Uses of Interface
edu.cmu.tetrad.graph.Node
Packages that use Node
Package
Description
This package contains classes for causal graph search algorithms.
This package contains classes for scoring causal graph models.
This package contains classes for testing causal graph search algorithms.
This package contains utility classes for causal graph search algorithms.
A package for algorithms that are not ready for prime time.
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Uses of Node in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
Method parameters in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag with type arguments of type NodeModifier and TypeMethodDescriptionvoidBossTrekMimic.setInputs(Collection<Node> inputs) Deprecated.Sets the observed variables known to be inputs to the latent structure.voidTgin.setInputs(Collection<Node> inputs) Deprecated.Sets the observed variables known to be inputs to the latent structure.voidTrekMimic.setInputs(Collection<Node> inputs) Sets the observed variables known to be inputs to the latent structure.voidBossTrekMimic.setOutputs(Collection<Node> outputs) Deprecated.Sets the observed variables known to be outputs from the latent structure.voidTgin.setOutputs(Collection<Node> outputs) Deprecated.Sets the observed variables known to be outputs from the latent structure.voidTrekMimic.setOutputs(Collection<Node> outputs) Sets the observed variables known to be outputs from the latent structure. -
Uses of Node in edu.cmu.tetrad.algcomparison.score
Methods in edu.cmu.tetrad.algcomparison.score that return NodeModifier and TypeMethodDescriptionBasisFunctionBgeScore.getVariable(String name) Returns the variable with the given name.BasisFunctionBicScore.getVariable(String name) Returns the variable with the given name.BasisFunctionBicScoreTabular.getVariable(String name) Returns the variable with the given name.BdeuScore.getVariable(String name) Returns the variable with the given name.BfBlocksBicScore.getVariable(String name) Retrieves a variable by its name from the associated data set.BgeScore.getVariable(String name) Returns the variable with the given name.BlocksBicScore.getVariable(String name) Retrieves a variable by its name from the associated data set.BlockScoreWrapper.getVariable(String name) Returns the variable with the given name.ConditionalGaussianBicScore.getVariable(String name) Returns the variable with the given name.DegenerateGaussianBgeScore.getVariable(String name) Returns the variable with the given name.DegenerateGaussianBicScore.getVariable(String name) Returns the variable with the given name.DiscreteBicScore.getVariable(String name) Returns the variable with the given name.EbicScore.getVariable(String name) Returns the variable with the given name.FfMl.getVariable(String name) d Retrieves the variable with the given name from the data set.FfMlContinuous.getVariable(String name) d Retrieves the variable with the given name from the data set.GicScores.getVariable(String name) Returns the variable with the given name.HeavyTailSemBicScore.getVariable(String name) Retrieves the variable with the given name from the data set.HuangMarginalLikelihoodScore.getVariable(String name) Retrieves the variable with the given name from the data set.InstanceAugmentedSemBicScoreWrapper.getVariable(String name) Retrieves a variable node by its name.IsBDeuScoreWrapper.getVariable(String name) Retrieves a variable from the model by its name.KcvBicScore.getVariable(String name) Retrieves the variable with the given name from the data set.KernelMarginalLikelihoodScore.getVariable(String name) Retrieves the variable with the given name from the data set.MagDgBicScore.getVariable(String name) Returns the variable with the given name.MSepScore.getVariable(String name) Returns the variable with the given name.MVPBicScore.getVariable(String name) Deprecated.Returns the variable with the given name.PoissonPriorScore.getVariable(String name) Returns the variable with the given name.RankBicScore.getVariable(String name) Retrieves the variable with the given name from the data set.RffBicScore.getVariable(String name) Retrieves the variable with the given name from the data set.ScoreWrapper.getVariable(String name) Returns the variable with the given name.SemBicScore.getVariable(String name) Retrieves the variable with the given name from the data set.TRffBicScore.getVariable(String name) Retrieves a variable from the dataset based on the given name.ZhangShenBoundScore.getVariable(String name) Retrieves the variable with the given name from the data set. -
Uses of Node in edu.cmu.tetrad.algcomparison.simulation
Methods in edu.cmu.tetrad.algcomparison.simulation that return types with arguments of type NodeModifier and TypeMethodDescriptionDaoSimulation.soficOrder(Graph dag) Returns a source-first consistent topological ordering of the DAG nodes.Methods in edu.cmu.tetrad.algcomparison.simulation with parameters of type NodeModifier and TypeMethodDescriptionLeeHastieSimulation.getEdgeParams(Node n1, Node n2, GeneralizedSemPm pm) getEdgeParams. -
Uses of Node in edu.cmu.tetrad.algcomparison.statistic
Methods in edu.cmu.tetrad.algcomparison.statistic with parameters of type NodeModifier and TypeMethodDescriptionbooleanNumDirectedEdgeNoMeasureAncestors.existsDirectedPathFromTo(Graph graph, Node node1, Node node2) existsDirectedPathFromTo. -
Uses of Node in edu.cmu.tetrad.algcomparison.utils
Methods in edu.cmu.tetrad.algcomparison.utils that return NodeModifier and TypeMethodDescriptionPooledScoreWrapper.getVariable(String name) Returns the variable with the given name. -
Uses of Node in edu.cmu.tetrad.bayes
Methods in edu.cmu.tetrad.bayes that return NodeModifier and TypeMethodDescriptionstatic Node[]GraphTools.getMaximumCardinalityOrdering(Graph graph) Perform Tarjan and Yannakakis (1984) maximum cardinality search (MCS) to get the maximum cardinality ordering.BayesIm.getNode(int nodeIndex) Returns the name of the given node.Returns the name of the given node.BayesPm.getNode(int index) Returns the node at the given index.Returns the node by the given name.DirichletBayesIm.getNode(int nodeIndex) Returns the name of the given node.getNode.Evidence.getNode(int nodeIndex) getNode.MlBayesIm.getNode(int nodeIndex) Retrieves the node at the specified index.getNode.MlBayesImObs.getNode(int nodeIndex) Returns the name of the given node.getNode.UpdatedBayesIm.getNode(int nodeIndex) Returns the name of the given node.getNode.BayesPm.getVariable(Node node) Returns the variable for the given node.BayesProperties.getVariable(String targetName) Retrieves a variable from the list of variables that matches the specified target name.Methods in edu.cmu.tetrad.bayes that return types with arguments of type NodeModifier and TypeMethodDescriptionGraphTools.getCliques(Node[] ordering, Graph graph) Get cliques in a decomposable graph.GraphTools.getCliques(Node[] ordering, Graph graph) Get cliques in a decomposable graph.GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) getCliqueTree.GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) getCliqueTree.BayesIm.getMeasuredNodes()Returns the list of measured variables.BayesPm.getMeasuredNodes()Returns the measured nodes.DirichletBayesIm.getMeasuredNodes()getMeasuredNodes.MlBayesIm.getMeasuredNodes()getMeasuredNodes.MlBayesImObs.getMeasuredNodes()getMeasuredNodes.UpdatedBayesIm.getMeasuredNodes()getMeasuredNodes.Calculate separator sets in clique tree.Calculate separator sets in clique tree.BayesIm.getVariables()Returns the list of variables.BayesImProbs.getVariables()Getter for the fieldvariables.BayesPm.getVariables()getVariables.CellTableProbs.getVariables()getVariables.DataSetProbs.getVariables()getVariables.DirichletBayesIm.getVariables()getVariables.DirichletDataSetProbs.getVariables()getVariables.IntAveDataSetProbs.getVariables()getVariables.MlBayesIm.getVariables()getVariables.MlBayesImObs.getVariables()getVariables.StoredCellProbs.getVariables()Getter for the fieldvariables.StoredCellProbsObs.getVariables()Getter for the fieldvariables.UpdatedBayesIm.getVariables()getVariables.Evidence.getVariablesInEvidence()getVariablesInEvidence.Methods in edu.cmu.tetrad.bayes with parameters of type NodeModifier and TypeMethodDescriptionstatic voidApply Tarjan and Yannakakis (1984) fill in algorithm for graph triangulation.BayesPm.getCategory(Node node, int index) Returns the index'th value for the given node.Evidence.getCategory(Node node, int j) getCategory.intBayesPm.getCategoryIndex(Node node, String category) Returns the index of the given category for the given node.GraphTools.getCliques(Node[] ordering, Graph graph) Get cliques in a decomposable graph.GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) getCliqueTree.intBayesIm.getNodeIndex(Node node) Returns the index of the given node.intDirichletBayesIm.getNodeIndex(Node node) Returns the index of the given node.intMlBayesIm.getNodeIndex(Node node) Returns the index of the given node in the nodes array.intMlBayesImObs.getNodeIndex(Node node) Returns the index of the given node.intUpdatedBayesIm.getNodeIndex(Node node) Returns the index of the given node.intBayesPm.getNumCategories(Node node) Returns the number of values for the given node.double[][]BdeMetricCache.getObservedCounts(Node node, BayesPm bayesPm, BayesIm bayesIm) This method is used in testing and debugging and not in the BDe metric calculations.intBdeMetricCache.getScoreCount(Node node, Set<Node> parents) This is just for testing the operation of the inner class and the map from nodes and parent sets to scores.Calculate separator sets in clique tree.BayesPm.getVariable(Node node) Returns the variable for the given node.doubleComputes the BDe score, using the logarithm of the gamma function, relative to the data, of the factor determined by a node and its parents.voidBayesPm.setCategories(Node node, List<String> categories) Sets the number of values for the given node to the given number.voidBayesPm.setNumCategories(Node node, int numCategories) Sets the number of values for the given node to the given number.Method parameters in edu.cmu.tetrad.bayes with type arguments of type NodeModifier and TypeMethodDescriptionstatic StoredCellProbsStoredCellProbs.createRandomCellTable(List<Node> variables) createRandomCellTable.GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) getCliqueTree.GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) getCliqueTree.intBdeMetricCache.getScoreCount(Node node, Set<Node> parents) This is just for testing the operation of the inner class and the map from nodes and parent sets to scores.Calculate separator sets in clique tree.Calculate separator sets in clique tree.doubleComputes the BDe score, using the logarithm of the gamma function, relative to the data, of the factor determined by a node and its parents.Constructor parameters in edu.cmu.tetrad.bayes with type arguments of type NodeModifierConstructorDescriptionStoredCellProbsObs(List<Node> variables) Constructor for StoredCellProbsObs. -
Uses of Node in edu.cmu.tetrad.classify
Methods in edu.cmu.tetrad.classify that return types with arguments of type NodeModifier and TypeMethodDescriptionClassifierBayesUpdaterDiscrete.getBayesImVars()Returns the variables of the BayesIM. -
Uses of Node in edu.cmu.tetrad.data
Subinterfaces of Node in edu.cmu.tetrad.dataModifier and TypeInterfaceDescriptioninterfaceInterface implemented by classes, instantiations of which are capable of serving as variables for columns in a DataSet.Classes in edu.cmu.tetrad.data that implement NodeModifier and TypeClassDescriptionclassBase class for variable specifications for DataSet.final classRepresents a real-valued variable.final classRepresents a discrete variable as a range of integer-valued categories 0, 1, ..., m - 1, where m is the number of categories for the variable.Methods in edu.cmu.tetrad.data that return NodeModifier and TypeMethodDescriptionHistogram.getTargetNode()Retrieves the target node.BoxDataSet.getVariable(int col) getVariable.BoxDataSet.getVariable(String varName) getVariable.CorrelationMatrixOnTheFly.getVariable(String name) Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.CovarianceMatrix.getVariable(String name) Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.CovarianceMatrixOnTheFly.getVariable(String name) Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.DataModel.getVariable(String name) getVariable.DataModelList.getVariable(String name) getVariable.DataSet.getVariable(int column) getVariable.DataSet.getVariable(String name) getVariable.ICovarianceMatrix.getVariable(String name) Retrieves a Node instance from the covariance matrix corresponding to the specified variable name.IndependenceFacts.getVariable(String name) getVariable.NumberObjectDataSet.getVariable(int col) getVariable.NumberObjectDataSet.getVariable(String varName) getVariable.TimeSeriesData.getVariable(String name) getVariable.abstract NodeCreates a new node of the same type as this one with the given name.Creates a new node of the same type as this one with the given name.Creates a new node of the same type as this one with the given name.Methods in edu.cmu.tetrad.data that return types with arguments of type NodeModifier and TypeMethodDescriptionDataUtils.createContinuousVariables(String[] varNames) createContinuousVariables.DataTransforms.getConstantColumns(DataSet dataSet) getConstantColumns.DataUtils.getExampleNonsingular(ICovarianceMatrix covarianceMatrix, int depth) getExampleNonsingular.NumberObjectDataSet.getSelectedVariables()getSelectedVariables.BoxDataSet.getVariables()Getter for the fieldvariables.CorrelationMatrixOnTheFly.getVariables()Getter for the fieldvariables.CovarianceMatrix.getVariables()Getter for the fieldvariables.CovarianceMatrixOnTheFly.getVariables()Getter for the fieldvariables.DataModelList.getVariables()getVariables.DataSet.getVariables()getVariables.ICovarianceMatrix.getVariables()Retrieves the list of Node variables for the covariance matrix.IndependenceFacts.getVariables()getVariables.NumberObjectDataSet.getVariables()Getter for the fieldvariables.TimeSeriesData.getVariables()getVariables.VariableSource.getVariables()Returns the list of variables associated with this object.Methods in edu.cmu.tetrad.data with parameters of type NodeModifier and TypeMethodDescriptionvoidBoxDataSet.addVariable(int index, Node variable) Adds the given variable at the given index.voidBoxDataSet.addVariable(Node variable) Adds the given variable to the data set.voidDataSet.addVariable(int index, Node variable) Adds the given variable at the given index.voidDataSet.addVariable(Node variable) Adds the given variable to the data set.voidMixedDataBox.addVariable(Node variable) addVariable.voidNumberObjectDataSet.addVariable(int index, Node variable) Adds the given variable at the given index.voidNumberObjectDataSet.addVariable(Node variable) Adds the given variable to the data set.voidBoxDataSet.changeVariable(Node from, Node to) Changes the variable for the given column fromfromtoto.voidDataSet.changeVariable(Node from, Node to) Changes the variable for the given column fromfromtoto.voidNumberObjectDataSet.changeVariable(Node from, Node to) Changes the variable for the given column fromfromtoto.static voidDataTransforms.copyColumn(Node node, DataSet source, DataSet dest) copyColumn.voidDiscretizer.equalCounts(Node node, int numCategories) Sets the given node to discretized using evenly distributed values using the given number of categories.voidDiscretizer.equalIntervals(Node node, int numCategories) Sets the given node to discretized using evenly spaced intervals using the given number of categories.intBoxDataSet.getColumnIndex(Node variable) getColumn.intDataSet.getColumnIndex(Node variable) getColumn.intNumberObjectDataSet.getColumnIndex(Node variable) getColumn.booleanIndependenceFacts.isIndependent(Node x, Node y, Node... z) isIndependent.booleanIndependenceFacts.isIndependent(Node x, Node y, Set<Node> z) isIndependent.intKnowledge.isInWhichTier(Node node) Returns the index of the tier of node if it's in a tier, otherwise -1.booleanBoxDataSet.isSelected(Node variable) isSelected.final booleanCorrelationMatrixOnTheFly.isSelected(Node variable) Checks if the specified node is selected in the covariance matrix.final booleanCovarianceMatrix.isSelected(Node variable) Checks if the specified node is selected in the covariance matrix.final booleanCovarianceMatrixOnTheFly.isSelected(Node variable) Checks if the specified node is selected in the covariance matrix.booleanDataSet.isSelected(Node variable) isSelected.booleanICovarianceMatrix.isSelected(Node variable) Checks if the specified node is selected in the covariance matrix.booleanNumberObjectDataSet.isSelected(Node variable) isSelected.voidBoxDataSet.removeColumn(Node variable) Removes the given variable, along with all of its data.voidDataSet.removeColumn(Node variable) Removes the given variable, along with all of its data.voidNumberObjectDataSet.removeColumn(Node variable) Removes the given variable, along with all of its data.static voidScales the values of a specified node in the given dataset to a specified range [scaleMin, scaleMax].final voidSelects a specified variable in the covariance matrix.final voidSelects a specified variable in the covariance matrix.final voidSelects a specified variable in the covariance matrix.voidSelects a specified variable in the covariance matrix.voidBoxDataSet.setSelected(Node variable, boolean selected) Marks the given column as selected if 'selected' is true or deselected if 'selected' is false.voidDataSet.setSelected(Node variable, boolean selected) Marks the given column as selected if 'selected' is true or deselected if 'selected' is false.voidNumberObjectDataSet.setSelected(Node variable, boolean selected) Marks the given column as selected if 'selected' is true or deselected if 'selected' is false.voidBoxDataSet.setVariable(int j, Node variable) Updates the variable at the specified index in the variables list.voidDataSet.setVariable(int j, Node variable) Sets the value of a variable to the provided instance.voidNumberObjectDataSet.setVariable(int j, Node variable) Sets the variable at the specified index in the list of variables.static MatrixsubMatrix.static MatrixsubMatrix.static MatrixsubMatrix.static MatrixsubMatrix.Method parameters in edu.cmu.tetrad.data with type arguments of type NodeModifier and TypeMethodDescriptionbooleanIndependenceFacts.isIndependent(Node x, Node y, Set<Node> z) isIndependent.static double[]MissingnessInjector.parseRateProfile(String text, List<Node> variables, double defaultRate) Parses a hand-specified rate profile.voidSetter for the fieldnodes.voidCorrelationMatrixOnTheFly.setVariables(List<Node> variables) Sets the list of Node variables for the covariance matrix.voidCovarianceMatrix.setVariables(List<Node> variables) Sets the list of Node variables for the covariance matrix.voidCovarianceMatrixOnTheFly.setVariables(List<Node> variables) Sets the list of Node variables for the covariance matrix.voidICovarianceMatrix.setVariables(List<Node> variables) Sets the list of Node variables for the covariance matrix.static MatrixDataTransforms.standardizeData(Matrix data, List<Node> variables) Standardizes the columns of the given data matrix by centering and scaling.static MatrixsubMatrix.static MatrixsubMatrix.static MatrixsubMatrix.static MatrixsubMatrix.static MatrixsubMatrix.static MatrixsubMatrix.BoxDataSet.subsetColumns(List<Node> vars) Creates and returns a dataset consisting of those variables in the list vars.DataSet.subsetColumns(List<Node> vars) Creates and returns a dataset consisting of those variables in the list vars.NumberObjectDataSet.subsetColumns(List<Node> vars) Creates and returns a dataset consisting of those variables in the list vars.Constructor parameters in edu.cmu.tetrad.data with type arguments of type NodeModifierConstructorDescriptionBoxDataSet(DataBox dataBox, List<Node> variables) Constructs a new data set with the given number of rows and columns, with all values set to missing.CorrelationMatrix(List<Node> variables, Matrix matrix, int sampleSize) Constructs a correlation matrix data set using the given information.CovarianceMatrix(List<Node> variables, double[][] matrix, int sampleSize) Constructor for CovarianceMatrix.CovarianceMatrix(List<Node> variables, Matrix matrix, int sampleSize) Protected constructor to construct a new covariance matrix using the supplied continuous variables and the the given symmetric, positive definite matrix and sample size.Discretizer(DataSet dataSet, Map<Node, DiscretizationSpec> specs) Constructor for Discretizer.MixedDataBox(List<Node> variables, int numRows) The variables here are used only to determine which columns are discrete and which are continuous; bounds checking is not done.MixedDataBox(List<Node> variables, int numRows, double[][] continuousData, int[][] discreteData) This constructor allows other data readers to populate the fields directly.NumberObjectDataSet(Number[][] data, List<Node> variables) Constructor for NumberObjectDataSet. -
