field sampleSize.
setSampleStyle(Cstar.SampleStyle) - Method in class edu.cmu.tetrad.search.Cstar: Sets the sample style.
setSanitizeNames(boolean) - Method in class edu.cmu.tetrad.data.LongToWide: If true (the default), output column names have runs of characters other than letters, digits, underscore, period, and hyphen replaced by a single underscore, so that they survive whitespace-delimited contexts such as knowledge files.
setSaveCPDAGs(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets whether to save CPDAGs.
setSaveCPDAGs(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field saveCPDAGs.
setSaveData(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the flag indicating whether to save data.
setSaveDiagnostics(boolean) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets whether to save diagnostics.
setSaveGraphs(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets whether to save graphs.
setSaveGraphs(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field saveGraphs.
setSavePags(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the value of 'savePags' flag.
setSavePags(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field savePags.
setScale(double) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets the scale value for this object.
setScale(double) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Sets the scale factor for the object.
setScale(double, double, double, double) - Method in class edu.cmu.tetrad.util.RandomPiecewiseLinearBijective: Sets the scale of the function by updating the minimum and maximum values of the x-axis and y-axis.
setScalingFactor(double) - Method in class edu.cmu.tetrad.search.test.ConditionalCorrelationIndependence: Sets the bandwidth adjustment value for the ConditionalCorrelationIndependence analysis.
setScalingFactor(double) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the scaling factor for the Gaussian bandwidth heuristic.
setScore(Pairwise.Score) - Method in class edu.cmu.tetrad.search.Pairwise: Sets the (LoFS) score to use.
setScore(ScoreType) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field score.
setScoreCheckPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets the penalty discount c in the MAG BIC, bic = 2 * logLik - c * k * ln(n).
setScoreCheckPenaltyDiscount(double) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the penalty discount c used by the MAG score check, in bic = 2 * logLik - c * k * ln(n).
setScorer(AdditiveLocalScorer) - Method in class edu.cmu.tetrad.search.Cam: Sets the scorer to be used by the current CAM instance.
setScoreType(ScoreType) - Method in class edu.cmu.tetrad.sem.SemEstimator: Setter for the field scoreType.
setScoreType(ScoreType) - Method in class edu.cmu.tetrad.sem.SemIm: Setter for the field scoreType.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.DirectLingam: Sets the score wrapper for this DirectLingam instance.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.Fask: Sets the score wrapper for the object.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.continuous.dag.FaskOrig: Deprecated.

Sets the score wrapper for the object.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskConcatenated: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskPool: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FaskVote: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FciIod: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.FgesConcatenated: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Images: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesBoss: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.ImagesFges: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.multi.Ion: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in interface edu.cmu.tetrad.algcomparison.algorithm.MultiDataSetAlgorithm: Sets a score wrapper if not null.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Boss: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossCdnodScore
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.BossTopK: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodBoss
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.CdnodPag
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Cstar: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Fges: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.FgesMb: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Grasp: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.RestrictedBoss: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Bfci: Sets the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryan: Sets the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciBryanScore: Sets the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciC: Sets the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciD: Sets the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BfciScoreCheck: Sets the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossCcd: Updates the score wrapper for this algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossFci: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.BossPod: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.DmFcit: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Fcit: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMagMarkov: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitMarkov: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSl: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitSlScoreCheck: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.FcitZm: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gfci: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GfciOld: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.Gmas: Sets the score wrapper.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.GraspFci: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.LvHeuristic: Sets the score wrapper for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Sets the ScoreWrapper object for the algorithm.
setScoreWrapper(ScoreWrapper) - Method in interface edu.cmu.tetrad.algcomparison.utils.TakesScoreWrapper: Sets the score wrapper.
setSd(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the standard deviation.
setSeed(long) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets the random seed for drawing resamples, for reproducibility, or -1 for time-based.
setSeed(long) - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Sets the seed of the draws; used by the stochastic form only.
setSeed(long) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the seed for generating random numbers.
setSeed(long) - Method in class edu.cmu.tetrad.search.Flop: Sets the random seed used for the ILS perturbations, for reproducibility.
setSeed(long) - Method in class edu.cmu.tetrad.search.GraspFci: Setter for the field seed.
setSeed(long) - Method in class edu.cmu.tetrad.search.PermutationSearch: Sets the seed value used for generating random numbers.
setSeed(long) - Method in class edu.cmu.tetrad.search.PermutationSearchTopK: Sets the seed value used for generating random numbers.
setSeed(long) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the random seed for the stage-1 BOSS search; −1 (default) leaves it unseeded.
setSeed(long) - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Sets the random seed for reproducibility.
setSeed(long) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Sets the seed value for random number generation.
setSeed(long) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the seed for the random number generator and updates any associated components.
setSeed(long) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the seed for the random number generator and updates any associated components.
setSeed(long) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the base random seed for the test.
setSeed(long) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the base random seed for the internal pseudo-random processes of this test.
setSeed(long) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets the seed for the random number generator used within the instance.
setSeed(long) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the seed value for generating random values or reproducible sequences.
setSeed(long) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets the random seed used for node-traversal order during VertexRepairSearch.search().
setSeed(long) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Sets the RNG seed for the bootstrap, for reproducibility.
setSeed(long) - Method in class edu.cmu.tetrad.search.work_in_progress.LvBossExperimental: Sets the seed for the restart shuffles.
setSeed(long) - Method in class edu.cmu.tetrad.util.RandomUtil: Sets the seed to the given value.
setSeed(long) - Method in class edu.cmu.tetrad.util.StratifiedPermutationRankTest: Sets the seed for the permutation stream.
setSeedVertices(Set) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Restricts INITIAL candidate enumeration under VertexRepairSearch.RepairStrategy.GLOBAL_QUEUE to the given vertices, so repair effort starts where an external diagnostic (e.g.
setSelected(Node, boolean) - Method in class edu.cmu.tetrad.data.BoxDataSet: Marks the given column as selected if 'selected' is true or deselected if 'selected' is false.
setSelected(Node, boolean) - Method in interface edu.cmu.tetrad.data.DataSet: Marks the given column as selected if 'selected' is true or deselected if 'selected' is false.
setSelected(Node, boolean) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Marks the given column as selected if 'selected' is true or deselected if 'selected' is false.
setSelectedModel(DataModel) - Method in class edu.cmu.tetrad.data.DataModelList: Setter for the field selectedModel.
setSelectionAlpha(double) - Method in class edu.cmu.tetrad.search.Cstar: Sets the selection alpha.
setSelectionBias(boolean) - Method in class edu.cmu.tetrad.data.AbstractVariable: Sets the selection bias status for this node.
setSelectionBias(boolean) - Method in class edu.cmu.tetrad.data.DiscreteVariable
setSelectionBias(boolean) - Method in class edu.cmu.tetrad.graph.GraphNode: Sets whether the node is selected as a bias node.
setSelectionBias(boolean) - Method in interface edu.cmu.tetrad.graph.Node: Returns the selection bias status for this node.
setSelectionBias(boolean) - Method in class edu.cmu.tetrad.search.Icd: If true (default), orientation rules R5/R6/R7 for possible selection bias are applied.
setSelfLoopCoef(double) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Setter for the field selfLoopCoef.
setSelfLoopStrength(double) - Method in class edu.cmu.tetrad.search.Pairwise: Sets the self-loop strength, if applicable.
setSemIm(SemIm) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Setter for the field semIm.
setSemIm(SemIm) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Setter for the field semIm.
setSemOptimizer(SemOptimizer) - Method in class edu.cmu.tetrad.sem.SemEstimator: Setter for the field semOptimizer.
setSemPm(SemPm) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Setter for the field semPm.
setSemPm(SemPm) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Setter for the field semPm.
setSeparator(String) - Method in class edu.cmu.tetrad.data.LongToWide: Sets the separator between a value variable's name and the level suffix.
setSepsetGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the frozen reference graph (G_0) used to compute separators for endpoint pairs that have no recorded separator.
setSepsetMap(SepsetMap) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the SepsetMap used by the R0R4StrategyTestBased.
setSetAlgorithmKnowledge(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the algorithm knowledge flag.
setSetType(ConditioningSetType) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the type of conditioning sets to use in the Markov check.
setShortDescription(String) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field shortDescription.
setShowErrorTerms(boolean) - Method in class edu.cmu.tetrad.graph.SemGraph: Setter for the field showErrorTerms.
setShowUtilities(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the value of the showUtilities property.
setShowUtilities(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field showUtilities.
setShrinkage(double) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the shrinkage parameter.
setShrinkageMode(IndTestFisherZ.ShrinkageMode) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the shrinkage mode for the instance.
setSignatureSimilarityThreshold(double) - Method in class edu.cmu.tetrad.search.mimic.DmPcHybrid: Sets the minimum output-signature similarity required to cluster outputs together.
setSignatureSimilarityThreshold(double) - Method in class edu.cmu.tetrad.search.mimic.DmPcRobust: Sets the threshold value for signature similarity, which is used to determine the degree of similarity needed between signatures.
setSignificance(double) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: setSignificance.
setSignificance(double) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: setSignificance.
setSignificance(double) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: setSignificance.
setSignificance(double) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: setSignificance.
setSimIndex(int) - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: Setter for the field simIndex.
setSimulation(Simulation) - Method in class edu.cmu.tetrad.algcomparison.algorithm.ExternalAlgorithm: Setter for the field simulation.
setSingularDepth(int) - Method in class edu.cmu.tetrad.search.GraspFci: Sets depth for singular tucks.
setSingularityLambda(double) - Method in class edu.cmu.tetrad.search.score.GicScores: Sets the Singularity lambda for the model.
setSingularityLambda(double) - Method in class edu.cmu.tetrad.search.score.PoissonPriorScore: Sets the Singularity lambda value used in computations.
setSkewEdgeThreshold(double) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the skew-edge threshold.
setSkipChecks(boolean) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Turns the trusted-caller fast path on or off; see the field.
setSortByUtility(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Set the flag to determine if utility should do the sorting.
setSortByUtility(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field sortByUtility.
setSpringConstant(double) - Method in class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Sets the spring constant; higher for more elasticity.
setStable(boolean) - Method in class edu.cmu.tetrad.search.Fas: Sets the stability flag for the search process, which may determine the search strategy or algorithm's behavior.
setStable(boolean) - Method in class edu.cmu.tetrad.search.Fasd
setStable(boolean) - Method in class edu.cmu.tetrad.search.Fci: Sets the stability status.
setStable(boolean) - Method in interface edu.cmu.tetrad.search.IFas: Sets whether the adjacency search should be stable.
setStage1Graph(Graph) - Method in class edu.cmu.tetrad.search.Rlcd: Supplies a stage-1 graph over the data variables directly, skipping the stage-1 search.
setStage1Method(Rlcd.Stage1Method) - Method in class edu.cmu.tetrad.search.Rlcd: Sets the stage-1 search method (default BOSS).
setStartFromCompleteGraph(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets whether the search should start from a complete graph.
setStartIm(SemIm) - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Setter for the field startIm.
setStartingValue(double) - Method in class edu.cmu.tetrad.sem.Parameter: Sets the starting value in case this is a fixed parameter.
setStartsWithParametersEstimationInitializationTemplate(String, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the parameter estimation initialization template for a given startsWith string.
setStartsWithParametersTemplate(String, String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the parameter template for expressions that start with the specified string.
setStartWith(Fcit.START_WITH) - Method in class edu.cmu.tetrad.search.Fcit: Sets the algorithm to use to get the initial CPDAG.
setStartWith(Fcit2.START_WITH) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitKeepKnowledgeOrientations.START_WITH) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitMagMarkov.START_WITH) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitMarkov.START_WITH) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitSl.START_WITH) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitSl2.START_WITH) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitSlKeepKnowledgeOrientations.START_WITH) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitSlScoreCheck.START_WITH) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitZm.START_WITH) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitZm2.START_WITH) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the algorithm to use to get the initial CPDAG.
setStartWith(FcitZmKeepKnowledgeOrientations.START_WITH) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the algorithm to use to get the initial CPDAG.
setStdout(PrintStream) - Method in class edu.cmu.tetrad.data.simulation.LoadDataAndGraphs: Setter for the field stdout.
setStep8MaxDepth(int) - Method in class edu.cmu.tetrad.search.mimic.Dm: Sets the maximum depth for step 8 in the process.
setStepsGenerated(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Sets the number of time steps to generate.
setStepsGenerated(int) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: setStepsGenerated.
setStochastic(boolean) - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Chooses the stochastic (Monte Carlo EM) form over the deterministic default.
setStopEnergy(double) - Method in class edu.cmu.tetrad.graph.LayoutUtil.KamadaKawaiLayout: Sets the max delta at which the algorithm will stop settling.
setStratifiedRankTest(StratifiedChi2RankTest) - Method in class edu.cmu.tetrad.search.Rlcd: Puts the search into stratified (domain-pooled) mode for data gathered in several domains -- batches, sites, cohorts -- whose causal structure is shared but whose linear coefficients, means, and noise variances may differ by domain; the Phase 1 upgrade of LCD-NOD (Dong et al., ICML 2026) applied to RLCD.
setStratifiedRankTests(StratifiedRankTests) - Method in class edu.cmu.tetrad.search.Fofc: Puts the search into stratified (domain-pooled) mode, or takes it out of that mode with null.
setStratifiedRankTests(StratifiedRankTests) - Method in class edu.cmu.tetrad.search.Tsc: Puts the search into stratified (domain-pooled) mode, or takes it out of that mode with null.
setStretch(double) - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbsParams: Setter for the field stretch.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Sets the structure prior value for the BDeu scoring metric.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Sets the structure prior value for this instance.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the structure prior value.
setStructurePrior(double) - Method in interface edu.cmu.tetrad.search.is.IsScore: Sets the structure prior used to influence the scoring calculations.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.score.BdeScore: Sets the structure prior for the BDe score.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.score.BDeuScore: Sets the structure prior for the BdeuScore object.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Sets the structure prior value for the scoring process.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Sets the structure prior.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Sets the structure prior.
setStructurePrior(double) - Method in interface edu.cmu.tetrad.search.score.DiscreteScore: Sets the structure prior.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Sets the structure prior for this score.
setStructurePrior(double) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: For BDeu score for discrete search; see Chickering (2002).
setStructurePrior(double) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field structurePrior.
setStudentTNu(double) - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Sets the degrees of freedom (nu) for the Student's t-distribution.
setSubstitutions(Map) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: setSubstitutions.
setSupersetScore(UnmixSpec.SupersetScore) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the score used for parent superset feature.
setSupersetTopM(int) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the top M value for the parent superset feature.
setSuperstructure(Graph) - Method in class edu.cmu.tetrad.search.Ccd: Sets an optional adjacency superstructure for Step A.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the verbosity level of the search algorithm.
setSuperVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the verbosity level of the search algorithm.
setSweepAddMaxSize(int) - Method in class edu.cmu.tetrad.search.Fcit: Caps the size of addition subsets in the sweep's inner layer.
setSweepAddMaxSize(int) - Method in class edu.cmu.tetrad.search.Fcit2: Caps the size of addition subsets in the sweep's inner layer.
setSweepAddMaxSize(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Caps the size of addition subsets in the sweep's inner layer.
setSweepNfMaxSize(int) - Method in class edu.cmu.tetrad.search.Fcit: Caps the size of not-followed subsets in the sweep's outer layer.
setSweepNfMaxSize(int) - Method in class edu.cmu.tetrad.search.Fcit2: Caps the size of not-followed subsets in the sweep's outer layer.
setSweepNfMaxSize(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Caps the size of not-followed subsets in the sweep's outer layer.
setSweepRemoveMaxSize(int) - Method in class edu.cmu.tetrad.search.Fcit: Caps the size of removal subsets in the sweep's inner layer.
setSweepRemoveMaxSize(int) - Method in class edu.cmu.tetrad.search.Fcit2: Caps the size of removal subsets in the sweep's inner layer.
setSweepRemoveMaxSize(int) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Caps the size of removal subsets in the sweep's inner layer.
setSymmetric(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets the symmetry property of the object and clears the cache.
setSymmetric(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets the symmetric property and clears the cache.
setSymmetricFirstStep(boolean) - Method in class edu.cmu.tetrad.search.Fges: Sets whether the first step of the procedure will score both X->Y and Y->X and prefer the higher score (for adding X--Y to the graph).
setSymmetricFirstStep(boolean) - Method in class edu.cmu.tetrad.search.FgesMb: Sets whether the first step of the procedure will score both X->Y and Y->X and prefer the higher score (for adding X--Y to the graph).
setSymmetrized(boolean) - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Sets whether the test is symmetrized over the (x, y) pair.
setTabDelimitedTables(boolean) - Method in class edu.cmu.tetrad.algcomparison.Comparison: Sets the flag indicating whether tab-delimited tables should be used.
setTabDelimitedTables(boolean) - Method in class edu.cmu.tetrad.algcomparison.TimeoutComparison: Setter for the field tabDelimitedTables.
setTailCompleteness(boolean) - Method in class edu.cmu.tetrad.search.Icd: If true (default), orientation rules R8/R9/R10 for tail-completeness are applied.
setTarget(String) - Method in class edu.cmu.tetrad.classify.ClassifierBayesUpdaterDiscrete: Sets the target variable.
setTargetDegree(int) - Method in class edu.cmu.tetrad.data.missing.MiceLiteImputer: Sets the number of powers of a continuous variable whose fitted values donors are matched on when that variable is imputed.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.BossCcd: Sets the independence test used by the CCD phase.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Ccd: Sets the independence test to be used during the algorithm's execution.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Cdnod
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.CdnodBoss
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Fas
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Fci: Sets the independence test for the current object.
setTest(IndependenceTest) - Method in interface edu.cmu.tetrad.search.IGraphSearch: Sets the test to be used by the search.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.mimic.Dm: Sets the independence test to use.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Pc: Sets the independence test for this instance.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.PcAR: Sets the independence test for this instance.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Pcmci: Sets a new independence test for the PCMCI algorithm.
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.Rfci
setTest(IndependenceTest) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Sets the independence test instance and rebuilds internal structures.
setTestingData(DataSet) - Method in class edu.cmu.tetrad.data.Knowledge: Sets the testing data for this instance.
setTestTimeout(long) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets the timeout for running tests.
setTestType(BpcTestType) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: setTestType.
setTestwiseCommonRowsDiff(boolean) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Experimental.
setTestwiseDeletion(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Puts the search into testwise-deletion mode.
setTetradLoggerConfig(TetradLoggerConfig) - Method in class edu.cmu.tetrad.util.TetradLogger: Sets what configuration should be used to determine which events to log.
setThr(double) - Method in class edu.cmu.tetrad.search.work_in_progress.Glasso: Setter for the field thr.
setThreadDataSets(List) - Method in class edu.cmu.tetrad.algcomparison.utils.PooledIndependenceWrapper: Registers the data sets to pool for tests subsequently built on the calling thread, overriding the default list.
setThreadDataSets(List) - Method in class edu.cmu.tetrad.algcomparison.utils.PooledScoreWrapper: Registers the data sets to pool for scores subsequently built on the calling thread, overriding the default list.
setThreshold(boolean) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Sets the threshold mode for the system.
setThreshold(boolean) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Sets the threshold mode for the model, determining whether a specific threshold-based mechanism is enabled during independence testing or computation.
setThreshold(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Sets whether the independence test should be thresholded (true) or randomized (false).
setThreshold(boolean) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the threshold.
setThreshold(double) - Method in class edu.cmu.tetrad.search.FactorAnalysis: Sets the threshold.
setThresholdNoRandomConstrainSearch(boolean) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Setter for the field thresholdNoRandomConstrainSearch.
setThresholdNoRandomDataSearch(boolean) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Setter for the field thresholdNoRandomDataSearch.
setTier(int, List) - Method in class edu.cmu.tetrad.data.Knowledge: Sets the variable in a given tier to the specified list.
setTierForbiddenWithin(int, boolean) - Method in class edu.cmu.tetrad.data.Knowledge: Forbids any variable from being parent of any other variable within the given tier, or cancels this forbidding.
setTierForbiddenWithin(int, boolean) - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Forbids edges within the given tier, or cancels this forbidding.
setTiesAccepted(boolean) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Sets whether the score step accepts a tie (a non-decreasing score) rather than requiring a strict improvement.
setTimeout(long) - Method in class edu.cmu.tetrad.search.Fcit: Sets the timeout for per-edge separator searches, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the timeout for per-edge separator searches, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the timeout for per-edge separator searches, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeout(long) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the timeout for the search algorithm, or -1 for unlimited.
setTimeoutMs(long) - Method in class edu.cmu.tetrad.search.Cdnod: Sets the timeout value in milliseconds for this instance.
setTimeoutMs(long) - Method in class edu.cmu.tetrad.search.CdnodBoss: Sets the timeout value in milliseconds for this instance.
setTimeoutMs(long) - Method in class edu.cmu.tetrad.search.Pc: Sets the timeout duration for this instance, specifying the maximum time (in milliseconds) the algorithm or operation is permitted to run.
setTimeoutMs(long) - Method in class edu.cmu.tetrad.search.PcAR: Sets the timeout duration for this instance, specifying the maximum time (in milliseconds) the algorithm or operation is permitted to run.
Setting - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
Settings - Section in class edu.cmu.tetrad.search.harness.ChemMaskHarness
Settings - Section in class edu.cmu.tetrad.search.harness.MissingDataRecoveryHarness
Settings - Section in class edu.cmu.tetrad.search.harness.MixedMissingDataHarness
setTNeighbors(Set) - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Sets the set of nodes that are in TNeighbors.
setToken(int, int, String) - Method in class edu.cmu.tetrad.util.TextTable: Sets the token at the given row and column, each of which must be >= 0 and less than the number of rows or columns, respectively.
setTol(double) - Method in class edu.cmu.tetrad.search.score.CamAdditivePsplineBic: Sets the tolerance value for the P-spline model, ensuring it is not lower than 1e-8.
setTolerance(double) - Method in class edu.cmu.tetrad.data.EmCovarianceEstimator: Sets the convergence tolerance, applied to the largest absolute change in any mean or covariance entry between successive iterations, scaled by the corresponding standard deviations.
setTolerance(double) - Method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Sets the convergence tolerance of the deterministic form: the largest absolute change in any covariance entry between iterations at which the iteration stops (default 1e-6).
setTolerance(double) - Method in class edu.cmu.tetrad.search.FastIca: Sets the tolerance value for convergence in the FastICA algorithm.
setTolerance(double) - Method in class edu.cmu.tetrad.search.score.MagRicfBicScorer: Sets the RICF convergence tolerance.
setTolerance(double) - Method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Sets the tolerance level for the optimizer.
setTolerance(int) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.

Integer tolerance on equality (e.g., 1 to allow off-by-one).
setToleranceDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field toleranceDepth.
setTopK(int) - Method in class edu.cmu.tetrad.search.BossTopK: Sets the number of top-scoring permutations (models) to retain.
setTopK(int) - Method in interface edu.cmu.tetrad.search.SuborderSearchTopK: Sets the number of top-scoring permutations (models) to retain.
setToTautology() - Method in class edu.cmu.tetrad.bayes.Proposition: Specifies that all variables in the proposition are either completely allowed (true) or completely disallowed (false) for all of their categories.
setTrekPenaltyMultiplier(double) - Method in class edu.cmu.tetrad.search.score.BlocksBicScore: Sets the penalty multiplier for trek mismatch penalties.
setTrial(int) - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: Setter for the field trial.
setTriangleEscapeOnly(boolean) - Method in class edu.cmu.tetrad.search.Gmas: Sets whether the two-move escape considers only triangle pairs.
setTrimmingStyle(int) - Method in class edu.cmu.tetrad.search.FgesMb: Sets the trimming style for the algorithm.
setTrueDag(Graph) - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: Setter for the field trueDag.
setTrueGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: If the true graph is set, askterisks will be printed in log output for the true edges.
setTrueGraph(Graph) - Method in interface edu.cmu.tetrad.search.utils.IPurify: setTrueGraph.
setTrueGraph(Graph) - Method in class edu.cmu.tetrad.search.utils.PurifyTetradBased: setTrueGraph.
setTrueInputs(int[]) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: Setter for the field trueInputs.
setTruePag(Graph) - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Setter for the field truePag.
setTwoCycleAlpha(double) - Method in class edu.cmu.tetrad.search.Fask: Sets the significance level (alpha) for the FASK algorithm.
setTwoCycleAlpha(double) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the alpha for the per-dataset two-cycle test.
setTwoCycleMinAbs(double) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets the minimum absolute value threshold for breaking two-cycle constraints in the processing.
setTwoCycleScreeningCutoff(double) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the cutoff for two-cycle screening.
setTwoPhasePagScoring(boolean) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: When true (the default as of 2026-9-8), PAG repair under the two ordered-local-Markov conditioning types scores candidates in two phases rather than fully canonicalizing every candidate.
setType(ParamConstraintType) - Method in class edu.cmu.tetrad.sem.ParamConstraint: Setter for the field type.
setUncoveredDepth(int) - Method in class edu.cmu.tetrad.search.Grasp: Sets the maximum depth at which uncovered tucks can be performed within the depth-first search of GRaSP.
setUncoveredDepth(int) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field uncoveredDepth.
setUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.Dag: Sets the underlined triples.
setUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: setUnderLineTriples.
setUnderLineTriples(Set) - Method in interface edu.cmu.tetrad.graph.Graph: setUnderLineTriples.
setUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.LagGraph: setUnderLineTriples.
setUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.SemGraph: setUnderLineTriples.
setUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Sets the underline triples.
setUnderLineTriples(Set) - Method in class edu.cmu.tetrad.graph.Underlines: Setter for the field underLineTriples.
setUnfoldCovers(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Whether discovered latent covers are unfolded into their children for further rank tests.
setUnlimitedRampCeiling(int) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the highest finite cap in the deepening ramp used when the depth is unlimited (-1); after this level one unlimited level is run.
setUnlimitedRampCeiling(int) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the highest finite cap in the deepening ramp used when the depth is unlimited (-1); after this level one unlimited level is run.
setUp() - Method in class edu.cmu.tetrad.graph.ReplicatingGraphTest: Sets up the test environment by initializing the graph nodes and creating a new instance of the ReplicatingGraph class with a predefined list of nodes and a lag replication policy.
setUp() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: Sets up a graph to randomize with 100 variables in it.
setUp() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.TestMeasurementSimulator: Sets up a GeneHistory object in the format for Richard's diagnostic.
setUpperBound(double) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets the upper bound.
setUpperBoundDouble(double) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field upperBoundDouble.
setUpperBoundInt(int) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field upperBoundInt.
setUpperBoundLong(long) - Method in class edu.cmu.tetrad.util.ParamDescription: Setter for the field upperBoundLong.
setUseAdaptiveLasso(boolean) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets whether the adaptive lasso method should be used in the two-step regression process.
setUseAdaptiveZBins(boolean) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Enables or disables adaptive selection of the number of quantile bins per continuous Z variable.
setUseAdjacencyOrientations(boolean) - Method in class edu.cmu.tetrad.search.FaskPool: Sets whether the adjacency stage's compelled orientations act as default orientations in the same way.
setUseAndersonDarling(boolean) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets whether to use the Anderson-Darling test during the repair process.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.Boss: Sets up BOSS to use the BES algorithm to render BOSS correct under the faithfulness assumption.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.BossCcd: Sets whether BOSS should run BES after each best-move sweep.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: Sets up BOSS to use the BES algorithm to render BOSS correct under the faithfulness assumption.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.Gmas: Sets whether BES is used in BOSS.
setUseBes(boolean) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets whether to use the BES (Backward Elimination Search) algorithm during the search.
setUseBossAdjacencies(boolean) - Method in class edu.cmu.tetrad.search.Fask: Sets whether the initial adjacency skeleton should come from a BOSS CPDAG rather than FAS-stable.
setUseClassWalk(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether the within-class candidate generator is the class walk (single mark changes over the Markov equivalence class) rather than the staged seeds/LEG/fork-flip search.
setUseClassWalk(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether the within-class candidate generator is the class walk (single mark changes over the Markov equivalence class) rather than the staged seeds/LEG/fork-flip search.
setUseClassWalk(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether the within-class candidate generator is the class walk (single mark changes over the Markov equivalence class) rather than the staged seeds/LEG/fork-flip search.
setUseClassWalk(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether the within-class candidate generator is the class walk (single mark changes over the Markov equivalence class) rather than the staged seeds/LEG/fork-flip search.
setUseClosureCoverSearch(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets whether the closure-cover generator replaces the staged representative search; see FcitSl.useClosureCoverSearch.
setUseClosureCoverSearch(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether the closure-cover generator replaces the staged representative search; see FcitSl2.useClosureCoverSearch.
setUseClosureCoverSearch(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets whether the closure-cover generator replaces the staged representative search; see FcitSlKeepKnowledgeOrientations.useClosureCoverSearch.
setUseClosureCoverSearch(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether the closure-cover generator replaces the staged representative search; see FcitSlScoreCheck.useClosureCoverSearch.
setUseCosSinPairs(boolean) - Method in class edu.cmu.tetrad.search.score.RffBicScore: Enables or disables the use of cosine and sine pairs in random Fourier feature (RFF) computations.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.Boss: True if the order of the variables in the data should be used for an initial best-order search, false if a random permutation should be used.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.BossCcd: Sets whether BOSS should use the data variable order for its first start.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: True if the order of the variables in the data should be used for an initial best-order search, false if a random permutation should be used.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets the flag indicating whether to use data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.Gmas: Sets whether BOSS uses the data order.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.Grasp: True if the order of the variables in the data should be used for an initial best-order search, false if a random permutation should be used.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.GraspFci: Sets whether to use data order for GRaSP (as opposed to random order) for the first step of GRaSP
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets whether the search algorithm should use the order of the data set during the search.
setUseDataOrder(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field useDataOrder.
setUseDisplayNames(boolean) - Method in class edu.cmu.tetrad.search.utils.BayesImParser: Sets whether to use display names.
setUseExternalOrientations(boolean) - Method in class edu.cmu.tetrad.search.Fask: Sets whether the external graph's compelled orientations act as default orientations, overturnable by the left-right statistic only under the bootstrap sign-consensus gate (see Fask.setOverrideBootstraps(int)).
setUseExternalOrientations(boolean) - Method in class edu.cmu.tetrad.search.FaskPool: Sets whether the external graphs' compelled orientations act as default orientations that the pooled statistic can overturn only on strong opposing evidence (see the class Javadoc).
setUseExternalOrientations(boolean) - Method in class edu.cmu.tetrad.search.FaskVote: Sets whether the external graphs' compelled orientations act as default orientations for the vote.
setUseFasAdjacencies(boolean) - Method in class edu.cmu.tetrad.search.Fask: Sets whether the FASK algorithm should use Fast Adjacency Search (FAS) for determining adjacencies.
setUseFgES(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch: setUseFgES.
setUseGuidedConstruction(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: Sets whether the guided witness construction replaces the configured generator for the within-class passes.
setUseHalfZConcatForKx(boolean) - Method in class edu.cmu.tetrad.search.test.Kci: Sets whether half Z concatenation is used for Kx operations.
setUseHenckelPruning(boolean) - Method in class edu.cmu.tetrad.search.RecursiveAdjustment: Enables or disables Henckel–Perković–Maathuis (HPM) pruning on the RA solutions.
setUseIlsRestarts(boolean) - Method in class edu.cmu.tetrad.search.Boss: Sets whether restarts after the first should use iterated local search (ILS) in the style of the FLOP algorithm (Wienobst, Henckel, & Weichwald, 2026): each restart perturbs the best suborder found so far with about ln(m) random transpositions, where m is the size of the suborder, rather than starting from a fresh random permutation.
setUseInteractions(boolean) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Sets whether the system should use interactions and updates the internal state accordingly.
setUseJointXZKernel(boolean) - Method in class edu.cmu.tetrad.search.test.Kci: Sets whether the joint XZ kernel is used in the operation.
setUseMarkFlipEscalation(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether the conflict-driven mark-flip escalation runs when the live-view sweep fails for a pair.
setUseMarkFlipEscalation(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: Sets whether the conflict-driven mark-flip escalation runs when the live-view sweep fails for a pair.
setUseMarkFlipEscalation(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether the conflict-driven mark-flip escalation runs when the live-view sweep fails for a pair.
setUseMarkFlipEscalation(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: Sets whether the conflict-driven mark-flip escalation runs when the live-view sweep fails to find a separator.
setUseMarkFlipEscalation(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: Sets whether the conflict-driven mark-flip escalation runs when the live-view sweep fails to find a separator.
setUseMarkFlipEscalation(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the conflict-driven mark-flip escalation runs when the live-view sweep fails to find a separator.
setUseMaxAcrossStrata(boolean) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Deprecated.
use MinimaxCITest.setQMinimax(double) with q = 1.0 for max aggregation.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether to use the maxP criterion during the sepset search.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets whether the "Use Max-P" option is enabled or not.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether to use the maxP criterion during the search process.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets whether to use the maxP criterion during the search process.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets whether to use the maxP criterion during the search process.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets whether to use the maxP criterion during the search process.
setUseMaxP(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets whether to use the maxP criterion during the search process.
setUseParentSuperset(boolean) - Method in class edu.cmu.tetrad.data.UnmixSpec: Sets the parent superset feature to enabled or disabled.
setUsePass0(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Turns Pass 0 (re-selection of test-refuted seeded sepsets) on or off; default on.
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether to do the possible-D-SEP pass (Pass 2).
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets whether to do the possible-D-SEP pass (Pass 2).
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Whether to do the possible d-sep step.
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether to do the possible-D-SEP pass (Pass 2).
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Whether to do the possible d-sep step (Pass 2).
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Whether to do the possible d-sep step (Pass 2).
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Whether to do the possible d-sep step (Pass 2).
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Whether to do the possible d-sep step; empirically we find this unnecessary, though the defiition of GFCI includes it.
setUsePossibleDsep(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Whether to do the possible d-sep step (Pass 2).
setUsePseudoinverse(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets whether to use the pseudoinverse in computations.
setUseR4(boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets whether R4 should be run.
setUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.Grasp: True if the Raskutti-Uhler method should be used, false if the Verma-Pearl method should be used.
setUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.GraspFci: Sets whether to use Raskutti and Uhler's modification of GRaSP.
setUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Setter for the field useRaskuttiUhler.
setUseRaskuttiUhler(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: setUseRaskuttiUhler.
setUseSaturatingStep(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether to run the saturating step after the passes.
setUseSaturatingStep(boolean) - Method in class edu.cmu.tetrad.search.Fcit: Sets whether the saturating pass (joint removal of all test-confirmed spurious pairs when no single-edge removal commits) runs.
setUseSaturatingStep(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Sets whether the saturating pass (joint removal of all test-confirmed spurious pairs when no single-edge removal commits) runs.
setUseSaturatingStep(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the saturating pass runs (option 3 of the three-way correctness choice).
setUseSaturatingStep(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether to run the saturating step after the passes.
setUseScore(boolean) - Method in class edu.cmu.tetrad.search.Grasp: True if the score should be used (if both a score and a test are provided), false if not.
setUseScore(boolean) - Method in class edu.cmu.tetrad.search.GraspFci: Sets whether to use score for GRaSP (as opposed to independence test) for GRaSP.
setUseScore(boolean) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Setter for the field useScore.
setUseScore(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field useScore.
setUseScoreCheck(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether the RICF-BIC score check is applied to gated commitments.
setUseScoreCheck(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: Sets whether the MAG score check is applied.
setUseScoreStep(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: Sets whether the score step is on (option 2 of the three-way correctness choice).
setUseScoreStep(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether the score step is on (see the class doc, CORRECTNESS WARRANT, option 2).
setUseScoreStep(boolean) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Turns the score step on or off.
setUseSkewAdjacencies(boolean) - Method in class edu.cmu.tetrad.search.Fask: Configures whether the FASK algorithm should utilize skew adjacencies during its execution.
setUseWildBootstrap(boolean) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: When true, Model-P and Node-P are the joint wild-bootstrap omnibus (sum T^2) rather than KS/AD on the pooled p-values.
setValue(double) - Method in class edu.cmu.tetrad.sem.Mapping: Sets the value of the array element at the stored coordinates (i, j).
setValue(double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Sets the parents of this component.
setValue(double) - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbComponent: Sets the parents of this component.
setValue(int, boolean) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Sets the boolean value in a given row of the table.
setValue(int, double) - Method in class edu.cmu.tetrad.sem.SemProposition: Sets the value at the specified index in the array of values.
setValue(int, int, double) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Sets the value of the covariance matrix at the specified indices.
setValue(int, int, double) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Sets the value of the covariance matrix at the specified indices.
setValue(int, int, double) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Sets the value of the covariance matrix at the specified indices.
setValue(int, int, double) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Sets the value of the covariance matrix at the specified indices.
setValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Assigns double x to matrix element (r, c).
setValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Assigns double x to matrix element at (r, c).
setValue(int, int, double) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrixF: Sets the value of element (row,col) to x
setValue(int, int, float) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrixF: Assigns float x to matrix element (r, c)
setValue(int, int, float) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.MatrixF: Assigns float x to matrix element at (r, c)
setValue(int, int, float) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrixF: Assigns float x to matrix element (r, c)
setValue(int, int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Assigns integer x to matrix element (r, c).
setValue(int, int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Assigns integer x to matrix element at (r, c).
setValue(int, int, int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrix: Sets the value of element (row,col) to x
setValue(int, int, short) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Assigns short x to matrix element (r, c)
setValue(int, int, short) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Assigns short x to matrix element at (r, c)
setValue(int, int, short) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrix: Assigns short x to matrix element (r, c)
setValue(Node, double) - Method in class edu.cmu.tetrad.sem.SemProposition: Sets the value for a given node in the SemProposition object.
setValueVariables(List) - Method in class edu.cmu.tetrad.data.LongToWide: Sets the value variables to spread.
setVarHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field varHigh.
setVarHigh(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field varHigh.
setVariable(int, boolean) - Method in class edu.cmu.tetrad.bayes.Proposition: Specifies that all categories for the given variable are either all allowed (true) or all disallowed (false).
setVariable(int, Node) - Method in class edu.cmu.tetrad.data.BoxDataSet: Updates the variable at the specified index in the variables list.
setVariable(int, Node) - Method in interface edu.cmu.tetrad.data.DataSet: Sets the value of a variable to the provided instance.
setVariable(int, Node) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Sets the variable at the specified index in the list of variables.
setVariables(List) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Sets the list of Node variables for the covariance matrix.
setVariables(List) - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Sets the list of Node variables for the covariance matrix.
setVariables(List) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Sets the list of Node variables for the covariance matrix.
setVariables(List) - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Sets the list of Node variables for the covariance matrix.
setVariables(List) - Method in class edu.cmu.tetrad.search.is.BDeuScoreWOprior: Sets the list of variables for the scoring metric.
setVariables(List) - Method in class edu.cmu.tetrad.search.is.IsBDeuScore2: Sets the list of variables for the current instance.
setVariables(List) - Method in class edu.cmu.tetrad.search.is.IsBicScore: Sets the list of variables for this object.
setVariables(List) - Method in class edu.cmu.tetrad.search.PcMb: Setter for the field variables.
setVariables(List) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: Sets the variables to a new list of the same size.
setVariables(List) - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: Sets the variables to a new list of the same size.
setVariables(List) - Method in class edu.cmu.tetrad.search.score.GicScores: Sets the variables of the dataset.
setVariables(List) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Sets the variables of the covariance matrix.
setVariables(List) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the list of variables for the instance.
setVariables(List) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Sets the varialbe to this list (of the same length).
setVariables(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Setter for the field variables.
setVariables(List) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Sets the variables used in the independence test.
setVariables(List) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Setter for the field variables.
setVariablesCopied(boolean) - Method in class edu.cmu.tetrad.data.Discretizer: Setter for the field variablesCopied.
setVariablesTemplate(String) - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Sets the variable template.
setVariance(int, int, double) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Sets the variance value for a continuous child node.
setVarianceFloor(double) - Method in class edu.cmu.tetrad.search.score.InstanceAugmentedSemBicScore: Sets the variance floor for this instance.
setVarLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.ConditionalGaussianSimulation: Setter for the field varLow.
setVarLow(double) - Method in class edu.cmu.tetrad.algcomparison.simulation.LinearSineSimulation: Setter for the field varLow.
setVarRange(double, double) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: setVarRange.
setVarsPerInd(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.LagGraphParams: Setter for the field varsPerInd.
setVarsPerInd(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraphParams: Sets the number of variables per individual.
setVerbose(boolean) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets whether to log progress to the Tetrad log.
setVerbose(boolean) - Method in interface edu.cmu.tetrad.cluster.ClusteringAlgorithm: True iff verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.cluster.KMeans: True iff verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.BfciBryan: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.BfciBryanScore: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.BfciScoreCheck: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Boss: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.BossCcd: Sets whether verbose output should be enabled.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.BossTopK: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Bpc: Enables or disables verbose logging for the Bpc instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Cam: Sets the verbosity level for the current Cam instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Ccd: Sets whether verbose output should be enabled for the current process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Cstar: Sets whether verbose output will be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fas: Sets the verbosity level for the logging or debugging output of the search process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fasd: Sets the verbose flag to control verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Sets the verbose mode.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FastIca: Enables or disables verbose mode for the FastICA algorithm.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fci: Sets the verbosity level for the system.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fcit: True just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fcit2: True just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: True just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitMagMarkov: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitMarkov: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitSl: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitSl2: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitSlScoreCheck: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitZm: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitZm2: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations: True, just in case good and restored changes are printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fges: Sets whether verbose output should be produced.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.FgesMb: Sets whether verbose output should be produced.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Flop: Sets whether progress should be logged.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Fofc: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Ftfc: Sets the verbose mode for the instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.GfciOld: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Gffc: Enables or disables verbose mode for the current instance and associated components.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Gmas: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Grasp: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Icd: Sets whether verbose output is enabled for this instance.
setVerbose(boolean) - Method in interface edu.cmu.tetrad.search.IFas: Sets the verbosity of the search process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.IndTestIod: Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticBDeu: Sets the verbosity setting for the independence test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.is.IndTestProbabilisticISBDeu: Sets the verbose mode for the model.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.is.IsGFci: Sets whether verbose mode is enabled for logging or detailed output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Lingam: Verbose logging.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.LvHeuristic: Sets the verbosity level of the search algorithm.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.MarkovCheck: Sets the verbosity level for logging or output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.mimic.BossTrekMimic: Deprecated.

Sets verbose output for measurement-model construction.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss: Sets verbose output forwarded to BOSS.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Sets verbosity.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.mimic.TrekMimic: Sets verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Pc: Sets whether this instance and its associated test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.PcAR: Sets whether this instance and its associated test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Pcd: Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.PcMb: Sets the verbosity level of the search.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Rfci: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Rlcd: Whether to log the search trace through TetradLogger.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.score.GicScores: Sets whether verbose output should be sent to out.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.score.IndTestScore: Sets whether verbose output should be sent to out.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Sets whether verbose output should be sent to out.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.ScoredClusterFinder: Enables or disables verbose mode for this instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.StarFci: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.StarFciB: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.StarFciC: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.StarFciD: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.StarFciOriginal: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.CachedIndependenceQueries: Sets the verbose mode for the wrapped test instance if it implements the IndependenceTest interface.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.CachingIndependenceTest: Sets the verbosity mode for the underlying independence test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Sets whether this test prints verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.FfCi: Sets the verbosity level for the current instance and its delegate.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.FfCi1: Sets the verbosity level for the current instance and its delegate.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.FfCiContinuous: Configures whether verbose output is enabled for this instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.Gcm
setVerbose(boolean) - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Sets whether verbose output is enabled.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionBlocks: Sets the verbosity mode for this instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrt: Deprecated.

Enables or disables verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBasisFunctionLrtFullSample: Deprecated.

Sets the verbose mode for logging or output behavior.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksLemma10: Deprecated.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestBlocksWilkes
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestCord: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Sets the verbosity for the independence test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrtFullSample: Deprecated.

Sets the verbose mode for logging or output behavior.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Sets whether verbose output is enabled, for this test and the underlying one.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestFdrWrapper: Sets the verbose output flag for the underlying independence test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Sets the verbosity setting for this instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Sets the verbose output flag.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestGin: Sets the verbosity of the independence test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Sets the verbose flag to enable or disable verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Sets the verbose mode for the IndTestHsic class.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestIndependenceFacts: Sets whether verbose output is to be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestMulti: Sets whether verbose output should be printed during the test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestMvpLrt: Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestProbabilistic: Sets the verbose flag indicating whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Sets whether verbose mode is enabled or disabled for the instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Sets the verbose output flag.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.Kci: Sets the verbose mode for the current instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Sets the verbosity level for the independence test.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.MinimaxCITest: Sets the verbosity mode of the application or process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.MsepTest: Sets the verbosity level for the program.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.ProcessPythonCiService
setVerbose(boolean) - Method in interface edu.cmu.tetrad.search.test.PythonCiService: Sets the verbosity mode for the service.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Sets whether verbose output is enabled for this instance of PythonKciIndependenceTest.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Sets whether verbose output is enabled for this instance of PythonRcitIndependenceTest.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.Rcit: Sets the verbosity level for the current instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Sets whether verbose output is enabled or not.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Sets the verbose mode for this instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Configures the verbose mode for this test instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Tgin: Deprecated.

Configures the verbosity of the process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.Tsc: Sets the verbose mode for the application or process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.TwoStep: Deprecated.

Sets whether verbose output is enabled for the process.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.Bes: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.BesPermutation: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Sets whether gate decisions are logged.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.DagSepsets: Sets the verbose mode of the SepsetProducer.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.FciOrient: Sets whether verbose output is printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.FgesOrienter: Sets whether verbose output should be produced.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.MagToPag: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.MeekRules: Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Sets the verbose mode for the FciOrientDataExaminationStrategy object.
setVerbose(boolean) - Method in interface edu.cmu.tetrad.search.utils.SepsetProducer: Sets the verbose mode of the SepsetProducer.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedy: Sets the verbosity level for this object.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.SepsetsGreedyMb: Sets the verbosity level for this object.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.SepsetsMaxP: Sets the verbosity level for this object.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.SepsetsMinP: Sets the verbosity level for this object.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.SepsetsPossibleDsep: Sets the verbose mode of the SepsetProducer.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.SepsetsSet: Sets the verbose mode of the SepsetProducer.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: Sets the verbosity level for this instance.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.utils.TsDagToPag: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Configures the verbosity level for logging output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.GraspTol: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Sets the verbose flag to determine if verbose output should be enabled or disabled.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Sets the verbose flag.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Sets the verbose mode to either enabled or disabled.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: Sets the verbose flag.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.Kpc: Deprecated.

Sets whether verbose output should be printed.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.ProbabilisticMapIndependence: Sets whether this test will print verbose output.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpc: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.SampleVcpcFast: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.SemBicScoreDeterministic: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcFas: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPc: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.simulation.GdistanceRandom: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.simulation.HsimAutoC: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.simulation.HsimAutoRun: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAC: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: Setter for the field verbose.
setVerbose(boolean) - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
setVerbose(boolean) - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Sets the verbose mode of the program.
setVerbose(boolean) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.PagSamplingRfci: Set the verbose flag.
setVerbose(boolean) - Method in class edu.pitt.dbmi.algo.bayesian.constraint.search.RfciBsc: Sets whether verbose output should be produced.
setWbNumBootstraps(int) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets the number of bootstrap replicates used when VertexRepairSearch.setUseWildBootstrap(boolean) is enabled.
setWbSeed(long) - Method in class edu.cmu.tetrad.search.VertexRepairSearch: Sets the random seed for the wild bootstrap, for reproducibility.
setWeight(int) - Method in class edu.cmu.tetrad.search.FciOrientDijkstra.DijkstraEdge: Sets the weight of the edge.
setWeight(String, double) - Method in class edu.cmu.tetrad.algcomparison.statistic.Statistics: Sets the utility weight of the statistic by the given name.
setWeighting(FaskPool.Weighting) - Method in class edu.cmu.tetrad.search.FaskPool: Sets the weighting scheme for pooling.
setWeights(double[]) - Method in class edu.cmu.tetrad.search.score.ImagesScore: Sets the weight schedule over datasets.
setWInit(Matrix) - Method in class edu.cmu.tetrad.search.FastIca: Sets the initial weights matrix to be used for the FastICA algorithm.
setWithReplacement(boolean) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sets whether resampling is with replacement.
setWithReplacement(boolean) - Method in class edu.cmu.tetrad.data.BootstrapSampler: This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
setWrite(boolean) - Method in class edu.cmu.tetrad.simulation.HsimAutoC: Setter for the field write.
setWrite(boolean) - Method in class edu.cmu.tetrad.simulation.HsimAutoRun: Setter for the field write.
setWrite(boolean) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAC: Setter for the field write.
setWrite(boolean) - Method in class edu.cmu.tetrad.simulation.HsimRepeatAutoRun: Setter for the field write.
setWThreshold(double) - Method in class edu.cmu.tetrad.search.Dagma: Sets the value of the wThreshold field.
setWThreshold(double) - Method in class edu.cmu.tetrad.search.Lingam: Optional pre-threshold for |W_ij| -> 0 before Hungarian; set to 0 to disable (default).
setWThreshold(double) - Method in class edu.cmu.tetrad.search.LingD: Sets the threshold value for the `wThreshold` field.
severe(String) - Method in class edu.cmu.tetrad.util.LogUtils: severe.
severity() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns the value of the severity record component.
Sextad - Class in edu.cmu.tetrad.search.utils: Represents an ordered sextad of nodes.
Sextad - Class in edu.cmu.tetrad.search.work_in_progress: Represents an ordered sextad of variables.
Sextad(int, int, int, int, int, int) - Constructor for class edu.cmu.tetrad.search.utils.Sextad: Constructor.
Sextad(Node[]) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Sextad: Constructor for Sextad.
Sextad(Node, Node, Node, Node, Node, Node) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Sextad: Constructor for Sextad.
SHALLOW_RECURSIVE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking.Strategy: Shallow recursive blocking.
ShallowRecursiveBlocking - Class in edu.cmu.tetrad.search: Shallow-commit ("near-x") variant of RecursiveBlocking.
ShallowRecursiveBlockingMemo - Class in edu.cmu.tetrad.search: ShallowRecursiveBlockingStack plus a delta-replay continuation memo.
ShallowRecursiveBlockingStack - Class in edu.cmu.tetrad.search: Explicit-stack (no JVM recursion) port of ShallowRecursiveBlocking (the recursive "near-x" shallow-commit reference).
shallowSearch - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: If provided, called on each subsample to get a shallow graph (e.g., PC-Max depth 1)
shd - Variable in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.GraphDiff: Represents the Structural Hamming Distance (SHD) between two graphs.
SHD - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: The structural Hamming distance.
SHD - Enum constant in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Constant SHD
ShiftSearch - Class in edu.cmu.tetrad.search.utils: Tries to find a good shifting of variables to minimize average BIC for time-series data.
ShiftSearch(List) - Constructor for class edu.cmu.tetrad.search.utils.ShiftSearch: Constructor for ShiftSearch.
ShortDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of short data.
ShortDataBox(int, int) - Constructor for class edu.cmu.tetrad.data.ShortDataBox: Constructs an 2D short array consisting entirely of missing values (-99).
ShortDataBox(short[][]) - Constructor for class edu.cmu.tetrad.data.ShortDataBox: Constructs a new data box using the given 2D short data array as data.
shouldEstimate() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility: Convenience: whether the caller should proceed to compute adjustment sets and estimate.
shouldForceZeroEffect() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility: Convenience: whether the caller should force a numeric 0 effect (rather than estimating).
showOverflowCount - Variable in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config: Indicates whether to display the count of overflow items in a concatenated string.
showOverflowCount(boolean) - Method in class edu.cmu.tetrad.search.blocks.LatentNameAssigner.Config.Builder: Sets whether the overflow count should be displayed in the resulting configuration.
SHRINKAGE_MODE - Static variable in class edu.cmu.tetrad.util.Params: Constant SHRINKAGE_MODE="shrinkageMode"
shrinkageMode() - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Returns the value of the shrinkageMode record component.
shuffle(List) - Static method in class edu.cmu.tetrad.util.RandomUtil: This is just the Collections.shuffle logic but using Tetrad RandomUtil to get random numbers.
shuffleColumns(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: shuffleColumns.
shuffleColumns2(List) - Static method in class edu.cmu.tetrad.data.DataTransforms: shuffleColumns2.
shuffleFolds - Variable in class edu.cmu.tetrad.sem.NNEstimatorParams: If true, rows are randomly permuted (using NNEstimatorParams.seed) before being cut into k contiguous folds for cross-validation.
sigma - Variable in class edu.cmu.tetrad.data.EmCovarianceEstimator.Result: The estimated covariance matrix.
signed(double, double) - Static method in class edu.cmu.tetrad.graph.EdgeShading: Returns the shade for a signed value: POSITIVE for values at or above zero, NEGATIVE otherwise.
signed(double, double, boolean) - Static method in class edu.cmu.tetrad.graph.EdgeShading: As EdgeShading.signed(double, double), for a light or a dark canvas; see EdgeShading.color(Hue, double, boolean).
Significance - Enum constant in enum class edu.cmu.tetrad.search.utils.ClusterSignificance.CheckType: Check the significance using a regression model.
SIGNIFICANCE_CHECKED - Static variable in class edu.cmu.tetrad.util.Params: Constant SIGNIFICANCE_CHECKED="significanceChecked"
significant(List, double) - Method in class edu.cmu.tetrad.search.utils.ClusterSignificance: Returns the p-value for the given cluster.
significantAt(double) - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: True if testable and pValue ≤ alpha.
signum(double) - Static method in class edu.cmu.tetrad.util.TMath: Calculates the signum function of the specified double value.
SimpleDataLoader - Class in edu.cmu.tetrad.data: SimpleDataLoader class.
SimpleRandomizer - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Randomizes a graph using existing factors by first removing all edges and then adding for each factor (a) an edge from the same factor at time lag 1 and (b) a given number of factors chosen uniformly from all lagged factors with lag > 0.
SimpleRandomizer(int, int, int, double) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.SimpleRandomizer: Constructor for SimpleRandomizer.
SIMPLIFIED_BPC - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: Even slower.
SIMPLIFIED_BPC_DEPTH_0 - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: This will work and does a good job for small models, no more than 4 latents.
SIMPLIFIED_BPC_DEPTH_1 - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: This is very slow.
simulate(int) - Method in class edu.cmu.tetrad.sem.NNEstimator: Simulates a dataset of the requested size from the fitted model and computes an AdequacyReport comparing it to the observed data.
simulate(int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Simulates the DAG with the given number of samples and random seed.
simulate(int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Simulates data by generating continuous and discrete values for all variables in the trained directed acyclic graph (DAG) model.
simulate(int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Simulates data by generating continuous and discrete values for all variables in the trained directed acyclic graph (DAG) model.
simulate(int) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Simulates data by generating continuous and discrete values for all variables in the trained directed acyclic graph (DAG) model.
simulate(int, long) - Method in class edu.cmu.tetrad.sem.PlotMatrixMimicSimulator: Simulates a synthetic dataset by sampling from a multivariate normal distribution, applying optional nonlinear distortions, and mapping the latent variables to real values through inverse empirical cumulative distribution functions.
simulate(int, long) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Simulates the DAG with the given number of samples and random seed.
simulate(int, long) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Simulates data by generating continuous and discrete values for all variables in the trained directed acyclic graph (DAG) model.
simulate(int, long) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Simulates data by generating continuous and discrete values for all variables in the trained directed acyclic graph (DAG) model.
simulate(int, long) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Simulates data by generating continuous and discrete values for all variables in the trained directed acyclic graph (DAG) model.
simulate(SemIm, int) - Static method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Simulates sampleSize rows of data from an already-parameterised SemIm.
simulate(GeneHistory) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.MeasurementSimulator: Simulates (optionally) neither, either, or both of two three-dimensionaly data sets, rawData and measuredData.
simulate(GeneHistory) - Method in class edu.cmu.tetrad.study.gene.tetradapp.model.MeasurementSimulatorParams: simulate.
simulateData(int, boolean) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Simulates a data set with the specified number of rows.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Simulates a data set with the specified number of rows.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Simulates a data set.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Simulates a data set with the specified number of rows.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Simulates a data set with the specified number of rows.
simulateData(int, boolean) - Method in interface edu.cmu.tetrad.data.Simulator: Simulates data from the model associated with this object.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Simulates data based on the given parameters.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.sem.SemIm: Simulates data from the model associated with this object.
simulateData(int, boolean) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: Simulates data from the model associated with this object.
simulateData(int, boolean, int[]) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Simulates a sample with the given sample size.
simulateData(DataSet, boolean) - Method in interface edu.cmu.tetrad.bayes.BayesIm: Simulates data based on the provided data set and saves the latent data if specified.
simulateData(DataSet, boolean) - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Simulates data based on the provided data set and saves the latent data if specified.
simulateData(DataSet, boolean) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Simulates data for the given data set.
simulateData(DataSet, boolean) - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Simulates data based on the provided data set and saves the latent data if specified.
simulateData(DataSet, boolean) - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Simulates data based on the provided data set and saves the latent data if specified.
simulateData(DataSet, boolean, int[]) - Method in class edu.cmu.tetrad.bayes.MlBayesIm: simulateData.
simulateDataAvoidInfinity(int, boolean) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Simulates data avoiding infinity values.
simulateDataCholesky(int, boolean) - Method in class edu.cmu.tetrad.sem.SemIm: Simulates data from this Sem using a Cholesky decomposition of the implied covariance matrix.
simulateDataFisher(double[][], int, double) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Simulates data using the model of R.
simulateDataFisher(int) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Simulates data using the model of R.
simulateDataFisher(int) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Simulates data using the model of R.
simulateDataFisher(int, int, double) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Simulates data using the model of R.
simulateDataFisher(int, int, int, double, boolean) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: simulateDataFisher.
simulateDataMinimizeSurface(int, boolean) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Simulates data by minimizing the surface defined by the given sample size and whether latent data is saved.
simulateDataNSteps(int, boolean) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Simulates data for a given number of steps.
simulateDataRecursive(int) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: This simulates data by picking random values for the exogenous terms and percolating this information down through the SEM, assuming it is acyclic.
simulateDataRecursive(int, boolean) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: This simulates data by picking random values for the exogenous terms and percolating this information down through the SEM, assuming it is acyclic.
simulateDataRecursive(int, boolean) - Method in class edu.cmu.tetrad.sem.SemIm: simulateDataRecursive.
simulateDataReducedForm(int) - Method in class edu.cmu.tetrad.sem.LargeScaleSimulation: Simulates data using the model X = (I - B)Y^-1 * e.
simulateDataReducedForm(int, boolean) - Method in class edu.cmu.tetrad.sem.SemIm: simulateDataReducedForm.
simulateDataReducedForm(int, boolean) - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: simulateDataReducedForm.
simulatedN - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Number of observed parent configurations the measures were averaged over, per repeat.
SimulateNetwork - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: SimulateNetwork class.
SimulateNetworkMain - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin: SimulateNetworkMain class.
SimulateNetworkMain() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.SimulateNetworkMain: Constructor for SimulateNetworkMain.
simulateOneRecord(Vector) - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: simulateOneRecord.
simulateOneRecord(Vector) - Method in class edu.cmu.tetrad.sem.SemIm: simulateOneRecord.
simulatePossibleShrinkage(Parameters, Graph) - Static method in class edu.cmu.tetrad.sem.SemIm: Simulates possible shrinkage scenarios for a given graph and parameters.
simulateStableFixedRadius(Graph, int, double, double, double, Parameters) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Simulate from an arbitrary graph with SCC-wise fixed spectral radius s.
simulateStableProductCapped(Graph, int, double, double, double, long, Parameters) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Simulate from an arbitrary graph with SCC-wise radius capped by sqrt(maxProd).
Simulation - Section in class edu.cmu.tetrad.search.harness.ChemMaskHarness
Simulation - Interface in edu.cmu.tetrad.algcomparison.simulation: The interface that simulations must implement.
SIMULATION_EXPANDED - Enum constant in enum class edu.cmu.tetrad.search.blocks.BlocksUtil.NamingMode: Represents a naming mode focused on scenarios where the names are expanded and adjusted for simulations, often leading to more detailed or descriptive names for latent variables
simulationExpanded_expandsRankIntoMultipleLatents() - Method in class edu.cmu.tetrad.search.blocks.GiveGoodLatentNamesTest: This test verifies the behavior of the `giveGoodLatentNames` method when the naming mode is set to `SIMULATION_EXPANDED`.
SimulationPath - Interface in edu.cmu.tetrad.algcomparison.statistic.utils: Some simulations may wish to implement this interface to specify a simulation path, which will be printed in the output.
Simulations - Class in edu.cmu.tetrad.algcomparison.simulation: A list of simulations to be compared.
Simulations() - Constructor for class edu.cmu.tetrad.algcomparison.simulation.Simulations: Constructor for Simulations.
SimulationStudy - Class in edu.cmu.tetrad.search.harness.tsc: Top-level harness for the TSC simulation study.
SimulationStudy() - Constructor for class edu.cmu.tetrad.search.harness.tsc.SimulationStudy: Constructs a new instance of the SimulationStudy class.
SimulationStudy.AlgorithmSummary - Record Class in edu.cmu.tetrad.search.harness.tsc: Mean metrics for one algorithm at one sample-size level.
SimulationStudy.ConditionResult - Record Class in edu.cmu.tetrad.search.harness.tsc: Results for one sample-size level within a study.
SimulationStudy.StudyResult - Record Class in edu.cmu.tetrad.search.harness.tsc: Aggregated results for one complete study (one rank condition).
SimulationStudyTsbi - Class in edu.cmu.tetrad.search.harness.tsbi: Simulation harness for the TSBI (Trek-Separation Block Independence) test.
SimulationStudyTsbi() - Constructor for class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi: Constructs a new instance of the SimulationStudyTsbi class.
SimulationStudyTsbi.ConditionResult - Record Class in edu.cmu.tetrad.search.harness.tsbi: Mean metrics for one sample-size level.
SimulationStudyTsbi.StudyResult - Record Class in edu.cmu.tetrad.search.harness.tsbi: Aggregated results for the complete TSBI study.
SimulationTypes - Class in edu.cmu.tetrad.algcomparison.simulation: Jun 4, 2019 3:10:49 PM
SimulationUtils - Class in edu.cmu.tetrad.algcomparison.simulation: Jun 4, 2019 5:21:45 PM
Simulator - Interface in edu.cmu.tetrad.data: Created by jdramsey on 12/22/15.
Simulators - Section in class edu.cmu.tetrad.search.MarkovCheckNullReference
sin(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the trigonometric sine of an angle given in radians.
SingleDatasetSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: A Simulation implementation that returns a single supplied data set.
SingleDatasetSimulation(DataSet) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.SingleDatasetSimulation: A Simulation implementation that returns a single supplied data set.
SingleGraph - Class in edu.cmu.tetrad.algcomparison.graph: Stores a single graph for use in simulations, etc.
SingleGraph(Graph) - Constructor for class edu.cmu.tetrad.algcomparison.graph.SingleGraph: Constructor for SingleGraph.
SingleGraphAlg - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: PC.
SingleGraphAlg(Graph) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.SingleGraphAlg: Constructor for SingleGraphAlg.
SingletonAttacher - Class in edu.cmu.tetrad.search: Utility to attach singleton (measured) variables as children of latent blocks using a Wilks rank-drop criterion, analogous to the latent->latent hierarchy test.
singletonClosureWithComposition(Set) - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Computes the singleton closure of independence facts using the composition rule, starting from an initial set of singleton independence facts.
SingletonClusterPolicy - Enum Class in edu.cmu.tetrad.search.blocks: Enum representing policies for handling clusters in a single cluster analysis context.
singletonFacts(Set) - Static method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Returns the subset of a given set of GraphoidIndFacts where both X and Y are singleton sets--i.e., facts of the form {x} ⊥⊥ {y} | Z.
SingleValue - Class in edu.cmu.tetrad.util.dist: A pretend distribution that always returns the given value when nextRandom() is called.
SingleValue(double) - Constructor for class edu.cmu.tetrad.util.dist.SingleValue: Constructs single value "distribution" using the given value.
SINGULARITY_LAMBDA - Static variable in class edu.cmu.tetrad.util.Params: Constant SINGULARITY_LAMBDA="singularityLambda"
sinh(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the hyperbolic sine of a given angle in radians.
size() - Method in class edu.cmu.tetrad.algcomparison.statistic.Statistics: The number of statistics.
size() - Method in interface edu.cmu.tetrad.data.Covariances: Returns the dimensiom of the matrix.
size() - Method in class edu.cmu.tetrad.data.CovariancesDoubleForkJoin: size.
size() - Method in class edu.cmu.tetrad.data.DataModelList
size() - Method in class edu.cmu.tetrad.data.IndependenceFacts: size.
size() - Method in class edu.cmu.tetrad.search.rlcd.Cover: Returns the size of this cover.
size() - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns the number of pairs carrying a non-neutral value.
size() - Method in class edu.cmu.tetrad.search.utils.MinimaxGroupCache: Retrieves the number of cached groups present in the internal cache.
size() - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Returns the number of {x, y} in the key set of the map.
size() - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: size.
size() - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: size.
size() - Method in class edu.cmu.tetrad.util.Vector: size.
skew - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Score: The skew.
skew(double[], double[], boolean) - Static method in class edu.cmu.tetrad.search.FaskOrig: Deprecated.