Uses of Node in edu.cmu.tetrad.data.missing
Methods in edu.cmu.tetrad.data.missing that return types with arguments of type NodeModifier and TypeMethodDescriptionResolves the dropped names to the dataset's variables, skipping any that are no longer present.Method parameters in edu.cmu.tetrad.data.missing with type arguments of type NodeModifier and TypeMethodDescriptionTestwiseCovariance.pairwiseCovarianceMatrix(List<Node> variables) 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. -
Uses of Node in edu.cmu.tetrad.estimate.v1
Methods in edu.cmu.tetrad.estimate.v1 with parameters of type NodeModifier and TypeMethodDescriptionAdjustmentEffectEstimatorV1.estimateAteBinaryVectorV1(DataSet data, String xName, int[] x01Full, Node y, Set<Node> z, AdjustmentEffectEstimatorV1.ConfigV1 cfg) Estimates the Average Treatment Effect (ATE) for a binary treatment variable provided as a vector.AdjustmentEffectEstimatorV1.estimateAteV1(DataSet data, Node x, Node y, Set<Node> z, AdjustmentEffectEstimatorV1.ConfigV1 cfg) v1: Estimate ATE using a provided adjustment set (already found by RA, Perkovic criterion, etc.).Method parameters in edu.cmu.tetrad.estimate.v1 with type arguments of type NodeModifier and TypeMethodDescriptionAdjustmentEffectEstimatorV1.estimateAteBinaryVectorV1(DataSet data, String xName, int[] x01Full, Node y, Set<Node> z, AdjustmentEffectEstimatorV1.ConfigV1 cfg) Estimates the Average Treatment Effect (ATE) for a binary treatment variable provided as a vector.AdjustmentEffectEstimatorV1.estimateAteV1(DataSet data, Node x, Node y, Set<Node> z, AdjustmentEffectEstimatorV1.ConfigV1 cfg) v1: Estimate ATE using a provided adjustment set (already found by RA, Perkovic criterion, etc.). -
Uses of Node in edu.cmu.tetrad.graph
Classes in edu.cmu.tetrad.graph that implement NodeModifier and TypeClassDescriptionclassImplements a basic node in a graph--that is, a node that is not itself a variable.Subinterfaces with type arguments of type Node in edu.cmu.tetrad.graphModifier and TypeInterfaceDescriptioninterfaceRepresents an object with a name, node type, and position that can serve as a node in a graph.Classes in edu.cmu.tetrad.graph that implement interfaces with type arguments of type NodeModifier and TypeClassDescriptionfinal classLazily enumerates EVERY valid causal order of a DAG or CPDAG, each exactly once.final classLazily enumerates EVERY valid causal order of a DAG or CPDAG, each exactly once.Methods in edu.cmu.tetrad.graph that return NodeModifier and TypeMethodDescriptionstatic NodeReturns 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.static NodeGraphUtils.getAssociatedNode(Node errorNode, Graph graph) Returns the associated node for the given error node in the specified graph.static NodeEdges.getDirectedEdgeHead(Edge edge) For a directed edge, returns the node adjacent to the arrow endpoint.static NodeEdges.getDirectedEdgeTail(Edge edge) For a directed edge, returns the node adjacent to the null endpoint.final NodeEdge.getDistalNode(Node node) Traverses the edge in an undirected fashion--given one node along the edge, returns the node at the opposite end of the edge.SemGraph.getErrorNode(Node node) getErrorNode.SemGraph.getExogenous(Node node) getExogenous.NodePair.getFirst()Getter for the fieldfirst.Retrieves the node in the graph with the specified name.getNode.getNode.getNode.getNode.Retrieves a Node from the graph based on the given name.getNode.final NodeEdge.getNode1()Getter for the fieldnode1.final NodeEdge.getNode2()Getter for the fieldnode2.NodePair.getSecond()Getter for the fieldsecond.static NodeGraphUtils.getTrekSource(Graph graph, List<Node> trek) This method returns the source node of a given trek in a graph.SemGraph.getVarNode(Node node) getVarNode.IndependenceFact.getX()Getter for the fieldx.Triple.getX()Getter for the fieldx.IndependenceFact.getY()Getter for the fieldy.Triple.getY()Getter for the fieldy.Triple.getZ()Getter for the fieldz.Creates a new node of the same type as this one with the given name.Creates a new node of the same type as this one with the given name.static NodeIf node is one endpoint of edge, returns the other endpoint.static NodeEdges.traverseDirected(Node node, Edge edge) For A -> B, given A, returns B; otherwise returns null.static NodeEdges.traverseNondirected(Node node, Edge edge) For A o-o B, given A, returns B; otherwise returns null.static NodeEdges.traversePotentiallyDirected(Node node, Edge edge) For A --* B or A o-* B, given A, returns B.static NodeGraphUtils.traversePotentiallyDirected(Node node, Edge edge) Traverses a potentially directed edge to identify the next node in the traversal.static NodeEdges.traverseReverseDirected(Node node, Edge edge) For A -> B, given B, returns A; otherwise returns null.Methods in edu.cmu.tetrad.graph that return types with arguments of type NodeModifier and TypeMethodDescriptionPaths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength) Computes and returns a list of adjustment sets for given nodes and parameters.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, boolean henckelPruning, boolean oSetCompatible) Computes and returns a list of adjustment sets for given nodes and parameters.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, String colliderPolicy) Computes the adjustment sets needed to estimate the causal effect of node X on node Y in a given graph structure under specified parameters.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, String colliderPolicy, boolean henckelPruning) Computes the adjustment sets needed to estimate the causal effect of node X on node Y in a given graph structure under specified parameters.Paths.allDirectedPaths(Node node1, Node node2, int maxLength) Finds all directed paths from node1 to node2 with a maximum length.Finds all paths from node1 to node2 within a specified maximum length.Paths.allPaths(Node node1, Node node2, int minLength, int maxLength, Set<Node> conditionSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int maxLength, Set<Node> conditionSet, boolean excludeSelectionBias) Finds all paths between two nodes within a given maximum length, considering optional condition set and selection bias.Paths.allPathsOutOf(Node node1, int maxLength, Set<Node> conditionSet, boolean excludeSelectionBias) Generates all paths out of a given node within a specified maximum length and conditional set.GraphUtils.anteriority(Graph G, Node... x) Computes the anteriority of the given nodes in a graph.Paths.anteriority(Node... X) Returns the set of nodes that are in the anteriority of the given nodes in the graph.Constructs a list of nodes from the givennodeslist at the given indices in that list.Converts an array of indices into a set of corresponding nodes from a given list of nodes.Converts the given array of nodes into a Set of nodes.GraphUtils.connectedComponents(Graph g) Identifies all connected components in a given graph.Paths.connectedComponents()Returns a list of connected components in the graph.Paths.directedPaths(Node node1, Node node2, int maxLength) Finds all directed paths from node1 to node2 with a maximum length.Calculates the district of a given node in a graph.Retrieves the set of nodes that belong to the same district as the given node.Returns D-SEP(x, y) for a MAG G.Returns D-SEP(x, y) for a maximal ancestral graph G (or inducing path graph G, as in Causation, Prediction and Search).Returns D-SEP(x, y) for a MAG G (or inducing path graph G, as in Causation, Prediction and Search).Returns D-SEP(x, y) for a MAG G.GraphUtils.dsepReachability(Node x, Node y, Graph G) Returns D-SEP(x, y) for a MAG G.Dag.getAdjacentNodes(Node node) Retrieves the adjacent nodes of a given node in the graph.EdgeListGraph.getAdjacentNodes(Node node) getAdjacentNodes.Graph.getAdjacentNodes(Node node) getAdjacentNodes.LagGraph.getAdjacentNodes(Node node) getAdjacentNodes.SemGraph.getAdjacentNodes(Node node) getAdjacentNodes.TimeLagGraph.getAdjacentNodes(Node node) Retrieves a list of adjacent nodes for the given node.Paths.getAmenablePathsMag(Node node1, Node node2, int maxLength, Set<Node> forceVisible) 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.Paths.getAmenablePathsPag(Node node1, Node node2, int maxLength, Set<Node> forceVisible) Finds amenable paths from the given source node to the given destination node with a maximum length, for a PAG.Paths.getAmenablePathsPdagMag(Node node1, Node node2, int maxLength) Finds amenable paths from the given source node to the given destination node with a maximum length.Paths.getAncestors(Node node) Retrieves the ancestors of a specified `Node` in the graph.Paths.getAncestors(List<Node> nodes) Returns a list of all ancestors of the given nodes.Paths.getAncestorsMap()Return a map from each node to its collection of ancestors.Paths.getAncestorsMap()Return a map from each node to its collection of ancestors.Paths.getBackdoorPaths(Node X, Node Y, String graphType, int maxPathLength) Finds and returns all valid backdoor paths between two nodes in a graph.Dag.getChildren(Node node) Retrieves the children of a specified Node in the graph.EdgeListGraph.getChildren(Node node) getChildren.Graph.getChildren(Node node) getChildren.LagGraph.getChildren(Node node) getChildren.SemGraph.getChildren(Node node) getChildren.TimeLagGraph.getChildren(Node node) Returns a list of children nodes for the given node.Paths.getDescendants(Node node) Returns a list of all descendants of the given node.Paths.getDescendants(List<Node> nodes) Retrieves the descendants of the given list of nodes.Paths.getDescendantsMap()Return a map from each node to its collection of descendants.Paths.getDescendantsMap()Return a map from each node to its collection of descendants.SemGraph.getFullTierOrdering()getFullTierOrdering.Paths.getInducingPath(Node x, Node y, Set<Node> selectionVariables) This method calculates the inducing path between two measured nodes in a graph.TimeLagGraph.getLag0Nodes()Getter for the fieldlag0Nodes.Paths.getMConnectedVars(Node y, Set<Node> z) Retrieves the set of nodes that are connected to the given nodeyand are also present in the set of nodesz.getMConnectedVars.Dag.getNodes()getNodes.EdgeListGraph.getNodes()getNodes.Graph.getNodes()getNodes.LagGraph.getNodes()getNodes.SemGraph.getNodes()getNodes.TimeLagGraph.getNodes()Retrieves a list of nodes from the graph.Dag.getNodesInTo(Node node, Endpoint n) Retrieves a list of nodes in the given graph that have edges pointing into the specified node and endpoint.EdgeListGraph.getNodesInTo(Node node, Endpoint endpoint) Nodes adjacent to the given node with the given proximal endpoint.Graph.getNodesInTo(Node node, Endpoint n) Nodes adjacent to the given node with the given proximal endpoint.LagGraph.getNodesInTo(Node node, Endpoint n) Nodes adjacent to the given node with the given proximal endpoint.SemGraph.getNodesInTo(Node node, Endpoint endpoint) Nodes adjacent to the given node with the given proximal endpoint.TimeLagGraph.getNodesInTo(Node node, Endpoint endpoint) Retrieves a list of nodes that have an incoming edge from a specific node and endpoint.Dag.getNodesOutTo(Node node, Endpoint n) Retrieves a list of nodes that have outgoing edges to a specified node and endpoint.EdgeListGraph.getNodesOutTo(Node node, Endpoint endpoint) Nodes adjacent to the given node with the given distal endpoint.Graph.getNodesOutTo(Node node, Endpoint n) Nodes adjacent to the given node with the given distal endpoint.LagGraph.getNodesOutTo(Node node, Endpoint n) Nodes adjacent to the given node with the given distal endpoint.SemGraph.getNodesOutTo(Node node, Endpoint n) Nodes adjacent to the given node with the given distal endpoint.TimeLagGraph.getNodesOutTo(Node node, Endpoint endpoint) Retrieves the list of nodes in a graph that have an outgoing edge to the given node and endpoint.Dag.getParents(Node node) Retrieves the list of parent nodes for a given node in the graph.EdgeListGraph.getParents(Node node) getParents.Graph.getParents(Node node) getParents.LagGraph.getParents(Node node) getParents.Paths.getParents(List<Node> pi, int p, Graph g, boolean verbose, boolean excludeSelectionBias) Returns the parents of the node at index p, calculated using Pearl's method.SemGraph.getParents(Node node) getParents.TimeLagGraph.getParents(Node node) Returns the list of parent nodes for the given node.Dag.getSepset(Node n1, Node n2, IndependenceTest test) Returns the sepset between two given nodes in the graph.Retrieves the set of nodes that form the sepset between two given nodes.EdgeListGraph.getSepset(Node x, Node y, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.Graph.getSepset(Node n1, Node n2, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.LagGraph.getSepset(Node n1, Node n2, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.Paths.getSepset(Node x, Node y, boolean excludeSelectionBias, IndependenceTest test, int depth) Finds a sepset for x and y, if there is one; otherwise, returns null.SemGraph.getSepset(Node n1, Node n2, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.TimeLagGraph.getSepset(Node n1, Node n2, IndependenceTest test) Retrieves the sepset of two nodes in the graph.EdgeListGraph.getSepsetContaining(Node x, Node y, Set<Node> containing, int maxLength) Retrieves the set of nodes that form the sepset between two given nodes.Paths.getSepsetContaining(Node x, Node y, Set<Node> containing, int maxPathLength) Retrieves a sepset (a set of nodes) between two given nodes.Paths.getValidOrder(List<Node> initialOrder, boolean forward) Returns a valid causal order for either a DAG or a CPDAG.Paths.getValidOrderMag(List<Node> initialOrder, boolean forward) Generates a valid topological ordering of nodes in a directed graph without cycles.IndependenceFact.getZ()getZ.ValidOrderIterator.iterator()GraphUtils.markovBlanket(Node x, Graph G) Computes the Markov blanket for a given node in a graph.Paths.markovBlanket(Node node) Returns the Markov Blanket of a given node in the graph.Paths.maxCliques()Returns a set of all maximum cliques in the graph.GraphUtils.maximalCliques(Graph graph, List<Node> nodes) Finds all maximal cliques in a given graph.ValidOrderIterator.next()LayoutUtil.orderRowsToReduceCrossings(Graph graph, List<List<Node>> rows) 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 (seeLayoutUtil.orderTiers(Graph, List, List)).Paths.possibleDsep(Node x, int maxPossibleDsepPathLength) Identifies and returns a list of possible d-separating nodes relative to a specified node in the graph.Paths.potentiallyDirectedPaths(Node node1, Node node2, int maxLength) Finds all potentially directed paths between two nodes up to a maximum length.GraphUtils.replaceNodes(List<Node> originalNodes, Graph graph) Converts the given list of nodes,originalNodes, to use the replacement nodes for them by the same name in the givengraph.GraphUtils.replaceNodes(List<Node> originalNodes, List<Node> newNodes) Converts the given list of nodes,originalNodes, to use the new variables (with the same names as the old).GraphUtils.stronglyConnectedComponents(Graph g) Compute strongly connected components (SCCs) of a directed graph.Finds all treks from node1 to node2 with a maximum length.Paths.treksIncludingBidirected(Node node1, Node node2) Finds all possible treks between two nodes, including bidirectional treks.GraphUtils.visibleEdgeAdjustments1(Graph G, Node x, Node y, int numSmallestSizes, GraphUtils.GraphType graphType) Calculates visual-edge adjustments given graph G between two nodes x and y that are subsets of MB(X).GraphUtils.visibleEdgeAdjustments3(Graph G, Node x, Node y, int numSmallestSizes, GraphUtils.GraphType graphType) This method calculates visible-edge adjustments for a given graph, two nodes, a number of smallest sizes, and a graph type.Methods in edu.cmu.tetrad.graph with parameters of type NodeModifier and TypeMethodDescriptionvoidDag.addAmbiguousTriple(Node x, Node y, Node z) Adds an ambiguous triple to the list of ambiguous triples.voidEdgeListGraph.addAmbiguousTriple(Node x, Node y, Node z) addAmbiguousTriple.voidGraph.addAmbiguousTriple(Node x, Node y, Node z) addAmbiguousTriple.voidLagGraph.addAmbiguousTriple(Node x, Node y, Node z) addAmbiguousTriple.voidSemGraph.addAmbiguousTriple(Node x, Node y, Node z) addAmbiguousTriple.voidTimeLagGraph.addAmbiguousTriple(Node x, Node y, Node z) Adds an ambiguous triple to the list of ambiguous triples.voidUnderlines.addAmbiguousTriple(Node x, Node y, Node z) addAmbiguousTriple.booleanDag.addBidirectedEdge(Node node1, Node node2) Bidirected edges are not allowed in DAGs.booleanEdgeListGraph.addBidirectedEdge(Node node1, Node node2) Adds a bidirected edges <-> to the graph.booleanGraph.addBidirectedEdge(Node node1, Node node2) Adds a bidirected edges <-> to the graph.booleanLagGraph.addBidirectedEdge(Node node1, Node node2) Adds a bidirected edges <-> to the graph.booleanSemGraph.addBidirectedEdge(Node nodeA, Node nodeB) Adds a bidirected edges <-> to the graph.booleanTimeLagGraph.addBidirectedEdge(Node node1, Node node2) Adds a bidirected edge between two nodes.booleanDag.addDirectedEdge(Node node1, Node node2) Adds a directed edge between two nodes.booleanEdgeListGraph.addDirectedEdge(Node node1, Node node2) Adds a directed edge --> to the graph.booleanGraph.addDirectedEdge(Node node1, Node node2) Adds a directed edge --> to the graph.booleanLagGraph.addDirectedEdge(Node node1, Node node2) Adds a directed edge --> to the graph.booleanSemGraph.addDirectedEdge(Node nodeA, Node nodeB) Adds a directed edge --> to the graph.booleanTimeLagGraph.addDirectedEdge(Node node1, Node node2) Adds a directed edge between two nodes to the graph.voidDag.addDottedUnderlineTriple(Node x, Node y, Node z) Adds a dotted underline triple to the graph.voidEdgeListGraph.addDottedUnderlineTriple(Node x, Node y, Node z) addDottedUnderlineTriple.voidGraph.addDottedUnderlineTriple(Node x, Node y, Node z) addDottedUnderlineTriple.voidLagGraph.addDottedUnderlineTriple(Node x, Node y, Node z) addDottedUnderlineTriple.voidSemGraph.addDottedUnderlineTriple(Node x, Node y, Node z) addDottedUnderlineTriple.voidTimeLagGraph.addDottedUnderlineTriple(Node x, Node y, Node z) Adds a triple with dotted underline to the list of triples.voidUnderlines.addDottedUnderlineTriple(Node x, Node y, Node z) addDottedUnderlineTriple.booleanAdds a Node to the graph.booleanAdds a node to the graph.booleanAdds a node to the graph.booleanAdds a node to the graph.booleanAdds a node to the graph.booleanAdds a node to the graph.booleanDag.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edge between two nodes in the graph.booleanEdgeListGraph.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edges o-o to the graph.booleanGraph.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edges o-o to the graph.booleanLagGraph.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edges o-o to the graph.booleanSemGraph.addNondirectedEdge(Node nodeA, Node nodeB) Adds a nondirected edges o-o to the graph.booleanTimeLagGraph.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edge between two nodes.booleanDag.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge between two nodes.booleanEdgeListGraph.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge o-> to the graph.booleanGraph.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge o-> to the graph.booleanLagGraph.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge o-> to the graph.booleanSemGraph.addPartiallyOrientedEdge(Node nodeA, Node nodeB) Adds a partially oriented edge o-> to the graph.booleanTimeLagGraph.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge between two given nodes.voidDag.addUnderlineTriple(Node x, Node y, Node z) Adds an underline triple to the current object.voidEdgeListGraph.addUnderlineTriple(Node x, Node y, Node z) addUnderlineTriple.voidGraph.addUnderlineTriple(Node x, Node y, Node z) addUnderlineTriple.voidLagGraph.addUnderlineTriple(Node x, Node y, Node z) addUnderlineTriple.voidSemGraph.addUnderlineTriple(Node x, Node y, Node z) addUnderlineTriple.voidTimeLagGraph.addUnderlineTriple(Node x, Node y, Node z) Adds an underline triple consisting of three nodes to the graph.voidUnderlines.addUnderlineTriple(Node x, Node y, Node z) addUnderlineTriple.booleanDag.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge between two nodes.booleanEdgeListGraph.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge --- to the graph.booleanGraph.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge --- to the graph.booleanLagGraph.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge --- to the graph.booleanSemGraph.addUndirectedEdge(Node nodeA, Node nodeB) Adds an undirected edge --- to the graph.booleanTimeLagGraph.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge between two nodes.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength) Computes and returns a list of adjustment sets for given nodes and parameters.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, boolean henckelPruning, boolean oSetCompatible) Computes and returns a list of adjustment sets for given nodes and parameters.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, String colliderPolicy) Computes the adjustment sets needed to estimate the causal effect of node X on node Y in a given graph structure under specified parameters.Paths.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, String colliderPolicy, boolean henckelPruning) Computes the adjustment sets needed to estimate the causal effect of node X on node Y in a given graph structure under specified parameters.Paths.allDirectedPaths(Node node1, Node node2, int maxLength) Finds all directed paths from node1 to node2 with a maximum length.Finds all paths from node1 to node2 within a specified maximum length.Paths.allPaths(Node node1, Node node2, int minLength, int maxLength, Set<Node> conditionSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int maxLength, Set<Node> conditionSet, boolean excludeSelectionBias) Finds all paths between two nodes within a given maximum length, considering optional condition set and selection bias.Paths.allPathsOutOf(Node node1, int maxLength, Set<Node> conditionSet, boolean excludeSelectionBias) Generates all paths out of a given node within a specified maximum length and conditional set.GraphUtils.anteriority(Graph G, Node... x) Computes the anteriority of the given nodes in a graph.Paths.anteriority(Node... X) Returns the set of nodes that are in the anteriority of the given nodes in the graph.Converts the given array of nodes into a Set of nodes.static EdgeEdges.bidirectedEdge(Node nodeA, Node nodeB) Constructs a new bidirected edge from nodeA to nodeB (<->).static booleanDetermines if the collider is allowed.default intCompares 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.booleanDag.containsNode(Node node) Checks if the given Node object is contained in the graph.booleanEdgeListGraph.containsNode(Node node) Determines whether this graph contains the given node.booleanGraph.containsNode(Node node) Determines whether this graph contains the given node.booleanLagGraph.containsNode(Node node) Determines whether this graph contains the given node.booleanSemGraph.containsNode(Node node) Determines whether this graph contains the given node.booleanTimeLagGraph.containsNode(Node node) Checks if the graph contains a specific node.static NodeReturns 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.booleanPaths.definiteNonDescendent(Node node1, Node node2) added by ekorber, 2004/06/12booleanPaths.defVisiblePag(Node A, Node B) Returns true if the edge form A to B is a definitely visible edge in a PAG.static EdgeEdges.directedEdge(Node nodeA, Node nodeB) Constructs a new directed edge from nodeA to nodeB (-->).Paths.directedPaths(Node node1, Node node2, int maxLength) Finds all directed paths from node1 to node2 with a maximum length.static booleanDetermines whether threeNodeobjects are distinct.Calculates the district of a given node in a graph.Retrieves the set of nodes that belong to the same district as the given node.Returns D-SEP(x, y) for a MAG G.Returns D-SEP(x, y) for a maximal ancestral graph G (or inducing path graph G, as in Causation, Prediction and Search).Returns D-SEP(x, y) for a MAG G (or inducing path graph G, as in Causation, Prediction and Search).Returns D-SEP(x, y) for a MAG G.GraphUtils.dsepReachability(Node x, Node y, Graph G) Returns D-SEP(x, y) for a MAG G.booleanPaths.existsDirectedPath(Node node1, Node node2) Checks if a directed path exists between two nodes in a graph.booleanPaths.existsDirectedPath(Node node1, Node node2, int depth) Checks if a directed path exists between two nodes within a certain depth.booleanPaths.existsDirectedPath(Node node1, Node node2, org.apache.commons.lang3.tuple.Pair<Node, Node> without) Checks if a directed path exists between two nodes in a graph, ignoring a specified edge.booleanPaths.existsInducingPath(Node x, Node y, Set<Node> selectionVariables) Determines whether an inducing path exists between two nodes in a graph.booleanPaths.existsInducingPathBFS(Node x, Node y, Set<Node> selectionVariables) Breadth-first version of the âinducing-path exists?â test.booleanPaths.existsInducingPathDFS(Node x, Node y, Set<Node> selectionVariables) Determines whether an inducing path exists between node1 and node2, given a set O of observed nodes and a set sem of conditioned nodes.booleanPaths.existsInducingPathVisit(Node a, Node b, Node x, Node y, Set<Node> selectionVariables, LinkedList<Node> path) Determines whether an inducing path exists between two nodes in a graph.booleanEdgeListGraph.existsPotentiallyDirectedPath(Node node1, Node node2) Determines whether one node is an ancestor of another.default booleanGraph.existsPotentiallyDirectedPath(Node node1, Node node2) Determines whether there is a potentially directed path from node1 to node2.booleanPaths.existsPotentiallyDirectedPath(Node from, Node to) existsPotentiallyDirectedPath.booleanPaths.existsPotentiallyDirectedPath(Node node1, Set<Node> nodes) existsPotentiallyDirectedPath.booleanPaths.existsTrek(Node node1, Node node2) Determines whether a trek exists between two nodes in the graph.static GraphGraphUtils.getAdjacencySubgraphWithTargetNode(Graph graph, Node target) Calculates the subgraph over the adjacency of a target node for a DAG, CPDAG, MAG, or PAG.Dag.getAdjacentNodes(Node node) Retrieves the adjacent nodes of a given node in the graph.EdgeListGraph.getAdjacentNodes(Node node) getAdjacentNodes.Graph.getAdjacentNodes(Node node) getAdjacentNodes.LagGraph.getAdjacentNodes(Node node) getAdjacentNodes.SemGraph.getAdjacentNodes(Node node) getAdjacentNodes.TimeLagGraph.getAdjacentNodes(Node node) Retrieves a list of adjacent nodes for the given node.GraphUtils.getAmbiguousTriplesFromGraph(Node node, Graph graph) Retrieves the list of ambiguous triples from the given graph for a given node.Paths.getAmenablePathsMag(Node node1, Node node2, int maxLength, Set<Node> forceVisible) 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.Paths.getAmenablePathsPag(Node node1, Node node2, int maxLength, Set<Node> forceVisible) Finds amenable paths from the given source node to the given destination node with a maximum length, for a PAG.Paths.getAmenablePathsPdagMag(Node node1, Node node2, int maxLength) Finds amenable paths from the given source node to the given destination node with a maximum length.Paths.getAncestors(Node node) Retrieves the ancestors of a specified `Node` in the graph.static NodeGraphUtils.getAssociatedNode(Node errorNode, Graph graph) Returns the associated node for the given error node in the specified graph.Paths.getBackdoorPaths(Node X, Node Y, String graphType, int maxPathLength) Finds and returns all valid backdoor paths between two nodes in a graph.Dag.getChildren(Node node) Retrieves the children of a specified Node in the graph.EdgeListGraph.getChildren(Node node) getChildren.Graph.getChildren(Node node) getChildren.LagGraph.getChildren(Node node) getChildren.SemGraph.getChildren(Node node) getChildren.TimeLagGraph.getChildren(Node node) Returns a list of children nodes for the given node.GraphSaveLoadUtils.getCollidersFromGraph(Node node, Graph graph) getCollidersFromGraph.intReturns the degree of a given node in the graph.intgetDegree.intgetDegree.intgetDegree.intgetDegree.intRetrieves the degree of a given node in the graph.Paths.getDescendants(Node node) Returns a list of all descendants of the given node.Dag.getDirectedEdge(Node node1, Node node2) Returns the directed edge between the given nodes, if one exists in the graph.EdgeListGraph.getDirectedEdge(Node node1, Node node2) getDirectedEdge.Graph.getDirectedEdge(Node node1, Node node2) getDirectedEdge.LagGraph.getDirectedEdge(Node node1, Node node2) getDirectedEdge.SemGraph.getDirectedEdge(Node node1, Node node2) getDirectedEdge.TimeLagGraph.getDirectedEdge(Node node1, Node node2) Retrieves the directed edge connecting two nodes in the graph.final EndpointEdge.getDistalEndpoint(Node node) getDistalEndpoint.final NodeEdge.getDistalNode(Node node) Traverses the edge in an undirected fashion--given one node along the edge, returns the node at the opposite end of the edge.GraphUtils.getDottedUnderlinedTriplesFromGraph(Node node, Graph graph) Retrieves the list of dotted and underlined triples from the given graph, with the specified node as the middle node.Retrieves the edge between two nodes in the graph.getEdge.getEdge.getEdge.getEdge.Retrieves the edge between the given nodes.Returns a list of edges connected to the given node.Returns a list of edges between the specified nodes in the graph.getEdges.getEdges.getEdges.getEdges.getEdges.getEdges.getEdges.getEdges.Returns the list of edges connected to the specified node.Finds all edges between two nodes.Dag.getEndpoint(Node node1, Node node2) Returns the endpoint between two nodes in the graph.EdgeListGraph.getEndpoint(Node node1, Node node2) getEndpoint.Graph.getEndpoint(Node node1, Node node2) getEndpoint.LagGraph.getEndpoint(Node node1, Node node2) getEndpoint.SemGraph.getEndpoint(Node node1, Node node2) getEndpoint.TimeLagGraph.getEndpoint(Node node1, Node node2) Returns the endpoint between two nodes in the graph.SemGraph.getErrorNode(Node node) getErrorNode.SemGraph.getExogenous(Node node) getExogenous.static GraphGraphUtils.getGraphWithoutXToY(Graph G, Node x, Node y, GraphUtils.GraphType graphType) Returns a graph that is obtained by removing the edge from node x to node y from the input graph.intDag.getIndegree(Node node) Returns the indegree of the specified node in the graph.intEdgeListGraph.getIndegree(Node node) getIndegree.intGraph.getIndegree(Node node) getIndegree.intLagGraph.getIndegree(Node node) getIndegree.intSemGraph.getIndegree(Node node) getIndegree.intTimeLagGraph.getIndegree(Node node) Returns the indegree of a given node in the graph.Paths.getInducingPath(Node x, Node y, Set<Node> selectionVariables) This method calculates the inducing path between two measured nodes in a graph.static GraphGraphUtils.getMarkovBlanketSubgraphWithTargetNode(Graph graph, Node target) Calculates the subgraph over the Markov blanket of a target node for a DAG, CPDAG, MAG, or PAG.Paths.getMConnectedVars(Node y, Set<Node> z) Retrieves the set of nodes that are connected to the given nodeyand are also present in the set of nodesz.getMConnectedVars.getNodeId.Dag.getNodesInTo(Node node, Endpoint n) Retrieves a list of nodes in the given graph that have edges pointing into the specified node and endpoint.EdgeListGraph.getNodesInTo(Node node, Endpoint endpoint) Nodes adjacent to the given node with the given proximal endpoint.Graph.getNodesInTo(Node node, Endpoint n) Nodes adjacent to the given node with the given proximal endpoint.LagGraph.getNodesInTo(Node node, Endpoint n) Nodes adjacent to the given node with the given proximal endpoint.SemGraph.getNodesInTo(Node node, Endpoint endpoint) Nodes adjacent to the given node with the given proximal endpoint.TimeLagGraph.getNodesInTo(Node node, Endpoint endpoint) Retrieves a list of nodes that have an incoming edge from a specific node and endpoint.Dag.getNodesOutTo(Node node, Endpoint n) Retrieves a list of nodes that have outgoing edges to a specified node and endpoint.EdgeListGraph.getNodesOutTo(Node node, Endpoint endpoint) Nodes adjacent to the given node with the given distal endpoint.Graph.getNodesOutTo(Node node, Endpoint n) Nodes adjacent to the given node with the given distal endpoint.LagGraph.getNodesOutTo(Node node, Endpoint n) Nodes adjacent to the given node with the given distal endpoint.SemGraph.getNodesOutTo(Node node, Endpoint n) Nodes adjacent to the given node with the given distal endpoint.TimeLagGraph.getNodesOutTo(Node node, Endpoint endpoint) Retrieves the list of nodes in a graph that have an outgoing edge to the given node and endpoint.intDag.getNumEdges(Node node) Returns the number of edges connected to the specified node.intEdgeListGraph.getNumEdges(Node node) getNumEdges.intGraph.getNumEdges(Node node) getNumEdges.intLagGraph.getNumEdges(Node node) getNumEdges.intSemGraph.getNumEdges(Node node) getNumEdges.intTimeLagGraph.getNumEdges(Node node) Retrieves the number of edges connected to a specific node.intDag.getOutdegree(Node node) Returns the outdegree of the given node.intEdgeListGraph.getOutdegree(Node node) getOutdegree.intGraph.getOutdegree(Node node) getOutdegree.intLagGraph.getOutdegree(Node node) getOutdegree.intSemGraph.getOutdegree(Node node) getOutdegree.intTimeLagGraph.getOutdegree(Node node) Retrieves the outdegree of the specified node in the graph.Dag.getParents(Node node) Retrieves the list of parent nodes for a given node in the graph.EdgeListGraph.getParents(Node node) getParents.Graph.getParents(Node node) getParents.LagGraph.getParents(Node node) getParents.SemGraph.getParents(Node node) getParents.TimeLagGraph.getParents(Node node) Returns the list of parent nodes for the given node.static GraphGraphUtils.getParentsSubgraphWithTargetNode(Graph graph, Node target) Calculates the subgraph over the parents of a target node for a DAG, CPDAG, MAG, or PAG.final EndpointEdge.getProximalEndpoint(Node node) getProximalEndpoint.Dag.getSepset(Node n1, Node n2, IndependenceTest test) Returns the sepset between two given nodes in the graph.Retrieves the set of nodes that form the sepset between two given nodes.EdgeListGraph.getSepset(Node x, Node y, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.Graph.getSepset(Node n1, Node n2, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.LagGraph.getSepset(Node n1, Node n2, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.Paths.getSepset(Node x, Node y, boolean excludeSelectionBias, IndependenceTest test, int depth) Finds a sepset for x and y, if there is one; otherwise, returns null.SemGraph.getSepset(Node n1, Node n2, IndependenceTest test) Returns the set of nodes that form the separating set between two given nodes.TimeLagGraph.getSepset(Node n1, Node n2, IndependenceTest test) Retrieves the sepset of two nodes in the graph.EdgeListGraph.getSepsetContaining(Node x, Node y, Set<Node> containing, int maxLength) Retrieves the set of nodes that form the sepset between two given nodes.Paths.getSepsetContaining(Node x, Node y, Set<Node> containing, int maxPathLength) Retrieves a sepset (a set of nodes) between two given nodes.EdgeListGraph.getTriplesLists(Node node) getTriplesLists.TripleClassifier.getTriplesLists(Node node) getTriplesLists.Underlines.getTriplesLists(Node node) getTriplesLists.GraphUtils.getUnderlinedTriplesFromGraph(Node node, Graph graph) Retrieves the underlined triples from the given graph that involve the specified node.SemGraph.getVarNode(Node node) getVarNode.doubleReturns the height of the rendered box of the given node in pixels.booleanDag.isAdjacentTo(Node node1, Node node2) Determines whether two nodes are adjacent in the graph.booleanEdgeListGraph.isAdjacentTo(Node node1, Node node2) isAdjacentTo.booleanGraph.isAdjacentTo(Node node1, Node node2) isAdjacentTo.booleanLagGraph.isAdjacentTo(Node node1, Node node2) isAdjacentTo.booleanSemGraph.isAdjacentTo(Node nodeX, Node nodeY) isAdjacentTo.booleanTimeLagGraph.isAdjacentTo(Node node1, Node node2) Determines whether two nodes are adjacent in the graph.booleanDag.isAmbiguousTriple(Node x, Node y, Node z) Determines if a triple of nodes is ambiguous.booleanEdgeListGraph.isAmbiguousTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanGraph.isAmbiguousTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanLagGraph.isAmbiguousTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanSemGraph.isAmbiguousTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanTimeLagGraph.isAmbiguousTriple(Node x, Node y, Node z) Checks whether a triple of nodes is ambiguous.booleanUnderlines.isAmbiguousTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanEdgeListGraph.isAncestorOf(Node