Calculates a left-right judgument using the skewness of two arrays of double values.
Skew - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: Skew.
Skew - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Skew rule.
Skew() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Constructor for Skew.
Skew(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Skew: Constructor for Skew.
SKEW - Enum constant in enum class edu.cmu.tetrad.search.Fask.LeftRight: Use Skew.
SKEW - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

The skew rule from the Hyvarinen and Smith paper.
SKEW - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: He3(Y)/sqrt(3!) = (Y^3-3Y)/sqrt(6): conditional-skew channel.
SKEW - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: He3(Y)/sqrt(3!) = (Y^3-3Y)/sqrt(6): conditional-skew channel.
SKEW_EDGE_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant SKEW_EDGE_THRESHOLD="skewEdgeThreshold"
SkewE - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: SkewE.
SkewE - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The SkewE rule.
SkewE() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Constructor for SkewE.
SkewE(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.SkewE: Constructor for SkewE.
skewness(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: skewness.
skewness(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: skewness.
skewness(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: skewness.
skewness(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: skewness.
SKIP_NUM_RECORDS - Static variable in class edu.cmu.tetrad.util.Params: Constant SKIP_NUM_RECORDS="skipNumRecords"
skipped - Variable in class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText.ApplyResult: Skipped lines with reasons, e.g., "foo (unknown parameter)".
skipped() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns the value of the skipped record component.
SLOPE_SHIFT - Enum constant in enum class edu.cmu.tetrad.search.Fask.TwoCycleRule: Compare partial regression SLOPES rather than correlations.
SMALL_MARGINAL_CELL - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A discrete variable has one or more observed categories with very small counts.
SMALL_PAIRWISE_CELLS - Enum constant in enum class edu.cmu.tetrad.data.audit.FindingCode: A pair of discrete variables has small expected cell counts under independence, so conditional independence tests involving this pair (or conditioning sets containing them) will be unreliable.
SMALL_PAIRWISE_COUNT - Static variable in class edu.cmu.tetrad.data.missing.MissingDataAudit: The pairwise complete count below which advice() warns that test-wise statistics may be unstable.
smallestUsableMargin(int, int) - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: The smallest margin this sample size could certify at all, at the current alpha.
soficOrder(Graph) - Static method in class edu.cmu.tetrad.algcomparison.simulation.DaoSimulation: Returns a source-first consistent topological ordering of the DAG nodes.
solve(Matrix) - Method in class edu.cmu.tetrad.util.Matrix: Solves the linear system A * X = B where this matrix is A and the argument is B.
sorted(Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: Sorts a set of covers deterministically.
sortEdges(List) - Static method in class edu.cmu.tetrad.graph.Edges: sortEdges.
Soundness - Section in class edu.cmu.tetrad.search.PairwiseMarkovProperty
Soundness sketch (NOLAC) - Section in class edu.cmu.tetrad.search.Tsc
sourceMutationForcesRebuildAndNewIdentity() - Method in class edu.cmu.tetrad.util.PagCacheTest: Verifies the behavior of the PagCache when the source graph (DAG) is modified after a PAG object has been retrieved from the cache.
Sp - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: SP (Sparsest Permutation)
Sp - Class in edu.cmu.tetrad.search: Implements the SP (Sparsest Permutation) algorithm.
Sp() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Constructor for Sp.
Sp(ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Sp: Constructor for Sp.
Sp(Score) - Constructor for class edu.cmu.tetrad.search.Sp: This algorithm will work with an arbitrary score.
SP - Enum constant in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Start with SP.
SP - Enum constant in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Start with SP.
SPACE - Enum constant in enum class edu.cmu.tetrad.util.TextTable.Delimiter: Constant SPACE
spanningSepset(Graph, IndependenceTest, Node, Node, int, int, int, long, Runnable) - Static method in class edu.cmu.tetrad.search.utils.FcitSepsets: Searches for a separating set S such that x and y are conditionally independent given S within the specified constraints.
spanningSepsetSearch(Graph, IndependenceTest, Node, Node, int, int, int, long, Runnable) - Static method in class edu.cmu.tetrad.search.utils.FcitSepsets: Searches for a set S with x _||_ y | S in the given graph.
SPEARMAN - Enum constant in enum class edu.cmu.tetrad.data.UnmixSpec.SupersetScore: Represents Spearman's rank-order correlation coefficient, a non-parametric measure of the monotonic relationship between two ranked variables.
SPEARMAN - Enum constant in enum class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.ScoreType: Spearman's rank correlation coefficient, which measures rank-based correlation.
spec() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.ParseResult: Returns the value of the spec record component.
spec() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the spec record component.
spec() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the spec record component.
Spec(double[], double) - Constructor for record class edu.cmu.tetrad.data.MissingnessInjector.Spec: Canonical constructor, validating the rates and the row effect.
Spec(double, double, boolean, boolean, boolean, String) - Constructor for record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Canonical constructor, with validation.
specialConfiguration(IndependenceTest, Knowledge, boolean) - Static method in class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased: Provides a special configuration for creating an instance of FciOrientDataExaminationStrategy.
spectralRadiusAbs(SemIm, Graph, List) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Computes the spectral radius of the absolute value of the coefficient matrix of the given strongly connected component (SCC) in a graph.
SpFci - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag: Adjusts GFCI to use a permutation algorithm (in this case SP) to do the initial steps of finding adjacencies and unshielded colliders.
SpFci - Class in edu.cmu.tetrad.search: Uses SP in place of FGES for the initial step in the *-FCI algorithm.
SpFci() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: The SpFci class represents a specific algorithm for structural learning called "Conditional Independence Test-based Fast Causal Inference" (SpFci).
SpFci(IndependenceWrapper, ScoreWrapper) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.pag.SpFci: Constructor for the SpFci class.
SpFci(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.SpFci: Constructor; requires by ta test and a score, over the same variables.
split(DataSet, double) - Static method in class edu.cmu.tetrad.data.DataTransforms: split.
split(DataSet, Node) - Static method in class edu.cmu.tetrad.util.StratifiedRankTests: Splits a data set into one data set per value of a discrete domain column, dropping that column.
Split - Class in edu.cmu.tetrad.util.dist: Wraps a chi square distribution for purposes of drawing random samples.
Split(double, double) - Constructor for class edu.cmu.tetrad.util.dist.Split: Creates a new split distribution, drawing uniformly from [-b, -a] U [a, b], where a and b are positive real numbers.
SPLIT_DELTA - Static variable in class edu.cmu.tetrad.util.Params: Constant SPLIT_DELTA="splitDelta"
SplitCasesSpec - Class in edu.cmu.tetrad.data: Specifies how a column (continuous or discrete) should be discretized.
SplitCasesSpec(int, int[], List) - Constructor for class edu.cmu.tetrad.data.SplitCasesSpec: Constructor for SplitCasesSpec.
Splitting strategy - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
sqrt() - Method in class edu.cmu.tetrad.util.Matrix: sqrt.
sqrt(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the square root of the given double value using the specified implementation.
SQUARE - Enum constant in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: The square; see LayoutUtil.squareLayout(Graph).
SQUARED - Enum constant in enum class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue.DistanceType: Calculate the squared distance between the true edge strength and the range of estimated coefficients.
SquaredErrorLoss - Class in edu.cmu.tetrad.cluster.metrics: Euclidean dissimilarity metric--i.e., the sum of the differences in corresponding variable values.
SquaredErrorLoss() - Constructor for class edu.cmu.tetrad.cluster.metrics.SquaredErrorLoss: Calculates the squared error dissimilarity between two vectors using the Euclidean dissimilarity metric.
squareLayout(Graph) - Static method in class edu.cmu.tetrad.graph.LayoutUtil: squareLayout.
sSquare(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sSquare.
sSquare(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: sSquare.
sSquare(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sSquare.
sSquare(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: sSquare.
ssx(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: ssx.
ssx(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: ssx.
ssx(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: ssx.
ssx(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: ssx.
stabilityAcrossRestarts(DataSet, EmUnmix.Config, ResidualRegressor, Function, Function, int, long) - Static method in class edu.cmu.tetrad.search.unmix.UnmixDiagnostics: Evaluates the stability of clustering results across multiple independent runs of the EM algorithm by calculating the average Adjusted Rand Index (ARI) and its standard deviation between all pairs of runs.
StabilityResult(double, double, int) - Constructor for class edu.cmu.tetrad.search.unmix.UnmixDiagnostics.StabilityResult: Constructs an object to represent the result of a stability analysis of clustering.
StabilitySelection - Class in edu.cmu.tetrad.algcomparison.algorithm: Stability selection: runs the wrapped algorithm on resamples of the data and returns the graph containing exactly those edges (with orientation) appearing in more than "percentStability" of the resample graphs.
StabilitySelection(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.StabilitySelection: Constructor for StabilitySelection.
stabilizeAllSccsFixedRadius(SemIm, Graph, double, double, double) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Stabilize an existing SemIm in-place: enforce per-SCC spectral radius target s.
stabilizeAllSccsFixedRadiusScaleOnly(SemIm, Graph, double) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Stabilizes all strongly connected components (SCCs) of a given graph by scaling internal edges such that the spectral radius of each SCC does not exceed the given target value s.
stable() - Method in record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Returns the value of the stable record component.
stable(boolean) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the stability parameter for the algorithm.
stable(boolean) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Deprecated.
This parameter is unused in CdnodBoss and will be removed.
stable(DataSet, Node, Set, Node, double) - Method in interface edu.cmu.tetrad.search.cdnod_pag.ChangeTest: Determines whether the data, with respect to a given context variable, is stable (i.e., there is no change or instability).
stable(Node, Set) - Method in class edu.cmu.tetrad.search.cdnod_pag.ChangeOracle: Determines if the given node and set of nodes are stable with respect to the current data, contexts, and alpha significance level.
STABLE_FAS - Static variable in class edu.cmu.tetrad.util.Params: Constant STABLE_FAS="stableFAS"
stableAll(DataSet, Node, Set, Collection, double) - Method in interface edu.cmu.tetrad.search.cdnod_pag.ChangeTest: Determines whether the data is stable with respect to all specified context variables, meaning no changes or instability are detected across any of the contexts provided.
stampSeedAttribute(Graph, Collection) - Static method in class edu.cmu.tetrad.search.MarkovAuditUtils: Writes seeds onto graph under MarkovAuditUtils.SEED_ATTRIBUTE as a comma-separated name list, the channel the Vertex Repair GUI reads to offer seeded repair.
stampWithBic(Graph, DataModel) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: stampWithBic.
stampWithScore(Graph, Score) - Static method in class edu.cmu.tetrad.search.utils.LogUtilsSearch: stampWithScore.
standardError() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the standardError record component.
standardError() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns the value of the standardError record component.
standardErrors() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns the bootstrap standard errors, one per treatment.
standardize(double[]) - Static method in class edu.cmu.tetrad.search.Fask: Standardizes the given array of values by converting the elements to have a mean of 0 and a standard deviation of 1.
standardize(DataSet) - Static method in class edu.cmu.tetrad.search.PooledAdjacencySearch: Standardizes a data set for the pooled FASK algorithms: each continuous variable to mean zero and unit variance; discrete variables left as they are, as discrete variables, so that a score for mixed data can still be computed from the result.
STANDARDIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant STANDARDIZE="standardize"
STANDARDIZED_STRUCTURAL_EQUATION_MODEL - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant STANDARDIZED_STRUCTURAL_EQUATION_MODEL="Standardized Structural Equation Model"
standardizeData(double[]) - Static method in class edu.cmu.tetrad.data.DataTransforms: standardizeData.
standardizeData(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: Standardizes the provided data array by subtracting the mean and scaling by the standard deviation.
standardizeData(DataSet) - Static method in class edu.cmu.tetrad.data.DataTransforms: standardizeData.
standardizeData(Matrix) - Static method in class edu.cmu.tetrad.data.DataTransforms: standardizeData.
standardizeData(Matrix, List) - Static method in class edu.cmu.tetrad.data.DataTransforms: Standardizes the columns of the given data matrix by centering and scaling.
standardizeData(Vector) - Static method in class edu.cmu.tetrad.util.StatUtils: Standardizes the provided vector data by removing the mean and scaling to unit variance.
standardizeData(List) - Static method in class edu.cmu.tetrad.data.DataTransforms: standardizeData.
standardizedFifthMoment(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: standardizedFifthMoment.
standardizedFifthMoment(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: standardizedFifthMoment.
StandardizedSemIm - Class in edu.cmu.tetrad.sem: A special SEM model in which variances of variables are always 1 and means of variables are always 0.
StandardizedSemIm(SemIm, StandardizedSemIm.Initialization, Parameters) - Constructor for class edu.cmu.tetrad.sem.StandardizedSemIm: Constructs a new standardized SEM IM from the freeParameters in the given SEM IM.
StandardizedSemIm(SemIm, Parameters) - Constructor for class edu.cmu.tetrad.sem.StandardizedSemIm: Constructs a new standardized SEM IM, initializing from the freeParameters in the given SEM IM.
StandardizedSemIm.Initialization - Enum Class in edu.cmu.tetrad.sem: The initialization method for the model.
StandardizedSemIm.ParameterRange - Class in edu.cmu.tetrad.sem: Stores a coefficient range--i.e.
StandardizedSemSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: Standardized SEM simulation.
StandardizedSemSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Constructor for StandardizedSemSimulation.
StandardizedSemSimulation(SemPm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Constructor for StandardizedSemSimulation.
StandardizedSemSimulation(StandardizedSemIm) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.StandardizedSemSimulation: Constructor for StandardizedSemSimulation.
standardizedSixthMoment(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: standardizedSixthMoment.
standardizedSixthMoment(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: standardizedSixthMoment.
STAR - Enum constant in enum class edu.cmu.tetrad.graph.Endpoint: Star endpoint.
StarFci - Class in edu.cmu.tetrad.search: *-FCI (StarFCI): the shipping latent-variable search of the StarFCI paper (Ramsey, Andrews, and Spirtes), in the sepset-map-as-state formulation due to Bryan Andrews.
StarFci(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFci: Constructor.
StarFciB - Class in edu.cmu.tetrad.search: *-FCI (StarFCI): the GFCI-template latent-variable search of the StarFCI paper (Ramsey, Andrews, and Spirtes, "StarFCI: A GFCI-Template Latent-Variable Search that Returns a PAG by Construction"), with the Markov-CPDAG initializer abstracted (StarFciB.getMarkovCpdag(boolean)) and supplied by subclasses: Gfci (from FGES), Bfci (from BOSS), GraspFci (from GRaSP), SpFci (from SP).
StarFciB(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFciB: Constructs a new StarFci algorithm with the given independence test.
StarFciC - Class in edu.cmu.tetrad.search: EXPERIMENTAL VARIANT (2026-10-19): StarFci with the PAG inference replaced by SEPSET PROJECTION.
StarFciC(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFciC: Constructs a new StarFciC algorithm with the given independence test.
StarFciD - Class in edu.cmu.tetrad.search: EXPERIMENTAL VARIANT (2026-10-19): StarFciC (sepset projection) PLUS the recursive-blocking R4 fallback.
StarFciD(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFciD: Constructs a new StarFciD algorithm with the given independence test.
StarFciGuaranteePag - Class in edu.cmu.tetrad.search: Deprecated.
Legacy shell over StarFciOriginal; extend StarFci instead.
StarFciGuaranteePag(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFciGuaranteePag: Deprecated.

Constructs a new instance with the given independence test.
StarFciKeepKnowledgeOrientations - Class in edu.cmu.tetrad.search: Deprecated.
The knowledge handling of this class has been consolidated into StarFci (formerly StarFciGuaranteePag); with empty knowledge the two already coincided. This standalone copy is retained only for the hand-run comparison harness (TestStarFciKeepKnowledgeOrientations) and receives no further changes; extend StarFci instead.
StarFciKeepKnowledgeOrientations(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFciKeepKnowledgeOrientations: Deprecated.

Constructs a new StarFci algorithm with the given independence test.
StarFciOriginal - Class in edu.cmu.tetrad.search: ORIGINAL *-FCI implementation (CPDAG-collider copy plus sepset stamps, frozen sepsets, saturating step, gated or ungated), preserved under this name on 2026-10-21 when StarFci was replaced by the sepset-projection / re-selection formulation (formerly BfciBryan).
StarFciOriginal(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.StarFciOriginal: Constructs a new StarFci algorithm with the given independence test.
StARS - Class in edu.cmu.tetrad.algcomparison.algorithm: StARS (Stability Approach to Regularization Selection): selects a value for one numeric parameter of a wrapped algorithm by sweeping the parameter over [low, high] in steps of 0.5, measuring the StARS adjacency instability of the algorithm's output over a fixed set of resamples at each value, choosing the value with the largest instability strictly below the cutoff (Params "StARS.cutoff"), and then running the wrapped algorithm on the full data at the chosen value.
StARS(Algorithm, String, double, double) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.StARS: Constructor for StARS.
STARS_CUTOFF - Static variable in class edu.cmu.tetrad.util.Params: Constant STARS_CUTOFF="StARS.cutoff"
START_FROM_COMPLETE_GRAPH - Static variable in class edu.cmu.tetrad.util.Params: Constant START_FROM_COMPLETE_GRAPH="startFromCompleteGraph"
startsWithPrefixes() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: startsWithPrefixes.
statistic - Variable in class edu.cmu.tetrad.search.CordEngine1.Result: Represents the test statistic (studentized score T) used in a conditional independence test.
statistic - Variable in class edu.cmu.tetrad.search.CordEngine2.Result: Represents the test statistic value (studentized score T) calculated from the data during a hypothesis test.
statistic - Variable in class edu.cmu.tetrad.search.CordEngine3.Result: The test statistic for a statistical hypothesis test, commonly referred to as the studentized score T.
statistic - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: Represents the GLS (Generalized Least Squares) mean-type test statistic (referred to as T_mean).
statistic - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: The representative generalized least squares (GLS) mean-type statistic T, derived from the median of p-values across rotations.
statistic - Variable in class edu.cmu.tetrad.search.utils.NonlinearityTests.TestResult: Represents the value of the test statistic computed during a statistical hypothesis test.
statistic() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns the value of the statistic record component.
statistic(int[], int[], int) - Method in class edu.cmu.tetrad.search.rlcd.Chi2RankTest: The statistic T of the class comment for the rank ≤ r test, to be referred to a chi-square distribution with (|P| − r)(|Q| − r) degrees of freedom.
statistic(int[], int[], int, double) - Method in class edu.cmu.tetrad.search.rlcd.Chi2RankTest: The statistic T of the class comment for the rank ≤ r test, with the sample size supplied by the caller in place of the one this test was built with.
Statistic - Interface in edu.cmu.tetrad.algcomparison.statistic: The interface that each statistic needs to implement.
statisticMax - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: The GLS max-type test statistic (T_max), representing the maximum test statistic value obtained in a statistical test.
statisticMax - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: The representative max-type test statistic T (median-p rotation).
Statistics - Class in edu.cmu.tetrad.algcomparison.statistic: A list of statistics and their utility weights.
Statistics() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.Statistics: Constructor for Statistics.
status - Variable in class edu.cmu.tetrad.search.CordEngine1.Result: Represents the status of the test result, indicating whether the outcome is valid or degenerate.
status - Variable in class edu.cmu.tetrad.search.CordEngine2.Result: Represents the status of the statistical test result.
status - Variable in class edu.cmu.tetrad.search.CordEngine3.Result: Represents the status of the test result in a hypothesis testing context.
status - Variable in class edu.cmu.tetrad.search.CordEngine4.Result: Represents the status of the test result, indicating whether the test outcome is valid or represents a degenerate case.
status - Variable in class edu.cmu.tetrad.search.CordEngine5.Result: Represents the status of the result, indicating whether it is valid or degenerate.
status() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility: Retrieves the high-level eligibility status for total-effect computation.
statusUpdated(String) - Method in interface edu.cmu.tetrad.search.VertexRepairSearch.RepairListener: Called whenever the search emits a human-readable status message.
StatUtils - Class in edu.cmu.tetrad.util: Contains a number of basic statistical functions.
step - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: Elimination round within this child at which the edge fell (0-based).
stop() - Method in class edu.cmu.tetrad.search.utils.ShiftSearch: stop.
StoredCellProbs - Class in edu.cmu.tetrad.bayes: Creates a table of stored cell probabilities for the given list of variables.
StoredCellProbsObs - Class in edu.cmu.tetrad.bayes: ///////////////////////////////////////////////////////////////////
StoredCellProbsObs(List) - Constructor for class edu.cmu.tetrad.bayes.StoredCellProbsObs: Constructor for StoredCellProbsObs.
StoredLagGraphParams - Class in edu.cmu.tetrad.study.gene.tetrad.gene.graph: Stores a file for reading in a lag graph from a file.
StoredLagGraphParams() - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.graph.StoredLagGraphParams: Constructs a new parameters object.
stouffer - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Stouffer et al.'s method
stouffer - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The stouffer method.
STRATIFIED_PERMUTATIONS - Static variable in class edu.cmu.tetrad.util.Params: Constant STRATIFIED_PERMUTATIONS="stratifiedPermutations"
StratifiedChi2RankTest - Class in edu.cmu.tetrad.search.rlcd: The RLCD rank test pooled over the strata of a discrete domain (batch, site, cohort) variable, after Theorem 4 of Dong, Dai, Ng, Spirtes, and Zhang (ICML 2026), the Phase 1 test of their LCD-NOD framework.
StratifiedChi2RankTest(List) - Constructor for class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: Constructs the test from one continuous data set per domain.
StratifiedPermutationRankTest - Class in edu.cmu.tetrad.util: The permutation form of the stratified (domain-pooled) rank test, after Theorem 5 of Dong, Dai, Ng, Spirtes, and Zhang (ICML 2026).
StratifiedPermutationRankTest(List) - Constructor for class edu.cmu.tetrad.util.StratifiedPermutationRankTest: Constructs the test from one continuous data set per domain, all with the same variables in the same order.
StratifiedRank - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for the stratified (domain-pooled) rank conditional independence test, IndTestStratifiedRank -- the Phase 1 test of LCD-NOD (Dong, Dai, Ng, Spirtes, and Zhang, ICML 2026).
StratifiedRank() - Constructor for class edu.cmu.tetrad.algcomparison.independence.StratifiedRank: Constructs a new instance of the algorithm.
StratifiedRankHarness - Class in edu.cmu.tetrad.search.harness: A watch-the-output harness for the stratified (domain-pooled) rank test of Dong et al.
StratifiedRankTests - Class in edu.cmu.tetrad.util: Rank tests for cross-covariance matrices pooled across the strata of a discrete domain (or batch, site, cohort) variable, after Theorem 4 of Dong, Dai, Ng, Spirtes, and Zhang, "Identifying Partially Observed Causal Models from Heterogeneous/Nonstationary Data" (ICML 2026), the Phase 1 test of their LCD-NOD framework.
StratifiedRankTests(List, int[]) - Constructor for class edu.cmu.tetrad.util.StratifiedRankTests: Constructs the test from per-stratum correlation (or covariance) matrices and sample sizes.
StratifiedRealDataRunner - Class in edu.cmu.tetrad.search.harness: A watch-the-output runner for trying the stratified (domain-pooled) rank tests on a real data file.
STRATIFY_BY_DOMAIN - Static variable in class edu.cmu.tetrad.util.Params: Constant STRATIFY_BY_DOMAIN="stratifyByDomain"
stratifyResidualsByDiscreteParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: A boolean variable that determines whether to stratify residual bootstrap by discrete-parent signature.
stratifyResidualsByDiscreteParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: Indicates whether residuals should be stratified based on discrete parent variables.
stratifyResidualsByDiscreteParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: Whether to stratify residual bootstrap by discrete-parent signature.
stratifyResidualsByDiscreteParents - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: Whether to stratify residual bootstrap by discrete-parent signature.
Strength(HybridCgEdgeStrengths.Kind, double, String) - Constructor for record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Creates an instance of a Strength record class.
STRICT - Enum constant in enum class edu.cmu.tetrad.util.TMath.Impl: Represents the strict implementation strategy for mathematical computations.
STRING - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: String.
StroblTestwiseFciStudy - Class in edu.cmu.tetrad.study.missingness: Replication harness for Strobl, Visweswaran and Spirtes, "Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion" (Int J Data Sci Anal, 2018).
strOfParents(int) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Returns a string with the indexes of all parents of node i separated by spaces (useful for printouts)
stronglyConnectedComponents(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Compute strongly connected components (SCCs) of a directed graph.
stronglyConnectedComponents(Graph) - Static method in class edu.cmu.tetrad.sem.CyclicStableUtils: Kosaraju's algorithm for strongly connected components.
STRUCTURAL_EQUATION_MODEL - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Deprecated.
This name covered both the Gaussian and the non-Gaussian linear SEM simulations, which was confusing. It is kept so that scripts and saved selections using the old name still resolve (to the Linear Gaussian SEM); use SimulationTypes.LINEAR_GAUSSIAN_SEM or SimulationTypes.LINEAR_NON_GAUSSIAN_SEM instead.
StructuralGraphScorer - Class in edu.cmu.tetrad.search.harness.tsbi: Scores a recovered structural graph against the true structural graph using adjacency and arrowhead precision and recall.
StructuralGraphScorer.GraphScore - Record Class in edu.cmu.tetrad.search.harness.tsbi: Immutable result of a structural graph scoring comparison.
structuralhammingdistance(Graph, Graph, boolean) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: Originally, Tsamardinos, I., Brown, L.
StructuralHammingDistance - Class in edu.cmu.tetrad.algcomparison.statistic: Calculates the structural Hamming distance (SHD) between the estimated graph and the true graph.
StructuralHammingDistance() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.StructuralHammingDistance: Constructs the statistic.
StructuralHammingDistance(boolean) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.StructuralHammingDistance: Constructs a StructuralHammingDistance instance with the specified comparison mode.
structuralVar(DataSet, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: structuralVar.
STRUCTURE_PRIOR - Static variable in class edu.cmu.tetrad.util.Params: Constant STRUCTURE_PRIOR="structurePrior"
STUDENT_T - Enum constant in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NoiseModel: A noise model based on the Student's t-distribution.
Study design - Section in class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi
studyName() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.StudyResult: Returns the value of the studyName record component.
StudyResult(String, List) - Constructor for record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.StudyResult: Creates an instance of a StudyResult record class.
StudyResult(List) - Constructor for record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.StudyResult: Creates an instance of a StudyResult record class.
style() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the style record component.
subgraph(List) - Method in class edu.cmu.tetrad.graph.Dag: Returns a subgraph of the current graph consisting only of the specified nodes.
subgraph(List) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
subgraph(List) - Method in interface edu.cmu.tetrad.graph.Graph: Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
subgraph(List) - Method in class edu.cmu.tetrad.graph.LagGraph: Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
subgraph(List) - Method in class edu.cmu.tetrad.graph.SemGraph: Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
subgraph(List) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Returns a subgraph of the current graph based on the provided nodes.
SublistGenerator - Class in edu.cmu.tetrad.util: Generates (nonrecursively) all of the sublists of size b from a list of size a, where a, b are nonnegative integers and a >= b.
SublistGenerator(int, int) - Constructor for class edu.cmu.tetrad.util.SublistGenerator: Constructs a new generator for sublists for a list of size b taken a at a time.
submatrix(double[][], int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: A copy of the original (square) matrix with the stated index row/column removed
subMatrix(ICovarianceMatrix, Node, Node, List) - Static method in class edu.cmu.tetrad.data.DataUtils: subMatrix.
subMatrix(ICovarianceMatrix, Map, Node, Node, List) - Static method in class edu.cmu.tetrad.data.DataUtils: subMatrix.
subMatrix(Matrix, List, Node, Node, List) - Static method in class edu.cmu.tetrad.data.DataUtils: subMatrix.
subMatrix(Matrix, Map, Node, Node, List) - Static method in class edu.cmu.tetrad.data.DataUtils: subMatrix.
SuborderSearch - Interface in edu.cmu.tetrad.search: An interface for suborder searches for various types of permutation algorithms.
SuborderSearchTopK - Interface in edu.cmu.tetrad.search: An interface for suborder searches for various types of permutation algorithms.
SUBSAMPLE - Enum constant in enum class edu.cmu.tetrad.search.Cstar.SampleStyle: Use subsample.
subset(List) - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns a SEM BIC score for the given subset of variables.
subsetColumns(int[]) - Method in class edu.cmu.tetrad.data.BoxDataSet: Creates a new DataSet object containing only the specified columns.
subsetColumns(int[]) - Method in interface edu.cmu.tetrad.data.DataSet: subsetColumns.
subsetColumns(int[]) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: subsetColumns.
subsetColumns(List) - Method in class edu.cmu.tetrad.data.BoxDataSet: Creates and returns a dataset consisting of those variables in the list vars.
subsetColumns(List) - Method in interface edu.cmu.tetrad.data.DataSet: Creates and returns a dataset consisting of those variables in the list vars.
subsetColumns(List) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Creates and returns a dataset consisting of those variables in the list vars.
subsetRows(int[]) - Method in class edu.cmu.tetrad.data.BoxDataSet: Creates a subset of rows from the existing DataSet.
subsetRows(int[]) - Method in interface edu.cmu.tetrad.data.DataSet: subsetRows.
subsetRows(int[]) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: subsetRows.
subsetRows(List) - Method in class edu.cmu.tetrad.data.BoxDataSet
subsetRows(List) - Method in interface edu.cmu.tetrad.data.DataSet: Creates a new DataSet containing only the rows specified by the given list of row indices.
subsetRows(List) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet
subsetRowsColumns(int[], int[]) - Method in class edu.cmu.tetrad.data.BoxDataSet: Generates a subset of the current DataSet by selecting specified rows and columns.
subsetRowsColumns(int[], int[]) - Method in interface edu.cmu.tetrad.data.DataSet: Generates a subset of the current DataSet by selecting specified rows and columns.
subsetRowsColumns(int[], int[]) - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: Generates a subset of the current DataSet by selecting specified rows and columns.
subtract(double[][], double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: subtract.
subtract(double[], double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: subtract.
success(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a success message prefixed with a checkmark emoji if the tracer is enabled.
successiveFactorVarimax(Matrix) - Method in class edu.cmu.tetrad.search.FactorAnalysis: Returns the matrix result for the varimax algorithm.
successiveResidual() - Method in class edu.cmu.tetrad.search.FactorAnalysis: Successive method with residual matrix.
suffix() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns the value of the suffix record component.
suffixLogs - Variable in class edu.cmu.tetrad.util.RankTests.RccaEntry: An array where each element at index `i` represents the cumulative sum of the logarithms of (1 - squared singular values) from index `i` to the end of the corresponding singular values array.
suggest(List) - Static method in class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester: Suggests removals for the determinism findings among the given findings.
Suggestion(String, FindingCode, String, boolean) - Constructor for record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Creates an instance of a Suggestion record class.
suggestShortLevelNames(List, int) - Static method in class edu.cmu.tetrad.data.LongToWide: Suggests short, unique, sanitized names for a set of levels, for the user to edit: each level is sanitized (as by LongToWide.setSanitizeNames(boolean)), split into tokens at underscores, tokens shared by more than half of the levels are dropped when at least three levels are present (so "Mastery" and "Assessment" fall away while "Unit_01" and "ver_A" stay), the remaining tokens are rejoined, and the result is truncated to maxLength characters.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.TestRevealEvaluator: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestIndexedConnectivity: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestIndexedLagGraph: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomial: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomialTerm: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
suite() - Static method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.TestMeasurementSimulator: This method uses reflection to collect up all of the test methods from this class and return them to the test runner.
sum() - Method in class edu.cmu.tetrad.util.Vector: Computes the sum of all elements in this vector.
sum(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sum.
sum(double[][], double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: sum.
sum(double[], double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: sum.
sum(int) - Method in class edu.cmu.tetrad.util.Matrix: sum.
SUM - Enum constant in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: The sum of the values (continuous only; missing values ignored).
sum0ToN(int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: sum0ToN.
sumBits(byte[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.akutsu.BoolSearch: Returns the sum of the bits in the byte array b.
sumBits(byte[]) - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.BoolSearch: sumBits.
summarize(List, List) - Static method in class edu.cmu.tetrad.study.performance.Comparison: summarize.
summarize(List, List) - Static method in class edu.cmu.tetrad.study.performance.Comparison2: summarize.
summary() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns a short summary suitable for a log entry.
sumOfArCoefficients(DataSet, int) - Static method in class edu.cmu.tetrad.search.utils.TsUtils: sumOfArCoefficients.
supersetCfg - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Configuration object for the parent superset used in the EmUnmix algorithm.
supersetScore - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Specifies the scoring type to be used when working with the parent superset configuration.
supersetTopM - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: The maximum size of the superset to be considered during processing or configuration.
support() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns the value of the support record component.
SUPPRESS - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.NoAmenablePolicy: Represents a policy option within the NoAmenablePolicy enumeration that is used to suppress or ignore cases where a certain condition or requirement is not amenable, without attempting to resolve or return alternatives.
svals - Variable in class edu.cmu.tetrad.util.RankTests.RccaEntry: An array of singular values sorted in descending order.
svar() - Static method in class edu.cmu.tetrad.graph.ReplicatingGraph: Creates a new instance of ReplicatingGraph using a default LagReplicationPolicy.
svar(List) - Static method in class edu.cmu.tetrad.graph.ReplicatingGraph: Constructs a ReplicatingGraph using the given list of nodes and applies a default LagReplicationPolicy for edge replication.
SVARFCI - Enum constant in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Constant for the SVARFCI algorithm.
swap(Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Swaps m and n in the permutation.
SWAP - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Swap two edges.
sweep() - Method in class edu.cmu.tetrad.search.test.AlphaReport: A sweep over the levels usually reached for, in decades and half decades from 0.1 down to 1e-6, plus the calibrated levels themselves so that they are always on screen.
sweep(double, double, double) - Method in class edu.cmu.tetrad.search.score.PenaltyDiscountReport: A sweep of PenaltyDiscountReport.rowAt(double) over a range of penalty discounts, for seeing where the false-discovery and effect-size criteria cross.
sweep(DataSet, MissingDataAudit, double...) - Static method in class edu.cmu.tetrad.data.missing.MissingnessThreshold: The effect of each of a series of thresholds, for reading off where the complete-case count turns.
sweep(DataSet, String, List) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sweeps a single parameter over the given values.
sweep(DataSet, String, List, String, List) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sweeps the cross product of two parameters over the given value lists, varying the second parameter fastest.
SweepReport - Class in edu.cmu.tetrad.algcomparison.sweep: The full record of a ParameterSweep: the evaluated settings with their evidence (point graphs, resample edge probabilities, adjacency instability, Markov-check statistics, timing) plus enough configuration metadata to make the sweep reproducible.
SweepReport(String, String, int, int, int, double, boolean, long, int, List) - Constructor for class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Constructs a report, recording whether resampling was by row or by block.
SweepReport(String, String, int, int, int, double, boolean, long, List) - Constructor for class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Constructs a report.
SweepResult - Class in edu.cmu.tetrad.algcomparison.sweep: The immutable result of evaluating one parameter setting in a ParameterSweep: the setting itself, the point-estimate graph searched on the full data, a probability-annotated graph aggregating the resample searches (in the same display convention as bootstrap results elsewhere in Tetrad), the StARS-style adjacency instability over the resamples, Markov-check statistics for the point graph (if a Markov-check test was configured), and timing.
SweepResult(Map, Graph, Graph, double, SweepResult.MarkovStats, int, long) - Constructor for class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Constructs a result.
SweepResult.MarkovStats - Record Class in edu.cmu.tetrad.algcomparison.sweep: Markov-check statistics for a point graph, mirroring the statistics returned by MarkovCheck: for the implied independencies ("Ind") and implied dependencies ("Dep") respectively, the Anderson-Darling p-value for uniformity of the local p-values, the Kolmogorov-Smirnov p-value, the binomial p-value, the fraction of facts judged dependent, and the number of facts tested.
sweepSettings(DataSet, List) - Method in class edu.cmu.tetrad.algcomparison.sweep.ParameterSweep: Sweeps an explicit list of settings, each a map from parameter name to value.
SwingProgressListener() - Constructor for class edu.cmu.tetrad.graph.LayoutUtil.SwingProgressListener: Constructs a SwingProgressListener instance.
sxy(double[], double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sxy.
sxy(double[], double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: sxy.
sxy(long[], long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: sxy.
sxy(long[], long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: sxy.
sxy(Vector, Vector, int) - Static method in class edu.cmu.tetrad.util.StatUtils: sxy.
SymMatrix - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a space-efficient symmetric matrix (of elements of type short), storing only the lower triangular portion of it
SymMatrix(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrix: Creates a symmetric matrix reading it from file fname.
SymMatrix(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrix: Creates a symmetric matrix with nrows rows.
SymMatrixF - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util: Implements a space-efficient symmetric matrix (of elements of type short), storing only the lower triangular portion of it
SymMatrixF(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrixF: Creates a symmetric matrix reading it from file fname.
SymMatrixF(String, int) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrixF: Creates a symmetric matrix with nrows rows.
symmetric - Variable in class edu.cmu.tetrad.search.CordEngine1: Indicates whether the conditional independence test should be performed in symmetric mode or not.
SYMMETRIC_FIRST_STEP - Static variable in class edu.cmu.tetrad.util.Params: Constant SYMMETRIC_FIRST_STEP="symmetricFirstStep"
symmetricDirichletIm(BayesPm, double) - Static method in class edu.cmu.tetrad.bayes.DirichletBayesIm: symmetricDirichletIm.
symmetrize(Matrix) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Computes the symmetrized version of the given square matrix.
symmetrizeInPlace(DMatrixRMaj) - Static method in class edu.cmu.tetrad.search.score.KcvBicScore: Ensures that the matrix is symmetric by averaging each pair of off-diagonal elements.
symmetry() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if symmetry holds--i.e., X ⊥⊥ Y | Z ==> Y ⊥⊥ X | Z