node1, Node node2) Determines whether one node is an ancestor of another.default booleanGraph.isAncestorOf(Node node1, Node node2) Determines whether one node is an ancestor of another.booleanLagGraph.isAncestorOf(Node node1, Node node2) isAncestorOf.booleanPaths.isAncestorOf(Node node1, Node node2) Determines whether one node is an ancestor of another.booleanPaths.isAncestorOfAnyZ(Node b, Set<Node> z) Return true if b is an ancestor of any node in zdefault booleanGraph.isBidirected(Node v, Node u) Determines if the edge between two nodes is bidirected.booleanChecks if the given node1 is a child of node2 in the graph.booleanisChildOf.booleanisChildOf.booleanisChildOf.booleanisChildOf.booleanChecks if a given node is a child of another node in the graph.static booleanChecks if the given trek in a graph is a confounding trek.static booleanGraphUtils.isCoveringAdjacency(Graph trueGraph, Graph estGraph, Node x, Node y) Determines whether an edge between two nodes in the estimated graph is covering a collider or noncollider in the true graph.booleanDag.isDefCollider(Node node1, Node node2, Node node3) Checks if there is a definite collider between three nodes in the graph.booleanEdgeListGraph.isDefCollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanGraph.isDefCollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanLagGraph.isDefCollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanSemGraph.isDefCollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanTimeLagGraph.isDefCollider(Node node1, Node node2, Node node3) Determines if there is a definite collider relationship between the given nodes.booleanDag.isDefNoncollider(Node node1, Node node2, Node node3) Checks if three given nodes form a definite non-collider in a graph.booleanEdgeListGraph.isDefNoncollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanGraph.isDefNoncollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanLagGraph.isDefNoncollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanSemGraph.isDefNoncollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanTimeLagGraph.isDefNoncollider(Node node1, Node node2, Node node3) Determines if the given nodes form a definite noncollider in the graph.default booleanGraph.isDescendentOf(Node n, Node x) Determines if the specified node x is a descendant of the node n in the current graph structure.booleanPaths.isDescendentOf(Node node1, Node node2) Determines whether one node is a descendent of another.booleanPaths.isDirected(Node node1, Node node2) Checks if there is a directed edge from node1 to node2 in the graph.booleanEdgeListGraph.isDirectedFromTo(Node x, Node y) default booleanGraph.isDirectedFromTo(Node x, Node y) Determines if there is a directed edge from node x to node y in the graph.booleanDag.isExogenous(Node node) Checks whether a given node is exogenous.booleanEdgeListGraph.isExogenous(Node node) isExogenous.booleanGraph.isExogenous(Node node) isExogenous.booleanLagGraph.isExogenous(Node node) isExogenous.booleanSemGraph.isExogenous(Node node) isExogenous.booleanTimeLagGraph.isExogenous(Node node) Checks if a given node is exogenous.booleanPaths.isGraphAmenable(Node node1, Node node2, String graphType, int maxLengthAdjustment, Set<Node> forceVisibility) Determines if the graph is amenable based on the provided nodes, graph type, and maximum allowable length adjustment.booleanPaths.isMConnectedTo(Node x, Node y, Set<Node> z, boolean excludeSelectionBias) Determmines whether x and y are d-connected given z.booleanDetemrmines whether x and y are d-connected given z.booleanEdgeListGraph.isMSeparatedFrom(Node x, Node y, Set<Node> z) Determines whether x and y are d-separated given z.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, boolean isPag) Determines whether one n ode is d-separated from another.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if two nodes are M-separated.booleanDag.isParameterizable(Node node) Checks if the given node is parameterizable.booleanEdgeListGraph.isParameterizable(Node node) isParameterizable.booleanGraph.isParameterizable(Node node) isParameterizable.booleanLagGraph.isParameterizable(Node node) isParameterizable.booleanSemGraph.isParameterizable(Node node) isParameterizable.booleanTimeLagGraph.isParameterizable(Node node) Checks if a node is parameterizable.booleanDag.isParentOf(Node node1, Node node2) Determines if a given node is a parent of another node in the graph.booleanEdgeListGraph.isParentOf(Node node1, Node node2) Determines whether node1 is a parent of node2.booleanGraph.isParentOf(Node node1, Node node2) Determines whether node1 is a parent of node2.booleanLagGraph.isParentOf(Node node1, Node node2) Determines whether node1 is a parent of node2.booleanSemGraph.isParentOf(Node node1, Node node2) Determines whether node1 is a parent of node2.booleanTimeLagGraph.isParentOf(Node node1, Node node2) Determines if a given node is a parent of another node in the graph.booleanCheck to see if a set of variables Z satisfies the back-door criterion relative to node x and node y.booleanDag.isUnderlineTriple(Node x, Node y, Node z) Determines if a triple of nodes is underlined.booleanEdgeListGraph.isUnderlineTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanGraph.isUnderlineTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanLagGraph.isUnderlineTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanSemGraph.isUnderlineTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanTimeLagGraph.isUnderlineTriple(Node x, Node y, Node z) Checks whether a given triple (x, y, z) is an underline triple.booleanUnderlines.isUnderlineTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanPaths.isUndirected(Node node1, Node node2) Checks if the edge between two nodes in the graph is undirected.GraphUtils.markovBlanket(Node x, Graph G) Computes the Markov blanket for a given node in a graph.Paths.markovBlanket(Node node) Returns the Markov Blanket of a given node in the graph.static GraphGraphUtils.markovBlanketSubgraph(Node target, Graph graph) Calculates the subgraph over the Markov blanket of a target node in a given DAG, CPDAG, MAG, or PAG.static EdgeEdges.nondirectedEdge(Node nodeA, Node nodeB) Constructs a new nondirected edge from nodeA to nodeB (o-o).static voidGraphUtils.orientCollider(Graph g, Node x, Node z, Node y) Orients the edges of the given graph by setting both specified nodes as arrow endpoints directed towards the specified target node.static EdgeEdges.partiallyOrientedEdge(Node nodeA, Node nodeB) Constructs a new partially oriented edge from nodeA to nodeB (o->).static StringGraphUtils.pathString(Graph graph, Node... x) Generates a string representation of a path in a given graph, starting from the specified nodes.static StringTriple.pathString(Graph graph, Node x, Node y, Node z) pathString.booleanEdge.pointsTowards(Node node) pointsTowards.booleanPaths.possibleAncestor(Node node1, Node node2) possibleAncestor.Paths.possibleDsep(Node x, int maxPossibleDsepPathLength) Identifies and returns a list of possible d-separating nodes relative to a specified node in the graph.Paths.potentiallyDirectedPaths(Node node1, Node node2, int maxLength) Finds all potentially directed paths between two nodes up to a maximum length.voidDag.removeAmbiguousTriple(Node x, Node y, Node z) Removes an ambiguous triple from the list of ambiguous triples.voidEdgeListGraph.removeAmbiguousTriple(Node x, Node y, Node z) removeAmbiguousTriple.voidGraph.removeAmbiguousTriple(Node x, Node y, Node z) removeAmbiguousTriple.voidLagGraph.removeAmbiguousTriple(Node x, Node y, Node z) removeAmbiguousTriple.voidSemGraph.removeAmbiguousTriple(Node x, Node y, Node z) removeAmbiguousTriple.voidTimeLagGraph.removeAmbiguousTriple(Node x, Node y, Node z) Removes an ambiguous triple from the collection.voidUnderlines.removeAmbiguousTriple(Node x, Node y, Node z) removeAmbiguousTriple.voidDag.removeDottedUnderlineTriple(Node x, Node y, Node z) Removes a dotted underline triple from the set of triples.voidEdgeListGraph.removeDottedUnderlineTriple(Node x, Node y, Node z) removeDottedUnderlineTriple.voidGraph.removeDottedUnderlineTriple(Node x, Node y, Node z) removeDottedUnderlineTriple.voidLagGraph.removeDottedUnderlineTriple(Node x, Node y, Node z) removeDottedUnderlineTriple.voidSemGraph.removeDottedUnderlineTriple(Node x, Node y, Node z) removeDottedUnderlineTriple.voidTimeLagGraph.removeDottedUnderlineTriple(Node x, Node y, Node z) Removes a triple of nodes from the set of dottedUnderLineTriples.voidUnderlines.removeDottedUnderlineTriple(Node x, Node y, Node z) removeDottedUnderlineTriple.booleanDag.removeEdge(Node node1, Node node2) Removes the edge between two nodes in the graph.booleanEdgeListGraph.removeEdge(Node node1, Node node2) Removes an edge between two given nodes.booleanGraph.removeEdge(Node node1, Node node2) Removes an edge between two given nodes.booleanLagGraph.removeEdge(Node node1, Node node2) Removes an edge between two given nodes.booleanReplicatingGraph.removeEdge(Node a, Node b) Removes an edge between the specified nodes, if such an edge exists in the graph.booleanSemGraph.removeEdge(Node node1, Node node2) Removes an edge between two given nodes.booleanTimeLagGraph.removeEdge(Node node1, Node node2) Removes the edge between two given nodes.booleanDag.removeEdges(Node node1, Node node2) Removes an edge between two nodes.booleanEdgeListGraph.removeEdges(Node node1, Node node2) Removes all edges connecting node A to node B.booleanGraph.removeEdges(Node node1, Node node2) Removes all edges connecting node A to node B.booleanLagGraph.removeEdges(Node node1, Node node2) Removes all edges connecting node A to node B.booleanSemGraph.removeEdges(Node node1, Node node2) Removes all edges connecting node A to node B.booleanTimeLagGraph.removeEdges(Node node1, Node node2) Removes edges between two nodes.booleanDag.removeNode(Node node) Removes the specified node from the graph.booleanEdgeListGraph.removeNode(Node node) Removes a node from the graph.booleanGraph.removeNode(Node node) Removes a node from the graph.booleanLagGraph.removeNode(Node node) Removes a node from the graph.booleanSemGraph.removeNode(Node node) Removes a node from the graph.booleanTimeLagGraph.removeNode(Node node) Removes the given node from the graph.voidDag.removeUnderlineTriple(Node x, Node y, Node z) Removes an underline triple from the list of underline triples.voidEdgeListGraph.removeUnderlineTriple(Node x, Node y, Node z) removeUnderlineTriple.voidGraph.removeUnderlineTriple(Node x, Node y, Node z) removeUnderlineTriple.voidLagGraph.removeUnderlineTriple(Node x, Node y, Node z) removeUnderlineTriple.voidSemGraph.removeUnderlineTriple(Node x, Node y, Node z) removeUnderlineTriple.voidTimeLagGraph.removeUnderlineTriple(Node x, Node y, Node z) Removes the specified triple (x, y, z) from the list of underline triples.voidUnderlines.removeUnderlineTriple(Node x, Node y, Node z) removeUnderlineTriple.booleanDag.setEndpoint(Node from, Node to, Endpoint endPoint) Sets the endpoint of a directed edge between two nodes in a graph.booleanEdgeListGraph.setEndpoint(Node from, Node to, Endpoint endPoint) Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.booleanGraph.setEndpoint(Node from, Node to, Endpoint endPoint) Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.booleanLagGraph.setEndpoint(Node from, Node to, Endpoint endPoint) Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.booleanSemGraph.setEndpoint(Node node1, Node node2, Endpoint endpoint) Sets the endpoint type at the 'to' end of the edge from 'from' to 'to' to the given endpoint.booleanTimeLagGraph.setEndpoint(Node from, Node to, Endpoint endPoint) Sets the endpoint of an edge between two nodes in the graph.voidEdge.setProximalEndpoint(Node node, Endpoint proximalEndpoint) Sets the endpoint nearest the given nodes to the given endpoint.static NodeIf node is one endpoint of edge, returns the other endpoint.static NodeEdges.traverseDirected(Node node, Edge edge) For A -> B, given A, returns B; otherwise returns null.static NodeEdges.traverseNondirected(Node node, Edge edge) For A o-o B, given A, returns B; otherwise returns null.static NodeEdges.traversePotentiallyDirected(Node node, Edge edge) For A --* B or A o-* B, given A, returns B.static NodeGraphUtils.traversePotentiallyDirected(Node node, Edge edge) Traverses a potentially directed edge to identify the next node in the traversal.static NodeEdges.traverseReverseDirected(Node node, Edge edge) For A -> B, given B, returns A; otherwise returns null.Finds all treks from node1 to node2 with a maximum length.Paths.treksIncludingBidirected(Node node1, Node node2) Finds all possible treks between two nodes, including bidirectional treks.static booleanChecks if the souce L of the given trek in a graph (not equal to x or y) is a latent.static booleanChecks if three nodes are connected in a graph.static EdgeEdges.undirectedEdge(Node nodeA, Node nodeB) Constructs a new undirected edge from nodeA to nodeB (--).GraphUtils.visibleEdgeAdjustments1(Graph G, Node x, Node y, int numSmallestSizes, GraphUtils.GraphType graphType) Calculates visual-edge adjustments given graph G between two nodes x and y that are subsets of MB(X).GraphUtils.visibleEdgeAdjustments3(Graph G, Node x, Node y, int numSmallestSizes, GraphUtils.GraphType graphType) This method calculates visible-edge adjustments for a given graph, two nodes, a number of smallest sizes, and a graph type.doubleReturns the width of the rendered box of the specified node in pixels.Method parameters in edu.cmu.tetrad.graph with type arguments of type NodeModifier and TypeMethodDescriptionPaths.allPaths(Node node1, Node node2, int minLength, int maxLength, Set<Node> conditionSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int minLength, int maxLength, Set<Node> conditionSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int minLength, int maxLength, Set<Node> conditionSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int maxLength, Set<Node> conditionSet, boolean excludeSelectionBias) Finds all paths between two nodes within a given maximum length, considering optional condition set and selection bias.Paths.allPathsOutOf(Node node1, int maxLength, Set<Node> conditionSet, boolean excludeSelectionBias) Generates all paths out of a given node within a specified maximum length and conditional set.Constructs a list of nodes from the givennodeslist at the given indices in that list.Converts an array of indices into a set of corresponding nodes from a given list of nodes.MisclassificationUtils.convertNodes(Set<Edge> edges, List<Node> newVariables) convertNodes.booleanPaths.existsDirectedPath(Node node1, Node node2, org.apache.commons.lang3.tuple.Pair<Node, Node> without) Checks if a directed path exists between two nodes in a graph, ignoring a specified edge.booleanPaths.existsDirectedPath(Node node1, Node node2, org.apache.commons.lang3.tuple.Pair<Node, Node> without) Checks if a directed path exists between two nodes in a graph, ignoring a specified edge.booleanPaths.existsInducingPath(Node x, Node y, Set<Node> selectionVariables) Determines whether an inducing path exists between two nodes in a graph.booleanPaths.existsInducingPathBFS(Node x, Node y, Set<Node> selectionVariables) Breadth-first version of the âinducing-path exists?â test.booleanPaths.existsInducingPathDFS(Node x, Node y, Set<Node> selectionVariables) Determines whether an inducing path exists between node1 and node2, given a set O of observed nodes and a set sem of conditioned nodes.booleanPaths.existsInducingPathVisit(Node a, Node b, Node x, Node y, Set<Node> selectionVariables, LinkedList<Node> path) Determines whether an inducing path exists between two nodes in a graph.booleanPaths.existsInducingPathVisit(Node a, Node b, Node x, Node y, Set<Node> selectionVariables, LinkedList<Node> path) Determines whether an inducing path exists between two nodes in a graph.booleanPaths.existsPotentiallyDirectedPath(Node node1, Set<Node> nodes) existsPotentiallyDirectedPath.Paths.getAmenablePathsMag(Node node1, Node node2, int maxLength, Set<Node> forceVisible) 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.Paths.getAmenablePathsPag(Node node1, Node node2, int maxLength, Set<Node> forceVisible) Finds amenable paths from the given source node to the given destination node with a maximum length, for a PAG.Paths.getAncestors(List<Node> nodes) Returns a list of all ancestors of the given nodes.static GraphGenerates a directed acyclic graph (DAG) based on the given list of nodes using Raskutti and Uhler's method.Returns the parent matrix for the graph.Paths.getDescendants(List<Node> nodes) Retrieves the descendants of the given list of nodes.Paths.getInducingPath(Node x, Node y, Set<Node> selectionVariables) This method calculates the inducing path between two measured nodes in a graph.Paths.getMConnectedVars(Node y, Set<Node> z) Retrieves the set of nodes that are connected to the given nodeyand are also present in the set of nodesz.getMConnectedVars.getMConnectedVars.getMConnectedVars.Paths.getParents(List<Node> pi, int p, Graph g, boolean verbose, boolean excludeSelectionBias) Returns the parents of the node at index p, calculated using Pearl's method.EdgeListGraph.getSepsetContaining(Node x, Node y, Set<Node> containing, int maxLength) Retrieves the set of nodes that form the sepset between two given nodes.Paths.getSepsetContaining(Node x, Node y, Set<Node> containing, int maxPathLength) Retrieves a sepset (a set of nodes) between two given nodes.static NodeGraphUtils.getTrekSource(Graph graph, List<Node> trek) This method returns the source node of a given trek in a graph.Paths.getValidOrder(List<Node> initialOrder, boolean forward) Returns a valid causal order for either a DAG or a CPDAG.Paths.getValidOrderMag(List<Node> initialOrder, boolean forward) Generates a valid topological ordering of nodes in a directed graph without cycles.GraphSaveLoadUtils.grabLayout(List<Node> nodes) grabLayout.booleanPaths.isAncestorOfAnyZ(Node b, Set<Node> z) Return true if b is an ancestor of any node in zstatic booleanGraphUtils.isClique(Collection<Node> set, Graph graph) Checks if the given set of nodes forms a clique in the specified graph.static booleanChecks if the given trek in a graph is a confounding trek.booleanPaths.isGraphAmenable(Node node1, Node node2, String graphType, int maxLengthAdjustment, Set<Node> forceVisibility) Determines if the graph is amenable based on the provided nodes, graph type, and maximum allowable length adjustment.booleanPaths.isMConnectedTo(Node x, Node y, Set<Node> z, boolean excludeSelectionBias) Determmines whether x and y are d-connected given z.booleanDetemrmines whether x and y are d-connected given z.booleanDetemrmines whether x and y are d-connected given z.booleanDetemrmines whether x and y are d-connected given z.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, boolean excludeSelectionBias) Checks if the given path is an m-connecting path.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, boolean excludeSelectionBias) Checks if the given path is an m-connecting path.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if the given path is an m-connecting path and doens't contain duplicate nodes.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if the given path is an m-connecting path and doens't contain duplicate nodes.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if the given path is an m-connecting path and doens't contain duplicate nodes.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if the given path is an m-connecting path and doens't contain duplicate nodes.booleanEdgeListGraph.isMSeparatedFrom(Node x, Node y, Set<Node> z) Determines whether x and y are d-separated given z.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, boolean isPag) Determines whether one n ode is d-separated from another.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if two nodes are M-separated.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if two nodes are M-separated.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, Map<Node, Set<Node>> ancestors, boolean excludeSelectionBias) Checks if two nodes are M-separated.booleanCheck to see if a set of variables Z satisfies the back-door criterion relative to node x and node y.static voidLayoutUtil.layoutInCenteredRows(List<List<Node>> tiers, LayoutUtil.NodeSize size) Places the given rows of nodes one under another, top row first, each row centered on the widest row.static GraphGraphSaveLoadUtils.loadGraphBNTPcMatrix(List<Node> vars, DataSet dataSet) loadGraphBNTPcMatrix.static doubleGraphUtils.localMarkovInitializePValues(Graph dag, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues) Initializes and evaluates p-values for local Markov properties in a given graph.static doubleGraphUtils.localMarkovInitializePValues(Graph dag, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues) Initializes and evaluates p-values for local Markov properties in a given graph.voidPaths.makeValidOrder(List<Node> order) Reorders the given order into a valid causal order for either a DAG or a CPDAG.GraphUtils.maximalCliques(Graph graph, List<Node> nodes) Finds all maximal cliques in a given graph.static GraphCreates a new instance of a Graph initialized with the provided list of nodes.LayoutUtil.orderRowsToReduceCrossings(Graph graph, List<List<Node>> rows) 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 (seeLayoutUtil.orderTiers(Graph, List, List)).static GraphGraphSaveLoadUtils.parseGraphXml(nu.xom.Element graphElement, Map<String, Node> nodes) parseGraphXml.static StringGraphUtils.pathString(Graph graph, List<Node> path, boolean showBlocked) Constructs a string representation of a path in a graph.static StringReturns a string representation of the given path in the graph, considering the conditioning variables.static StringReturns a string representation of the given path in the graph, considering the conditioning variables.static StringGraphUtils.pathString(Graph graph, List<Node> path, Set<Node> conditioningVars, boolean showBlocked, boolean excludeSelectionBias) Returns a string representation of the given path in the graph, with additional information about conditioning variables.static StringGraphUtils.pathString(Graph graph, List<Node> path, Set<Node> conditioningVars, boolean showBlocked, boolean excludeSelectionBias) Returns a string representation of the given path in the graph, with additional information about conditioning variables.static doubleCalculates the p-value using the Anderson-Darling test for a given set of p-values from an independence test.static doubleCalculates the p-value using the Anderson-Darling test for a given set of p-values from an independence test.static DagRandomGraph.randomDag(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected) Generates a random Directed Acyclic Graph (DAG).static DagRandomGraph.randomDag(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed) Generates a random Directed Acyclic Graph (DAG) based on the given parameters.static GraphRandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected) Generates a random graph based on the given parameters.static GraphRandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed) Generates a random graph with the specified parameters and properties.static GraphRandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected) Generates a random graph with forward edges.static GraphRandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, boolean layoutAsCircle, long seed) Generates a random graph with forward edges.static GraphRandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed) Generates a random directed acyclic graph with specified properties, including constraints on maximum degree, indegree, and outdegree, with random forward edges.static GraphRandomGraph.randomGraphUniform(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, int numIterations) Generates a random graph using UniformGraphGenerator with the specified parameters.booleanDag.removeNodes(List<Node> nodes) Removes the specified nodes from the graph.booleanEdgeListGraph.removeNodes(List<Node> newNodes) Iterates through the list and removes any permissible nodes found.booleanGraph.removeNodes(List<Node> nodes) Iterates through the list and removes any permissible nodes found.booleanLagGraph.removeNodes(List<Node> nodes) Iterates through the list and removes any permissible nodes found.booleanSemGraph.removeNodes(List<Node> nodes) Iterates through the list and removes any permissible nodes found.booleanTimeLagGraph.removeNodes(List<Node> nodes) Removes the specified nodes from the graph.static booleanGraphUtils.repairMaximality(Graph pag, boolean verbose, Set<Node> selection, FciOrient fciOrient, Knowledge knowledge, Set<Triple> knownColliders) 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.static GraphGraphUtils.replaceNodes(Graph originalGraph, List<Node> newVariables) Converts the given graph,originalGraph, to use the new variables (with the same names as the old).GraphUtils.replaceNodes(List<Node> originalNodes, Graph graph) Converts the given list of nodes,originalNodes, to use the replacement nodes for them by the same name in the givengraph.GraphUtils.replaceNodes(List<Node> originalNodes, List<Node> newNodes) Converts the given list of nodes,originalNodes, to use the new variables (with the same names as the old).voidSet the nodes of the graph.voidsetNodes.voidsetNodes.voidsetNodes.voidsetNodes.voidSets the nodes of the graph.Returns a subgraph of the current graph consisting only of the specified nodes.Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.Returns a subgraph of the current graph based on the provided nodes.static ReplicatingGraphConstructs a ReplicatingGraph using the given list of nodes and applies a default LagReplicationPolicy for edge replication.static booleanGraphUtils.trekIsUncovered(Graph trueGraph, Graph estGraph, List<Node> trek) 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.static booleanChecks if the souce L of the given trek in a graph (not equal to x or y) is a latent.static GraphTrims the given graph based on the specified trimming style.Constructors in edu.cmu.tetrad.graph with parameters of type NodeModifierConstructorDescriptionConstructs a new edge by specifying the nodes it connects and the endpoint types.Constructs an Edge by specifying two nodes, their corresponding endpoints, and whether to flip the direction of the edge if it is pointing backwards.IndependenceFact(Node x, Node y, Node... z) Constructor for IndependenceFact.IndependenceFact(Node x, Node y, Set<Node> z) Constructor for IndependenceFact.Constructor for NodePair.Constructs a triple of nodes.Constructor parameters in edu.cmu.tetrad.graph with type arguments of type NodeModifierConstructorDescriptionConsistentDagIterator(Graph graph, List<Node> initialOrder, boolean forward, boolean distinct) Iterates over DAGs consistent with a given CPDAG.Constructor for Dag.EdgeListGraph(List<Node> nodes) Constructs a new graph, with no edges, using the given variable names.IndependenceFact(Node x, Node y, Set<Node> z) Constructor for IndependenceFact.ReplicatingGraph(List<Node> nodes, EdgeReplicationPolicy policy) Constructs a new ReplicatingGraph using the provided list of nodes and edge replication policy.ValidOrderIterator(Graph graph, List<Node> initialOrder, boolean forward) Iterates over valid orders for a CPDAG. -
Uses of Node in edu.cmu.tetrad.hybridcg
Methods in edu.cmu.tetrad.hybridcg that return NodeModifier and TypeMethodDescriptionNode[]HybridCgModel.HybridCgPm.getNodes()Retrieves the ordered list of nodes in this probabilistic model.Methods in edu.cmu.tetrad.hybridcg that return types with arguments of type NodeModifier and TypeMethodDescriptionHybridCgVars.materializeDataVariables(HybridCgModel.HybridCgPm pm) Build data variables (ContinuousVariable / DiscreteVariable) from the PM’s schema.Methods in edu.cmu.tetrad.hybridcg with parameters of type NodeModifier and TypeMethodDescriptionvoidHybridCgModel.HybridCgPm.autoCutpointsForDiscreteChild(Node child, DataSet data, int binsPerParent) Populate cutpoints for each continuous parent of a DISCRETE child using equal-frequency binning.intHybridCgModel.HybridCgPm.discretizeFor(Node child, Node contParent, double value) Discretizes the given value of a continuous parent node for a specific discrete child node into a bin index based on predefined cutpoints.intRetrieves the index of a given node within the ordered list of nodes.voidHybridCgModel.HybridCgPm.setContParentCutpointsForDiscreteChild(Node child, Map<Node, double[]> cutpointsByContParent) Sets the continuous parent cutpoints for a specified discrete child node.Method parameters in edu.cmu.tetrad.hybridcg with type arguments of type NodeModifier and TypeMethodDescriptionvoidHybridCgModel.HybridCgPm.setContParentCutpointsForDiscreteChild(Node child, Map<Node, double[]> cutpointsByContParent) Sets the continuous parent cutpoints for a specified discrete child node.Constructor parameters in edu.cmu.tetrad.hybridcg with type arguments of type NodeModifierConstructorDescriptionHybridCgPm(Graph dag, List<Node> nodeOrder, Map<Node, Boolean> discreteFlags, Map<Node, List<String>> categoryMap) Constructs a HybridCgPm instance based on the provided directed acyclic graph (DAG), node ordering, discrete flags for nodes, and a mapping of node categories.HybridCgPm(Graph dag, List<Node> nodeOrder, Map<Node, Boolean> discreteFlags, Map<Node, List<String>> categoryMap) Constructs a HybridCgPm instance based on the provided directed acyclic graph (DAG), node ordering, discrete flags for nodes, and a mapping of node categories. -
Uses of Node in edu.cmu.tetrad.regression
Methods in edu.cmu.tetrad.regression with parameters of type NodeModifier and TypeMethodDescriptionRegressestargeton theregressors, yielding a regression plane.Regressestargeton theregressors, yielding a regression plane.regress.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.Regresses the target on the given regressors.Method parameters in edu.cmu.tetrad.regression with type arguments of type NodeModifier and TypeMethodDescriptionLogisticRegression.regress(DiscreteVariable x, List<Node> regressors) x must be binary; regressors must be continuous or binary.Regressestargeton theregressors, yielding a regression plane.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.Regresses the target on the given regressors.Constructor parameters in edu.cmu.tetrad.regression with type arguments of type NodeModifierConstructorDescriptionRegressionDataset(Matrix data, List<Node> variables) Constructor for RegressionDataset. -
Uses of Node in edu.cmu.tetrad.search
Fields in edu.cmu.tetrad.search declared as NodeModifier and TypeFieldDescriptionfinal NodePc.Triple.xThe first node in the triplet represented by this Triple object.final NodePcAR.Triple.xThe first node in the triplet represented by this Triple object.final NodePc.Triple.yThe third node in the triplet represented by this Triple object.final NodePcAR.Triple.yThe third node in the triplet represented by this Triple object.final NodePc.Triple.zThe second node in the triplet represented by this Triple object.final NodePcAR.Triple.zThe second node in the triplet represented by this Triple object.Methods in edu.cmu.tetrad.search that return NodeModifier and TypeMethodDescriptionRecursiveBlocking.Mark.at()Returns the value of theatrecord component.RecursiveBlocking.Mark.from()Returns the value of thefromrecord component.Cstar.Record.getCauseNode()Returns the cause node associated with this record.Cstar.Record.getEffectNode()Retrieves the effect node of the record.Return the node associated with the given variable in the graph.IndTestIod.getVariable(Node node) Returns the variable associated with the given node in the graph.IndTestIod.getVariable(String name) Retrieves a variable with the given name.MarkovCheck.getVariable(String name) Returns the variable with the given name.FciOrientDijkstra.DijkstraEdge.gety()Returns the node.GRegression.BootstrapResult.outcome()Returns the value of theoutcomerecord component.GRegressionPag.BootstrapResult.outcome()Returns the value of theoutcomerecord component.GRegressionPag.BootstrapResult.treatment()Returns the value of thetreatmentrecord component.PcAR.OrientationClash.u()Returns the value of theurecord component.CdnodBoss.Disagreement.x()Returns the value of thexrecord component.Fas.BlockedDeletion.x()Returns the value of thexrecord component.PcAR.ContestedDeletion.x()Returns the value of thexrecord component.PcAR.MarkovAuditFailure.x()Returns the value of thexrecord component.CdnodBoss.Disagreement.y()Returns the value of theyrecord component.Fas.BlockedDeletion.y()Returns the value of theyrecord component.PcAR.ContestedDeletion.y()Returns the value of theyrecord component.PcAR.MarkovAuditFailure.y()Returns the value of theyrecord component.CdnodBoss.Disagreement.z()Returns the value of thezrecord component.PcAR.ContestedDeletion.z()Returns the value of thezrecord component.PcAR.OrientationClash.z()Returns the value of thezrecord component.Methods in edu.cmu.tetrad.search that return types with arguments of type NodeModifier and TypeMethodDescriptionGRegressionPag.adjustmentSet(Node x, Node y) Returns a valid adjustment set for (x, y) in the PAG, or null if none is found.GRegressionPag.adjustmentSets(Node x, Node y, int maxNumSets) Returns up tomaxNumSetsvalid adjustment sets for (x, y) in the PAG.RecursiveAdjustment.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, RecursiveAdjustment.ColliderPolicy colliderPolicy, boolean avoidAmenable, Set<Node> notFollowed, Set<Node> containing, Set<Node> forceVisibility) Computes a list of adjustment sets for estimating the causal effect of X on Y within a given graph structure.RecursiveAdjustmentMultiple.adjustmentSets(Set<Node> X, Set<Node> Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, boolean avoidAmenable, Set<Node> notFollowed, Set<Node> containing) Entry point analogous to Adjustment.adjustmentSetsRB, but for set-valued X and Y.GRegression.ancestorsAvoiding(Graph graph, Node y, Set<Node> excluded) 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.Given an initial permutation, 'order,' of the variables, searches for a best permutation of the variables by rearranging the variables in 'order.'RecursiveBlocking.BlockingResult.blockingSet()Returns the value of theblockingSetrecord component.SepsetFinder.blockPathsLocalMarkov(Graph graph, Node x) Returns a set of nodes that are the parents of the given node in the graph.RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength) Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Variant ofRecursiveBlocking1.blockPathsRecursively(Graph, Node, Node, Set, Set, int)that additionally accepts an optionalKnowledgeobject.RecursiveBlockingChokePointA.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength) 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.RecursiveBlockingChokePointB.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength) Identifies and blocks paths in the given graph by iteratively finding and addressing chokepoints and eligible non-collider nodes.RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength) Blocks paths recursively between two nodes in a graph, based on specified constraints and conditions.RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Recursively blocks paths in a graph between two nodes under specified constraints.RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge, PrintStream out) Blocks paths recursively between two nodes in a graph with detailed verbose tracing output.RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, PrintStream out) Blocks paths recursively between two nodes in a graph while tracing the process.SepsetFinder.blockPathsWithMarkovBlanket(Node x, Graph G) Identifies the set of nodes that form the Markov Blanket for a given node in a graph.GRegression.bucketDecomposition(Graph graph) Returns the bucket decomposition of the graph: the maximal connected components of its undirected subgraph (Section 3.4.1 of Guo and Perković, 2022).Returns a map from nodes in V \ {Y} to their minimum absolute effects on Y.Returns a map from nodes in V \ {Y} to their minimum effects.PdagPagIda.calculateMinimumTotalEffectsOnY(Node y) Calculates the minimum total effects of all potential cause nodes on the specified target node y.GacTotalEffectElibility.AdjustmentSetProvider.compute(Graph graph, Node x, Node y, String graphType, int maxPathLength) Computes adjustment sets based on the provided graph, pair of nodes, graph type, and maximum path length.GacTotalEffectElibility.JointAdjustmentSetProvider.compute(Graph graph, Set<Node> X, Node y, String graphType, int maxPathLength) Computes a list of sets of nodes based on the provided graph, set of nodes, target node, graph type, and maximum path length.GacTotalEffectElibility.computeAdjustmentSetsIfEligible(Node x, Node y, GacTotalEffectElibility.AdjustmentSetProvider provider) Optional convenience: compute adjustment sets using a provided provider, but only if eligibility is OK.GacTotalEffectElibility.computeJointAdjustmentSetsIfEligible(Set<Node> X, Node y, GacTotalEffectElibility.JointAdjustmentSetProvider provider) Same idea asGacTotalEffectElibility.computeAdjustmentSetsIfEligible(Node, Node, AdjustmentSetProvider)but for joint.PcAR.MarkovAuditFailure.conditioningSet()Returns the value of theconditioningSetrecord component.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node start, boolean uncovered, Map<Node, Node> predecessors) Finds shortest distances from a start node to all other nodes in a graph.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node x, Node y, Map<Node, Node> predecessors, boolean uncovered) Finds shortest distances from a x node to all other nodes in a graph.GRegression.externalParents(Graph graph, Collection<Node> set) Returns the external parents of a set of nodes: the union of their parents, minus the set itself.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, long timeout) 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.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter) 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.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter, long timeout) Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter, long timeout, boolean useMaxP) 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.Tgin.TginOrientationStages.findImpureClusters()Groups original latents into connected components joined by undirected edges.Finds the Markov blanket (MB) for a given target node.