T

t - Variable in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Length-K observed standardized statistics T_j, aligned with WildBootstrapMarkovCheck.Result.labels.
t() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the t record component.
ta - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Tail-to-arrow
TAB - Enum constant in enum class edu.cmu.tetrad.util.TextTable.Delimiter: Constant TAB
TAB - Static variable in class edu.cmu.tetrad.data.DelimiterType: Constant TAB
TABULAR - Enum constant in enum class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Kind: No linear coefficient; strength is a distribution-shift effect size in [0, 1].
TAIL - Enum constant in enum class edu.cmu.tetrad.graph.Endpoint: Tail endpoint.
TailConfusion - Class in edu.cmu.tetrad.algcomparison.statistic.utils: A confusion matrix for tails--i.e.
TailConfusion(Graph, Graph) - Constructor for class edu.cmu.tetrad.algcomparison.statistic.utils.TailConfusion: Constructor for TailConfusion.
TailPrecision - Class in edu.cmu.tetrad.algcomparison.statistic: TailPrecision is a class that implements the Statistic interface.
TailPrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TailPrecision: Constructs the statistic.
TailRecall - Class in edu.cmu.tetrad.algcomparison.statistic: Implements the TailRecall statistic, which calculates the tail recall value for a given true graph, estimated graph, and data model.
TailRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TailRecall: Constructs the statistic.
takeOne() - Method in class edu.cmu.tetrad.search.rlcd.Cover: Returns a representative variable name.
TakesCovarianceMatrix - Interface in edu.cmu.tetrad.algcomparison.algorithm: Tags algorithm comparison wrappers that can take a covariance matrix as input.
TakesData - Interface in edu.cmu.tetrad.algcomparison.utils: Tags a gadget that takes a data model list as argument to its constructor.
takesExternalGraph(Class) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Checks if the algorithm takes an external graph.
TakesExternalGraph - Interface in edu.cmu.tetrad.algcomparison.utils: Tags an algorithm that can take an external graph as input.
TakesGraph - Interface in edu.cmu.tetrad.algcomparison.independence: TakesGraph interface.
TakesIndependenceWrapper - Interface in edu.cmu.tetrad.algcomparison.utils: Tags an algorithm as using an independence wrapper.
takesKnowledge(Class) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Checks if the algorithm takes knowledge.
takesMultipleDataset(Class) - Method in class edu.cmu.tetrad.annotation.AlgorithmAnnotations: Checks if the algorithm takes multiple data sets.
TakesScoreWrapper - Interface in edu.cmu.tetrad.algcomparison.utils: Tags an algorithm as using a score wrapper.
tan(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the trigonometric tangent of an angle.
tanh(double) - Static method in class edu.cmu.tetrad.util.TMath: Computes the hyperbolic tangent of a given value.
Tanh - Class in edu.cmu.tetrad.algcomparison.algorithm.pairwise: Tanh.
Tanh - Enum constant in enum class edu.cmu.tetrad.search.Pairwise.Rule: The Tahn rule.
Tanh(Algorithm) - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.pairwise.Tanh: Constructor for Tanh.
TANH - Enum constant in enum class edu.cmu.tetrad.search.Fask.LeftRight: Use Tanh.
TANH - Enum constant in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

The tanh rule from the Hyvarinen and Smith paper.
target() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Returns the value of the target record component.
TARGET_NAME - Static variable in class edu.cmu.tetrad.util.Params: Constant TARGET_NAME="targetName"
TARGETS - Static variable in class edu.cmu.tetrad.util.Params: Constant TARGETS="targets"
TaskManager - Class in edu.cmu.tetrad.util: This cancels all processes that check the TaskManager.getInstance().isCanceled() method.
TaskRunner<T> - Class in edu.cmu.tetrad.util: This class is for running a list of tasks that implement Callable.
TaskRunner() - Constructor for class edu.cmu.tetrad.util.TaskRunner: This class is responsible for running a list of tasks that implement the Callable interface in parallel using multiple threads.
TaskRunner(int) - Constructor for class edu.cmu.tetrad.util.TaskRunner: Initializes a TaskRunner with the specified number of threads.
tautology(VariableSource) - Static method in class edu.cmu.tetrad.bayes.Evidence: tautology.
tautology(VariableSource) - Static method in class edu.cmu.tetrad.bayes.Proposition: tautology.
tautology(SemIm) - Static method in class edu.cmu.tetrad.sem.SemProposition: Creates a tautology by wrapping the given SemIm object.
TaylorSeries - Class in edu.cmu.tetrad.util: Represents a Taylor series expansion for a mathematical function.
TaylorSeriesDerivativeCheck - Class in edu.cmu.tetrad.util: Tests if the derivative f'(x) is positive for a Taylor series function over a range of x given its derivatives at 0.
tCdf(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: CACM Algorithm 395, by G.
tearDown() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: tearDown.
TemplateExpander - Class in edu.cmu.tetrad.sem: Expands templates for the generalized SEM PM.
test() - Method in class edu.cmu.tetrad.hybridcg.TestHybridCgModel: Tests the HybridCgModel with a random mixed node list.
test(double[][], double[], double[]) - Method in class edu.cmu.tetrad.search.CordEngine1: Conducts a conditional independence test to evaluate the hypothesis H0: Y is independent of Z given X.
test(double[][], double[], double[]) - Method in class edu.cmu.tetrad.search.CordEngine2: Tests the relationships between the given datasets x, y, and z by performing statistical computations and returning a result based on the analysis.
test(double[][], double[], double[]) - Method in class edu.cmu.tetrad.search.CordEngine3: Tests the model using the provided data and auxiliary features.
test(double[][], double[], double[]) - Method in class edu.cmu.tetrad.search.CordEngine4: Performs a test based on the provided input data.
test(double[][], double[], double[]) - Method in class edu.cmu.tetrad.search.CordEngine5: Executes a statistical test using the given input data.
test(double[][], double[], double[][]) - Method in class edu.cmu.tetrad.search.CordEngine1: Performs a conditional independence test to evaluate the hypothesis H0: Y is independent of Z given X.
test(double[][], double[], double[][]) - Method in class edu.cmu.tetrad.search.CordEngine2: Tests the relationships between the provided datasets by performing statistical computations and returns the results based on the analysis.
test(double[][], double[], double[][]) - Method in class edu.cmu.tetrad.search.CordEngine3: Executes the core functionality of the CORD engine using the provided inputs.
test(double[][], double[], double[][]) - Method in class edu.cmu.tetrad.search.CordEngine4: Conducts a test using the provided input data and returns the results.
test(double[][], double[], double[][]) - Method in class edu.cmu.tetrad.search.CordEngine5: Executes a statistical test using the provided input data.
test(double[], double[], double[][], double, int, long) - Static method in class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest: Runs the test.
test(IndependenceTest) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the independence test for the builder.
test(IndependenceTest) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Sets the IndependenceTest instance to be used by the Builder.
TEST_TIMEOUT - Static variable in class edu.cmu.tetrad.util.Params: Constant TEST_TIMEOUT="testTimeout"
testable() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: True if the test could be carried out (df > 0 and a finite statistic).
testAdditivityCheckFlagsPureInteraction() - Method in class edu.cmu.tetrad.test.TestNonlinearityTests: cvAdditiveVsRff must have power on its one job: a pure interaction, which no additive model can represent.
testAdditivityCheckNotFooledBySmoothAdditive() - Method in class edu.cmu.tetrad.test.TestNonlinearityTests: cvAdditiveVsRff must NOT flag additive-but-smooth truths.
TestAdjustment - Class in edu.cmu.tetrad.search.mimic: Sanity test for Adjustment: For random graphs and random (X,Y), each returned adjustment set Z must: - NOT block any amenable (causal) path X => Y (i.e., those must stay m-connecting) - Block every backdoor path between X and Y (i.e., those must be NOT m-connecting)
TestAdjustment() - Constructor for class edu.cmu.tetrad.search.mimic.TestAdjustment: Default constructor for the TestAdjustment class.
testAdjustmentSetsRespectPaths() - Method in class edu.cmu.tetrad.search.mimic.TestAdjustment: Tests the correctness of adjustment sets generated for random graphs and verifies whether they respect causal and backdoor path properties.
testAllMissingColumn() - Method in class edu.cmu.tetrad.test.TestDataAudit: A column with no non-missing values at all should fire CONSTANT_COLUMN with numNonMissing = 0.
testAteOrAndDrAreReasonableV1() - Method in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1SmokeTest: Tests that the estimated Average Treatment Effects (ATE) using Outcome Regression (OR) and Doubly Robust (DR) methods in version 1 of the adjustment effect estimator are reasonable under simulated data with a discrete confounder, binary treatment, and continuous outcome.
TestBooleanFunction - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Tests the BooleanFunction class.
TestBooleanFunction(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: Standard constructor for JUnit test cases.
TestBoss - Class in edu.cmu.tetrad.algcomparison.examples: Test the degenerate Gaussian score.
TestBoss() - Constructor for class edu.cmu.tetrad.algcomparison.examples.TestBoss: Initializes a new instance of the TestBoss class.
testCallerArraysNotModified() - Method in class edu.cmu.tetrad.test.TestNonlinearityTests: Internal centering must operate on copies: caller arrays are not modified.
TestCausalUnmixer - Class in edu.cmu.tetrad.search.unmix: Better-targeted unmixing tests with ground-truth labels + ARI.
TestCausalUnmixer() - Constructor for class edu.cmu.tetrad.search.unmix.TestCausalUnmixer: Default constructor for the TestCausalUnmixer class.
TestCausalUnmixer.LabeledData - Class in edu.cmu.tetrad.search.unmix: Represents labeled data consisting of a dataset and corresponding labels.
testCleanGaussianNoWarnings() - Method in class edu.cmu.tetrad.test.TestDataAudit: Clean Gaussian data should produce zero WARNING findings.
testCollinearPair() - Method in class edu.cmu.tetrad.test.TestDataAudit: A near-copy pair should fire HIGH_CORRELATION and NEAR_DETERMINISM_LINEAR but not EXACT_LINEAR_DEPENDENCE.
testCompareDagToCPDAG(int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testCompareDagToCPDAG.
testComparePcVersions(int, double, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testComparePcVersions.
testConstantColumn() - Method in class edu.cmu.tetrad.test.TestDataAudit: An exactly constant continuous column, a continuous column constant on its two non-missing entries, and a single-category discrete column should each fire CONSTANT_COLUMN (and not NEAR_CONSTANT); a varying Gaussian column should fire neither.
testConstantColumnDoesNotMaskDownstreamChecks() - Method in class edu.cmu.tetrad.test.TestDataAudit: A constant column must not mask downstream findings among the remaining variables: with a constant X1 present, a near-linear-dependence among X2, X3, X4 should still fire NEAR_DETERMINISM_LINEAR, the continuous-name accessors should exclude X1, and the correlation matrix should cover only the non-constant variables.
testConstantIndegree() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: Tests whether the randomizer can randomly make a graph where all of the factors have the same indegree.
testConstruction() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestIndexedConnectivity: testConstruction.
testConstruction() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestIndexedLagGraph: testConstruction.
testConstruction() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomial: Tests to make sure that null parent throw an exception.
testConstruction() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomialTerm: Tests to make sure that null parent throw an exception.
testCpc(int, double, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testCpc.
testCpcStable(int, double, int, double) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testCpcStable.
testCvTestsDeterministic() - Method in class edu.cmu.tetrad.test.TestNonlinearityTests: The CV tests must be deterministic given their inputs (local seeded generators), independent of the global RandomUtil stream's state.
TestDataAudit - Class in edu.cmu.tetrad.test: Tests the pre-search data audit.
TestDataAudit() - Constructor for class edu.cmu.tetrad.test.TestDataAudit: Constructs a new test.
testDefaultParameterSettings() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.TestMeasurementSimulator: Test to make sure the accessor methods are working correctly.
TestDegenerateGaussian - Class in edu.cmu.tetrad.algcomparison.examples: Test the degenerate Gaussian score.
TestDegenerateGaussian() - Constructor for class edu.cmu.tetrad.algcomparison.examples.TestDegenerateGaussian: Initializes a new instance of the TestDegenerateGaussian class.
testDerivativePositivity(double[], double, double, double) - Static method in class edu.cmu.tetrad.util.TaylorSeriesDerivativeCheck: Tests if the first derivative f'(x) is positive for all x in the given range.
testDiscChildWithContParent() - Method in class edu.cmu.tetrad.hybridcg.HybridCgRowShapeTest: Tests the behavior of HybridCgPm row shapes when a discrete child has continuous parents.
testDoInterventionRemovesParents_preservesOtherCpts_andUpdatersAgree() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdaterTest: (3) do(X): incoming edges removed, other CPTs preserved, manipulated CPT set to intervention distribution.
testDrDoesNotExplodeUnderNearPositivityViolationV1() - Method in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1SmokeTest: Tests that the Doubly Robust (DR) Average Treatment Effect (ATE) estimate in version 1 of the adjustment effect estimator does not suffer from instability or explosion under scenarios with near-positivity violations in propensity scores.
testEvaluation() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomial: Test the evaluation of terms.
testEvaluation() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomialTerm: Test the evaluation of terms.
testEvidencePropagatesUpAndDown_andUpdatersAgree() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdaterTest: Structure: X1 -> X2 -> X3 -> X5 \-> X4
testExactLinearDependence() - Method in class edu.cmu.tetrad.test.TestDataAudit: An exact linear copy should fire EXACT_LINEAR_DEPENDENCE.
testFci(int, double, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testFci.
testFges(int, double, int, double) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testFges.
testFgesComparisonContinuous(int, double, int, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testFgesComparisonContinuous.
testFgesComparisonDiscrete(int, double, int, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testFgesComparisonDiscrete.
testFgesFci(int, double) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testGfci.
testFgesMbComparisonContinuous(int, double, int, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testFgesMbComparisonContinuous.
testFgesMbComparisonDiscrete(int, double, int, int) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testFgesMbComparisonDiscrete.
testGFciComparison() - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testGFciComparison.
TestHybridCgModel - Class in edu.cmu.tetrad.hybridcg: Tests for HybridCgModel.
TestHybridCgModel() - Constructor for class edu.cmu.tetrad.hybridcg.TestHybridCgModel: Default constructor for the TestHybridCgModel class.
TestIGFCI_TCGA - Class in edu.cmu.tetrad.search.is: CLI runner used to reproduce the original IGFCI-on-TCGA workflow.
TestIGFCI_TCGA() - Constructor for class edu.cmu.tetrad.search.is.TestIGFCI_TCGA: Constructs a new instance of the TestIGFCI_TCGA class.
TestIndexedConnectivity - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Tests the IndexedConnectivity class by constructing graphs with randomly chosen parameters and seeing if they have the required properties.
TestIndexedConnectivity(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestIndexedConnectivity: Change the name of this constructor to match the name of the test class.
TestIndexedLagGraph - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Tests the IndexedLagGraph class by constructing graphs with randomly chosen parameters and seeing if they have the required properties.
TestIndexedLagGraph(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestIndexedLagGraph: Change the name of this constructor to match the name of the test class.
testIsCanalyzing() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: Tests to see whether some known canalyzing functions (AND, OR, ...) pass the isCanalyzing() test.
testIsEffective() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: Tests to see whether some known effective functions pass the isEffective() test.
TestISFGS_TCGA - Class in edu.cmu.tetrad.search.is: CLI runner for instance-specific FGES on TCGA-like discrete data.
TestISFGS_TCGA() - Constructor for class edu.cmu.tetrad.search.is.TestISFGS_TCGA: Default constructor for the TestISFGS_TCGA class.
testJsonWellFormed() - Method in class edu.cmu.tetrad.test.TestDataAudit: The JSON rendering should contain the expected top-level keys and have balanced braces.
testKnowledgeReplicationWithBoss() - Method in class edu.cmu.tetrad.search.TestTsKnowledgeReplication: Constructs a small 3-variable time series dataset, defines within-lag knowledge over two tiers, expands it across lags, verifies the expansion, then runs BOSS with the expanded knowledge and a replicating graph.
testLaggedVariableInKnowledgeThrows() - Method in class edu.cmu.tetrad.search.TestTsKnowledgeReplication: Verifies that createLagData(DataSet, int, Knowledge) throws an IllegalArgumentException when the supplied knowledge contains lagged variable names (i.e.
testLiangFigure6() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.TestRevealEvaluator: Tests whether the calculations in Liang, Figure 6 come out to the correct values.
testLinearCancellationFaithlessness() - Method in class edu.cmu.tetrad.search.harness.ViolationsTest: Test for linear cancellation faithlessness in PC algorithm.
testLinearWithInterceptNotFlagged() - Method in class edu.cmu.tetrad.test.TestNonlinearityTests: A linear relationship with a large intercept, passed raw (uncentered), must not be flagged nonlinear.
Test logic - Section in class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence
testLowSampleRatio() - Method in class edu.cmu.tetrad.test.TestDataAudit: n = 30 with p = 10 should fire LOW_SAMPLE_RATIO.
testMain() - Method in class edu.pitt.dbmi.algo.bayesian.constraint.inference.BayesianConstraintInferenceTest: Test of main method, of class BayesianConstraintInference.
testManyLevelsAndFewValues() - Method in class edu.cmu.tetrad.test.TestDataAudit: A 13-level discrete variable should fire DISCRETE_MANY_LEVELS, and a continuous variable taking three values should fire CONTINUOUS_FEW_VALUES.
testMaxIndegree() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: Tests whether the randomizer can randomly make a graph where the maximum indegree across factors is the given factor.
testMeanIndegree() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: Tests whether the randomizer can randomly make a graph where the mean ofindegree across factors is the given number.
TestMeasurementSimulator - Class in edu.cmu.tetrad.study.gene.tetrad.gene.simulation: Tests the MeasurementSimulator class using diagnostics devised by Richard Scheines.
TestMeasurementSimulator(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.simulation.TestMeasurementSimulator: Standard constructor for JUnit test cases.
testMissingData() - Method in class edu.cmu.tetrad.test.TestDataAudit: MCAR-injected missingness should produce exactly one MISSING_DATA finding, a non-null delegated MissingDataAudit, and a Little's MCAR p-value among the finding's values.
TestMissingDataPolicyWiring - Class in edu.cmu.tetrad.test: Checks that a score wrapper's missing-data policies agree across the three independent places a policy has to be admitted before a user can reach it: the score class's own policy switch, which throws on a policy it does not implement; the wrapper's getParameters(), which is what an interface reads to decide whether to render the control at all; the native-policy set the wrapper passes to MissingDataUtils.gate, which rejects anything outside it before the score is ever constructed.
TestMissingDataPolicyWiring() - Constructor for class edu.cmu.tetrad.test.TestMissingDataPolicyWiring: Default constructor for the TestMissingDataPolicyWiring class.
testMultiTreatmentSharedBackdoor() - Method in class edu.cmu.tetrad.search.RecursiveAdjustmentMultipleTest: Tests the adjustment set computation for a Directed Acyclic Graph (DAG) with multiple treatments sharing a common backdoor path to the outcome.
testNearConstant() - Method in class edu.cmu.tetrad.test.TestDataAudit: A negligible-variance (but varying) continuous column and a 99.5-percent-modal (but two-category) discrete column should each fire NEAR_CONSTANT, and neither should fire CONSTANT_COLUMN.
testNearDeterminismDiscreteContinuous() - Method in class edu.cmu.tetrad.test.TestDataAudit: A class variable with well-separated class means nearly determining a continuous variable should fire NEAR_DETERMINISM_DISCRETE_CONTINUOUS.
testNearWhichEndpointDoesNotChangeSolutions() - Method in class edu.cmu.tetrad.search.RecursiveAdjustmentMultipleTest: Tests whether specifying different possible endpoint options in the adjustment set computation produces solutions from the same only slightly reordered but identical solution families.
TestNonlinearityTests - Class in edu.cmu.tetrad.test: Regression tests for NonlinearityTests, pinning two fixes verified by the calibration harness: Internal centering: the tests fit without an intercept column, so before the fix any linear relationship with a nonzero intercept (the typical case for raw real data) was misspecified under the null and all of RESET, the conditional-moment test, and the additive-hinge test rejected "linearity" with probability near 1.
TestNonlinearityTests() - Constructor for class edu.cmu.tetrad.test.TestNonlinearityTests: Constructs a new test.
testNullConstruction() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: Tests to make sure that null parent throw an exception.
TestOfIndependence - Annotation Interface in edu.cmu.tetrad.annotation: Aug 31, 2017 4:42:08 PM
TestOfIndependenceAnnotations - Class in edu.cmu.tetrad.annotation: Sep 26, 2017 1:18:28 AM
testOSetCpdag_agreesWithDag_inSimpleChainNoConfounding() - Method in class edu.cmu.tetrad.search.OSetTest: Tests that OSet.oSetCpdag(Graph, Node, Node, int) agrees with OSet.oSetDag(Graph, Node, Node) in a simple unconfounded causal chain.
testOSetCpdag_matchesDag() - Method in class edu.cmu.tetrad.search.OSetTest: Tests the behavior of OSet.oSetCpdag(Graph, Node, Node, int) compared to OSet.oSetDag(Graph, Node, Node) using a minimal confounding example.
testOSetDag_HenckelExample() - Method in class edu.cmu.tetrad.search.OSetTest: Tests the OSet.oSetDag(Graph, Node, Node) method using a classic Henckel–Perković–Maathuis example DAG.
testOSetDag_simpleChainNoConfounding() - Method in class edu.cmu.tetrad.search.OSetTest: Tests OSet.oSetDag(Graph, Node, Node) on a simple causal chain with no confounding variables.
testPc(int, double, int, double) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testPc.
testPcStable(int, double, int, double) - Method in class edu.cmu.tetrad.study.performance.PerformanceTests: testPcStable.
TestPolynomial - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Tests the Polynomial class.
TestPolynomial(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomial: Standard constructor for JUnit test cases.
TestPolynomialTerm - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Tests the PolynomialTerm class.
TestPolynomialTerm(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestPolynomialTerm: Standard constructor for JUnit test cases.
testPrint(int) - Static method in class edu.cmu.tetrad.util.PermutationGenerator: This static method will print the series of combinations for numObjects choose b to System.out.
testPrint(int) - Static method in class edu.cmu.tetrad.util.SelectionGenerator: This static method will print the series of combinations for a choose b to System.out.
testPrint(int[]) - Static method in class edu.cmu.tetrad.util.CombinationGenerator: This static method will print the series of combinations for a choose b to System.out.
testPrint(int, int) - Static method in class edu.cmu.tetrad.util.ChoiceGenerator: This static method will print the series of combinations for a choose b to System.out.
testPrint(int, int) - Static method in class edu.cmu.tetrad.util.SublistGenerator: This static method will print the series of combinations for a choose depth to System.out.
testQuadraticFlaggedByAllFour() - Method in class edu.cmu.tetrad.test.TestNonlinearityTests: A strong quadratic must be flagged by all four tests.
TestResult(double, double, boolean) - Constructor for class edu.cmu.tetrad.search.utils.NonlinearityTests.TestResult: Constructs a TestResult instance with the specified test statistic, p-value, and decision on whether to reject the null hypothesis.
TestRevealEvaluator - Class in edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal: Test the Reveal evaluator.
TestRevealEvaluator(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.reveal.TestRevealEvaluator: Standard constructor for JUnit test cases.
testRowOrder() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: Tests whether rows are stored in the correct order.
testSerialDependenceAr1Message() - Method in class edu.cmu.tetrad.test.TestDataAudit: An AR(1) series with substantial lag-1 autocorrelation should fire SERIAL_DEPENDENCE with the AR(1) effective-sample-size clause in the message and without the periodicSuspect flag.
testSerialDependencePeriodicSuspectMessage() - Method in class edu.cmu.tetrad.test.TestDataAudit: A periodic series with near-zero lag-1 autocorrelation but a dominant autocorrelation at a deeper lag (here a period-4 cycle, whose lag-2 autocorrelation is near -1 while lag-1 is near 0) should fire SERIAL_DEPENDENCE with the periodic-dependence clause in place of the AR(1) effective-sample-size clause, and should carry periodicSuspect = 1 in its values.
testSimpleChain_X_to_Z_in_PAG() - Method in class edu.cmu.tetrad.search.PdagPagIdaTest: Tests the calculation of total causal effects between variables in a partially directed acyclic graph (PAG) using the PagIda algorithm.
TestSimpleRandomizer - Class in edu.cmu.tetrad.study.gene.tetrad.gene.history: Tests the SimpleRandomizer class by constructing graphs with randomly chosen parameters and seeing if they have the required properties.
TestSimpleRandomizer(String) - Constructor for class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestSimpleRandomizer: Change the name of this constructor to match the name of the test class.
testSinglePairSimpleDAG() - Method in class edu.cmu.tetrad.search.RecursiveAdjustmentMultipleTest: Tests the adjustment set computation for a simple Directed Acyclic Graph (DAG) with a single treatment-outcome pair.
testSkewedColumn() - Method in class edu.cmu.tetrad.test.TestDataAudit: A lognormal column should fire NON_GAUSSIAN at severity INFO; a Gaussian column should not fire.
testSmallMarginalCell() - Method in class edu.cmu.tetrad.test.TestDataAudit: A discrete variable with a 3-case category should fire SMALL_MARGINAL_CELL.
testSmallPairwiseCells() - Method in class edu.cmu.tetrad.test.TestDataAudit: Two 5-level discrete variables at n = 100 should fire SMALL_PAIRWISE_CELLS.
testsPerPair(double, int) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: The number of conditioning sets tried per adjacent pair up to a given depth, sum over k of C(a, k) for k = 0 to depth, with a the size of the adjacency set the sets are drawn from.
Test statistic - Section in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest
Test statistic - Section in class edu.cmu.tetrad.search.test.Rcit
Test statistic and p-value - Section in class edu.cmu.tetrad.search.test.FfCi
testTableSize() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.TestBooleanFunction: Tests to make sure the table is the correct size.
TestTsKnowledgeReplication - Class in edu.cmu.tetrad.search: Tests the behavior of TsUtils.createLagData(DataSet, int, Knowledge) and its interaction with BOSS via a ReplicatingGraph.
TestTsKnowledgeReplication() - Constructor for class edu.cmu.tetrad.search.TestTsKnowledgeReplication: Default constructor.
testwise() - Static method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A spec for test-wise deletion.
TESTWISE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: For each local score or independence test, use only the rows that are complete on the variables involved in that particular calculation.
TESTWISE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingValueSupport: The component performs test-wise (pairwise/local) deletion: each local calculation uses the rows complete on the variables involved in that calculation.
TestwiseCovariance - Class in edu.cmu.tetrad.data.missing: Test-wise (family-wise) covariance estimation for components whose local calculations consume a covariance submatrix and a sample size: for a requested column set, the sample covariance matrix is computed over exactly the rows that are complete on those columns, together with the number of such rows.
TestwiseCovariance(Matrix) - Constructor for class edu.cmu.tetrad.data.missing.TestwiseCovariance: Constructs the helper over a data matrix.
TestwiseCovariance.Family - Record Class in edu.cmu.tetrad.data.missing: The covariance matrix of a column set over the rows complete on that set, the number of such rows, and the rows themselves.
TestwiseDeletionRankTest - Class in edu.cmu.tetrad.search.rlcd: A rank test by testwise deletion: each rank query is answered from the rows that are complete on the columns that query mentions, with its own sample size.
TestwiseDeletionRankTest(DataSet) - Constructor for class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: Constructs the test over a data set that may contain missing values.
TestwiseDeletionRankTest(DataSet, double) - Constructor for class edu.cmu.tetrad.search.rlcd.TestwiseDeletionRankTest: Constructs the test over a data set that may contain missing values.
TestwiseDeletionStudy - Class in edu.cmu.tetrad.study.missingness: Maps out where test-wise deletion fails and where it is fine, before any decision about its place in the interface.
TestwiseDeletionStudy() - Constructor for class edu.cmu.tetrad.study.missingness.TestwiseDeletionStudy: Default constructor for the TestwiseDeletionStudy class.
TestwiseRows - Class in edu.cmu.tetrad.data.missing: Computes, and caches, the row subsets used by test-wise deletion: for a given set of columns, the rows of a dataset on which all of those columns are observed.
Tetrad - Class in edu.cmu.tetrad.search.utils: Represents an ordered sextad of nodes.
Tetrad(int, int, int, int) - Constructor for class edu.cmu.tetrad.search.utils.Tetrad: Constructor.
TETRAD_BASED - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Tetrad-based test.
TETRAD_BOLLEN - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Bollen Tetrad test.
TETRAD_DELTA - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Delta Tetrad test.
TETRAD_PURIFY_WASHDOWN - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: This option doesn't do any purify.
TETRAD_TEST_BPC - Static variable in class edu.cmu.tetrad.util.Params: Constant TETRAD_TEST_BPC="tetrad_test_bpc"
TETRAD_TEST_FOFC - Static variable in class edu.cmu.tetrad.util.Params: Constant TETRAD_TEST_FOFC="tetrad_test_fofc"
TETRAD_WISHART - Enum constant in enum class edu.cmu.tetrad.search.utils.BpcTestType: Wishart Tetrad test.
tetradHolds(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: tetradHolds.
tetradHolds(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: tetradHolds.
tetradHolds(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: tetradHolds.
tetradHolds(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: tetradHolds.
TetradInt - Record Class in edu.cmu.tetrad.search.utils: Represents an ordered tetrad (quartet) of nodes, where the order of nodes within {i, j} and {k, l} does not matter, but the order of the pairs <{i, j}, {k, l}> does matter.
TetradInt(int, int, int, int) - Constructor for record class edu.cmu.tetrad.search.utils.TetradInt: Initializes a TetradInt record while ensuring that all provided nodes are distinct.
TetradLogger - Class in edu.cmu.tetrad.util: Represents a logging utility used throughout tetrad.
TetradLogger.EmptyConfig - Class in edu.cmu.tetrad.util: Represents an empty configuration for the logger.
TetradLogger.LogDisplayOutputStream - Interface in edu.cmu.tetrad.util: Represents an output stream that can get its own length.
TetradLoggerConfig - Interface in edu.cmu.tetrad.util: Represents the configuration for the logger.
TetradLoggerConfig.Event - Interface in edu.cmu.tetrad.util: Represents an event which is just an id and a description.
TetradLoggerEvent - Class in edu.cmu.tetrad.util: An event associated with the TetradLoggerListener.
TetradLoggerEvent(Object, TetradLoggerConfig) - Constructor for class edu.cmu.tetrad.util.TetradLoggerEvent: Constructs the event given the source and the TetradLoggerConfig associated with the event if there is one
TetradLoggerListener - Interface in edu.cmu.tetrad.util: A listener for tetrad's logger.
TetradNode - Class in edu.cmu.tetrad.search.utils: Represents a ordered tetrad of variables, (i, j, k, l).
TetradNode(Node, Node, Node, Node) - Constructor for class edu.cmu.tetrad.search.utils.TetradNode: Constructor for Tetrad.
TetradNode(Node, Node, Node, Node, double) - Constructor for class edu.cmu.tetrad.search.utils.TetradNode: Constructor for Tetrad.
TetradProperties - Class in edu.cmu.tetrad.util: Nov 10, 2017 4:14:31 PM
tetradPValue(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: tetradPValue.
tetradPValue(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: tetradPValue.
tetradPValue(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: tetradPValue.
tetradPValue(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: tetradPValue.
tetradScore(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: tetradScore.
tetradScore(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: tetradScore.
tetradScore(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: tetradScore.
tetradScore(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: tetradScore.
tetradScore1(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: tetradScore1.
tetradScore1(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: tetradScore1.
tetradScore1(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: tetradScore1.
tetradScore1(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: tetradScore1.
tetradScore3(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: tetradScore3.
tetradScore3(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: tetradScore3.
tetradScore3(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: tetradScore3.
tetradScore3(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: tetradScore3.
TetradSerializable - Interface in edu.cmu.tetrad.util: Interface to tag a class that is part of the set of serializable classes in the Tetrad API.
TetradSerializableExcluded - Interface in edu.cmu.tetrad.util: Interface to tag a class that should be exluded from the set of TetradSerializable classes, even if it implements the TetradSerializable interface.
TetradSerializableUtils - Class in edu.cmu.tetrad.util: Contains methods used by TestSerialization to ensure that previous "stable" versions of Tetrad will by loadable by later "stable" versions of Tetrad.
TetradSerializableUtils(String, String, String) - Constructor for class edu.cmu.tetrad.util.TetradSerializableUtils: Blank constructor.
TetradTest - Interface in edu.cmu.tetrad.search.utils: Provides an interface for classes that test tetrad constraints.
TetradTestContinuous - Class in edu.cmu.tetrad.search.utils: Implements different tests of tetrad constraints: using Wishart's test (CPS, Wishart 1928); Bollen's test (Bollen, 1990) or a more computationally intensive test that fits one/two factor Gaussian models.
TetradTestContinuous(CorrelationMatrix, BpcTestType, double) - Constructor for class edu.cmu.tetrad.search.utils.TetradTestContinuous: Constructor for TetradTestContinuous.
TetradTestContinuous(DataSet, BpcTestType, double) - Constructor for class edu.cmu.tetrad.search.utils.TetradTestContinuous: Constructor for TetradTestContinuous.
TetradTestDiscrete - Class in edu.cmu.tetrad.search.utils: Implements a test of tetrad constraints with discrete variables.
TetradTestDiscrete(DataSet, double) - Constructor for class edu.cmu.tetrad.search.utils.TetradTestDiscrete: Constructor for TetradTestDiscrete.
TetradTestPopulation - Class in edu.cmu.tetrad.search.utils: Implements a test of tetrad constraints in a known correlation matrix.
TetradTestPopulation(CorrelationMatrix) - Constructor for class edu.cmu.tetrad.search.utils.TetradTestPopulation: Constructor for TetradTestPopulation.
TextTable - Class in edu.cmu.tetrad.util: Stores a 2D array of Strings for printing out tables.
TextTable(int, int) - Constructor for class edu.cmu.tetrad.util.TextTable: Construct the text table; the table has a fixed number of rows and columns, each greater than zero.
TextTable.Delimiter - Enum Class in edu.cmu.tetrad.util: An enum of delimiters.
TeyssierScorer - Class in edu.cmu.tetrad.search.utils: Implements and extends a scorer extending Teyssier, M., and Koller, D.
TeyssierScorer(IndependenceTest, Score) - Constructor for class edu.cmu.tetrad.search.utils.TeyssierScorer: Constructor that takes both a test or a score.
Tgin - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Deprecated.
Tgin - Class in edu.cmu.tetrad.search: Deprecated.
Tgin() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.Tgin: Deprecated.