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.SepsetFinder.findSepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) Returns the sepset that contains the greedy test for variables x and y in the given graph.MarkovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(IndependenceTest independenceTest, Graph graph, Double threshold, Double shuffleThreshold) Calculates the Anderson-Darling test and classifies nodes as accepted or rejected based on the given threshold.MarkovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(IndependenceTest independenceTest, Graph estimatedCpdag, Graph trueGraph, Double threshold, Double shuffleThreshold, Double lowRecallBound) Get accepts and rejects nodes for all nodes from Anderson-Darling test and generate the plot data for confusion statistics.MarkovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(IndependenceTest independenceTest, Graph estimatedCpdag, Graph trueGraph, Double threshold, Double shuffleThreshold, Double lowRecallBound) Get accepts and rejects nodes for all nodes from Anderson-Darling test and generate the plot data for confusion statistics.NtadExplorer.NtadResult.getBlockA()Returns the list of nodes in Block A associated with the NTAD analysis result.NtadExplorer.NtadResult.getBlockB()Returns the list of nodes in Block B associated with the NTAD analysis result.GRegression.getBuckets()Returns the bucket decomposition used by this estimator.DirectLingam.getCausalOrder()Returns the causal order estimated by the last call toDirectLingam.search(), earliest (most nearly exogenous) first.MimbuildTrek.getClustering()Deprecated.The clustering used.MarkovCheck.getConditioningNodes()Returns the nodes that are possible Z1,...,Zn for X _||_ Y | Z1,...,Zn.GRegressionPag.getForceVisibility()Returns the nodes b for which an edge X -> b is treated as visible by fiat.MarkovCheck.getIndependenceNodes()Returns the nodes that are possible X and Y for X _||_ Y | Z1,...,Zn.Fas.getNodes()Retrieves a list of nodes associated with the initialized independence test.FciOrientDijkstra.Graph.getNodes()Returns the nodes in the graph.Ida.NodeEffects.getNodes()Returns the nodes.IdaCheck.getNodes()Returns a list of nodes.Pcd.getNodes()Retrieves the list of nodes in the graph.PdagPagIda.NodeEffects.getNodes()Retrieves the list of nodes contained in this object.PermutationSearch.getOrder()Retrieves the order list.PermutationSearchTopK.getOrder()Retrieves the order list.IdaCheck.getOrderedPairs()Retrieves a list of OrderedPair objects representing all possible pairs of distinct nodes in the graph.Boss.getParents()Returns the map from nodes to the sets of their parents.Boss.getParents()Returns the map from nodes to the sets of their parents.BossTopK.getParents()Returns the map from nodes to the sets of their parents (for the top model after a search).BossTopK.getParents()Returns the map from nodes to the sets of their parents (for the top model after a search).Sp.getParents()Retrieves a mapping of nodes to their parent nodes.Sp.getParents()Retrieves a mapping of nodes to their parent nodes.SuborderSearch.getParents()The map from nodes to parents resulting from the search.SuborderSearch.getParents()The map from nodes to parents resulting from the search.SuborderSearchTopK.getParents()The map from nodes to parents resulting from the search.SuborderSearchTopK.getParents()The map from nodes to parents resulting from the search.Rlcd.getPartition()Returns the stage-2 partition.Returns the shortest path from the start node to the end node.SepsetFinder.getSepsetContainingGreedySubsetMb(Graph graph, Graph cpdag, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) 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}.SepsetFinder.getSepsetContainingMaxPHybrid(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) Returns the set of nodes that act as a separating set between two given nodes (x and y) in a graph.SepsetFinder.getSepsetContainingMinPHybrid(Graph graph, Node x, Node y, IndependenceTest test, int depth) Returns the sepset containing the minimum p-value for the given variables x and y.SepsetFinder.getSmallestSubset(Node x, Node y, Set<Node> blocking, Set<Node> containing, Graph graph, boolean isPag) Finds a smallest subset S ofblockingthat renders two nodes x and y conditionally d-separated conditional on S in the given graph.PcMb.getTargets()Return the targets of the most recent search.BossTopK.getTopKParents(int i) Returns the parent map of the i-th top permutation (0 = highest scoring), suitable for building the corresponding graph viaPermutationSearchTopK.getGraph(...).BossTopK.getTopKParents(int i) Returns the parent map of the i-th top permutation (0 = highest scoring), suitable for building the corresponding graph viaPermutationSearchTopK.getGraph(...).SuborderSearchTopK.getTopKParents(int i) Returns the parent map of the i-th top permutation (0 = highest scoring), suitable for building the corresponding graph viaPermutationSearchTopK.getGraph(...).SuborderSearchTopK.getTopKParents(int i) Returns the parent map of the i-th top permutation (0 = highest scoring), suitable for building the corresponding graph viaPermutationSearchTopK.getGraph(...).GRegression.getVariableOrder()The variable order that indexes the rows and columns ofGRegression.getLambda(): the covariance matrix's variable order, mapped to graph nodes.GRegressionPag.getVariableOrder()The variable order indexingGRegressionPag.getSigmaHat(): the covariance matrix's order, mapped to graph nodes.Boss.getVariables()Returns the variables.BossTopK.getVariables()Returns the variables.Grasp.getVariables()Returns the variables.IndTestIod.getVariables()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.MarkovCheck.getVariables(List<Node> graphNodes, List<Node> independenceNodes, List<Node> conditioningNodes) Returns the variables of the independence test.PermutationSearch.getVariables()Retrieves the list of variables.PermutationSearchTopK.getVariables()Retrieves the list of variables.Sp.getVariables()Returns the list of variables associated with this object.SuborderSearch.getVariables()The list of all variables, in order.SuborderSearchTopK.getVariables()The list of all variables, in order.MarkovAuditUtils.implicatedVertices(Graph graph, IndependenceTest test, ConditioningSetType setType, double q, boolean negativelyCorrelated) 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.MarkovAuditUtils.implicatedVertices(Collection<IndependenceResult> failures) The endpoints of the given audit failures: the x and y of each rejected implied fact, deduplicated, in first-appearance order.PcAR.implicatedVertices()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.GRegressionPag.nonAmenabilityWitness(Node x, Node y) 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.GRegression.nonIdentificationWitness(Graph mpdag, Set<Node> treatments, Node outcome) 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).GRegression.nonIdentificationWitness(Collection<Node> treatments, Node outcome) Instance form ofGRegression.nonIdentificationWitness(Graph, Set, Node), resolving nodes by name.Fask.orientationCovariates(Graph graph, Node xi, Node xj, int maxPathLength) Identifies a set of covariates (nodes) that help orient causal relationships between two given nodes xi and xj within a graph.O_G(X -> Y) for an amenable CPDAG or maxPDAG.O_G(X -> Y) for a DAG.GacTotalEffectElibility.Eligibility.potentiallyDirectedPaths()Potentially directed paths from X to Y, as returned bygraph.paths().potentiallyDirectedPaths(...).CdnodBoss.Disagreement.sepset()Returns the value of thesepsetrecord component.Fas.BlockedDeletion.sepset()Returns the value of thesepsetrecord component.PcAR.ContestedDeletion.sepset()Returns the value of thesepsetrecord component.BfciScoreCheck.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.GfciOld.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciB.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciC.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciD.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciKeepKnowledgeOrientations.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Deprecated.Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciOriginal.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.GRegression.BootstrapResult.treatments()Returns the value of thetreatmentsrecord component.treks.PcAR.OrientationClash.witnesses()Returns the value of thewitnessesrecord component.Methods in edu.cmu.tetrad.search with parameters of type NodeModifier and TypeMethodDescriptionstatic booleanFciOrientDijkstra.adjacent(FciOrientDijkstra.Graph graph, Node currentVertex, Node predecessor) Checks whether a node is adjacent to another node in the graph.GRegressionPag.adjustmentSet(Node x, Node y) Returns a valid adjustment set for (x, y) in the PAG, or null if none is found.GRegressionPag.adjustmentSets(Node x, Node y, int maxNumSets) Returns up tomaxNumSetsvalid adjustment sets for (x, y) in the PAG.RecursiveAdjustment.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, RecursiveAdjustment.ColliderPolicy colliderPolicy, boolean avoidAmenable, Set<Node> notFollowed, Set<Node> containing, Set<Node> forceVisibility) Computes a list of adjustment sets for estimating the causal effect of X on Y within a given graph structure.doubleGRegressionPag.adjustmentSpread(Node x, Node y, int maxNumSets) Diagnostic: the spread (maximum minus minimum) of the estimate over up tomaxNumSetsvalid adjustment sets, all evaluated atSigma-hat.GRegression.ancestorsAvoiding(Graph graph, Node y, Set<Node> excluded) 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.SepsetFinder.blockPathsLocalMarkov(Graph graph, Node x) Returns a set of nodes that are the parents of the given node in the graph.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Recursively blocks paths in a graph between two nodes based on specified parameters and constraints.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Single canonical entry point for separating-set search.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs, RecursiveBlocking.Strategy strategy) Blocks paths in a graph recursively based on the given parameters and strategy.RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength) Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Variant ofRecursiveBlocking1.blockPathsRecursively(Graph, Node, Node, Set, Set, int)that additionally accepts an optionalKnowledgeobject.RecursiveBlockingChokePointA.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength) 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.RecursiveBlockingChokePointB.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength) Identifies and blocks paths in the given graph by iteratively finding and addressing chokepoints and eligible non-collider nodes.RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength) Blocks paths recursively between two nodes in a graph, based on specified constraints and conditions.RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Recursively blocks paths in a graph between two nodes under specified constraints.ShallowRecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Shallow ("near-x") separating-set search betweenxandy, with no deadline.ShallowRecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Shallow ("near-x") separating-set search betweenxandy.ShallowRecursiveBlockingMemo.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Shallow ("near-x") separating-set search, no deadline.ShallowRecursiveBlockingMemo.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Shallow ("near-x") separating-set search.ShallowRecursiveBlockingStack.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Shallow ("near-x") separating-set search, no deadline.ShallowRecursiveBlockingStack.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Shallow ("near-x") separating-set search.RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge, PrintStream out) Blocks paths recursively between two nodes in a graph with detailed verbose tracing output.RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, PrintStream out) Blocks paths recursively between two nodes in a graph while tracing the process.SepsetFinder.blockPathsWithMarkovBlanket(Node x, Graph G) Identifies the set of nodes that form the Markov Blanket for a given node in a graph.static GRegression.BootstrapResultGRegression.bootstrap(Graph mpdag, DataSet data, List<Node> treatments, Node outcome, int numBootstraps) 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.Estimates a total effect on the full data set and its standard error by the nonparametric bootstrap.Returns a map from nodes in V \ {Y} to their minimum absolute effects on Y.Returns a map from nodes in V \ {Y} to their minimum effects.PdagPagIda.calculateMinimumTotalEffectsOnY(Node y) Calculates the minimum total effects of all potential cause nodes on the specified target node y.IndTestIod.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of nodes.MarkovCheck.checkIndependenceForTargetNode(Node x) Retrieves the list of local independence facts for a given node.GacTotalEffectElibility.checkJoint(Set<Node> X, Node y) Joint check for a set X (treat as "treatments") and a single outcome y.GacTotalEffectElibility.checkPairwise(Node x, Node y) Pairwise check for a single (x, y).GacTotalEffectElibility.AdjustmentSetProvider.compute(Graph graph, Node x, Node y, String graphType, int maxPathLength) Computes adjustment sets based on the provided graph, pair of nodes, graph type, and maximum path length.GacTotalEffectElibility.JointAdjustmentSetProvider.compute(Graph graph, Set<Node> X, Node y, String graphType, int maxPathLength) Computes a list of sets of nodes based on the provided graph, set of nodes, target node, graph type, and maximum path length.GacTotalEffectElibility.computeAdjustmentSetsIfEligible(Node x, Node y, GacTotalEffectElibility.AdjustmentSetProvider provider) Optional convenience: compute adjustment sets using a provided provider, but only if eligibility is OK.static List<IndependenceFact> MarkovCheck.computeImpliedFactsForVertex(Graph graph, Node x, ConditioningSetType conditioningSetType) Computes the implied independence facts for a given vertex within the specified graph based on the provided conditioning set type.static List<IndependenceFact> MarkovCheck.computeImpliedFactsForVertex(Graph graph, Node x, ConditioningSetType conditioningSetType, Graph preparedMag) Variant ofMarkovCheck.computeImpliedFactsForVertex(Graph, Node, ConditioningSetType)accepting a MAG prepared once per graph byMarkovCheck.prepareMagForVertexFacts(Graph, ConditioningSetType).GacTotalEffectElibility.computeJointAdjustmentSetsIfEligible(Set<Node> X, Node y, GacTotalEffectElibility.JointAdjustmentSetProvider provider) Same idea asGacTotalEffectElibility.computeAdjustmentSetsIfEligible(Node, Node, AdjustmentSetProvider)but for joint.static double[][]Fask.covariates(Graph graph, Node xi, Node xj, List<Node> nodes, double[][] data, int maxPathLength) 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.booleanFas.DeterminismGuard.determines(Node v, Set<Node> S) Determines whether a nodevis determined by a set of nodesS.booleanIndTestIod.determines(List<Node> z, Node x) Determines whether the variables in z determine x.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node start, boolean uncovered, Map<Node, Node> predecessors) Finds shortest distances from a start node to all other nodes in a graph.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node x, Node y, Map<Node, Node> predecessors, boolean uncovered) Finds shortest distances from a x node to all other nodes in a graph.static List<IndependenceFact> MarkovCheck.factsForUniformZ(Graph g, Node x, Set<Node> z) Generates a list of independence facts for a given node with respect to a given graph and a conditioning set.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, long timeout) 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.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter) 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.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter, long timeout) Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter, long timeout, boolean useMaxP) 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.Finds the Markov blanket (MB) for a given target node.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Finds and returns a path from node 'a' to the target node 'b' in a given graph considering various constraints.