Constructs the wrapper.
Tgin(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.Tgin: Deprecated.

Constructs a TGIN search with data and parameters.
Tgin.TginOrientationStages - Class in edu.cmu.tetrad.search: Deprecated.

Helper class for the orientation stages.
TginOrientationStages(Graph, List, Map, DataSet, double, int, RawMarginalIndependenceTest) - Constructor for class edu.cmu.tetrad.search.Tgin.TginOrientationStages: Deprecated.

Constructs a new instance of TginOrientationStages.
The four questions, one axis each - Section in class edu.cmu.tetrad.study.missingness.TestwiseDeletionStudy
Theoretical foundation - Section in class edu.cmu.tetrad.search.Tsc
The PAIRWISE arm - Section in class edu.cmu.tetrad.search.harness.MissingDataRecoveryHarness
THR - Static variable in class edu.cmu.tetrad.util.Params: Constant THR="thr"
Thread-safety contract - Section in class edu.cmu.tetrad.search.test.IndTestFisherZ
threshold() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns the value of the threshold record component.
threshold(Matrix, double) - Static method in class edu.cmu.tetrad.search.LingD: Hard threshold (copy).
Threshold - Enum constant in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Choose an edge iff its prob.
THRESHOLD_B - Static variable in class edu.cmu.tetrad.util.Params: Constant THRESHOLD_B="thresholdBHat"
THRESHOLD_FOR_NUM_EIGENVALUES - Static variable in class edu.cmu.tetrad.util.Params: Constant THRESHOLD_FOR_NUM_EIGENVALUES="thresholdForNumEigenvalues"
THRESHOLD_NO_RANDOM_CONSTRAIN_SEARCH - Static variable in class edu.cmu.tetrad.util.Params: Constant THRESHOLD_NO_RANDOM_CONSTRAIN_SEARCH="thresholdNoRandomConstrainSearch"
THRESHOLD_NO_RANDOM_DATA_SEARCH - Static variable in class edu.cmu.tetrad.util.Params: Constant THRESHOLD_NO_RANDOM_DATA_SEARCH="thresholdNoRandomDataSearch"
THRESHOLD_W - Static variable in class edu.cmu.tetrad.util.Params: Constant THRESHOLD_W="thresholdW"
TierPrecedence - Class in edu.cmu.tetrad.search.rlcd: The part of background knowledge that constrains ancestry, not only adjacency: a variable in a strictly later temporal tier than another cannot be its cause, directly or through any intermediary, latent ones included.
TIME_LAG - Static variable in class edu.cmu.tetrad.util.Params: Constant TIME_LAG="timeLag"
TIME_LAG_REPLICATING_GRAPH - Static variable in class edu.cmu.tetrad.util.Params: Constant REPEATING_GRAPH="repeatingGraph" Whether to use a repeating graph for time lag search.
TIME_SERIES - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant TIME_SERIES="Time Series"
TimeLagGraph - Class in edu.cmu.tetrad.graph: Represents a time series graph--that is, a graph with a fixed number S of lags, with edges into initial lags only--that is, into nodes in the first R lags, for some R.
TimeLagGraph() - Constructor for class edu.cmu.tetrad.graph.TimeLagGraph: Constructor for TimeLagGraph.
TimeLagGraph(TimeLagGraph) - Constructor for class edu.cmu.tetrad.graph.TimeLagGraph: Constructor for TimeLagGraph.
TimeLagGraph.NodeId - Class in edu.cmu.tetrad.graph: Represents a NodeId with a name and a lag value.
timeMillis() - Static method in class edu.cmu.tetrad.util.MillisecondTimes: timeMillis.
TIMEOUT - Static variable in class edu.cmu.tetrad.util.Params: Constant TIMEOUT="timeout"
TimeoutComparison - Class in edu.cmu.tetrad.algcomparison: Nov 14, 2017 12:00:31 PM
TimeoutComparison() - Constructor for class edu.cmu.tetrad.algcomparison.TimeoutComparison: Represents a comparison of two time values for timeout purposes.
TimeoutComparison.ComparisonGraph - Enum Class in edu.cmu.tetrad.algcomparison: An enum of graph types to compare.
times(Matrix) - Method in class edu.cmu.tetrad.util.Matrix: times.
times(Vector) - Method in class edu.cmu.tetrad.util.Matrix: times.
TimeSeries - Annotation Interface in edu.cmu.tetrad.annotation: Aug 7, 2019 6:17:29 PM
TimeSeriesData - Class in edu.cmu.tetrad.data: Stores time series data as a list of continuous columns.
TimeSeriesData(Matrix, List) - Constructor for class edu.cmu.tetrad.data.TimeSeriesData: Constructs a new time series data contains for the given row-major data array and the given list of variables.
TimeSeriesSemSimulation - Class in edu.cmu.tetrad.algcomparison.simulation: Time series SEM simulation.
TimeSeriesSemSimulation(RandomGraph) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Creates a new TimeSeriesSemSimulation with the given random graph and no within-lag knowledge.
TimeSeriesSemSimulation(RandomGraph, Knowledge) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: Creates a new TimeSeriesSemSimulation with the given random graph and within-lag knowledge over base variable names (no lag suffix).
tippett - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Tippett's method
tippett - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The tippett method.
titles - Variable in class edu.cmu.tetrad.algcomparison.utils.ParameterSettingsText.ApplyResult: The section titles encountered, in order (lines without an equals sign).
tKeys() - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Returns the set of nodes that are keys in the map of triples.
TMath - Class in edu.cmu.tetrad.util: Unified math interface for Tetrad.
TMath.Impl - Enum Class in edu.cmu.tetrad.util: Enumerates the available implementation strategies for mathematical computations.
toAdjacency() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Writes the discovered structure as a signed adjacency matrix over the partition's observed variables followed by the latent variables (Python getLfromLatentGroups).
toArray() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: toArray.
toArray() - Method in class edu.cmu.tetrad.util.Matrix: toArray.
toArray() - Method in class edu.cmu.tetrad.util.Vector: toArray.
toArray(List) - Static method in class edu.cmu.tetrad.util.RankTests: Converts a List of Integer objects into an array of primitive int values.
toContinuousDataModel(ContinuousData) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toContinuousDataModel.
toCovarianceMatrix(CovarianceData) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toCovarianceMatrix.
toDataModel(Data) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toDataModel.
toDataModel(Data, Metadata) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toDataModel.
toDataSet() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Converts the current simulation result into a DataSet object, combining both continuous and discrete data variables.
toDataSet() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Converts the current simulation result into a DataSet object, combining both continuous and discrete data variables.
toDataSet() - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Converts the current simulation result into a DataSet object, combining both continuous and discrete data variables.
toDataSet(HybridCgModel.HybridCgIm.Sample) - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Convert a sampled matrix into a Tetrad DataSet with the provided node ordering.
toDegrees(double) - Static method in class edu.cmu.tetrad.util.TMath: Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
toDelimiter(char) - Static method in class edu.cmu.tetrad.util.DelimiterUtils: Get the enum delimiter corresponding to char delimiter: tab, space, comma, colon, semicolon, pipe.
toDisplayString() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report: Returns a readable text report.
toJson() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns a JSON rendering of the report.
toJson() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the findings and summary statistics as a JSON string, suitable for consumption by py-tetrad or other tools.
toJson(HybridCgModel.HybridCgIm) - Static method in class edu.cmu.tetrad.hybridcg.HybridCgIo: Renders the given model as pretty-printed JSON.
token() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns the value of the token record component.
Token - Enum Class in edu.cmu.tetrad.calculator.parser: Allowable tokens.
tol - Variable in class edu.cmu.tetrad.search.CordEngine1: The tolerance level for early stopping during iterative algorithms.
tol - Variable in class edu.cmu.tetrad.search.CordEngine2: Tolerance value used as a threshold for numerical convergence and stopping criteria.
tol - Variable in class edu.cmu.tetrad.search.CordEngine3: A tolerance value utilized as a convergence threshold in numerical methods or iterative algorithms within the CordEngine3 class.
tol - Variable in class edu.cmu.tetrad.search.CordEngine4: A tolerance level used as a convergence criterion for iterative algorithms or as a threshold for numerical comparisons.
tol - Variable in class edu.cmu.tetrad.search.CordEngine5: The tolerance level to be used for numerical calculations or comparisons.
tol - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Config: A threshold value used for numerical tolerance in computations to determine acceptable precision or convergence in iterative algorithms.
toMarkdown() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns a markdown rendering of the report: a header describing the sweep and a table with one row per setting (setting, point-graph edge count, adjacency instability, Anderson-Darling p for implied independencies, fraction of implied dependencies detected, elapsed milliseconds).
toMixedDataBox(MixedTabularData) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toMixedDataBox.
toMixedDataBox(MixedTabularData, Metadata) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: Converting using metadata
toNamesCluster(Collection, List) - Static method in class edu.cmu.tetrad.search.Tsc: Constructs a StringBuilder containing a formatted string representation of the names of nodes corresponding to the provided cluster indices.
toNamesClusters(Set, List) - Static method in class edu.cmu.tetrad.search.Tsc: Converts a set of clusters represented as sets of integers into a string representation that associates cluster IDs with node names.
toNodes(DataColumn[]) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toNodes.
toNodes(DiscreteDataColumn[]) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toNodes.
toNodes(List) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toNodes.
toNumericMatrix(DataSet) - Static method in class edu.cmu.tetrad.sem.DagScores: Converts the given dataset into a numeric matrix representation, where each value in the dataset is cast to a double.
TOP_BRACKET - Static variable in class edu.cmu.tetrad.util.Params: Constant TOP_BRACKET="topBracket"
TOP_K - Static variable in class edu.cmu.tetrad.util.Params: Constant TOP_K="topK"
topConstraints(int) - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result: Constraints with the largest |T_j|, most-violated first.
topM - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: Specifies the maximum number of strongest candidates to retain per node during the statistical screening process.
topToBottomLayout(TimeLagGraph) - Static method in class edu.cmu.tetrad.algcomparison.simulation.TimeSeriesSemSimulation: topToBottomLayout.
toRadians(double) - Static method in class edu.cmu.tetrad.util.TMath: Converts an angle measured in degrees to an approximately equivalent angle in radians.
toSpec(List, DataSet) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Converts a list of block indices and a dataset into a BlockSpec object, ensuring the blocks are canonicalized and generating the appropriate block variables.
toSpec(List, List, DataSet) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Converts a list of blocks, ranks, and a dataset into a BlockSpec object.
toStatusLine() - Method in class edu.cmu.tetrad.sem.CVReport: Generates a single-line summary of the cross-validation (CV) results for the current instance, including details such as the number of CV folds, mean out-of-sample squared maximum mean discrepancy (MMD²), mean out-of-sample R², and the count of nodes that improved over the baseline.
toString() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepReport: Returns the markdown rendering.
toString() - Method in record class edu.cmu.tetrad.algcomparison.sweep.SweepResult.MarkovStats: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.algcomparison.sweep.SweepResult: Returns a one-line rendering of this result.
toString() - Method in class edu.cmu.tetrad.annotation.AnnotatedClass
toString() - Method in class edu.cmu.tetrad.bayes.ApproximateUpdater: Prints out the most recent marginal.
toString() - Method in interface edu.cmu.tetrad.bayes.BayesIm: Returns a string representation for this Bayes net.
toString() - Method in class edu.cmu.tetrad.bayes.BayesPm: Prints out the list of values for each node.
toString() - Method in class edu.cmu.tetrad.bayes.DirichletBayesIm: Prints out the probability table for each variable.
toString() - Method in class edu.cmu.tetrad.bayes.Evidence: toString.
toString() - Method in class edu.cmu.tetrad.bayes.Identifiability: toString.
toString() - Method in class edu.cmu.tetrad.bayes.JunctionTreeUpdater: Returns a string representation of the JunctionTreeUpdater2 instance, summarizing its inference method and current evidence state.
toString() - Method in class edu.cmu.tetrad.bayes.Manipulation: toString.
toString() - Method in class edu.cmu.tetrad.bayes.MlBayesIm: Prints out the probability table for each variable.
toString() - Method in class edu.cmu.tetrad.bayes.MlBayesImObs: Prints out the probability table for each variable.
toString() - Method in class edu.cmu.tetrad.bayes.Proposition: toString.
toString() - Method in class edu.cmu.tetrad.bayes.RowSummingExactUpdater
toString() - Method in class edu.cmu.tetrad.bayes.StoredCellProbs: toString.
toString() - Method in class edu.cmu.tetrad.bayes.StoredCellProbsObs: toString.
toString() - Method in class edu.cmu.tetrad.bayes.UpdatedBayesIm: Prints out the probability table for each variable.
toString() - Method in class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Returns a string representation of the VariableElimiinationUpdater instance, summarizing its inference method and current evidence state.
toString() - Method in class edu.cmu.tetrad.calculator.expression.ConstantExpression: toString.
toString() - Method in class edu.cmu.tetrad.calculator.expression.VariableExpression: toString.
toString() - Method in class edu.cmu.tetrad.cluster.KMeans: toString.
toString() - Method in class edu.cmu.tetrad.data.AbstractVariable: toString.
toString() - Method in class edu.cmu.tetrad.data.audit.AuditFinding: Returns a one-line rendering of this finding, of the form "SEVERITY CODE [vars]: message".
toString() - Method in class edu.cmu.tetrad.data.audit.DataAudit: Returns the report for this audit.
toString() - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.audit.DeterministicClusters.Constraint: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.data.BoxDataSet: toString.
toString() - Method in class edu.cmu.tetrad.data.CorrelationMatrixOnTheFly: Prints out the matrix
toString() - Method in class edu.cmu.tetrad.data.CovarianceMatrix: Prints out the matrix
toString() - Method in class edu.cmu.tetrad.data.CovarianceMatrixOnTheFly: Prints out the matrix
toString() - Method in interface edu.cmu.tetrad.data.DataModel: Renders the data model as as String.
toString() - Method in class edu.cmu.tetrad.data.DataModelList: toString.
toString() - Method in interface edu.cmu.tetrad.data.DataSet: toString.
toString() - Method in class edu.cmu.tetrad.data.DelimiterType: Prints out the name of the type.
toString() - Method in class edu.cmu.tetrad.data.DiscreteVariable: toString.
toString() - Method in class edu.cmu.tetrad.data.DiscreteVariableType: Prints out the name of the type.
toString() - Method in class edu.cmu.tetrad.data.Discretizer.Discretization: Returns a string representation of the Discretization object.
toString() - Method in interface edu.cmu.tetrad.data.ICovarianceMatrix: Renders the covariance matrix as a string representation.
toString() - Method in class edu.cmu.tetrad.data.IndependenceFacts: toString.
toString() - Method in record class edu.cmu.tetrad.data.JoinDatasets.Result: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.data.Knowledge: toString.
toString() - Method in class edu.cmu.tetrad.data.KnowledgeEdge: toString.
toString() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Returns a short description of this structure.
toString() - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.LongToWide.Result: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.data.missing.MissingDataAudit: A string representation of the audit.
toString() - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: A string representation of the spec.
toString() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Candidate: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.missing.MissingnessThreshold.Effect: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.missing.TestwiseCovariance.Family: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.MechanismReport: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Report: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Result: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Spec: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.data.NumberObjectDataSet: toString.
toString() - Method in class edu.cmu.tetrad.graph.Dag: toString.
toString() - Method in class edu.cmu.tetrad.graph.Edge: Produces a string representation of the edge.
toString() - Method in class edu.cmu.tetrad.graph.EdgeListGraph: toString.
toString() - Method in interface edu.cmu.tetrad.graph.Graph: toString.
toString() - Method in class edu.cmu.tetrad.graph.GraphNode: toString.
toString() - Method in class edu.cmu.tetrad.graph.GraphUtils.TwoCycleErrors: Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.graph.IndependenceFact: toString.
toString() - Method in interface edu.cmu.tetrad.graph.Node: Returns the intervention type for this node.
toString() - Method in class edu.cmu.tetrad.graph.NodePair: toString.
toString() - Method in class edu.cmu.tetrad.graph.OrderedPair: toString.
toString() - Method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Returns a string representation of the CPDAG legality check result.
toString() - Method in class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: A string representation of the structural information for the generated graph.
toString() - Method in record class edu.cmu.tetrad.graph.RandomMim.LatentGroupSpec: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.graph.RandomMimic.MimicGroupSpec: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.graph.SemGraph: toString.
toString() - Method in class edu.cmu.tetrad.graph.TimeLagGraph: toString.
toString() - Method in class edu.cmu.tetrad.graph.Triple: toString.
toString() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.ModelFit: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeSignificance.Result: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm.Sample: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.hybridcg.HybridCgModel.HybridCgIm: Returns a string representation of the HybridIm model.
toString() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport.Deletion
toString() - Method in class edu.cmu.tetrad.hybridcg.HybridCgPruneReport
toString() - Method in class edu.cmu.tetrad.regression.LogisticRegression.Result: Returns a string representation of the regression results.
toString() - Method in class edu.cmu.tetrad.regression.RegressionResult: toString.
toString() - Method in class edu.cmu.tetrad.search.blocks.BlockSpec
toString() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Issue: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.ParseResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns a string representation of this record class.
toString() - Method in enum class edu.cmu.tetrad.search.ConditioningSetType
toString() - Method in class edu.cmu.tetrad.search.CordEngine1.Result: Returns a string representation of the hypothesis test result.
toString() - Method in class edu.cmu.tetrad.search.CordEngine2.Result: Generates a string representation of the result, including the hypothesis test details and the test verdict based on the status and calculated values.
toString() - Method in class edu.cmu.tetrad.search.CordEngine3.Result: Returns a string representation of the result, including the null hypothesis, test statistic, p-value, and status verdict.
toString() - Method in class edu.cmu.tetrad.search.CordEngine4.Result: Represents a string representation of the statistical test result for the null hypothesis H0: Y is conditionally independent of Z given X.
toString() - Method in class edu.cmu.tetrad.search.CordEngine5.Result
toString() - Method in record class edu.cmu.tetrad.search.CyclicSemScorer.ScoreResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.FciOrientDijkstra.DijkstraEdge: Returns a string representation of the DijkstraEdge.
toString() - Method in class edu.cmu.tetrad.search.GacTotalEffectElibility.Eligibility
toString() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Config: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.Uniformity: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Returns a string representation of this facade.
toString() - Method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Returns a string representation of this facade, reporting the number of pairs carrying a prior.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.StudyResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.ConditionResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.StudyResult: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.Ida.NodeEffects: Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.search.IndTestIod: Returns a string representation of this test.
toString() - Method in record class edu.cmu.tetrad.search.MarkovCheck.MarkovCheckRecord: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.MarkovCheckNullReference.Result: A report in the same format as the py-tetrad prototype.
toString() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentPair: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.LatentSignature: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.mimic.LatentLatentEvaluator.Report
toString() - Method in class edu.cmu.tetrad.search.mimic.MimicEvaluation: Returns a string representation of the MimicEvaluation object.
toString() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.NtadExplorer.NtadResult: Returns a string representation of the NtadResult object, including the values of its fields: blockA, blockB, blockSize, rank, and pValue.
toString() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.PcAR.OrientationClash: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.PdagPagIda.NodeEffects: Converts the object to a readable string representation by iterating through the nodes and their corresponding effects, appending them in the format "node=effect" with a space delimiter.
toString() - Method in record class edu.cmu.tetrad.search.PdagPagIda.PagEffects: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.BlockingResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.RecursiveBlocking.Mark: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.rlcd.Cover
toString() - Method in record class edu.cmu.tetrad.search.rlcd.LatentGroups.AdjacencyResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Result: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBgeScore: A string representation of the score.
toString() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScore: Returns a string representation of the BasisFunctionBicScore object.
toString() - Method in class edu.cmu.tetrad.search.score.BasisFunctionBicScoreFullSample: Returns a string representation of the BasisFunctionBicScore object.
toString() - Method in class edu.cmu.tetrad.search.score.BdeScore: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.score.BDeuScore: Returns a string representation of this BDeu Score object.
toString() - Method in class edu.cmu.tetrad.search.score.BgeScore: A string representation of the score.
toString() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianLikelihood.Ret: Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.search.score.ConditionalGaussianScore: Returns a string representation of the Conditional Gaussian Score Penalty.
toString() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianBgeScore: A string representation of the score.
toString() - Method in class edu.cmu.tetrad.search.score.DegenerateGaussianScore: A string representation of the score.
toString() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScore: A string representation of the score.
toString() - Method in class edu.cmu.tetrad.search.score.DiscreteBicScoreAdTree: A string representation of the score.
toString() - Method in class edu.cmu.tetrad.search.score.EdgePriorScore: Returns a string representation of this score.
toString() - Method in class edu.cmu.tetrad.search.score.FfCordContinuous: Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.search.score.FfDmlContinuous: Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.search.score.FfMl: Returns a string representation of the object, describing the type of kernel configuration as "KFF-ML Mixed (continuous+categorical product-kernel)".
toString() - Method in class edu.cmu.tetrad.search.score.FfMlContinuous: Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.search.score.GicScores: Returns a string for this object.
toString() - Method in class edu.cmu.tetrad.search.score.HeavyTailSemBicScore: Returns a string representation of the HeavyTailSemBicScore instance.
toString() - Method in class edu.cmu.tetrad.search.score.HuangMarginalLikelihoodScore: Returns a string representation of the HuangMarginalScore object.
toString() - Method in class edu.cmu.tetrad.search.score.KcvBicScore: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScore
toString() - Method in class edu.cmu.tetrad.search.score.KernelMarginalLikelihoodScoreContinuous: Retrieves the name of the score.
toString() - Method in record class edu.cmu.tetrad.search.score.LegendreBicScore.LocalFit: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Returns a string representation of the object.
toString() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration.NullFit: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.score.PenaltyDiscountReport.Row: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.score.RffBicScore: Returns a string representation of the score.
toString() - Method in interface edu.cmu.tetrad.search.score.Score: A string representation of the score.
toString() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.CovAndCoefs: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.score.SemBicScore.LikelihoodResult: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.score.SemBicScore: Returns a string representation of this score.
toString() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.LocalFit: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.score.TRffBicScore.NestedFits: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Returns a string representation of this MinimaxTRffBicScore instance.
toString() - Method in class edu.cmu.tetrad.search.ScoredClusterFinder.ClusterHit: Returns a string representation of the ClusterHit instance.
toString() - Method in record class edu.cmu.tetrad.search.test.AlphaReport.Row: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Eval: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndependenceResult: Returns a string representation of this independence fact.
toString() - Method in interface edu.cmu.tetrad.search.test.IndependenceTest: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestBasisExpandedGcm: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestChiSquare: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalCorrelation: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestConditionalGaussianLrt: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestCord: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestCordEric: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestDegenerateGaussianLrt: Returns a string representation of the degenerate Gaussian likelihood ratio test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestEquivalence: Returns a description of this test.
toString() - Method in record class edu.cmu.tetrad.search.test.IndTestFisherZ.Result: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZ: Returns a string representation of this instance, including details about the test configuration.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZConcatenateResiduals: Deprecated.

Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestGSquare: Returns a String representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestHsic: Deprecated.

Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestMulti
toString() - Method in class edu.cmu.tetrad.search.test.IndTestRcotCcaWilkes: Generates a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestRegression: Deprecated.

Returns a string representation of the Linear Regression Test object.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestStratifiedRank: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.IndTestTrekSep: Deprecated.

Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.LegendreLrIndependenceTest: Returns a string representation of the LegendreLrIndependenceTest.
toString() - Method in class edu.cmu.tetrad.search.test.MsepTest: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.test.PythonKciIndependenceTest: Returns a string representation of the PythonKciIndependenceTest instance, providing a concise description intended to identify the use of the KCI method for causal independence tests integrated with a Python backend.
toString() - Method in class edu.cmu.tetrad.search.test.PythonRcitIndependenceTest: Returns a string representation of the PythonRcitIndependenceTest instance, providing a concise description intended to identify the use of the RCIT method for causal independence tests integrated with a Python backend.
toString() - Method in class edu.cmu.tetrad.search.test.ScoreIndTest: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.test.TRffLrTest: Returns a string representation of this test.
toString() - Method in class edu.cmu.tetrad.search.utils.Bes.Arrow: Returns the index of the arrow.
toString() - Method in class edu.cmu.tetrad.search.utils.DiscriminatingPath: Returns a string representation of the DiscriminatingPath object.
toString() - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns a string representation of this store.
toString() - Method in record class edu.cmu.tetrad.search.utils.Embedding.EmbeddedData: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.utils.FcitSepsets.SepsetSearch: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms.GraphoidIndFact: Returns a string representation of this object.
toString() - Method in record class edu.cmu.tetrad.search.utils.MinimaxBinningConfig: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Result: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.utils.NonlinearityTests.TestResult: Returns a string representation of this TestResult instance.
toString() - Method in class edu.cmu.tetrad.search.utils.PermutationMatrixPair: Prints a string representation of this.
toString() - Method in class edu.cmu.tetrad.search.utils.PossibleMConnectingPath: toString.
toString() - Method in class edu.cmu.tetrad.search.utils.R5R9Dijkstra.DijkstraEdge: Returns a string representation of the DijkstraEdge object.
toString() - Method in class edu.cmu.tetrad.search.utils.SepsetMap: Returns a string representation of this sepset map.
toString() - Method in class edu.cmu.tetrad.search.utils.Sextad: toString.
toString() - Method in class edu.cmu.tetrad.search.utils.Tetrad: toString.
toString() - Method in record class edu.cmu.tetrad.search.utils.TetradInt: Returns a string representation of this TetradInt object.
toString() - Method in class edu.cmu.tetrad.search.utils.TetradNode: toString.
toString() - Method in enum class edu.cmu.tetrad.search.VertexRepairSearch.RepairStrategy
toString() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Result
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.DMSearch.LatentStructure: toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Anly outputs ops which have elements, not empty structures.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move: Returns a string representation of this move.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestCramerT: Deprecated.

Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent: Deprecated.

Returns a string representation of this object.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestFisherZRecursive: Deprecated.

toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMixedMultipleTTest: Deprecated.

Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestPositiveCorr: Deprecated.

Returns a string representation of the Fisher Z object.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.IndTestSepsetDci: toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.MagCgBicScore: toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.MagDgBicScore: toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.MagSemBicScore: toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.SepsetMapDci: toString.
toString() - Method in class edu.cmu.tetrad.search.work_in_progress.Sextad: toString.
toString() - Method in class edu.cmu.tetrad.sem.CVReport
toString() - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.sem.DagScorer: toString.
toString() - Method in class edu.cmu.tetrad.sem.EdgeStrengthResult
toString() - Method in class edu.cmu.tetrad.sem.GeneralizedSemIm: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.sem.GeneralizedSemPm: Returns a relatively brief String representation of this SEM PM--the equations and distributions of the model.
toString() - Method in class edu.cmu.tetrad.sem.Mapping: toString.
toString() - Method in class edu.cmu.tetrad.sem.NodeCVSummary
toString() - Method in enum class edu.cmu.tetrad.sem.ParamComparison
toString() - Method in enum class edu.cmu.tetrad.sem.ParamConstraintType
toString() - Method in class edu.cmu.tetrad.sem.Parameter: toString.
toString() - Method in enum class edu.cmu.tetrad.sem.ParamType
toString() - Method in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult
toString() - Method in class edu.cmu.tetrad.sem.PredictionPruneReport
toString() - Method in class edu.cmu.tetrad.sem.Ricf.FitConGraphResult: Returns a string representation of the FitConGraphResult object.
toString() - Method in class edu.cmu.tetrad.sem.Ricf.RicfResult: Returns a string representation of the RicfResult object.
toString() - Method in class edu.cmu.tetrad.sem.RicfEjml.RicfResult
toString() - Method in interface edu.cmu.tetrad.sem.Scorer: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemEstimator: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemEstimatorGibbs: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemEvidence: toString.
toString() - Method in record class edu.cmu.tetrad.sem.SemIm.CyclicSimResult: Returns a string representation of this record class.
toString() - Method in record class edu.cmu.tetrad.sem.SemIm.Result: Returns a string representation of this record class.
toString() - Method in class edu.cmu.tetrad.sem.SemIm: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemManipulation: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemOptimizerEm: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemOptimizerPowell: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemOptimizerRegression: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemOptimizerRicf: Returns a string representation of the SemOptimizerRicf instance, providing information about the algorithm and underlying libraries.
toString() - Method in class edu.cmu.tetrad.sem.SemOptimizerScattershot: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemPm: toString.
toString() - Method in class edu.cmu.tetrad.sem.SemProposition: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm.ParameterRange: toString.
toString() - Method in class edu.cmu.tetrad.sem.StandardizedSemIm: toString.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.biolingua.BiolinguaDigraph: Returns a specially formatted string with all the contents of this Graph.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicGraph: Returns a specially formatted string with all the contents of this Graph.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicLTMatrix: Returns a specially formatted string with all the contents of this matrix
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.BasicMatrix: Returns a specially formatted string with all the contents of this matrix
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.LTMatrix: Returns a specially formatted string with all the contents of this matrix
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.Matrix: Returns a specially formatted string with all the contents of this matrix
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrix: Returns a specially formatted string with all the contents of this matrix
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.util.SymMatrixF: Returns a specially formatted string with all the contents of this matrix
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.graph.ManualLagGraph: toString.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BasicLagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.BooleanFunction: Returns a string representation of the boolean function.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedConnectivity: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedLagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.IndexedParent: Prints out the factor index and lag.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LaggedFactor: Returns a string representing this lagged factor.
toString() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.history.LagGraph: Returns a string representation of the graph, indicating for each factor which lagged factors map into it.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.LinearFunction: Prints out the linear function of each factor of its parents.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.Polynomial: Prints out a representation of the term.
toString() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.PolynomialTerm: Prints out a representation of the term.
toString() - Method in class edu.cmu.tetrad.study.missingness.PooledDgLrtTest
toString() - Method in class edu.cmu.tetrad.study.performance.ComparisonParameters: toString.
toString() - Method in class edu.cmu.tetrad.study.performance.ComparisonResult: toString.
toString() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig.DefaultEvent: toString.
toString() - Method in class edu.cmu.tetrad.util.DefaultTetradLoggerConfig: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Beta: A string representation of the distribution.
toString() - Method in class edu.cmu.tetrad.util.dist.ChiSquare: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Discrete: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Exponential: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Gamma: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Indicator: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.LogNormal: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.MixtureOfGaussians: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Normal: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Poisson: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.SingleValue: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.util.dist.Split: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.TruncatedNormal: toString.
toString() - Method in class edu.cmu.tetrad.util.dist.Uniform: toString.
toString() - Method in class edu.cmu.tetrad.util.EssLikePython.Result: Returns a string representation of the Result object, which includes the average row correlation (avgRowCorr), effective sample size (ESS), and the number of rows used in the computation (m).
toString() - Method in interface edu.cmu.tetrad.util.Function: toString.
toString() - Method in class edu.cmu.tetrad.util.Matrix: toString.
toString() - Method in class edu.cmu.tetrad.util.MView
toString() - Method in class edu.cmu.tetrad.util.Parameters
toString() - Method in class edu.cmu.tetrad.util.PartialCorrelationPdf: toString.
toString() - Method in class edu.cmu.tetrad.util.Point: toString.
toString() - Method in class edu.cmu.tetrad.util.PointXy: toString.
toString() - Method in class edu.cmu.tetrad.util.RowCorrelationEffN.Result: Returns a string representation of the result, including the adjusted average row correlation value (rho_hat), the effective sample size (Neff), and the number of row pairs used in the computation.
toString() - Method in class edu.cmu.tetrad.util.SublistGenerator: toString.
toString() - Method in interface edu.cmu.tetrad.util.TetradLoggerConfig: Returns a string representation of the object.
toString() - Method in class edu.cmu.tetrad.util.TextTable: toString.
toString() - Method in class edu.cmu.tetrad.util.Vector: toString.
toString() - Method in class edu.cmu.tetrad.util.Version: toString.
toString() - Method in class edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald: Returns a string representation of the object.
toString(boolean[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Copies the given array, starting each line with a tab character..
toString(double[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toString.
toString(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Copies the given array, using a standard scientific notation number formatter and beginning each line with a tab character.
toString(double[][], NumberFormat) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Converts a 2D array of doubles into a String representation using the specified NumberFormat.
toString(double[][], NumberFormat, List) - Static method in class edu.cmu.tetrad.util.MatrixUtils: Converts a two-dimensional array representing a matrix into a string representation using the specified number format and variable names.
toString(double[][], List) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toString.
toString(double[], NumberFormat) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toString.
toString(int[]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toString.
toString(int[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toString.
toString(int[][], List) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toString.
toStringDep() - Method in class edu.cmu.tetrad.search.MarkovCheck.AllSubsetsIndependenceFacts: Returns a string representation of the m-connection facts.
toStringIndep() - Method in class edu.cmu.tetrad.search.MarkovCheck.AllSubsetsIndependenceFacts: Returns a string representation of the m-separation facts.
toStringSquare(double[][], NumberFormat, List) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toStringSquare.
toStringSquare(double[][], List) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toStringSquare.
toStringSquare(int[][], List) - Static method in class edu.cmu.tetrad.util.MatrixUtils: toStringSquare.
toSummaryLine() - Method in class edu.cmu.tetrad.sem.EdgeStrengthResult: One-line summary suitable for a status bar.
toSummaryLine() - Method in class edu.cmu.tetrad.sem.PartialEdgeStrengthResult: Returns a human-readable one-line summary.
total() - Method in class edu.cmu.tetrad.search.utils.IndependenceCheckCounter: Total across all sites.
totalEffect(Node, Node) - Method in class edu.cmu.tetrad.search.GRegression: Convenience method for a point intervention.
totalEffect(Node, Node) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the estimated total effect of x on y: the adjustment functional evaluated at the fitted Sigma-hat rather than at the sample covariance.
totalEffect(List, Node) - Method in class edu.cmu.tetrad.search.GRegression: Returns the estimated total effect of the treatments on the outcome (Eq.
totalEffectSampleCov(Node, Node) - Method in class edu.cmu.tetrad.search.GRegressionPag: Returns the same estimate computed from the sample covariance instead of Sigma-hat, using the same adjustment set: the ordinary adjustment estimator, provided for comparison.
totalReps() - Method in record class edu.cmu.tetrad.search.harness.tsbi.SimulationStudyTsbi.ConditionResult: Returns the value of the totalReps record component.
totalReps() - Method in record class edu.cmu.tetrad.search.harness.tsc.SimulationStudy.AlgorithmSummary: Returns the value of the totalReps record component.
toText() - Method in class edu.cmu.tetrad.sem.AdequacyReport: Converts the adequacy report data into a human-readable textual representation.
toText() - Method in class edu.cmu.tetrad.sem.CVReport: Returns a human-readable multi-line summary of the CV results.
toText() - Method in class edu.cmu.tetrad.sem.PredictionPruneReport: A human-readable summary, one line per proposed deletion.
toVerticalDiscreteDataModel(VerticalDiscreteTabularData) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: toVerticalDiscreteDataModel.
toVerticalDiscreteDataModel(VerticalDiscreteTabularData, Metadata) - Static method in class edu.cmu.tetrad.util.DataConvertUtils: Converting using metadata
toWeightsAnchoredAtAlpha(double) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Converts prior log-odds to p-value weights anchored at a given significance level, so that beta_ij = 0 reproduces that level exactly.
toWeightsViaBicBridge(double, int, double) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Converts prior log-odds to p-value weights via the BIC bridge.
toXML() - Method in class edu.pitt.isp.sverchkov.data.AdTree: Converts to XML.
toXML(DocumentBuilder) - Method in class edu.pitt.isp.sverchkov.data.AdTree: Converts to XML.
tpAdj() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the tpAdj record component.
tpAhd() - Method in record class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer.GraphScore: Returns the value of the tpAhd record component.
tPdf(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: tPdf.
tQuantile(double, double) - Static method in class edu.cmu.tetrad.util.ProbUtils: CACM Algorithm 396, by G.
trace() - Method in class edu.cmu.tetrad.util.Matrix: trace.
trace(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: trace.
trace(Set, Set) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: trace.
trace(Set, Set, Set) - Method in class edu.cmu.tetrad.search.utils.GrowShrinkTree: trace.
TrainAndResimulate - Class in edu.cmu.tetrad.sem: Simple driver: args[0] = input data path (tab-delimited with header) args[1] = input DAG path (one directed edge per line, like "A -> B" or "A-->B" or "A->B") args[2] = output data path (tab-delimited) args[3] = output DAG path (edge list) args[4] = (optional) nSamples (default = input N)
TRAINED_DAG_GNM - Enum constant in enum class edu.cmu.tetrad.search.MarkovCheckNullReference.SimulatorType: TrainedDagSimulatorGNM: neural local mechanisms with bootstrapped noise.
TRAINED_DAG_SIMULATION - Static variable in class edu.cmu.tetrad.algcomparison.simulation.SimulationTypes: Constant TRAINED_DAG_SIMULATION="Trained DAG Simulation"
TrainedDagAdequacy - Class in edu.cmu.tetrad.sem: The TrainedDagAdequacy class provides methods to evaluate the adequacy of a trained Directed Acyclic Graph (DAG) by comparing real data and simulated data generated from the DAG model.
TrainedDagModel - Class in edu.cmu.tetrad.algcomparison.simulation: Trained DAG model.
TrainedDagModel(RandomGraph, DataSet) - Constructor for class edu.cmu.tetrad.algcomparison.simulation.TrainedDagModel: Constructs a TrainedDagModel using the given randomGraph and data set.
Trained DAG simulator (ANM): learn local mechanisms from a real dataset and resimulate - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM
Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulate - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM
Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulate - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1
Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulate - Section in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2
TrainedDagSimulatorANM - Class in edu.cmu.tetrad.sem: Trained DAG simulator (ANM): learn local mechanisms from a real dataset and resimulate
TrainedDagSimulatorANM(DataSet, Graph, TrainedDagSimulatorANM.Params) - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Constructs a TrainedDagSimulatorANM
TrainedDagSimulatorANM.NodeReport - Class in edu.cmu.tetrad.sem: Class representing a report for a node in the simulation results.
TrainedDagSimulatorANM.Params - Class in edu.cmu.tetrad.sem: Defines various configuration parameters for simulations involving directed acyclic graphs (DAGs).
TrainedDagSimulatorANM.SimResult - Class in edu.cmu.tetrad.sem: Represents the result of a simulation, encapsulating the simulation data, variables, the true Directed Acyclic Graph (DAG), and optional diagnostic information.
TrainedDagSimulatorGNM - Class in edu.cmu.tetrad.sem: Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulate
TrainedDagSimulatorGNM(DataSet, Graph, TrainedDagSimulatorGNM.Params) - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Constructs a new instance of the TrainedDagSimulatorGNM class.
TrainedDagSimulatorGNM.NodeReport - Class in edu.cmu.tetrad.sem: Represents a report for a single node in a trained directed acyclic graph (DAG) structure.
TrainedDagSimulatorGNM.Params - Class in edu.cmu.tetrad.sem: Represents a configuration class containing various hyperparameters and settings used for the simulation and training of a directed acyclic graph (DAG) model.
TrainedDagSimulatorGNM.SimResult - Class in edu.cmu.tetrad.sem: Represents the result of a simulation, containing continuous and discrete data for variables, metadata about the simulation, and support for generating reports.
TrainedDagSimulatorGNM1 - Class in edu.cmu.tetrad.sem: Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulate
TrainedDagSimulatorGNM1(DataSet, Graph, TrainedDagSimulatorGNM1.Params) - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Constructs a new instance of the TrainedDagSimulatorGNM class.
TrainedDagSimulatorGNM1.NodeReport - Class in edu.cmu.tetrad.sem: Represents a report for a single node in a trained directed acyclic graph (DAG) structure.
TrainedDagSimulatorGNM1.Params - Class in edu.cmu.tetrad.sem: Encapsulates configuration parameters for the TrainedDagSimulatorGNM class.
TrainedDagSimulatorGNM1.SimResult - Class in edu.cmu.tetrad.sem: Represents the result of a simulation, containing continuous and discrete data for variables, metadata about the simulation, and support for generating reports.
TrainedDagSimulatorGNM2 - Class in edu.cmu.tetrad.sem: Trained DAG simulator (GNM): learn local mechanisms with non-additive noise and resimulate
TrainedDagSimulatorGNM2(DataSet, Graph, TrainedDagSimulatorGNM2.Params) - Constructor for class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Constructs a new instance of the TrainedDagSimulatorGNM class.
TrainedDagSimulatorGNM2.NodeReport - Class in edu.cmu.tetrad.sem: Represents a report for a single node in a trained directed acyclic graph (DAG) structure.
TrainedDagSimulatorGNM2.Params - Class in edu.cmu.tetrad.sem: Encapsulates configuration parameters for the TrainedDagSimulatorGNM class.
TrainedDagSimulatorGNM2.SimResult - Class in edu.cmu.tetrad.sem: Represents the result of a simulation, containing continuous and discrete data for variables, metadata about the simulation, and support for generating reports.
trainingRowsUsed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Number of rows used for training the node in the simulation.
trainingRowsUsed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Number of training rows used for the node.
trainingRowsUsed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Number of training rows used for the node.
trainingRowsUsed - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Number of training rows used for the node.
tRand(double) - Static method in class edu.cmu.tetrad.util.ProbUtils: T distribution random generator.
transferAttributes(Graph) - Method in class edu.cmu.tetrad.graph.Dag: Transfers attributes from the given graph to the current graph.
transferAttributes(Graph) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: transferAttributes.
transferAttributes(Graph) - Method in interface edu.cmu.tetrad.graph.Graph: transferAttributes.
transferAttributes(Graph) - Method in class edu.cmu.tetrad.graph.LagGraph: transferAttributes.
transferAttributes(Graph) - Method in class edu.cmu.tetrad.graph.SemGraph: transferAttributes.
transferAttributes(Graph) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Transfers attributes from the given graph to the current graph.
transferNodesAndEdges(Graph) - Method in class edu.cmu.tetrad.graph.Dag: Transfers nodes and edges from the given graph to the current graph.
transferNodesAndEdges(Graph) - Method in class edu.cmu.tetrad.graph.EdgeListGraph: Transfers nodes and edges from one graph to another.
transferNodesAndEdges(Graph) - Method in interface edu.cmu.tetrad.graph.Graph: Transfers nodes and edges from one graph to another.
transferNodesAndEdges(Graph) - Method in class edu.cmu.tetrad.graph.LagGraph: Transfers nodes and edges from one graph to another.
transferNodesAndEdges(Graph) - Method in class edu.cmu.tetrad.graph.SemGraph: Transfers nodes and edges from one graph to another.
transferNodesAndEdges(Graph) - Method in class edu.cmu.tetrad.graph.TimeLagGraph: Transfers nodes and edges from the given graph to the current graph.
transform(DataSet, String...) - Static method in class edu.cmu.tetrad.calculator.Transformation: Transforms the given data using the given representations of transforming equations.
Transformation - Class in edu.cmu.tetrad.calculator: Represents a transformation on some dataset.
transformCpdagIntoDag(Graph, Knowledge, boolean) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Transforms a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG) by orienting the undirected edges in the CPDAG.
translate(String, List) - Static method in class edu.cmu.tetrad.search.utils.GraphSearchUtils: translate.
transpose() - Method in class edu.cmu.tetrad.util.Matrix: transpose.
transpose(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: transpose.
traverse(Node, Edge) - Static method in class edu.cmu.tetrad.graph.Edges: If node is one endpoint of edge, returns the other endpoint.
traverseDirected(Node, Edge) - Static method in class edu.cmu.tetrad.graph.Edges: For A -> B, given A, returns B; otherwise returns null.
traverseNondirected(Node, Edge) - Static method in class edu.cmu.tetrad.graph.Edges: For A o-o B, given A, returns B; otherwise returns null.
traversePotentiallyDirected(Node, Edge) - Static method in class edu.cmu.tetrad.graph.Edges: For A --* B or A o-* B, given A, returns B.
traversePotentiallyDirected(Node, Edge) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Traverses a potentially directed edge to identify the next node in the traversal.
traverseReverseDirected(Node, Edge) - Static method in class edu.cmu.tetrad.graph.Edges: For A -> B, given B, returns A; otherwise returns null.
TREAT_AS_DEPENDENT - Enum constant in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.ErrorPolicy: Indicates that the error has dependencies and may affect other operations or processes.
TREAT_AS_INDEPENDENT - Enum constant in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.ErrorPolicy: Indicates that the error is considered independent and does not affect other operations or processes.
treatment() - Method in record class edu.cmu.tetrad.search.GRegressionPag.BootstrapResult: Returns the value of the treatment record component.
treatments() - Method in record class edu.cmu.tetrad.search.GRegression.BootstrapResult: Returns the value of the treatments record component.
trekIsUncovered(Graph, Graph, List) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Checks whether the given trek is "uncovered" relative to the estimated graph in the following sense: every consecutive pair along the trek is adjacent in the true graph, and among the consecutive pairs whose BOTH endpoints are measured, at least one is non-adjacent in the estimated graph.
TrekMeasurementModelBuilderBoss - Class in edu.cmu.tetrad.search.mimic: Builds the shared TSC/BOSS-based measurement model used by BossTrekMimic-style searches.
TrekMeasurementModelBuilderBoss(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss: Constructs the builder.
TrekMeasurementModelBuilderBoss.MeasurementBuildResult - Record Class in edu.cmu.tetrad.search.mimic: Result of building the shared measurement model.
TrekMeasurementModelBuilderPc - Class in edu.cmu.tetrad.search.mimic: Builds the shared PC/TSC-based measurement model used by TrekMimic-style searches.
TrekMeasurementModelBuilderPc(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc: Constructs the builder.
TrekMeasurementModelBuilderPc.MeasurementBuildResult - Record Class in edu.cmu.tetrad.search.mimic: Result of building the shared measurement model.
TrekMimic - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Algorithm-comparison wrapper for the Trek-MIMIC search.
TrekMimic - Class in edu.cmu.tetrad.search.mimic: Implements the Trek-MIMIC algorithm for recovering latent structure from measured data, including both the latent-latent skeleton and the measured parents (inputs) of each latent variable.
TrekMimic() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TrekMimic: Constructs the wrapper.
TrekMimic() - Constructor for class edu.cmu.tetrad.search.mimic.TrekMimic: Constructs an uninitialized TrekMimic search.
TrekMimic(DataSet, Parameters) - Constructor for class edu.cmu.tetrad.search.mimic.TrekMimic: Constructs a TrekMimic search with data and parameters.
TrekMimicRunner - Class in edu.cmu.tetrad.search.mimic: Benchmark runner for Trek-MIMIC.
TrekMimicRunner() - Constructor for class edu.cmu.tetrad.search.mimic.TrekMimicRunner: Constructor for the Trek-MIMIC runner.
treks(Graph, Node, Node) - Static method in class edu.cmu.tetrad.search.Ion: treks.
treks(Node, Node, int) - Method in class edu.cmu.tetrad.graph.Paths: Finds all treks from node1 to node2 with a maximum length.
TrekSeparationBlocksIndependence - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for Rank independence test.
TrekSeparationBlocksIndependence - Class in edu.cmu.tetrad.search.test: A block-level conditional independence test based on trek separation, operating under linear latent factor measurement models.
TrekSeparationBlocksIndependence() - Constructor for class edu.cmu.tetrad.algcomparison.independence.TrekSeparationBlocksIndependence: Constructs a new instance of the algorithm.
TrekSeparationBlocksIndependence(BlockSpec) - Constructor for class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Constructs an instance of IndTestBlocksTs using the provided block specification.
TrekSeparationBlocksIndependence(BlockSpec, RankTester) - Constructor for class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Constructs the test with a supplied rank engine.
TrekSeparationBlocksIndependence(BlockSpec, List) - Constructor for class edu.cmu.tetrad.search.test.TrekSeparationBlocksIndependence: Constructs the test pooled over several imputations of one data set.
treksIncludingBidirected(Node, Node) - Method in class edu.cmu.tetrad.graph.Paths: Finds all possible treks between two nodes, including bidirectional treks.
trekSourceLatent(Graph, List, Node, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Checks if the souce L of the given trek in a graph (not equal to x or y) is a latent.
TRFF_FF_FEATURES - Static variable in class edu.cmu.tetrad.util.Params: Constant TRFF_FF_FEATURES="trffFfFeatures"
TRFF_NU - Static variable in class edu.cmu.tetrad.util.Params: Constant TRFF_NU="trffNu"
TRFF_RIDGE - Static variable in class edu.cmu.tetrad.util.Params: Constant TRFF_RIDGE="trffRidge"
TRFF_SYMMETRIZED - Static variable in class edu.cmu.tetrad.util.Params: Constant TRFF_SYMMETRIZED="trffSymmetrized"
TRffBicScore - Class in edu.cmu.tetrad.algcomparison.score: The TRffBicScore class is a scoring mechanism designed to evaluate the fit and complexity of a data model.
TRffBicScore - Class in edu.cmu.tetrad.search.score: TRFF BIC score (mixed)
TRffBicScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.TRffBicScore: Constructor for the TRffBicScore class.
TRffBicScore(DataSet) - Constructor for class edu.cmu.tetrad.search.score.TRffBicScore: Constructs an instance of MinimaxTRffBicScore using the provided dataset.
TRffBicScore.LocalFit - Record Class in edu.cmu.tetrad.search.score: A record that encapsulates the results of a local fit operation, consisting of the log-likelihood value, the effective degrees of freedom, and the number of observations used.
TRffBicScore.NestedFits - Record Class in edu.cmu.tetrad.search.score: A pair of local fits computed on a shared design matrix in which the reduced model's columns are a prefix of the full model's columns, so the models are nested by construction.
TRffLrTest - Class in edu.cmu.tetrad.algcomparison.independence: Wrapper for the TRFF likelihood-ratio CI test built on TRffBicScore.
TRffLrTest - Class in edu.cmu.tetrad.search.test: Likelihood-ratio CI test based on nested TRffBicScore local fits.
TRffLrTest() - Constructor for class edu.cmu.tetrad.algcomparison.independence.TRffLrTest: Required no-arg ctor for algcomparison discovery/serialization.
TRffLrTest(TRffBicScore) - Constructor for class edu.cmu.tetrad.search.test.TRffLrTest: Constructs a TRffLrTest instance using the given score.
triangle(Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns true iff [a, b, c] is a triangle.
trimEdgesAmongParents(Graph, Node) - Static method in class edu.cmu.tetrad.search.utils.MbUtils: Removes edges among the parents of the target.
trimEdgesAmongParentsOfChildren(Graph, Node) - Static method in class edu.cmu.tetrad.search.utils.MbUtils: Removes edges among the parents of children of the target.
trimGraph(List, Graph, int) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Trims the given graph based on the specified trimming style.
TRIMMING_STYLE - Static variable in class edu.cmu.tetrad.util.Params: Constant TRIMMING_STYLE="trimmingStyle"
trimToAdjacents(Graph, Node) - Static method in class edu.cmu.tetrad.search.utils.MbUtils: Trims the graph to just the adjacents of the target.
trimToMbNodes(Graph, Node, boolean) - Static method in class edu.cmu.tetrad.search.utils.MbUtils: Trims the graph to the target, the parents and children of the target, and the parents of the children of the target.
trimTrailingEmptyTiers() - Method in class edu.cmu.tetrad.data.KnowledgeTierStructure: Removes empty tiers from the end of the structure, so that saved and rendered forms do not accumulate trailing blanks.
triple(Graph, Node, Node, Node) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Checks if three nodes are connected in a graph.
Triple - Class in edu.cmu.tetrad.graph: Stores a triple (x, y, z) of nodes.
Triple(Node, Node, Node) - Constructor for class edu.cmu.tetrad.graph.Triple: Constructs a triple of nodes.
Triple(Node, Node, Node) - Constructor for class edu.cmu.tetrad.search.Pc.Triple: Constructs a Triple object with the specified nodes.
Triple(Node, Node, Node) - Constructor for class edu.cmu.tetrad.search.PcAR.Triple: Constructs a Triple object with the specified nodes.
tripleAllowed(Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.AlmostCycleRemover: Determines whether a triple consisting of three given nodes is allowed.
TripleClassifier - Interface in edu.cmu.tetrad.graph: Marks a search algorithm as a triad classifier and returns the triad classifications if makes.
triplesToText(Set, String) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts a set of triples into a formatted string.
true_DAG - Enum constant in enum class edu.cmu.tetrad.algcomparison.Comparison.ComparisonGraph: Constant for the true DAG.
true_DAG - Enum constant in enum class edu.cmu.tetrad.algcomparison.TimeoutComparison.ComparisonGraph: The true dag.
TrueClusterExtractor - Class in edu.cmu.tetrad.search.harness.tsc: Extracts true latent clusters from a graph produced by RandomMim.
trueCount() - Method in record class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer.ClusterScore: Returns the value of the trueCount record component.
trueDag - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Represents the true Directed Acyclic Graph (DAG) associated with the simulation results.
trueDag - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Graph representing the true causal structure of the simulation.
trueDag - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Graph representing the true causal structure of the simulation.
trueDag - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Graph representing the true causal structure of the simulation.
TrueDagFalseNegativesArrows - Class in edu.cmu.tetrad.algcomparison.statistic: Represents the statistic of False Negatives for Arrows compared to the true DAG.
TrueDagFalseNegativesArrows() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesArrows: Constructs the statistic.
TrueDagFalseNegativesTails - Class in edu.cmu.tetrad.algcomparison.statistic: The class TrueDagFalseNegativesTails implements the Statistic interface to calculate the number of false negatives for tails compared to the true Directed Acyclic Graph (DAG).
TrueDagFalseNegativesTails() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalseNegativesTails: Constructs the statistic.
TrueDagFalsePositiveArrow - Class in edu.cmu.tetrad.algcomparison.statistic: Represents a statistic that calculates the false positives for arrows compared to the true directed acyclic graph (DAG).
TrueDagFalsePositiveArrow() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveArrow: This class represents a statistic that calculates the false positives for arrows compared to the true DAG.
TrueDagFalsePositiveTails - Class in edu.cmu.tetrad.algcomparison.statistic: TrueDagFalsePositiveTails is a class that implements the Statistic interface.
TrueDagFalsePositiveTails() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagFalsePositiveTails: Constructs a new TrueDagFalsePositiveTails object.
trueDagOrGraph(Graph, Graph) - Static method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns the graph a statistic should use for DAG-level questions--ancestry, latent confounding, or converting the truth to a CPDAG or PAG.
TrueDagPrecisionArrow - Class in edu.cmu.tetrad.algcomparison.statistic: The proportion of X*->Y in the estimated graph for which there is no path Y~~>X in the true graph.
TrueDagPrecisionArrow() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionArrow: This class represents a statistic that calculates the precision for arrows compared to the true DAG.
TrueDagPrecisionTails - Class in edu.cmu.tetrad.algcomparison.statistic: A class that implements the Statistic interface to calculate the proportion of X-->Y edges in the estimated graph for which there is a path X~~>Y in the true graph.
TrueDagPrecisionTails() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagPrecisionTails: This class represents a statistic that calculates the precision for tails compared to the true DAG.
TrueDagRecallArrows - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
TrueDagRecallArrows() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallArrows: This class represents a statistic that calculates the recall for arrows compared to the true DAG.
TrueDagRecallTails - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
TrueDagRecallTails() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagRecallTails: This class represents a statistic that calculates the true positives for tails compared to the true DAG.
TrueDagTruePositiveArrow - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
TrueDagTruePositiveArrow() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveArrow: This class represents a statistic that calculates the true positives for arrows compared to the true DAG.
TrueDagTruePositiveDirectedPathNonancestor - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
TrueDagTruePositiveDirectedPathNonancestor() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveDirectedPathNonancestor: This class represents a statistic that calculates the true positives for arrows compared to the true DAG.
TrueDagTruePositiveTails - Class in edu.cmu.tetrad.algcomparison.statistic: The bidirected true positives.
TrueDagTruePositiveTails() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TrueDagTruePositiveTails: This class represents a statistic that calculates the true positives for tails compared to the true DAG.
trueEdgeLossBound(double, double) - Static method in class edu.cmu.tetrad.search.test.AlphaCalibration: An upper bound on the probability that a true edge is removed, given a per-test probability of failing to reject and the number of sets tried: 1 - (1 - beta)^T.
TruncatedNormal - Class in edu.cmu.tetrad.util.dist: A normal distribution that allows its parameters to be set and allows random sampling.
TRUNCATION_LIMIT - Static variable in class edu.cmu.tetrad.util.Params: Constant TRUNCATION_LIMIT="truncationLimit"
tryZhangMag(Graph) - Static method in class edu.cmu.tetrad.search.utils.CorrectnessGates: The Zhang MAG of the given PAG, or null if the conversion fails (which both gates treat as a veto).
TsbiRunner - Class in edu.cmu.tetrad.search.harness.tsbi: Runs the PC algorithm with the TSBI (Trek-Separation Block Independence) test as its conditional independence oracle, given a pre-determined block clustering.
TsbiRunner(double, int) - Constructor for class edu.cmu.tetrad.search.harness.tsbi.TsbiRunner: Creates a TSBI runner with the given settings.
tsc(DataSet, double, int, double, int, SingletonClusterPolicy, int, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: Creates a new instance of a TscTestBlockDiscoverer, which is an implementation of the BlockDiscoverer interface.
tsc(DataSet, CovarianceMatrix, double, int, double, int, SingletonClusterPolicy, int, boolean) - Static method in class edu.cmu.tetrad.search.blocks.BlockDiscoverers: TSC over a pooled covariance matrix; see TscTestBlockDiscoverer.
Tsc - Class in edu.cmu.tetrad.search: Trek Separation Clusters (TSC): a rank-based algorithm for recovering the latent-cluster structure of a pure measurement model from an observed correlation or covariance matrix.
Tsc(List, CovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.Tsc: Constructs an instance of the TscScored class using the provided variables and covariance matrix.
Tsc(List, DataSet) - Constructor for class edu.cmu.tetrad.search.Tsc: Constructs an instance of the Tsc class by initializing variables, correlation matrix, sample size, and data array from the provided nodes and dataset.
TSC_CLUSTER_RANK - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_CLUSTER_RANK="tscClusterRank"
TSC_CLUSTER_SIZE - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_CLUSTER_SIZE="tscClusterSize"
TSC_ENABLE_HIERARCHY - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_ENABLE_HIERARCHY="tscEnableHierarchy"
TSC_MIN_RANK_DROP - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_MIN_RANK_DROP="tscMinRankDrop"
TSC_MIN_REDUNDANCY - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_MIN_REDUNDANCY="tscMinRedundancy"
TSC_MODE - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_MODE="tscMode"
tsc_onMixedRankMIMs_hasHighQuality() - Method in class edu.cmu.tetrad.search.TscHarnessTest: Validates the performance of the TSC (Time Series Clustering) algorithm on datasets with mixed ranks using a MIM (Manifest Independence Model).
tsc_onNoLatentDAG_producesAtMostTinyArtifacts() - Method in class edu.cmu.tetrad.search.TscHarnessTest: Tests the Time Series Clustering (TSC) algorithm on a directed acyclic graph (DAG) with no latent variables, verifying its behavior in a controlled environment.
TSC_PC_USE_BOSS - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_PC_USE_BOSS="tscPcUseBoss"
TSC_SINGLETON_POLICY - Static variable in class edu.cmu.tetrad.util.Params: Constant TSC_SINGLETON_POLICY="tscSingletonPolicy"
TscHarnessTest - Class in edu.cmu.tetrad.search: The TscHarnessTest class validates the Time Series Clustering (TSC) algorithm and its ability to handle various data scenarios, particularly with mixed ranks and different structural configurations.
TscHarnessTest() - Constructor for class edu.cmu.tetrad.search.TscHarnessTest: Constructs a new instance of the TscHarnessTest class.
TscRunner - Class in edu.cmu.tetrad.search.harness.tsc: AlgorithmRunner implementation for the TSC algorithm.
TscRunner(double, int, int, int) - Constructor for class edu.cmu.tetrad.search.harness.tsc.TscRunner: Creates a TSC runner with the given settings.
TscTestBlockDiscoverer - Class in edu.cmu.tetrad.search.blocks: The TscTestBlockDiscoverer class is an implementation of the BlockDiscoverer interface that utilizes the Testing Strong Causal structures (TSC) algorithm to identify and discover clusters or blocks of variables from a given dataset.
TscTestBlockDiscoverer(DataSet, double, int, double, int, SingletonClusterPolicy, int, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.TscTestBlockDiscoverer: Constructs a TscTestBlockDiscoverer instance with the specified parameters for discovering and analyzing blocks of variables in a dataset using the TSC algorithm.
TscTestBlockDiscoverer(DataSet, CovarianceMatrix, double, int, double, int, SingletonClusterPolicy, int, boolean) - Constructor for class edu.cmu.tetrad.search.blocks.TscTestBlockDiscoverer: Constructs a TscTestBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
TsDagToPag - Class in edu.cmu.tetrad.search.utils: Finds the PAG to which a DAG belongs, for a time series model.
TsDagToPag(Graph) - Constructor for class edu.cmu.tetrad.search.utils.TsDagToPag: Constructs a new FCI search for the given independence test and background knowledge.
tStat - Variable in class edu.cmu.tetrad.sem.PredictionPruneReport.Deletion: meanImprovement / seImprovement; NaN when SE is zero.
TsUtils - Class in edu.cmu.tetrad.search.utils: Contains some utilities for doing autoregression.
TsUtils.VarResult - Class in edu.cmu.tetrad.search.utils: Gives a result consisting of the residuals and collapsed var graphs.
tt - Enum constant in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Tail-to-tail
tuck(Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Moves j to before k and moves all the ancestors of j betwween k and j to before k.
tuck(Node, Node...) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Moves all j's to before k and moves all the ancestors of all ji's betwween k and ji to before k.
TWO_CYCLE - Enum constant in enum class edu.cmu.tetrad.search.Fask.Origin: Output as a two-cycle by the two-cycle test.
TWO_CYCLE - Enum constant in enum class edu.cmu.tetrad.search.FaskPool.Origin: Output as a two-cycle on unanimous per-dataset two-cycle tests.
TWO_CYCLE_ALPHA - Static variable in class edu.cmu.tetrad.util.Params: Constant TWO_CYCLE_ALPHA="twoCycleAlpha"
TWO_CYCLE_SCREENING_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant TWO_CYCLE_SCREENING_THRESHOLD="twoCycleScreeningThreshold"
twoCycCor - Variable in class edu.cmu.tetrad.graph.GraphUtils.TwoCycleErrors: The number of correct edges.
twoCycFn - Variable in class edu.cmu.tetrad.graph.GraphUtils.TwoCycleErrors: The number of false negatives.
twoCycFp - Variable in class edu.cmu.tetrad.graph.GraphUtils.TwoCycleErrors: The number of false positives.
TwoCycleErrors(int, int, int) - Constructor for class edu.cmu.tetrad.graph.GraphUtils.TwoCycleErrors: Constructs a new TwoCycleErrors.
TwoCycleFalseNegative - Class in edu.cmu.tetrad.algcomparison.statistic: The 2-cycle precision.
TwoCycleFalseNegative() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalseNegative: This class represents a statistic that calculates the 2-cycle false negative.
TwoCycleFalsePositive - Class in edu.cmu.tetrad.algcomparison.statistic: The 2-cycle precision.
TwoCycleFalsePositive() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TwoCycleFalsePositive: This class represents a statistic that calculates the 2-cycle precision.
TwoCyclePrecision - Class in edu.cmu.tetrad.algcomparison.statistic: The 2-cycle precision.
TwoCyclePrecision() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TwoCyclePrecision: This class represents a statistic that calculates the 2-cycle precision.
TwoCycleRecall - Class in edu.cmu.tetrad.algcomparison.statistic: The 2-cycle recall.
TwoCycleRecall() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TwoCycleRecall: This class represents a statistic that calculates the 2-cycle recall.
twoCycleTest(double[], double[], double[][], double) - Static method in class edu.cmu.tetrad.search.Fask: The FASK two-cycle test, in static form so that multi-dataset algorithms (e.g., FaskPool) can run it per dataset.
twoCycleTest(double[], double[], double[][], double, Fask.TwoCycleRule) - Static method in class edu.cmu.tetrad.search.Fask: The two-cycle test with the choice of discrepancy statistic made explicit; see Fask.TwoCycleRule.
TwoCycleTruePositive - Class in edu.cmu.tetrad.algcomparison.statistic: The 2-cycle precision.
TwoCycleTruePositive() - Constructor for class edu.cmu.tetrad.algcomparison.statistic.TwoCycleTruePositive: The TwoCycleTruePositive class represents a statistic that calculates the number of true positives for 2-cycles in both the true and estimated graphs.
twoFactorTest(int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: twoFactorTest.
twoFactorTest(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: twoFactorTest.
twoFactorTest(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: twoFactorTest.
twoFactorTest(int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: twoFactorTest.
twoFactorTest(int, int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: twoFactorTest.
twoFactorTest(int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: twoFactorTest.
twoFactorTest(int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: twoFactorTest.
twoFactorTest(int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: twoFactorTest.
twoFactorTest(int, int, int, int, int, int) - Method in interface edu.cmu.tetrad.search.utils.TetradTest: twoFactorTest.
twoFactorTest(int, int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestContinuous: twoFactorTest.
twoFactorTest(int, int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: twoFactorTest.
twoFactorTest(int, int, int, int, int, int) - Method in class edu.cmu.tetrad.search.utils.TetradTestPopulation: twoFactorTest.
TwoIndicatorTest - Class in edu.cmu.tetrad.search.rlcd: Tells a direct edge between two indicators from a latent common cause, using third-order cumulants.
TwoIndicatorTest.Answer - Enum Class in edu.cmu.tetrad.search.rlcd: The answer.
TwoIndicatorTest.Result - Record Class in edu.cmu.tetrad.search.rlcd: The result of the test.
TwoStep - Class in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag: Deprecated.
TwoStep - Class in edu.cmu.tetrad.search: Deprecated.
TwoStep() - Constructor for class edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag.TwoStep: Deprecated.