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap, RecursiveBlockingVerbose.Tracer tracer) Recursive method to find and evaluate the path between two nodes in a graph.static RecursiveBlocking1.BlockableRecursiveBlocking1.findPathToTargetVisit(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.SepsetFinder.findSepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) Returns the sepset that contains the greedy test for variables x and y in the given graph.Ida.getAbsTotalEffects(Node x, Node y) This method calculates the absolute total effects of node x on node y.PdagPagIda.getAbsTotalEffects(Node x, Node y) Calculates the absolute total effects of one variable on another.MarkovCheck.getF1StatsForTargetNodeAdjacencySubgraph(Node x, Graph estimatedGraph, Graph trueGraph) Calculates F1 statistics for a target node's adjacency subgraph in terms of adjacency, arrow types, circle types, and tail types.MarkovCheck.getF1StatsForTargetNodeMBSubgraph(Node x, Graph estimatedGraph, Graph trueGraph) Computes the F1 statistics for the Markov blanket subgraph with the target node in the given estimated graph and true graph.MarkovCheck.getF1StatsForTargetNodeParentsSubgraph(Node x, Graph estimatedGraph, Graph trueGraph) 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.Retrieves the GrowShrinkTree (GST) associated with the given Node.Retrieves the GrowShrinkTree (GST) associated with the given Node.doubleIdaCheck.getIdaMinEffect(Node x, Node y) Gets the signed minimum absolute total effect value between two nodes.doubleIdaCheck.getMaxTotalEffect(Node x, Node y) Returns the maximum total effect value between two nodes.doubleIdaCheck.getMinTotalEffect(Node x, Node y) Gets the minimum total effect value between two nodes.static Set<IndependenceFact> OrderedLocalMarkovProperty.getModelForNode(Graph admg, Node x) Returns the subset of OLMP independence facts that havexas 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.static Set<IndependenceFact> OrderedLocalMarkovPropertySinkElimination.getModelForNode(Graph mag, Node x) Returns the subset of ordered-local-Markov independence facts whose "sink" is the given nodex.static Set<IndependenceFact> PairwiseMarkovProperty.getModelForNode(Graph admg, Node x) Returns the subset of pairwise independence facts that involve nodex(as either the left or right endpoint), normalised so thatxis always the left-hand (getX()) node.FciOrientDijkstra.Graph.getNeighbors(Node node) Returns the neighbors of a node, reachable via DijkstraEdges in the grph.Return the node associated with the given variable in the graph.PdagPagIda.getPagEffects(Node x, Node y) Returns the possible total effects of x on y together with the count of refinements that could not be estimated; seePdagPagIda.PagEffects.Returns the shortest path from the start node to the end node.MarkovCheck.getPrecisionAndRecallGraphPlotData(Node x, Graph estimatedGraph, Graph trueGraph, ConditioningSetType conditioningSetType, String subgraphFeature) Computes the precision and recall values for a specified subgraph structure between an estimated graph and a true graph.voidMarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(Node x, Graph estimatedGraph, Graph trueGraph) Calculates the precision and recall on the Markov Blanket graph for a given node.voidMarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraph2(Node x, Graph estimatedGraph, Graph trueGraph) Calculates the precision and recall using LocalGraphConfusion (which calculates the combination of Adjacency and ArrowHead) on the Markov Blanket graph for a given node.MarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraphPlotData(Node x, Graph estimatedGraph, Graph trueGraph) Calculates the precision and recall on the markov blanket graph plot data.MarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraphPlotData2(Node x, Graph estimatedGraph, Graph trueGraph) This method calculates the precision and recall of a target node's Markov Blanket in the given estimated graph.SepsetFinder.getSepsetContainingGreedySubsetMb(Graph graph, Graph cpdag, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) 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}.SepsetFinder.getSepsetContainingMaxPHybrid(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) Returns the set of nodes that act as a separating set between two given nodes (x and y) in a graph.SepsetFinder.getSepsetContainingMinPHybrid(Graph graph, Node x, Node y, IndependenceTest test, int depth) Returns the sepset containing the minimum p-value for the given variables x and y.SepsetFinder.getSmallestSubset(Node x, Node y, Set<Node> blocking, Set<Node> containing, Graph graph, boolean isPag) Finds a smallest subset S ofblockingthat renders two nodes x and y conditionally d-separated conditional on S in the given graph.Ida.getTotalEffects(Node x, Node y) Calculates the total effects of node x on node y.PdagPagIda.getTotalEffects(Node x, Node y) Calculates the total effects of one variable on another.IndTestIod.getVariable(Node node) Returns the variable associated with the given node in the graph.booleanGRegressionPag.isAmenable(Node x, Node y) Returns true if the PAG is amenable with respect to (x, y), i.e.booleanRecursiveAdjustment.isGraphAmenable(Node X, Node Y, String graphType, int maxPathLength, Set<Node> forceVisibility) 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.static booleanGRegression.isIdentified(Graph mpdag, Set<Node> treatments, Node outcome) 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.booleanGRegression.isIdentified(Collection<Node> treatments, Node outcome) Returns true if the total effect of the treatments on the outcome is identified from the MPDAG.booleanIndTestIod.isJointlyMeasured(Node x, Node y) 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.booleanGRegressionPag.isPossibleAncestor(Node x, Node y) Returns true if x is a possible ancestor of y in the PAG.static doubleFask.leftRightDiffResidualized(int ruleIndex, Graph graph, Node xi, Node xj, List<Node> nodes, double[][] data) Computes the left-right difference residualized on covariates for the given nodes in a graph.GRegressionPag.nonAmenabilityWitness(Node x, Node y) 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.GRegression.nonIdentificationWitness(Graph mpdag, Set<Node> treatments, Node outcome) 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).GRegression.nonIdentificationWitness(Collection<Node> treatments, Node outcome) Instance form ofGRegression.nonIdentificationWitness(Graph, Set, Node), resolving nodes by name.Fask.orientationCovariates(Graph graph, Node xi, Node xj, int maxPathLength) Identifies a set of covariates (nodes) that help orient causal relationships between two given nodes xi and xj within a graph.O_G(X -> Y) for an amenable CPDAG or maxPDAG.O_G(X -> Y) for a DAG.doubleComputes 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.doubleReturns the posterior odds that the deleted pair (x, y) is a genuine edge, given the clash that flagged it.VertexRepairSearch.repairNode(Node node) Repairs the graph at one vertex only: repeatedly applies the top-ranked candidate edit at that vertex until none improves on the current graph.VertexRepairSearch.searchForNode(Node node) Computes and returns the ranked list of scored candidate edits for the given node in the current working graph.BfciScoreCheck.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.GfciOld.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciB.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciC.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciD.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciKeepKnowledgeOrientations.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Deprecated.Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciOriginal.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.voidFcitSl.setFocusPair(Node a, Node b) Sets the focus pair by combining the names of the given nodes in a specific order.voidFcitSl2.setFocusPair(Node a, Node b) Sets the focus pair by combining the names of the given nodes in a specific order.voidFcitSlKeepKnowledgeOrientations.setFocusPair(Node a, Node b) Sets the focus pair by combining the names of the given nodes in a specific order.voidFcitSlScoreCheck.setFocusPair(Node a, Node b) Sets the focus pair by combining the names of the given nodes in a specific order.doubleGRegression.totalEffect(Node treatment, Node outcome) Convenience method for a point intervention.double[]GRegression.totalEffect(List<Node> treatments, Node outcome) Returns the estimated total effect of the treatments on the outcome (Eq.doubleGRegressionPag.totalEffect(Node x, Node y) Returns the estimated total effect of x on y: the adjustment functional evaluated at the fittedSigma-hatrather than at the sample covariance.doubleGRegressionPag.totalEffectSampleCov(Node x, Node y) Returns the same estimate computed from the sample covariance instead ofSigma-hat, using the same adjustment set: the ordinary adjustment estimator, provided for comparison.treks.Method parameters in edu.cmu.tetrad.search with type arguments of type NodeModifier and TypeMethodDescriptionRecursiveAdjustment.adjustmentSets(Node X, Node Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, RecursiveAdjustment.ColliderPolicy colliderPolicy, boolean avoidAmenable, Set<Node> notFollowed, Set<Node> containing, Set<Node> forceVisibility) Computes a list of adjustment sets for estimating the causal effect of X on Y within a given graph structure.RecursiveAdjustmentMultiple.adjustmentSets(Set<Node> X, Set<Node> Y, String graphType, int maxNumSets, int maxRadius, int nearWhichEndpoint, int maxPathLength, boolean avoidAmenable, Set<Node> notFollowed, Set<Node> containing) Entry point analogous to Adjustment.adjustmentSetsRB, but for set-valued X and Y.GRegression.ancestorsAvoiding(Graph graph, Node y, Set<Node> excluded) 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.Given an initial permutation, 'order,' of the variables, searches for a best permutation of the variables by rearranging the variables in 'order.'RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Recursively blocks paths in a graph between two nodes based on specified parameters and constraints.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Single canonical entry point for separating-set search.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs, RecursiveBlocking.Strategy strategy) Blocks paths in a graph recursively based on the given parameters and strategy.RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength) Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Variant ofRecursiveBlocking1.blockPathsRecursively(Graph, Node, Node, Set, Set, int)that additionally accepts an optionalKnowledgeobject.RecursiveBlockingChokePointA.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength) 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.RecursiveBlockingChokePointB.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength) Identifies and blocks paths in the given graph by iteratively finding and addressing chokepoints and eligible non-collider nodes.RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength) Blocks paths recursively between two nodes in a graph, based on specified constraints and conditions.RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Recursively blocks paths in a graph between two nodes under specified constraints.ShallowRecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Shallow ("near-x") separating-set search betweenxandy, with no deadline.ShallowRecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Shallow ("near-x") separating-set search betweenxandy.ShallowRecursiveBlockingMemo.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Shallow ("near-x") separating-set search, no deadline.ShallowRecursiveBlockingMemo.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Shallow ("near-x") separating-set search.ShallowRecursiveBlockingStack.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge) Shallow ("near-x") separating-set search, no deadline.ShallowRecursiveBlockingStack.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs) Shallow ("near-x") separating-set search.RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge, PrintStream out) Blocks paths recursively between two nodes in a graph with detailed verbose tracing output.RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, PrintStream out) Blocks paths recursively between two nodes in a graph while tracing the process.static GRegression.BootstrapResultGRegression.bootstrap(Graph mpdag, DataSet data, List<Node> treatments, Node outcome, int numBootstraps) 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.IndTestIod.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of nodes.GacTotalEffectElibility.checkJoint(Set<Node> X, Node y) Joint check for a set X (treat as "treatments") and a single outcome y.GacTotalEffectElibility.JointAdjustmentSetProvider.compute(Graph graph, Set<Node> X, Node y, String graphType, int maxPathLength) Computes a list of sets of nodes based on the provided graph, set of nodes, target node, graph type, and maximum path length.GacTotalEffectElibility.computeJointAdjustmentSetsIfEligible(Set<Node> X, Node y, GacTotalEffectElibility.JointAdjustmentSetProvider provider) Same idea asGacTotalEffectElibility.computeAdjustmentSetsIfEligible(Node, Node, AdjustmentSetProvider)but for joint.static double[][]Fask.covariates(Graph graph, Node xi, Node xj, List<Node> nodes, double[][] data, int maxPathLength) 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.booleanFas.DeterminismGuard.determines(Node v, Set<Node> S) Determines whether a nodevis determined by a set of nodesS.booleanIndTestIod.determines(List<Node> z, Node x) Determines whether the variables in z determine x.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node start, boolean uncovered, Map<Node, Node> predecessors) Finds shortest distances from a start node to all other nodes in a graph.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node start, boolean uncovered, Map<Node, Node> predecessors) Finds shortest distances from a start node to all other nodes in a graph.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node x, Node y, Map<Node, Node> predecessors, boolean uncovered) Finds shortest distances from a x node to all other nodes in a graph.FciOrientDijkstra.distances(FciOrientDijkstra.Graph graph, Node x, Node y, Map<Node, Node> predecessors, boolean uncovered) Finds shortest distances from a x node to all other nodes in a graph.GRegression.externalParents(Graph graph, Collection<Node> set) Returns the external parents of a set of nodes: the union of their parents, minus the set itself.static List<IndependenceFact> MarkovCheck.factsForUniformZ(Graph g, Node x, Set<Node> z) Generates a list of independence facts for a given node with respect to a given graph and a conditioning set.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Finds and returns a path from node 'a' to the target node 'b' in a given graph considering various constraints.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Finds and returns a path from node 'a' to the target node 'b' in a given graph considering various constraints.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Finds and returns a path from node 'a' to the target node 'b' in a given graph considering various constraints.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap, RecursiveBlockingVerbose.Tracer tracer) Recursive method to find and evaluate the path between two nodes in a graph.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap, RecursiveBlockingVerbose.Tracer tracer) Recursive method to find and evaluate the path between two nodes in a graph.RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap, RecursiveBlockingVerbose.Tracer tracer) Recursive method to find and evaluate the path between two nodes in a graph.static RecursiveBlocking1.BlockableRecursiveBlocking1.findPathToTargetVisit(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.static RecursiveBlocking1.BlockableRecursiveBlocking1.findPathToTargetVisit(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.static RecursiveBlocking1.BlockableRecursiveBlocking1.findPathToTargetVisit(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap) Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.SepsetFinder.findSepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) Returns the sepset that contains the greedy test for variables x and y in the given graph.doubleIdaCheck.getAverageSquaredDistance(List<OrderedPair<Node>> pairs) Returns the average of the squared distances between the true total effects and the IDA effect ranges the list of node pairs indicated.doubleIdaCheck.getAvgMaxSquaredDiffEstTrue(List<OrderedPair<Node>> pairs) Returns the average of the squared differences between the maximum total effects and the true total effects for the list of node pairs indicated.doubleIdaCheck.getAvgMinSquaredDiffEstTrue(List<OrderedPair<Node>> pairs) Returns the average of the squared differences between the minimum total effects and the true total effects for the list of node pairs indicated.static GraphConstruct a graph given a specification of the parents for each node.static GraphConstruct a graph given a specification of the parents for each node.static GraphConstruct a graph given a specification of the parents for each node.static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpdag, boolean replicating) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpdag, boolean replicating) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpdag, boolean replicating) Constructs a graph given a specification of the parents for each node.static GraphConstruct a graph given a specification of the parents for each node.static GraphConstruct a graph given a specification of the parents for each node.static GraphConstruct a graph given a specification of the parents for each node.static GraphPermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Constructs a graph given a specification of the parents for each node, with graph replication disabled.static GraphPermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Constructs a graph given a specification of the parents for each node, with graph replication disabled.static GraphPermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Constructs a graph given a specification of the parents for each node, with graph replication disabled.static GraphPermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpdag, boolean replicating) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpdag, boolean replicating) Constructs a graph given a specification of the parents for each node.static GraphPermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpdag, boolean replicating) Constructs a graph given a specification of the parents for each node.Returns the shortest path from the start node to the end node.Returns the shortest path from the start node to the end node.Cstar.getRecords(DataSet dataSet, List<Node> possibleCauses, List<Node> possibleEffects, int topBracket, String path) Returns records for a set of variables with expected number of false positives bounded by q.SepsetFinder.getSepsetContainingGreedySubsetMb(Graph graph, Graph cpdag, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) 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}.SepsetFinder.getSepsetContainingMaxPHybrid(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth) Returns the set of nodes that act as a separating set between two given nodes (x and y) in a graph.SepsetFinder.getSmallestSubset(Node x, Node y, Set<Node> blocking, Set<Node> containing, Graph graph, boolean isPag) Finds a smallest subset S ofblockingthat renders two nodes x and y conditionally d-separated conditional on S in the given graph.doubleIdaCheck.getSquaredDistance(OrderedPair<Node> pair) 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.doubleIdaCheck.getSquaredMaxTrueDist(OrderedPair<Node> pair) Returns the squared difference between the maximum total effect and the true total effect for the given pair of nodes.doubleIdaCheck.getSquaredMinTrueDistance(OrderedPair<Node> pair) Returns the squared difference between the minimum total effect and the true total effect for the given pair of nodes.doubleIdaCheck.getTrueTotalEffect(OrderedPair<Node> pair) Calculates the true total effect between two nodes in the graph.MarkovCheck.getVariables(List<Node> graphNodes, List<Node> independenceNodes, List<Node> conditioningNodes) Returns the variables of the independence test.static booleanGRegression.hasRestrictiveProperty(Graph graph, Set<Node> bucket) 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.IndTestIod.indTestSubset(List<Node> vars) Calculates the independence test for a subset of variables.booleanRecursiveAdjustment.isGraphAmenable(Node X, Node Y, String graphType, int maxPathLength, Set<Node> forceVisibility) 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.static booleanGRegression.isIdentified(Graph mpdag, Set<Node> treatments, Node outcome) 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.booleanGRegression.isIdentified(Collection<Node> treatments, Node outcome) Returns true if the total effect of the treatments on the outcome is identified from the MPDAG.static doubleFask.leftRightDiffResidualized(int ruleIndex, Graph graph, Node xi, Node xj, List<Node> nodes, double[][] data) Computes the left-right difference residualized on covariates for the given nodes in a graph.static List<NtadExplorer.NtadResult> NtadExplorer.listRankDeficientNtads(CorrelationMatrix data, List<Node> vars, int blockSize, int maxResults, double alpha, Cca ccaTest) Identifies and returns a list of rank-deficient NTAD (near-total aggregate decomposition) results, based on the given correlation matrix and other input parameters.static @NotNull GraphConstructs a directed graph based on the input binary adjacency matrix and a list of nodes.static org.ejml.simple.SimpleMatrixTwoStep.maskFromUndirected(Graph skeleton, List<Node> vars) Deprecated.Constructs a mask matrix from an undirected graph, indicating the adjacency relationships between a given list of nodes.GRegression.nonIdentificationWitness(Graph mpdag, Set<Node> treatments, Node outcome) 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).GRegression.nonIdentificationWitness(Collection<Node> treatments, Node outcome) Instance form ofGRegression.nonIdentificationWitness(Graph, Set, Node), resolving nodes by name.voidTgin.TginOrientationStages.orientImpureCluster(List<Node> impureCluster) Orients all latent-latent edges within one impure cluster using the LaHiCaSl Algorithm 6 procedure (Xie et al.static StringGRegression.pathString(Graph graph, List<Node> path) Renders a path as a string using the graph's edge marks, e.g.doubleComputes 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.doubleReturns the posterior odds that the deleted pair (x, y) is a genuine edge, given the clash that flagged it.voidIdaCheck.recompute(List<OrderedPair<Node>> currentPairs) Recomputes the total effects and absolute total effects for all ordered pairs of nodes in the graph.static voidNtadExplorerHarness.runDemo(DataSet data, List<Node> vars, Cca ccaTest, int blockSize, int maxResults, double alpha) Runs the NTAD Explorer demo using the specified dataset, variable list, CCA test, and parameters.TscHarnessTest.runOnce(List<Node> vars, CovarianceMatrix cov, double alpha, int ess, int minRedundancy) Executes the time series clustering (TSC) algorithm once with the given parameters and returns canonicalized clusters as a set of sets of integers.Searches for conditional independence relationships in a graph constructed from the given list of nodes.Greedy equivalence search: Start from the empty graph, add edges till the model is significant.MimbuildTrek.search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov) Deprecated.Does the search and returns the graph.Performs a search to generate a graph structure based on the provided list of nodes.Performs the search over the given nodes.Searches for a graph using the given IFas instance and list of nodes.