Default constructor for the TwoStep class.
TwoStep() - Constructor for class edu.cmu.tetrad.search.TwoStep: Deprecated.

Default constructor for the TwoStep class.
TwoStep.Result - Class in edu.cmu.tetrad.search: Deprecated.

The Result class encapsulates the output of the TwoStep algorithm's search process.
type - Static variable in class edu.cmu.tetrad.util.MillisecondTimes: Constant type

U

u() - Method in record class edu.cmu.tetrad.search.PcAR.OrientationClash: Returns the value of the u record component.
ulp(double) - Static method in class edu.cmu.tetrad.util.TMath: Returns the size of an ulp (unit in the last place) of the argument.
UNBLOCKABLE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking.Blockable: Some path is unblockable regardless of Z (e.g.
UNBLOCKABLE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlocking1.Blockable: Indicates that the entity cannot be blocked.
UNBLOCKABLE - Enum constant in enum class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Blockable: Represents the state where an entity cannot be blocked from performing actions or being accessed under any circumstances.
UNDECIDED - Enum constant in enum class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Answer: Not decided.
Underlines - Class in edu.cmu.tetrad.graph: Underlines class.
Underlines(Graph) - Constructor for class edu.cmu.tetrad.graph.Underlines: Constructor for Underlines.
Underlines(Underlines) - Constructor for class edu.cmu.tetrad.graph.Underlines: Constructor for Underlines.
undirectedEdge(Node, Node) - Static method in class edu.cmu.tetrad.graph.Edges: Constructs a new undirected edge from nodeA to nodeB (--).
undirectedGraph(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: undirectedGraph.
undirectedToBidirected(Graph) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Converts an undirected graph to a bidirected graph.
uniform(int, double, double) - Static method in record class edu.cmu.tetrad.data.MissingnessInjector.Spec: A uniform-rate spec, reproducing the historical constant-rate behavior.
Uniform - Class in edu.cmu.tetrad.util.dist: For given a, b (a < b), returns a point chosen uniformly from [a, b].
Uniform(double, double) - Constructor for class edu.cmu.tetrad.util.dist.Uniform: Constructor for Uniform.
UniformGraphGenerator(int) - Constructor for class edu.cmu.tetrad.graph.RandomGraph.UniformGraphGenerator: Constructs a random graph generator for the given structure.
uniformity - Variable in class edu.cmu.tetrad.search.harness.CiTestHarness.Result: An array of uniformity test results represented using the CiTestHarness.Uniformity record.
Uniformity(double, double) - Constructor for record class edu.cmu.tetrad.search.harness.CiTestHarness.Uniformity: Creates an instance of a Uniformity record class.
UniformityTest - Class in edu.cmu.tetrad.util: The UniformityTest class provides methods to calculate the p-value of a list of points using the Kolmogorov-Smirnov test and determine if the distribution is uniform.
UniformityTest() - Constructor for class edu.cmu.tetrad.util.UniformityTest: The UniformityTest class is used to calculate the p-value of a list of points using the Kolmogorov-Smirnov test and determine if the distribution is uniform.
uniformPairDofHistogram(int, int) - Static method in class edu.cmu.tetrad.search.score.PenaltyDiscountCalibration: Convenience for a homogeneous population: p variables all with the same block size.
uniformRand() - Static method in class edu.cmu.tetrad.util.ProbUtils: uniformRand.
uniformSeeds(long, long) - Static method in class edu.cmu.tetrad.util.ProbUtils: uniformSeeds.
union(int[], int[]) - Static method in class edu.cmu.tetrad.util.RankTests: Computes the union of two integer arrays and returns the result as an array.
union(GraphChange) - Method in class edu.cmu.tetrad.search.work_in_progress.GraphChange: Absorbs all changes from the GraphChange other into the calling GraphChange.
union(List, int) - Static method in class edu.cmu.tetrad.util.RankTests: Computes the union of a list of integers and a single integer.
unitKeys() - Method in record class edu.cmu.tetrad.data.LongToWide.Result: Returns the value of the unitKeys record component.
UNKNOWN - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Unknown.
unlog() - Method in record class edu.cmu.tetrad.data.LogTransformSpec: Returns the value of the unlog record component.
Unmarshallable - Interface in edu.cmu.tetrad.util: Interface to tag classes that should not be cloned by marshalling.> 0
unmatchedNames(List) - Method in class edu.cmu.tetrad.search.utils.EdgePriors: Returns the names mentioned by this store that do not occur among the given variables.
UnmeasuredConfounder - Annotation Interface in edu.cmu.tetrad.annotation: Sep 19, 2017 1:56:20 PM
UnmixDiagnostics - Class in edu.cmu.tetrad.search.unmix: The UnmixDiagnostics class provides utilities for evaluating and diagnosing results from unsupervised mixture models, such as Gaussian Mixture Models and related clustering techniques.
UnmixDiagnostics.BicDelta - Class in edu.cmu.tetrad.search.unmix: Represents the Bayesian Information Criterion (BIC) values for mixture models with K=1 and K=2 clusters and the difference (delta) between these values.
UnmixDiagnostics.EntropyStats - Class in edu.cmu.tetrad.search.unmix: Represents the entropy statistics of clustering results in the context of mixture models.
UnmixDiagnostics.GraphDiff - Class in edu.cmu.tetrad.search.unmix: Represents the difference between two clustering graphs in terms of structural and adjacency metrics.
UnmixDiagnostics.StabilityResult - Class in edu.cmu.tetrad.search.unmix: Represents the result of a stability analysis of clustering, typically performed by evaluating the Adjusted Rand Index (ARI) across multiple independent runs.
UnmixResult - Class in edu.cmu.tetrad.search.unmix: Container for unmixing output (labels, per-cluster datasets, optional graphs).
UnmixResult(int[], int, List, GaussianMixtureEM.Model) - Constructor for class edu.cmu.tetrad.search.unmix.UnmixResult: Constructs an instance of UnmixResult containing essential clustering outcome information without graphical representations.
UnmixResult(int[], int, List, List, GaussianMixtureEM.Model) - Constructor for class edu.cmu.tetrad.search.unmix.UnmixResult: Constructs an instance of UnmixResult containing information about the results of a clustering algorithm.
UnmixSpec - Class in edu.cmu.tetrad.data: Minimal, UI-friendly spec for the unmixer.
UnmixSpec() - Constructor for class edu.cmu.tetrad.data.UnmixSpec: Constructor for the UnmixSpec class.
UnmixSpec.ColliderStyle - Enum Class in edu.cmu.tetrad.data: The ColliderStyle enum represents different styles for handling colliders during graph learning processes.
UnmixSpec.CovarianceMode - Enum Class in edu.cmu.tetrad.data: The CovarianceMode enum represents different modes for handling covariance calculations during graph learning processes.
UnmixSpec.GraphLearner - Enum Class in edu.cmu.tetrad.data: The GraphLearner enum represents different algorithms or strategies used for graph learning processes within a system.
UnmixSpec.SupersetScore - Enum Class in edu.cmu.tetrad.data: The SupersetScore enum represents statistical ranking correlation coefficients that can be used to assess the relationship between two rankings.
unshieldedCollider(Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns true iff [a, b, c] is an unshielded collider.
unshieldedTriple(Node, Node, Node) - Method in class edu.cmu.tetrad.search.utils.TeyssierScorer: Returns true iff [a, b, c] is an unshielded triple.
UNSIGNED - Enum constant in enum class edu.cmu.tetrad.graph.EdgeShading.Hue: Effect with no sign, e.g.
unsoundColliders - Variable in class edu.cmu.tetrad.search.utils.SeedMarkovCheck.Result: Unshielded colliders in the seed that the true model cannot justify.
update() - Method in interface edu.cmu.tetrad.search.ModelObserver: This method is called when the model changes.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.AbstractNbComponent: Updates.
update() - Method in interface edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbComponent: Updates.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunction: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionAnd: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionOr: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionSum: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbFunctionSV: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbGene: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbGeneAnd: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.algorithm.urchin.NbGeneOr: update.
update() - Method in class edu.cmu.tetrad.study.gene.tetrad.gene.history.GeneHistory: Updates the history to the next time slice using some formula.
updateActiveNonSinkSet() - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Resets the active non-sink set to the current candidate non-sinks (Python updateactiveNonSinkSet).
updateActiveSet(boolean) - Method in class edu.cmu.tetrad.search.rlcd.LatentGroups: Refreshes the active set after covers are added (Python updateActiveSet): all observed covers plus atomic cover keys (all keys when forFinish), minus anything that is a child of a key, then deduplicated.
UpdatedBayesIm - Class in edu.cmu.tetrad.bayes: Represents a Bayes IM in which all of the conditional probability tables have been updated to take into account evidence.
UpdatedBayesIm(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.UpdatedBayesIm: Constructs Bayes net in which CPT's updated for the given evidence are calculated on the fly.
UpdatedBayesIm(BayesIm, Evidence) - Constructor for class edu.cmu.tetrad.bayes.UpdatedBayesIm: Constructs Bayes net in which CPT's updated for the given evidence are calculated on the fly.
updatedIm(Matrix, Vector) - Method in class edu.cmu.tetrad.sem.SemIm: updatedIm.
UpdateFunction - Interface in edu.cmu.tetrad.study.gene.tetrad.gene.history: Implements a function from the previous time steps of a history array to the getModel time step.
updateParams(Map) - Method in class edu.cmu.tetrad.search.test.ProcessPythonCiService
updateParams(Map) - Method in interface edu.cmu.tetrad.search.test.PythonCiService: Updates the parameters for the service with the provided map of key-value pairs.
UPPER_BOUND - Static variable in class edu.cmu.tetrad.util.Params: Constant UPPER_BOUND="upperBound"
upperBound(double[], double) - Static method in class edu.cmu.tetrad.search.utils.MinimaxBinning: Finds the smallest index in the sorted array edges such that the value at that index is greater than or equal to v.
upperBoundsFromObservedMaxima(DataSet) - Static method in class edu.cmu.tetrad.data.missing.CensoredMvnCovarianceEstimator: Upper bounds taken as each column's largest observed value, for data whose censored values were removed rather than recorded at the bound; columns with no missing values get no bound.
Usage - Section in class edu.cmu.tetrad.search.harness.EdgePriorsUsage
Usage - Section in class edu.cmu.tetrad.search.harness.tsbi.StructuralGraphScorer
Usage - Section in class edu.cmu.tetrad.search.harness.tsbi.TsbiRunner
Usage - Section in class edu.cmu.tetrad.search.harness.tsc.BestJaccardScorer
Usage - Section in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer
USE_ADAPTIVE_Z_BINS - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_ADAPTIVE_Z_BINS="useAdaptiveZBins"
USE_BES - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_BES="useBes"
USE_BES_DEFAULT_TRUE - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_BES_DEFAULT_TRUE="useBesDefaultTrue".
USE_BOSS_ADJACENCIES - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_BOSS_ADJACENCIES="useBossAdjacencies"
USE_CORR_DIFF_ADJACENCIES - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_CORR_DIFF_ADJACENCIES="useCorrDiffAdjacencies"
USE_DATA_ORDER - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_DATA_ORDER="useDataOrder"
USE_EQUIVALENCE_REMOVAL - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_EQUIVALENCE_REMOVAL="useEquivalenceRemoval"
USE_FAS_ADJACENCIES - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_FAS_ADJACENCIES="useFasAdjacencies"
USE_GAP - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_GAP="useGap"
USE_ILS_RESTARTS - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_ILS_RESTARTS="useIlsRestarts"
USE_MAX_ACROSS_STRATA - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_MAX_ACROSS_STRATA="useMaxAcrossStrata"
USE_MAX_P_HEURISTIC - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_MAX_P_HEURISTIC="useMaxPHeuristic"
USE_MAX_P_ORIENTATION_HEURISTIC - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_MAX_P_ORIENTATION_HEURISTIC="useMaxPOrientationHeuristic"
USE_PASS_0 - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_PASS_0="usePass0".
USE_SATURATING_STEP - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_SATURATING_STEP="useSaturatingStep".
USE_SKEW_ADJACENCIES - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_SKEW_ADJACENCIES="useSkewAdjacencies"
USE_VARIMAX - Static variable in class edu.cmu.tetrad.util.Params: Constant USE_VARIMAX="useVarimax"
useBagging - Variable in class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.Config: Determines whether bagging should be applied during the parent superset construction process.
useBes(boolean) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Configures whether BOSS runs a BES step after each permutation pass.
useDataOrder(boolean) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Configures whether BOSS uses the data order for its initial permutation.
useMAP - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Indicates whether to use Maximum A Posteriori (MAP) estimation for parameter initialization.
useParentSuperset - Variable in class edu.cmu.tetrad.search.unmix.CausalUnmixer.Config: Indicates whether to use the parent superset configuration during the clustering or unmixing process within the CausalUnmixer framework.
useParentSuperset - Variable in class edu.cmu.tetrad.search.unmix.EmUnmix.Config: Indicates whether to use a parent superset for subset initialization in the EmUnmix algorithm.
User - Enum constant in enum class edu.cmu.tetrad.util.MillisecondTimes.Type: User time.
userTimeMillis() - Static method in class edu.cmu.tetrad.util.MillisecondTimes: userTimeMillis.
useScoreStep() - Method in class edu.cmu.tetrad.search.utils.CorrectnessGates: Reports whether the score step is on.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.AverageDegreeEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.BicEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.BidirectedEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.DensityEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.ElapsedCpuTime: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderAlternative: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.ImpliesLegalMag: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.KnowledgeSatisfied: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalCpdag: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.LegalPag: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagCgScore: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagDgScore: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MagSemScore: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPasses: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAdPassesBestOf10: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingP: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckAndersonDarlingPBestOf10: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialP: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckBinomialPBestOf10: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFdrCutoffP: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH0: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckFractionDependentH1: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffP: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKolmogorovSmirnoffPBestOf10: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPasses: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MarkovCheckKsPassesBestOf10: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.Maximal: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.MaximalityCondition: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH0: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.McGetNumTestsH1: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoAlmostCyclicPathsCondition: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NoCyclicPathsCondition: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumAmbiguousTriples: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberArrowsNotInUnshieldedCollidersEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberCollidersEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInCollidersEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberEdgesInUnshieldedCollidersEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberOfEdgesEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberTailsEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumberUnshieldedCollidersEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumBidirectedEdgesEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredDD: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredNL: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPD: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumColoredPL: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyDirected: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDefinitelyNotDirectedPaths: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedEdges: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumDirectedPathsEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumLatentCommonAncestorBidirected: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumNondirectedEdges: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumParametersEst: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPartiallyOrientedEdges: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumPotentiallyDirected: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumUndirectedEdges: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.NumVisibleEdges: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.ParameterColumn: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentAmbiguous: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.PercentBidirectedEdges: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in class edu.cmu.tetrad.algcomparison.statistic.PvalueDistanceToAlpha: This method does not use the truth so is suitable for analyzing empirical data.
usesTruth() - Method in interface edu.cmu.tetrad.algcomparison.statistic.Statistic: Returns true if this statistics makes use of knowledge of the true graph.
useTestCache - Static variable in class edu.cmu.tetrad.search.Fcit: Whether the constructor wraps the supplied test in CachedIndependenceQueries.
useTestCache - Static variable in class edu.cmu.tetrad.search.Fcit2: Whether the constructor wraps the supplied test in CachedIndependenceQueries.
useTestCache - Static variable in class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations: Whether the constructor wraps the supplied test in CachedIndependenceQueries.

V

VALID - Enum constant in enum class edu.cmu.tetrad.search.RecursiveAdjustment.RaMode: Represents a valid mode in the RaMode enumeration.
validateBlocks(List, DataSet) - Static method in class edu.cmu.tetrad.search.blocks.BlocksUtil: Validates the provided list of blocks to ensure that all indices within each block are non-negative, within the range of columns in the given dataset, and not null.
validationFraction - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents the fraction of the dataset to be used for validation during training.
validationFraction - Variable in class edu.cmu.tetrad.search.CordEngine2: Proportion of the training data to set aside for validation during model training.
validationFraction - Variable in class edu.cmu.tetrad.search.CordEngine3: Proportion of the training data to set aside as validation data during model training.
validationFraction - Variable in class edu.cmu.tetrad.search.CordEngine4: The fraction of the dataset to allocate to the validation split during training.
validationFraction - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the fraction of the dataset to be used for validation during model training.
ValidOrderIterator - Class in edu.cmu.tetrad.graph: Lazily enumerates EVERY valid causal order of a DAG or CPDAG, each exactly once.
ValidOrderIterator(Graph) - Constructor for class edu.cmu.tetrad.graph.ValidOrderIterator: Enumerate preferring the graph's own node order (forward).
ValidOrderIterator(Graph, List, boolean) - Constructor for class edu.cmu.tetrad.graph.ValidOrderIterator: Iterates over valid orders for a CPDAG.
validRows(int[]) - Method in class edu.cmu.tetrad.data.missing.TestwiseRows: The rows on which every one of the given columns is observed, over all rows of the dataset, cached.
validRows(int[], List) - Method in class edu.cmu.tetrad.data.missing.TestwiseRows: The rows on which every one of the given columns is observed, in increasing order, as an immutable list.
validRowsForUnion(int, int[]) - Method in class edu.cmu.tetrad.search.score.LegendreBicScore: Determines the valid rows for a union operation based on the given child and parent arrays.
validRowsForUnion(int, int[]) - Method in class edu.cmu.tetrad.search.score.TRffBicScore: Computes and retrieves the valid rows for a union operation based on the given child variable and an array of parent variables.
value() - Method in record class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Strength: Returns the value of the value record component.
value() - Method in record class edu.cmu.tetrad.sem.DagMetricResult: Returns the value of the value record component.
valueAt(double) - Method in interface edu.cmu.tetrad.util.Function: valueAt.
valueAt(double) - Method in class edu.cmu.tetrad.util.PartialCorrelationPdf: valueAt.
valueAt(double...) - Method in interface edu.cmu.tetrad.sem.ConnectionFunction: valueAt.
valueEffects() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Returns the value of the valueEffects record component.
valueOf(String) - Static method in enum class edu.cmu.tetrad.algcomparison.Comparison.ComparisonGraph: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.algcomparison.TimeoutComparison.ComparisonGraph: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.annotation.AlgType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.bayes.MlBayesIm.CptMapType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.bayes.MlBayesIm.InitializationMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.calculator.parser.ExpressionParser.RestrictionType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.calculator.parser.Token: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.audit.AuditFinding.Severity: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.audit.FindingCode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.DataType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.JoinDatasets.JoinType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.missing.MissingDataSpec.EffectiveSampleSizeMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.missing.MissingValueSupport: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.MissingnessInjector.MechanismClass: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.ColliderStyle: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.CovarianceMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.GraphLearner: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.SupersetScore: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.Edge.Property: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.EdgeShading.Hue: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.Endpoint: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.GraphUtils.GraphType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.NodeType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.NodeVariableType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.Paths.CpdagViolation: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.graph.RandomMim.LatentLinkMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.hybridcg.HybridCgCutpoints.Method: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Kind: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Severity: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.blocks.BlocksUtil.NamingMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.blocks.SingletonClusterPolicy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Ccd.ColliderRule: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Cdnod.ColliderOrientationStyle: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.CdnodBoss.ColliderOrientationStyle: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.ConditioningSetType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue.DistanceType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Cstar.CpdagAlgorithm: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Cstar.SampleStyle: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Fask.LeftRight: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Fask.Origin: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Fask.TwoCycleRule: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FaskPool.Origin: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FaskPool.Weighting: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Fci.ColliderRule: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSl.CommitGate: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSl2.CommitGate: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.CommitGate: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.CommitGate: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitZm.COMMIT_ROUTE: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitZm2.COMMIT_ROUTE: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.COMMIT_ROUTE: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.GacTotalEffectElibility.Status: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.harness.GraphoidClosureHarness.ClosureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.harness.VertexRepairSimulation.StartingGraph: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Ida.IDA_TYPE: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.MarkovCheckNullReference.SimulatorType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Pairwise.Rule: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Pairwise.Score: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Pc.AllowBidirected: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Pc.ColliderOrientationStyle: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.PcAR.AllowBidirected: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.PcAR.ColliderOrientationStyle: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.PcAR.RescueAction: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.PdagPagIda.IDA_TYPE: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.ColliderPolicy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.GraphType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.NoAmenablePolicy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.RaMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveBlocking.Blockable: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveBlocking.Strategy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveBlocking1.Blockable: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Blockable: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.Rlcd.Stage1Method: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Answer: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.FfCordContinuous.FeatureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.FeatureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.FfMl.FeatureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.FfMlContinuous.FeatureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NoiseModel: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NonGaussianBonus: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.ImagesScore.WeightingScheme: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.KcvBicScore.BandwidthMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.RffBicScore.BandwidthMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.score.SemBicScore.RuleType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Dedup: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.ErrorPolicy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.ChiSquareTest.CellTableType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.ChiSquareTest.TestType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.FfCiContinuous.Approx: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.FfCiContinuous.FeatureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.Gcm.RegressorType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.IndTestBlocksLemma10.EqualityMode: Deprecated.

Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.IndTestFisherZ.ShrinkageMode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.Kci.KernelType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.Rcit.Approx: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.test.Rcit.FeatureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.CovarianceType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.ScoreType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.BpcTestType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.ClusterSignificance.CheckType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.EdgePriors.Origin: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.EdgePriors.Semantics: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.GraphSearchUtils.CpcTripleType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased.BlockingType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Rule: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.VertexRepairSearch.RepairStrategy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Multiplier: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.work_in_progress.VcPc.CpcTripleType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.search.work_in_progress.VcPcFast.CpcTripleType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.DagMetricResult.Better: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear.DistortionType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.ParamComparison: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.ParamConstraintType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.ParamType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.ScoreType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.sem.StandardizedSemIm.Initialization: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.missingness.PooledDgLrtTest.Pooling: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.DataType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.IndependenceTestType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.ResultType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.util.MillisecondTimes.Type: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.util.TextTable.Delimiter: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.cmu.tetrad.util.TMath.Impl: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCCausalInference.OP: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference.OP: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Returns the enum constant of this class with the specified name.
values() - Static method in enum class edu.cmu.tetrad.algcomparison.Comparison.ComparisonGraph: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.algcomparison.TimeoutComparison.ComparisonGraph: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.annotation.AlgType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.bayes.MlBayesIm.CptMapType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.bayes.MlBayesIm.InitializationMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.calculator.expression.ExpressionDescriptor.Position: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.calculator.parser.ExpressionParser.RestrictionType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.calculator.parser.Token: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.audit.AuditFinding.Severity: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.audit.FindingCode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.DataType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.JoinDatasets.JoinType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.LongToWide.Aggregation: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.missing.MissingDataPolicy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.missing.MissingDataSpec.EffectiveSampleSizeMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.missing.MissingValueSupport: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.MissingnessInjector.MechanismClass: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.ColliderStyle: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.CovarianceMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.GraphLearner: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.data.UnmixSpec.SupersetScore: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.Edge.Property: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.EdgeShading.Hue: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.EdgeTypeProbability.EdgeType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.Endpoint: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.GraphUtils.GraphType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.LayoutUtil.DefaultLayout: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.NodeType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.NodeVariableType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.Paths.CpdagViolation: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.graph.RandomMim.LatentLinkMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.hybridcg.HybridCgCutpoints.Method: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.hybridcg.HybridCgEdgeStrengths.Kind: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Severity: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.blocks.BlocksUtil.NamingMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.blocks.SingletonClusterPolicy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Ccd.ColliderRule: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Cdnod.ColliderOrientationStyle: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.CdnodBoss.ColliderOrientationStyle: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.ConditioningSetType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.CpdagParentDistancesFromTrue.DistanceType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Cstar.CpdagAlgorithm: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Cstar.SampleStyle: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Fask.LeftRight: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Fask.Origin: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Fask.TwoCycleRule: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FaskOrig.AdjacencyMethod: Deprecated.

Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FaskOrig.LeftRight: Deprecated.

Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FaskPool.Origin: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FaskPool.Weighting: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Fci.ColliderRule: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Fcit.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Fcit2.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitKeepKnowledgeOrientations.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitMagMarkov.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitMarkov.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSl.CommitGate: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSl.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSl2.CommitGate: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSl2.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.CommitGate: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSlKeepKnowledgeOrientations.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.CommitGate: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitSlScoreCheck.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitZm.COMMIT_ROUTE: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitZm.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitZm2.COMMIT_ROUTE: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitZm2.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.COMMIT_ROUTE: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.FcitZmKeepKnowledgeOrientations.START_WITH: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.GacTotalEffectElibility.Status: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.harness.GraphoidClosureHarness.ClosureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.harness.VertexRepairSimulation.StartingGraph: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Ida.IDA_TYPE: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.MarkovCheckNullReference.SimulatorType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Pairwise.Rule: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Pairwise.Score: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Pc.AllowBidirected: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Pc.ColliderOrientationStyle: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.PcAR.AllowBidirected: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.PcAR.ColliderOrientationStyle: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.PcAR.RescueAction: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.PdagPagIda.IDA_TYPE: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.PooledAdjacencySearch.Method: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.ColliderPolicy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.GraphType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.NoAmenablePolicy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveAdjustment.RaMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveBlocking.Blockable: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveBlocking.Strategy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveBlocking1.Blockable: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Blockable: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.Rlcd.Stage1Method: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Answer: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.FfCordContinuous.FeatureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.FeatureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.FfMl.FeatureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.FfMlContinuous.FeatureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.GicScores.RuleType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NoiseModel: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.HeavyTailSemBicScore.NonGaussianBonus: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.ImagesScore.WeightingScheme: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.KcvBicScore.BandwidthMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.RffBicScore.BandwidthMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.score.SemBicScore.RuleType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Dedup: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.ErrorPolicy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.ChiSquareTest.CellTableType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.ChiSquareTest.TestType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.FfCiContinuous.Approx: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.FfCiContinuous.FeatureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.Gcm.RegressorType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.IndTestBlocksLemma10.EqualityMode: Deprecated.

Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.IndTestFisherZ.ShrinkageMode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.Kci.KernelType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.Rcit.Approx: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.test.Rcit.FeatureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.CovarianceType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.unmix.ParentSupersetBuilder.ScoreType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.BpcAlgorithmType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.BpcTestType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.ClusterSignificance.CheckType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.EdgePriors.Origin: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.EdgePriors.Semantics: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.GraphSearchUtils.CpcTripleType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.R0R4StrategyTestBased.BlockingType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.R5R9Dijkstra.Rule: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.VertexRepairSearch.AdjustmentGraphType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.VertexRepairSearch.RepairStrategy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.WildBootstrapMarkovCheck.Multiplier: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.work_in_progress.HbsmsBeam.Move.Type: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.work_in_progress.VcPc.CpcTripleType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.search.work_in_progress.VcPcFast.CpcTripleType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.DagMetricResult.Better: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.NonlinearFunctionOfLinear.DistortionType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.ParamComparison: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.ParamConstraintType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.ParamType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.ScoreType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.sem.StandardizedSemIm.Initialization: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.missingness.PooledDgLrtTest.Pooling: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.performance.Comparison.TableColumn: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.performance.Comparison2.TableColumn: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.Algorithm: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.DataType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.IndependenceTestType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.study.performance.ComparisonParameters.ResultType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.util.MillisecondTimes.Type: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.util.TextTable.Delimiter: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.cmu.tetrad.util.TMath.Impl: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCCausalInference.OP: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.pitt.dbmi.algo.bayesian.constraint.inference.BCInference.OP: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class edu.pitt.dbmi.algo.resampling.ResamplingEdgeEnsemble: Returns an array containing the constants of this enum class, in the order they are declared.
values(A) - Method in class edu.pitt.isp.sverchkov.data.AdTree: Returns the number of rows in the data set.
valuesToString() - Method in class edu.cmu.tetrad.simulation.PRAOerrors: valuesToString.
VAR - Enum constant in enum class edu.cmu.tetrad.search.score.FfCordContinuous.Channel: He2(Y)/sqrt(2!) = (Y^2-1)/sqrt(2): conditional-variance / co-volatility channel.
VAR - Enum constant in enum class edu.cmu.tetrad.search.score.FfDmlContinuous.Channel: He2(Y)/sqrt(2!) = (Y^2-1)/sqrt(2): conditional-variance / co-volatility channel.
VAR - Enum constant in enum class edu.cmu.tetrad.sem.ParamType: Represents the error variance parameter in a structural equation modeling (SEM) model.
VAR_HIGH - Static variable in class edu.cmu.tetrad.util.Params: Constant VAR_HIGH="varHigh"
VAR_LOW - Static variable in class edu.cmu.tetrad.util.Params: Constant VAR_LOW="varLow"
varFloor - Variable in class edu.cmu.tetrad.search.CordEngine1: Represents a lower threshold value for variance within the class.
varFloor - Variable in class edu.cmu.tetrad.search.CordEngine2: Minimum allowed value for variance during calculations to prevent numerical instability or division by zero errors.
varFloor - Variable in class edu.cmu.tetrad.search.CordEngine3: Represents the minimum allowable floor value for variance calculations.
varFloorFrac - Variable in class edu.cmu.tetrad.search.CordEngine4: Fractional multiplier used to compute the adaptive floor during the scoring process.
varFloorFrac - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the adaptive floor fraction used in a specific calculation.
varFloorMin - Variable in class edu.cmu.tetrad.search.CordEngine4: Represents the minimum floor value used in adaptive calculations within the CORD framework.
varFloorMin - Variable in class edu.cmu.tetrad.search.CordEngine5: Represents the minimum allowable floor value for a particular computation or setting.
varHat(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: varHat.
varHat(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: varHat.
varHat(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: varHat.
varHat(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: varHat.
variable() - Method in record class edu.cmu.tetrad.data.audit.DeterminismRemovalSuggester.Suggestion: Returns the value of the variable record component.
Variable - Interface in edu.cmu.tetrad.data: Interface implemented by classes, instantiations of which are capable of serving as variables for columns in a DataSet.
VariableEliminationInference - Class in edu.cmu.tetrad.bayes: Exact inference engine over a BayesIm supporting: Soft evidence via allowed categories (Proposition) Hard evidence via node=category Marginals, conditionals, and joint marginals (all conditional on evidence) This class is designed specifically to satisfy the methods required by JunctionTreeUpdater.
VariableEliminationInference(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.VariableEliminationInference: Construct inference over the given BayesIm.
VariableEliminationUpdater - Class in edu.cmu.tetrad.bayes: RowSummingExactUpdater-style updater plumbing, but uses VariableEliminationInference for marginals/joints/conditionals.
VariableEliminationUpdater(BayesIm) - Constructor for class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Constructs a new instance of VariableElimiinationUpdater using the provided Bayesian network representation.
VariableEliminationUpdater(BayesIm, Evidence) - Constructor for class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Constructs a new instance of VariableElimiinationUpdater, using the provided Bayesian network representation and associated evidence.
VariableEliminationUpdater(BayesIm, Evidence, BayesIm, BayesIm, VariableEliminationInference) - Constructor for class edu.cmu.tetrad.bayes.VariableEliminationUpdater: Creates an instance of the VariableElimiinationUpdater class which performs updates on a variable elimination structure based on Bayesian networks and evidence.
VariableExpression - Class in edu.cmu.tetrad.calculator.expression: An Expression for a variable.
VariableExpression(String) - Constructor for class edu.cmu.tetrad.calculator.expression.VariableExpression: Constructor for VariableExpression.
variables - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: A list of variables involved in the simulation, including both continuous and discrete variables.
variables - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: List of variables in the simulation result.
variables - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: List of variables in the simulation result.
variables - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: List of variables in the simulation result.
variables() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderBoss.MeasurementBuildResult: Returns the value of the variables record component.
variables() - Method in record class edu.cmu.tetrad.search.mimic.TrekMeasurementModelBuilderPc.MeasurementBuildResult: Returns the value of the variables record component.
variables() - Method in class edu.cmu.tetrad.search.ntad_test.NtadTest: Generates a list of strings representing the variable indices from 0 to p-1.
variables() - Method in interface edu.pitt.isp.sverchkov.data.DataTable: variables.
variables() - Method in class edu.pitt.isp.sverchkov.data.DataTableImpl: variables.
variablesForIndices(List, List) - Static method in class edu.cmu.tetrad.search.utils.ClusterSignificance: Converts a list of indices into a list of Nodes representing a cluster.
variablesForIndices(Set, List) - Static method in class edu.cmu.tetrad.search.utils.ClusterSignificance: Converts a list of indices into a list of Nodes representing a cluster.
VariableSource - Interface in edu.cmu.tetrad.data: Inteface implemented by classes, instantiations of which are associated with lists of variables.
variance(double[]) - Static method in class edu.cmu.tetrad.util.StatUtils: variance.
variance(double[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: variance.
variance(long[]) - Static method in class edu.cmu.tetrad.util.StatUtils: variance.
variance(long[], int) - Static method in class edu.cmu.tetrad.util.StatUtils: variance.
Variance - Interface in edu.cmu.tetrad.stat: Feb 9, 2016 3:18:03 PM
VARIANCE_MISFIT_FACTOR - Static variable in class edu.cmu.tetrad.sem.SemEstimator: The factor by which an implied variance may differ from its sample variance before the estimate is reported as not reproducing the variances.
varianceDiff - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Mean over parent configurations of var(Y | pa, X randomized) − var(Y | pa), in the child's units squared.
varianceDiffFrac - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: EdgeStrengthResult.varianceDiff divided by the child's observed marginal variance, so it is comparable across children.
varianceDiffFracSd - Variable in class edu.cmu.tetrad.sem.EdgeStrengthResult: Standard deviation of EdgeStrengthResult.varianceDiffFrac across repeats; NaN if one repeat or discrete.
varianceMisfitWarning(SemIm) - Static method in class edu.cmu.tetrad.sem.SemEstimator: A warning when the estimate does not even reproduce the sample variances -- some implied variance is more than a factor of SemEstimator.VARIANCE_MISFIT_FACTOR away from its sample value -- which is what an optimizer that never left its starting point looks like, and what a user would otherwise discover only by reading the implied covariance matrix.
VarianceVector - Class in edu.cmu.tetrad.stat: Feb 9, 2016 3:18:44 PM
VarianceVector(float[][]) - Constructor for class edu.cmu.tetrad.stat.VarianceVector: Constructor for VarianceVector.
VarianceVectorForkJoin - Class in edu.cmu.tetrad.stat: Feb 9, 2016 3:19:52 PM
VarianceVectorForkJoin(float[][], int) - Constructor for class edu.cmu.tetrad.stat.VarianceVectorForkJoin: Constructor for VarianceVectorForkJoin.
varRange(double, double) - Method in class edu.cmu.tetrad.search.harness.tsc.SemParameterizer: Sets the uniform range for error variances.
VarResult(DataSet, Graph) - Constructor for class edu.cmu.tetrad.search.utils.TsUtils.VarResult: Constructs a new result.
vars(Collection) - Static method in class edu.cmu.tetrad.search.rlcd.CoverSets: All variable names appearing in any of the covers (Python getVars).
VarsortabilityHarness - Class in edu.cmu.tetrad.search.harness: Harness to test whether BOSS performance correlates with varsortability.
VarsortabilityHarness() - Constructor for class edu.cmu.tetrad.search.harness.VarsortabilityHarness: Constructs a new VarsortabilityHarness.
VcFas - Class in edu.cmu.tetrad.search.work_in_progress: Implements the "fast adjacency search" used in several causal algorithm in this package.
VcFas(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.VcFas: Constructor for VcFas.
VcPc - Class in edu.cmu.tetrad.search.work_in_progress: Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
VcPc(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.VcPc: Constructs a CPC algorithm that uses the given independence test as oracle.
VcPc.CpcTripleType - Enum Class in edu.cmu.tetrad.search.work_in_progress: An enum of triple types.
VcPcAlt - Class in edu.cmu.tetrad.search.work_in_progress: Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
VcPcAlt(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.VcPcAlt: Constructs a CPC algorithm that uses the given independence test as oracle.
VcPcFast - Class in edu.cmu.tetrad.search.work_in_progress: Implements a convervative version of PC, in which the Markov condition is assumed but faithfulness is tested locally.
VcPcFast(IndependenceTest) - Constructor for class edu.cmu.tetrad.search.work_in_progress.VcPcFast: Constructs a CPC algorithm that uses the given independence test as oracle.
VcPcFast.CpcTripleType - Enum Class in edu.cmu.tetrad.search.work_in_progress: An enum of the types of triples that can be found in a graph.
vec(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: vec.
vech(double[][]) - Static method in class edu.cmu.tetrad.util.MatrixUtils: vech.
vechToVecLeft(int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: The matrix which, when postmultiplied by vech, return vec.
vector() - Method in class edu.cmu.tetrad.util.MView: Converts a matrix view to a vector representation if the view corresponds to either a single row or a single column.
Vector - Class in edu.cmu.tetrad.util: Vector wrapping matrix library.
Vector(double[]) - Constructor for class edu.cmu.tetrad.util.Vector: Constructs a new Vector object from an array of double values.
Vector(int) - Constructor for class edu.cmu.tetrad.util.Vector: Constructs a new Vector object with the specified size.
Vector(RealVector) - Constructor for class edu.cmu.tetrad.util.Vector: Creates a new Vector object from a RealVector object.
Vector(SimpleMatrix) - Constructor for class edu.cmu.tetrad.util.Vector: Constructs a new Vector object using a SimpleMatrix instance.
verbose - Variable in class edu.cmu.tetrad.search.utils.TetradTestDiscrete: Whether to print out verbose information.
verbose(boolean) - Method in class edu.cmu.tetrad.search.Cdnod.Builder: Sets the verbose flag for the builder.
verbose(boolean) - Method in class edu.cmu.tetrad.search.CdnodBoss.Builder: Configures whether the builder operates in verbose mode.
verbose(boolean) - Method in class edu.cmu.tetrad.search.Pcmci.Builder: Sets the verbosity level for the Pcmci algorithm.
VERBOSE - Static variable in class edu.cmu.tetrad.graph.LegEnumerator: Static verbose.
VERBOSE - Static variable in class edu.cmu.tetrad.util.Params: Constant VERBOSE="verbose"
Version - Class in edu.cmu.tetrad.util: Represents the getModel Tetrad version.
Version(String) - Constructor for class edu.cmu.tetrad.util.Version: Parses string version specs into Versions.
VERSION - Static variable in class edu.cmu.tetrad.hybridcg.HybridCgIo: The current format version.
VertexRepairBattery - Class in edu.cmu.tetrad.search.harness: The VertexRepairBattery class is responsible for executing a comprehensive suite of tests to evaluate vertex repair algorithms.
VertexRepairBattery() - Constructor for class edu.cmu.tetrad.search.harness.VertexRepairBattery: Creates a new VertexRepairBattery object.
VertexRepairBattery2 - Class in edu.cmu.tetrad.search.harness: The VertexRepairBattery2 class is responsible for conducting and evaluating a comprehensive suite of tests on the VertexRepair algorithm under various configurations.
VertexRepairBattery2() - Constructor for class edu.cmu.tetrad.search.harness.VertexRepairBattery2: Constructs a new VertexRepairBattery2 object.
VertexRepairSearch - Class in edu.cmu.tetrad.search: Greedy local-search model-repair algorithm for causal graphs.
VertexRepairSearch(Graph, IndependenceTest, ConditioningSetType) - Constructor for class edu.cmu.tetrad.search.VertexRepairSearch: Constructs a new instance of VertexRepairSearch.
VertexRepairSearch.AdjustmentGraphType - Enum Class in edu.cmu.tetrad.search: Graph types supported by the repair search.
VertexRepairSearch.CandidateEdit - Interface in edu.cmu.tetrad.search: A single proposed graph modification, together with metadata needed for display and deduplication.
VertexRepairSearch.RepairListener - Interface in edu.cmu.tetrad.search: Callback interface for repair-progress events fired by VertexRepairSearch.
VertexRepairSearch.RepairStrategy - Enum Class in edu.cmu.tetrad.search: Which repair sweep strategy to use.
VertexRepairSearch.ScoredCandidate - Record Class in edu.cmu.tetrad.search: Represents a scored candidate with various metrics including violations, probabilities, and whether it passes certain guards.
VertexRepairSimulation - Class in edu.cmu.tetrad.search.harness: Simulation harness for VertexRepairSearch.
VertexRepairSimulation() - Constructor for class edu.cmu.tetrad.search.harness.VertexRepairSimulation: Default constructor for the VertexRepairSimulation class.
VertexRepairSimulation.StartingGraph - Enum Class in edu.cmu.tetrad.search.harness: Which algorithm to use to produce the initial graph before repair.
VertexRepairSpeedBenchmark - Class in edu.cmu.tetrad.search.harness: Hand-run wall-time benchmark for VertexRepairSearch, comparing full per-edit invalidation against affected-only invalidation on simulated CPDAG and PAG repair problems.
VerticalDoubleDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of double data.
VerticalDoubleDataBox(double[][]) - Constructor for class edu.cmu.tetrad.data.VerticalDoubleDataBox: Constructs a new data box using the given 2D double data array as data.
VerticalDoubleDataBox(int, int) - Constructor for class edu.cmu.tetrad.data.VerticalDoubleDataBox: Constructs an 2D double array consisting entirely of missing values (Double.NaN).
VerticalDoubleDataBox(DataBox) - Constructor for class edu.cmu.tetrad.data.VerticalDoubleDataBox: Copies the data from the given data box into this one.
VerticalIntDataBox - Class in edu.cmu.tetrad.data: Stores a 2D array of int data.
VerticalIntDataBox(int[][]) - Constructor for class edu.cmu.tetrad.data.VerticalIntDataBox: Constructs a new data box using the given 2D int data array as data.
VerticalIntDataBox(int, int) - Constructor for class edu.cmu.tetrad.data.VerticalIntDataBox: Constructs an 2D int array consisting entirely of missing values (int.NaN).
VerticalIntDataBox(DataBox) - Constructor for class edu.cmu.tetrad.data.VerticalIntDataBox: Constructor for VerticalIntDataBox.
VIA_COVARIANCE - Enum constant in enum class edu.cmu.tetrad.data.missing.MissingValueSupport: The component consumes a covariance matrix (or can be constructed from one), so missingness can be handled by supplying a covariance matrix estimated under MAR, e.g., by EmCovarianceEstimator.
Vicinity - Class in edu.cmu.tetrad.simulation: This version of Vicinity finds nearby nodes by searching with an expanding cube Prior to Vicinity4, versions of Vicinity looked at the 3 axis independently instead of collectively.
Vicinity(List, DataSet, int, int, int, int, int, int, double, double, double) - Constructor for class edu.cmu.tetrad.simulation.Vicinity: Constructor for Vicinity.
view() - Method in class edu.cmu.tetrad.util.Matrix: Creates a view of the entire matrix.
view() - Method in class edu.cmu.tetrad.util.MView: Creates a view of the entire matrix.
view(int[], int[]) - Method in class edu.cmu.tetrad.util.Matrix: Creates a view of a matrix using the specified row and column indices.
view(int[], int[]) - Method in class edu.cmu.tetrad.util.MView: Creates a view of a matrix using the specified row and column indices.
viewColumn(int) - Method in class edu.cmu.tetrad.util.Matrix: Creates a view of a specific column from the matrix.
viewColumn(int) - Method in class edu.cmu.tetrad.util.MView: Creates a view of a specific column from the matrix.
viewPart(int, int, int, int) - Method in class edu.cmu.tetrad.util.Matrix: Creates a view of a specified submatrix defined by the given row and column ranges.
viewPart(int, int, int, int) - Method in class edu.cmu.tetrad.util.MView: Creates a view of a specified submatrix defined by the given row and column ranges.
viewRow(int) - Method in class edu.cmu.tetrad.util.Matrix: Creates a sub-view of the matrix representing a specific row.
viewRow(int) - Method in class edu.cmu.tetrad.util.MView: Creates a sub-view of the matrix representing a specific row.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.ByteDataBox: viewSelection.
viewSelection(int[], int[]) - Method in interface edu.cmu.tetrad.data.DataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.DoubleDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.FloatDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.IntDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.LongDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.MixedDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.ShortDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.VerticalDoubleDataBox: viewSelection.
viewSelection(int[], int[]) - Method in class edu.cmu.tetrad.data.VerticalIntDataBox: viewSelection.
violation() - Method in record class edu.cmu.tetrad.graph.Paths.CpdagCheckResult: Returns the value of the violation record component.
violationsAfter() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the violationsAfter record component.
violationsBaseline() - Method in record class edu.cmu.tetrad.search.VertexRepairSearch.ScoredCandidate: Returns the value of the violationsBaseline record component.
ViolationsTest - Class in edu.cmu.tetrad.search.harness: Unit tests for violations in causal discovery algorithms.
ViolationsTest() - Constructor for class edu.cmu.tetrad.search.harness.ViolationsTest: Constructor.
visibleEdgeAdjustments1(Graph, Node, Node, int, GraphUtils.GraphType) - Static method in class edu.cmu.tetrad.graph.GraphUtils: Calculates visual-edge adjustments given graph G between two nodes x and y that are subsets of MB(X).
visibleEdgeAdjustments3(Graph, Node, Node, int, GraphUtils.GraphType) - Static method in class edu.cmu.tetrad.graph.GraphUtils: This method calculates visible-edge adjustments for a given graph, two nodes, a number of smallest sizes, and a graph type.

W

w - Variable in class edu.cmu.tetrad.regression.v1.RegressionUtilV1.FitLogitV1: v1: Coefficients w
W() - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Returns the value of the W record component.
W_THRESHOLD - Static variable in class edu.cmu.tetrad.util.Params: Constant W_THRESHOLD="wThreshold"
Wall - Enum constant in enum class edu.cmu.tetrad.util.MillisecondTimes.Type: Wall time.
wallTimeMillis() - Static method in class edu.cmu.tetrad.util.MillisecondTimes: wallTimeMillis.
warn(String) - Method in class edu.cmu.tetrad.search.RecursiveBlockingVerbose.Tracer: Logs a warning message if the tracer is enabled.
warn(String) - Method in class edu.cmu.tetrad.util.TetradLogger: Logs a warning: a condition the caller did not ask about and would want to know, such as a search hitting an iteration cap, a local score that could not be computed, or a resource that could not be read.
warning(String) - Method in class edu.cmu.tetrad.util.LogUtils: warning.
WARNING - Enum constant in enum class edu.cmu.tetrad.data.audit.AuditFinding.Severity: A property that typically degrades or invalidates common analyses if ignored.
WARNING - Enum constant in enum class edu.cmu.tetrad.search.blocks.BlockSpecTextCodec.Severity: Represents a less critical issue that might still require attention.
Washdown - Class in edu.cmu.tetrad.search.work_in_progress: Implements the Washdown algorithm,
Washdown(DataSet, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Washdown: Constructor.
Washdown(ICovarianceMatrix, double) - Constructor for class edu.cmu.tetrad.search.work_in_progress.Washdown: Constructor.
weakUnion() - Method in class edu.cmu.tetrad.search.utils.GraphoidAxioms: Checks if weak union holds, e.g., X _||_ Y ∪ W | Z ==> X _||_ Y | Z ∪ W
weight() - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Effect: Returns the value of the weight record component.
weight(Node, Node) - Method in class edu.cmu.tetrad.search.test.EdgePriorTest: Returns the weight for the unordered pair {x, y}.
weights - Variable in class edu.cmu.tetrad.search.unmix.GaussianMixtureEM.Model: The weights for each mixture component in the Gaussian Mixture Model (GMM).
weights - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult1D: The weights associated with each data point.
weights - Variable in class edu.cmu.tetrad.util.ConditionalKDE.ConditionalResult2D: The weights associated with each data point.
WEIGHTS - Enum constant in enum class edu.cmu.tetrad.search.utils.EdgePriors.Semantics: Multiplicative p-value weights w_ij; neutral element 1.
What it does - Section in class edu.cmu.tetrad.search.AdjustmentHarness
What this can and cannot show - Section in class edu.cmu.tetrad.study.missingness.BossMissingDataStudy
What this remedy is and is not - Section in class edu.cmu.tetrad.data.missing.MissingnessThreshold
What to expect, stated before running - Section in class edu.cmu.tetrad.study.missingness.BossMultipleImputationStudy
WHITESPACE - Enum constant in enum class edu.cmu.tetrad.calculator.parser.Token: Whitespace.
WHITESPACE - Static variable in class edu.cmu.tetrad.data.DelimiterType: Constant WHITESPACE
wide() - Method in record class edu.cmu.tetrad.data.LongToWide.Result: Returns the value of the wide record component.
width(Node) - Method in interface edu.cmu.tetrad.graph.LayoutUtil.NodeSize: Returns the width of the rendered box of the specified node in pixels.
WildBootstrapMarkovCheck - Class in edu.cmu.tetrad.search: Joint, dependence-aware Markov check for a DAG or CPDAG.
WildBootstrapMarkovCheck(DataSet) - Constructor for class edu.cmu.tetrad.search.WildBootstrapMarkovCheck: Constructs a check over the given data.
WildBootstrapMarkovCheck.Multiplier - Enum Class in edu.cmu.tetrad.search: Multiplier distribution for the wild bootstrap.
WildBootstrapMarkovCheck.OlsResidualizer - Class in edu.cmu.tetrad.search: Ordinary least squares residualization.
WildBootstrapMarkovCheck.Residualizer - Interface in edu.cmu.tetrad.search: Pluggable residualization.
WildBootstrapMarkovCheck.Result - Class in edu.cmu.tetrad.search: Outcome of a check: omnibus p-values plus per-constraint diagnostics.
winsorFrac - Variable in class edu.cmu.tetrad.estimate.v1.AdjustmentEffectEstimatorV1.ConfigV1: Fraction of data to winsorize for min/max scaling to [-1,1].
Wishart - Class in edu.cmu.tetrad.search.ntad_test: The Wishart class is a concrete implementation of the NtadTest abstract class, specifically for statistical tests based on the Wishart distribution.
Wishart(SimpleMatrix, boolean, int) - Constructor for class edu.cmu.tetrad.search.ntad_test.Wishart: Constructs a Wishart object using the provided data matrix, flag for correlation or covariance matrix, and the effective sample size.
withAdAlpha(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given Anderson-Darling alpha.
withAttrition(int...) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Sets the attrition order.
withBoundaryAtomMaxGapFraction(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given small-gap fraction (see DataAudit.Config.boundaryAtomMaxGapFraction).
withBoundaryAtomMinMass(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given minimum mass for BOUNDARY_ATOM.
withCellDeterminismMaxCardinality(int) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given maximum distinct-value count for members of determining sets in the cell-determinism check.
withCellDeterminismMaxSetSize(int) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given maximum determining-set size for the cell-determinism check (0 disables the check).
withCellDeterminismTolerance(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given relative tolerance for the cell-determinism check.
withDiscreteIndicators(boolean) - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns a copy of this spec with the given indicator type.
withDuplicateCorrelationTolerance(double) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Returns a config identical to this one but with the given duplicate-correlation tolerance.
withEmMaxIterations(int) - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Returns a copy of this spec with the given maximum number of EM iterations.
withEmRidge(double) - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Returns a copy of this spec with the given EM ridge.
withEmTolerance(double) - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Returns a copy of this spec with the given EM convergence tolerance.
withEssMode(MissingDataSpec.EffectiveSampleSizeMode) - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Returns a copy of this spec with the given effective sample size mode.
withExcludeContextsFromS(boolean) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPag: Configures whether contexts are excluded from the conditioning sets S offered to the change tests.
withExcludeContextsFromS(boolean) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Configures whether contexts are excluded from the conditioning sets S offered to the change tests.
withExtremeVarianceRatio(double) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Returns a config identical to this one but with the given extreme-variance-ratio threshold.
withHighCorrelation(double) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Returns a config identical to this one but with the given high-correlation threshold.
withHighCorrelation(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given high-correlation threshold.
withHigherIsBetterBic(boolean) - Method in class edu.cmu.tetrad.search.CyclicSemScorer: Sets whether higher values of BIC (Bayesian Information Criterion) are considered better and returns the updated instance of CyclicSemScorer.
WITHIN_INPUT - Enum constant in enum class edu.cmu.tetrad.search.test.CachedIndependenceQueries.Dedup: Strategy indicating deduplication will occur within the current input data.
withIndicatorEffect(int, int, double) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Adds an indicator effect: whether driver was deleted in a case shifts the log odds that target is deleted in that case.
withinDomainPooledCovariance() - Method in class edu.cmu.tetrad.search.rlcd.StratifiedChi2RankTest: The within-domain pooled covariance matrix: the per-domain covariance matrices averaged with weights (n_t - 1), which equals the covariance of the data after each domain has been centered at its own means, at sample size N - (number of domains) to account for the per-domain centering.
withinVariance() - Method in record class edu.cmu.tetrad.data.missing.RubinsRules.Pooled: Returns the value of the withinVariance record component.
withKnowledge(boolean, boolean) - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns a copy of this spec with the given knowledge settings.
withLowSampleRatio(double) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Returns a config identical to this one but with the given low-sample-ratio threshold.
withMaxSubsetSize(int) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPag: Sets the maximum size of conditioning sets S considered per edge by the orienter; negative values are clamped to 0.
withMaxSubsetSize(int) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Sets the maximum size of conditioning sets S considered per edge; negative values are clamped to 0.
withModifiedDerivatives(double[]) - Method in class edu.cmu.tetrad.util.TaylorSeries: Creates a new TaylorSeries instance with modified derivatives while maintaining the same center.
withNewCenter(double) - Method in class edu.cmu.tetrad.util.TaylorSeries: Creates a new TaylorSeries object with the same derivatives but a specified new center.
withNonlinearDeterminismMaxSetSize(int) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given nonlinear near-determinism maximum determining-set size (0 disables the check).
withNonlinearR2Determinism(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given nonlinear near-determinism threshold.
withNumImputations(int) - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Returns a copy of this spec with the given number of imputations.
withR2Determinism(double) - Method in class edu.cmu.tetrad.data.audit.CovarianceAudit.Config: Returns a config identical to this one but with the given R-squared determinism threshold.
withRateWindow(double, double) - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns a copy of this spec with the given rate window.
withReducedParents(int, int[], long) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Returns a new simulator that is identical to this one except that the mechanism for nodeIndex has been retrained using only the specified reduced parent set.
withRidgeOmega(double) - Method in class edu.cmu.tetrad.search.CyclicSemScorer: Sets the ridge omega value for the CyclicSemScorer and returns the updated instance.
withSeed(long) - Method in class edu.cmu.tetrad.data.missing.MissingDataSpec: Returns a copy of this spec with the given random seed.
withSelfMasking(int, double) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Adds self-masking: the variable's own true value shifts the log odds that it is deleted.
withSentinelGapRatio(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given minimum gap-to-median-spacing ratio for SENTINEL_VALUE.
withSentinelMinCount(int) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given minimum cell count for SENTINEL_VALUE.
withSentinelMinDistinct(int) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given minimum distinct-value count for the SENTINEL_VALUE check to run.
withSentinelMinMass(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given minimum observed-value share for SENTINEL_VALUE.
withSerialAlpha(double) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Sets the serial correlation alpha value.
withSerialGroupVariable(String) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given serial dependence grouping variable.
withSmallCellCount(int) - Method in class edu.cmu.tetrad.data.audit.DataAudit.Config: Returns a config identical to this one but with the given small-cell count threshold.
withStabilityTol(double) - Method in class edu.cmu.tetrad.search.CyclicSemScorer: Sets the stability tolerance for the CyclicSemScorer and returns the updated instance.
withSuffix(String) - Method in record class edu.cmu.tetrad.search.utils.MissingnessIndicatorAdder.Spec: Returns a copy of this spec with the given indicator name suffix.
withTiers(Map) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Merges the given tier assignments (smaller = earlier) into the tier map.
withValueEffect(int, int, double) - Method in record class edu.cmu.tetrad.data.MissingnessInjector.Mechanism: Adds a value effect: the standardized true value of driver shifts the log odds that target is deleted.
withVerbose(boolean) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPag: Enables or disables logging of individual orientation decisions and summary telemetry.
withVerbose(boolean) - Method in class edu.cmu.tetrad.search.cdnod_pag.CdnodPagOrienter: Enables or disables logging of individual commit decisions.
witnesses() - Method in record class edu.cmu.tetrad.search.PcAR.OrientationClash: Returns the value of the witnesses record component.
WitteReplicationStudy - Class in edu.cmu.tetrad.study.missingness: Replication harness for Witte, Foraita and Didelez, "Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data" (arXiv:2108.13331, 2021).
worsleyfriston - Enum constant in enum class edu.cmu.tetrad.search.utils.ResolveSepsets.Method: Worsley and Friston's method
worsleyfriston - Enum constant in enum class edu.cmu.tetrad.search.work_in_progress.ResolveSepsetsDci.Method: The wilkinson method.
wouldBeSatisfied(double) - Method in class edu.cmu.tetrad.sem.ParamConstraint: This method is for testing whether a value that might be assigned to a parameter would satisfy it.
wrap(IndependenceTest) - Method in class edu.cmu.tetrad.search.harness.EdgePriorFacade: Wraps a test built on a repeat's sub-covariance, restricting the locus prior to that repeat's variables.
wrapScore(Score) - Method in class edu.cmu.tetrad.search.harness.EdgePriorScoreFacade: Wraps a score built on a repeat's sub-covariance, restricting the locus prior to that repeat's variables.
writeCovMatrixLowerTriangle(ICovarianceMatrix, PrintWriter, NumberFormat) - Static method in class edu.cmu.tetrad.data.DataWriter: Writes the lower triangle of a covariance matrix to file.
writeCovMatrixSquare(ICovarianceMatrix, PrintWriter, NumberFormat) - Static method in class edu.cmu.tetrad.data.DataWriter: Writes the covariance matrix to file as a square matrix.
writeDecisions(File, List, double[], List, int[][][]) - Method in class edu.cmu.tetrad.search.harness.CiTestHarness: Writes a CSV file containing decisions for conditional independence tests.
writeFitReportTxt(File) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM: Writes a human-readable fit report to a specified text file.
writeFitReportTxt(File) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM: Writes a human-readable textual representation of the fit report to the specified file.
writeFitReportTxt(File) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1: Writes a human-readable textual representation of the fit report to the specified file.
writeFitReportTxt(File) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2: Writes a human-readable textual representation of the fit report to the specified file.
writePValues(File, List, List, double[][]) - Method in class edu.cmu.tetrad.search.harness.CiTestHarness: Writes a CSV file containing p-values for conditional independence tests.
writeRectangularData(DataSet, Writer, char) - Static method in class edu.cmu.tetrad.data.DataWriter: Writes a dataset to file.
writeSimReportTxt(File, double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Writes the simulation report generated by the getSimReportText method to a specified output file in plain text format.
writeSimReportTxt(File, double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Writes the simulation report generated by the getSimReportText method to a specified output file in plain text format.
writeSimReportTxt(File, double, boolean) - Method in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Writes the simulation report generated by the getSimReportText method to a specified output file in plain text format.
writeSummaryReport(File, List, double[], CiTestHarness.Result) - Method in class edu.cmu.tetrad.search.harness.CiTestHarness: Generates a summary report of the statistical tests and writes it to the specified output file.

X

x - Variable in class edu.cmu.tetrad.search.Pc.Triple: The first node in the triplet represented by this Triple object.
x - Variable in class edu.cmu.tetrad.search.PcAR.Triple: The first node in the triplet represented by this Triple object.
x() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the x record component.
x() - Method in record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Returns the value of the x record component.
x() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Returns the value of the x record component.
x() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns the value of the x record component.
x() - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Returns the value of the x record component.
X - Variable in class edu.cmu.tetrad.search.utils.NonlinearityTests.CleanData: The 2D array of independent variable values, cleaned of NaNs.
X() - Method in record class edu.cmu.tetrad.search.FastIca.IcaResult: Returns the value of the X record component.
X1_TO_X2 - Enum constant in enum class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Answer: X1 --> X2.
X2_TO_X1 - Enum constant in enum class edu.cmu.tetrad.search.rlcd.TwoIndicatorTest.Answer: X2 --> X1.
xentTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.NodeReport: Cross-Entropy (XENT) for the training data associated with the node.
xentTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.NodeReport: Cross-entropy loss for the training dataset associated with the node.
xentTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.NodeReport: Cross-entropy loss for the training dataset associated with the node.
xentTrain - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.NodeReport: Cross-entropy loss for the training dataset associated with the node.

Y

y - Variable in class edu.cmu.tetrad.search.Pc.Triple: The third node in the triplet represented by this Triple object.
y - Variable in class edu.cmu.tetrad.search.PcAR.Triple: The third node in the triplet represented by this Triple object.
y - Variable in class edu.cmu.tetrad.search.utils.NonlinearityTests.CleanData: The array of dependent variable values, cleaned of NaNs.
y() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the y record component.
y() - Method in record class edu.cmu.tetrad.search.Fas.BlockedDeletion: Returns the value of the y record component.
y() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Returns the value of the y record component.
y() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns the value of the y record component.
y() - Method in record class edu.cmu.tetrad.search.PcAR.MarkovAuditFailure: Returns the value of the y record component.

Z

z - Variable in class edu.cmu.tetrad.search.Pc.Triple: The second node in the triplet represented by this Triple object.
z - Variable in class edu.cmu.tetrad.search.PcAR.Triple: The second node in the triplet represented by this Triple object.
z() - Method in record class edu.cmu.tetrad.search.CdnodBoss.Disagreement: Returns the value of the z record component.
z() - Method in record class edu.cmu.tetrad.search.FaskPool.EdgeStat: Returns the value of the z record component.
z() - Method in record class edu.cmu.tetrad.search.harness.CiTestHarness.CiFact: Returns the value of the z record component.
z() - Method in record class edu.cmu.tetrad.search.PcAR.ContestedDeletion: Returns the value of the z record component.
z() - Method in record class edu.cmu.tetrad.search.PcAR.OrientationClash: Returns the value of the z record component.
Z_SCORE - Enum constant in enum class edu.cmu.tetrad.search.harness.AnnotationNormalization: The legacy scheme: the pooled finite entries are standardised to mean zero and unit sample standard deviation (n - 1 denominator).
zeros(int, int) - Static method in class edu.cmu.tetrad.util.MatrixUtils: zeros.
zExceedWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Array storing the count of exceedance warnings for each variable index during simulations.
zExceedWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zExceedWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zExceedWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zExceedWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Array storing the count of exceedance warnings for each variable index during simulations.
zExceedWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zExceedWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zExceedWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zhangMagFromPag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Transforms a partial ancestral graph (PAG) into a maximal ancestral graph (MAG) using Zhang's 2008 Theorem 2.
zhangMagsFromPag(Graph) - Static method in class edu.cmu.tetrad.graph.GraphTransforms: Lazily enumerates the Zhang MAGs of a PAG: one MAG per consistent DAG orientation of the PAG's unoriented component.
ZhangShenBoundScore - Class in edu.cmu.tetrad.algcomparison.score: Wrapper for linear, Gaussian Extended BIC score (Chen and Chen).
ZhangShenBoundScore() - Constructor for class edu.cmu.tetrad.algcomparison.score.ZhangShenBoundScore: This class represents the constructor for the ZhangShenBoundScore class.
ZS_RISK_BOUND - Static variable in class edu.cmu.tetrad.util.Params: Constant ZS_RISK_BOUND="zSRiskBound"
ZsbScore - Class in edu.cmu.tetrad.search.score: Implements an unpublished score based on a risk bound due to Zhang and Shen.
ZsbScore(DataSet, boolean) - Constructor for class edu.cmu.tetrad.search.score.ZsbScore: Constructs the score using a covariance matrix.
ZsbScore(ICovarianceMatrix) - Constructor for class edu.cmu.tetrad.search.score.ZsbScore: Constructs the score using a covariance matrix.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Thresholds for warning levels based on z-scores of continuous parents during simulation.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Array storing the count of exceedance warnings for each variable index during simulations.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: A threshold parameter used for controlling or warning about a specific condition or operation within the application.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: A threshold value used to issue a warning for the "z" score in statistical or analytical calculations.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: A threshold value used to issue a warning for the "z" score in statistical or analytical calculations.
zWarn1 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Tracks the count of occurrences where a specific variable index exceeds a certain z-score warning threshold during simulation.
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.Params: Thresholds for warning levels based on z-scores of continuous parents during simulation.
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorANM.SimResult: Array storing the count of exceedance warnings for each variable index during simulations.
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.Params: A configurable parameter used to define a specific warning threshold value.
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM.SimResult: Tracks the count
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.Params: A threshold value used to issue a warning for the "z" score in statistical or analytical calculations.
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM1.SimResult: Tracks the count
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.Params: A threshold value used to issue a warning for the "z" score in statistical or analytical calculations.
zWarn2 - Variable in class edu.cmu.tetrad.sem.TrainedDagSimulatorGNM2.SimResult: Tracks the count

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