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.Searches for the MB CPDAG for the given targets.Runs the search and returns the RFCI PAG.Searches of a specific sublist of nodes.voidBoss.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches a suborder of the variables.voidBoss.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches a suborder of the variables.voidBossTopK.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches a suborder of the variables.voidBossTopK.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches a suborder of the variables.voidSp.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches for the best suborder of nodes given a prefix and a suborder.voidSp.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches for the best suborder of nodes given a prefix and a suborder.voidSuborderSearch.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches the suborder.voidSuborderSearch.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches the suborder.voidSuborderSearchTopK.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches the suborder.voidSuborderSearchTopK.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) Searches the suborder.BfciScoreCheck.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.GfciOld.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.GfciOld.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciB.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciC.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciD.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciKeepKnowledgeOrientations.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Deprecated.Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.StarFciOriginal.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP) Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.voidGRegressionPag.setForceVisibility(Set<Node> forceVisibility) 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.voidSets the nodes.voidSets the list of nodes in this object.voidSets the order list for the search.voidSets the order list for the search.voidVertexRepairSearch.setSeedVertices(Set<Node> seeds) Restricts INITIAL candidate enumeration underVertexRepairSearch.RepairStrategy.GLOBAL_QUEUEto the given vertices, so repair effort starts where an external diagnostic (e.g.voidPcMb.setVariables(List<Node> variables) Setter for the fieldvariables.static voidMarkovAuditUtils.stampSeedAttribute(Graph graph, Collection<Node> seeds) WritesseedsontographunderMarkovAuditUtils.SEED_ATTRIBUTEas a comma-separated name list, the channel the Vertex Repair GUI reads to offer seeded repair.static @NotNull StringBuilderTsc.toNamesCluster(Collection<Integer> cluster, List<Node> nodes) Constructs a StringBuilder containing a formatted string representation of the names of nodes corresponding to the provided cluster indices.static @NotNull StringConverts a set of clusters represented as sets of integers into a string representation that associates cluster IDs with node names.double[]GRegression.totalEffect(List<Node> treatments, Node outcome) Returns the estimated total effect of the treatments on the outcome (Eq.Constructors in edu.cmu.tetrad.search with parameters of type NodeModifierConstructorDescriptionBlockedDeletion(Node x, Node y, Set<Node> sepset) Creates an instance of aBlockedDeletionrecord class.BootstrapResult(List<Node> treatments, Node outcome, double[] effect, Matrix covariance) Creates an instance of aBootstrapResultrecord class.BootstrapResult(Node treatment, Node outcome, double effect, double standardError, int numFailed) Creates an instance of aBootstrapResultrecord class.Creates an instance of aContestedDeletionrecord class.DijkstraEdge(Node y, int weight) Creates a new DijkstraEdge.Disagreement(Node x, Node z, Node y, boolean bossCollider, CdnodBoss.ColliderOrientationStyle style, Set<Node> sepset, double pValue) Creates an instance of aDisagreementrecord class.Creates an instance of aMarkrecord class.MarkovAuditFailure(Node x, Node y, Set<Node> conditioningSet, double pValue) Creates an instance of aMarkovAuditFailurerecord class.OrientationClash(Node u, Node z, Set<Node> witnesses) Creates an instance of aOrientationClashrecord class.Constructs a Triple object with the specified nodes.Constructs a Triple object with the specified nodes.Constructor parameters in edu.cmu.tetrad.search with type arguments of type NodeModifierConstructorDescriptionBlockedDeletion(Node x, Node y, Set<Node> sepset) Creates an instance of aBlockedDeletionrecord class.BlockingResult(Set<Node> blockingSet, boolean indeterminate) Creates an instance of aBlockingResultrecord class.BootstrapResult(List<Node> treatments, Node outcome, double[] effect, Matrix covariance) Creates an instance of aBootstrapResultrecord class.Creates an instance of aContestedDeletionrecord class.Disagreement(Node x, Node z, Node y, boolean bossCollider, CdnodBoss.ColliderOrientationStyle style, Set<Node> sepset, double pValue) Creates an instance of aDisagreementrecord class.Fci(IndependenceTest test, List<Node> searchVars) Constructs an instance of the Fci algorithm using the specified independence test and a subset of variables to include in the search.Constructor.Constructor for the LatentPurifier class, which implements the purification step of Silva et al.'s BuildPureClusters algorithm.MarkovAuditFailure(Node x, Node y, Set<Node> conditioningSet, double pValue) Creates an instance of aMarkovAuditFailurerecord class.NtadResult(List<Node> blockA, List<Node> blockB, int blockSize, int rank, double pValue) Constructs an instance of NtadResult with specified properties.OrientationClash(Node u, Node z, Set<Node> witnesses) Creates an instance of aOrientationClashrecord class.PdagPagIda(DataSet dataSet, Graph graph, List<Node> possibleCauses) 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.Rfci(IndependenceTest test, List<Node> searchVars) Constructs a new RFCI search for the given independence test and background knowledge and a list of variables to search over.TginOrientationStages(Graph graph, List<Node> allLatents, Map<Node, Node> nodeMap, DataSet dataSet, double alpha, int sampleSize, RawMarginalIndependenceTest hsic) Constructs a new instance of TginOrientationStages.TginOrientationStages(Graph graph, List<Node> allLatents, Map<Node, Node> nodeMap, DataSet dataSet, double alpha, int sampleSize, RawMarginalIndependenceTest hsic) Constructs a new instance of TginOrientationStages.TginOrientationStages(Graph graph, List<Node> allLatents, Map<Node, Node> nodeMap, DataSet dataSet, double alpha, int sampleSize, RawMarginalIndependenceTest hsic) Constructs a new instance of TginOrientationStages.Tsc(List<Node> variables, CovarianceMatrix cov) Constructs an instance of the TscScored class using the provided variables and covariance matrix.Constructs an instance of the Tsc class by initializing variables, correlation matrix, sample size, and data array from the provided nodes and dataset. -
Uses of Node in edu.cmu.tetrad.search.blocks
Methods in edu.cmu.tetrad.search.blocks that return types with arguments of type NodeModifier and TypeMethodDescriptionBlockSpec.blockVariables()Returns the list of block variables associated with this BlockSpec instance.BlocksUtil.expandLatents(BlockSpec spec) Expand ranks -> per-latent variables named Lk-1..Lk-r.BlocksUtil.makeBlockVariables(List<List<Integer>> blocks, DataSet dataSet) Creates a list of block variables based on the provided list of blocks and the dataset. -
Uses of Node in edu.cmu.tetrad.search.cdnod_pag
Methods in edu.cmu.tetrad.search.cdnod_pag that return NodeModifier and TypeMethodDescriptionChangeOracle.env()Retrieves the first context (environment) from the list of contexts.Methods in edu.cmu.tetrad.search.cdnod_pag that return types with arguments of type NodeModifier and TypeMethodDescriptionChangeOracle.contexts()Retrieves the list of contexts associated with this ChangeOracle instance.Methods in edu.cmu.tetrad.search.cdnod_pag with parameters of type NodeModifier and TypeMethodDescriptionbooleanDetermines whether the conditional distribution of y given Z changes with the environment variable, by testing the OLS residuals of y on Z against env.booleanDetermines if any changes occur with respect to the specified node and set of nodes given the current data, contexts, and alpha significance level.booleanEvaluates whether there is a change or instability with respect to the given context variable.booleanReturns true iff y and env are m-connected given Z in the true graph.default booleanChangeTest.changesAny(DataSet data, Node y, Set<Node> Z, Collection<Node> contexts, double alpha) Determines whether there is any change or instability with respect to any of the specified context variables.CdnodPagOrienter.forbidArrowheadsInto(Node node) Prohibits arrowheads into the given node (it is never used in the Y role).static voidPagEdgeUtils.orientArrowheadAt(Graph pag, Node parentCand, Node child) 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.static voidChangeTest.requireNonNulls(DataSet data, Node y, Set<Node> Z) Ensures that the specified objects are not null.booleanDetermines if the given node and set of nodes are stable with respect to the current data, contexts, and alpha significance level.default booleanDetermines whether the data, with respect to a given context variable, is stable (i.e., there is no change or instability).default booleanDetermines 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.Method parameters in edu.cmu.tetrad.search.cdnod_pag with type arguments of type NodeModifier and TypeMethodDescriptionbooleanDetermines whether the conditional distribution of y given Z changes with the environment variable, by testing the OLS residuals of y on Z against env.booleanDetermines if any changes occur with respect to the specified node and set of nodes given the current data, contexts, and alpha significance level.booleanEvaluates whether there is a change or instability with respect to the given context variable.booleanReturns true iff y and env are m-connected given Z in the true graph.default booleanChangeTest.changesAny(DataSet data, Node y, Set<Node> Z, Collection<Node> contexts, double alpha) Determines whether there is any change or instability with respect to any of the specified context variables.default booleanChangeTest.changesAny(DataSet data, Node y, Set<Node> Z, Collection<Node> contexts, double alpha) Determines whether there is any change or instability with respect to any of the specified context variables.CdnodPagOrienter.forbidArrowheadsInto(Collection<Node> nodes) Prohibits arrowheads into the given nodes (they are never used in the Y role).static voidChangeTest.requireNonNulls(DataSet data, Node y, Set<Node> Z) Ensures that the specified objects are not null.booleanDetermines if the given node and set of nodes are stable with respect to the current data, contexts, and alpha significance level.default booleanDetermines whether the data, with respect to a given context variable, is stable (i.e., there is no change or instability).default booleanDetermines 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.default booleanDetermines 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.Merges the given tier assignments (smaller = earlier) into the tier map.Constructors in edu.cmu.tetrad.search.cdnod_pag with parameters of type NodeModifierConstructorDescriptionChangeOracle(DataSet data, Node env, double alpha, ChangeTest test) Constructor for the ChangeOracle class using a single context.Constructor parameters in edu.cmu.tetrad.search.cdnod_pag with type arguments of type NodeModifierConstructorDescriptionChangeOracle(DataSet data, Collection<Node> contexts, double alpha, ChangeTest test) Constructs a new instance of the ChangeOracle class with multiple contexts. -
Uses of Node in edu.cmu.tetrad.search.harness
Method parameters in edu.cmu.tetrad.search.harness with type arguments of type NodeModifier and TypeMethodDescriptionstatic Set<IndependenceFact> GraphoidClosureHarness.computeClosure(Set<IndependenceFact> facts, List<Node> variables, GraphoidClosureHarness.ClosureType closure) Computes the closure of a set ofIndependenceFacts under the given graphoid assumption type, using the variables in the provided list as the full variable universe. -
Uses of Node in edu.cmu.tetrad.search.harness.tsbi
Method parameters in edu.cmu.tetrad.search.harness.tsbi with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.search.harness.tsc
Methods in edu.cmu.tetrad.search.harness.tsc that return types with arguments of type NodeModifier and TypeMethodDescriptionTrueClusterExtractor.extractClusters(Graph graph) Extracts true latent clusters fromgraph.TrueClusterExtractor.extractLatentLeaders(Graph graph) Extracts the primary latent node for each group in the same order asTrueClusterExtractor.extractClusters(Graph).Runs the algorithm ondataSetand returns the recovered clusters. -
Uses of Node in edu.cmu.tetrad.search.is
Methods in edu.cmu.tetrad.search.is that return NodeModifier and TypeMethodDescriptionBDeuScoreWOprior.getVariable(String targetName) Retrieves a Node from the list of variables that matches the specified target name.IndTestProbabilisticBDeu.getVariable(String name) Retrieves a Node variable by its name from the list of nodes.IndTestProbabilisticISBDeu.getVariable(String name) Retrieves a node corresponding to the given variable name.IsBDeuScore2.getVariable(String targetName) Retrieves the variable node with the specified name.IsBicScore.getVariable(String targetName) Searches the list of variables for a node with the specified name and returns it.IsScore.getVariable(String targetName) Resolve a variable by name.Methods in edu.cmu.tetrad.search.is that return types with arguments of type NodeModifier and TypeMethodDescriptionBDeuScoreWOprior.getVariables()Retrieves the list of variables associated with the scoring metric.IndTestProbabilisticBDeu.getVariables()Retrieves the list of variables associated with this independence test.IndTestProbabilisticISBDeu.getVariables()Retrieves the list of variables represented as nodes in the model.IsBDeuScore.getVariables()Retrieves the list of variables represented by nodes in the score computation.IsBDeuScore2.getVariables()IsBicScore.getVariables()Retrieves the list of variable nodes.IsScore.getVariables()Retrieves the list of variables (nodes) associated with the current implementation.Methods in edu.cmu.tetrad.search.is with parameters of type NodeModifier and TypeMethodDescriptionIndTestProbabilisticBDeu.checkIndependence(Node x, Node y, Node... z) Checks the independence of two variables, x and y, given a set of conditioning variables z.IndTestProbabilisticBDeu.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two variables, x and y, given a set of conditioning variables z.IndTestProbabilisticISBDeu.checkIndependence(Node x, Node y, Node... z) Evaluates the independence between two nodes, x and y, given a set of conditioning nodes, z.IndTestProbabilisticISBDeu.checkIndependence(Node x, Node y, Set<Node> z) Checks the probabilistic independence between two nodes, x and y, given a set of conditioning nodes, z.doubleIndTestProbabilisticBDeu.computeInd(Node x, Node y, Node... z) Computes the independence score between two nodes (x and y) conditioned on a set of other nodes (z).booleanBDeuScoreWOprior.determines(List<Node> z, Node y) Determines whether a given list of nodes (z) allows the determination of another node (y) based on specific criteria within the scoring framework.booleanIsBDeuScore2.determines(List<Node> z, Node y) Determines if variable y is determined by variables z.booleanIsBicScore.determines(List<Node> z, Node y) Determines whether the given set of nodeszimplies or determines the state or value of the nodeybased on certain criteria.booleanIsScore.determines(List<Node> Z, Node y) Determines whether the given set of nodesZdetermines the target nodey.Method parameters in edu.cmu.tetrad.search.is with type arguments of type NodeModifier and TypeMethodDescriptionIndTestProbabilisticBDeu.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two variables, x and y, given a set of conditioning variables z.IndTestProbabilisticISBDeu.checkIndependence(Node x, Node y, Set<Node> z) Checks the probabilistic independence between two nodes, x and y, given a set of conditioning nodes, z.booleanBDeuScoreWOprior.determines(List<Node> z, Node y) Determines whether a given list of nodes (z) allows the determination of another node (y) based on specific criteria within the scoring framework.booleanIsBDeuScore2.determines(List<Node> z, Node y) Determines if variable y is determined by variables z.booleanIsBicScore.determines(List<Node> z, Node y) Determines whether the given set of nodeszimplies or determines the state or value of the nodeybased on certain criteria.booleanIsScore.determines(List<Node> Z, Node y) Determines whether the given set of nodesZdetermines the target nodey.IndTestProbabilisticBDeu.indTestSubset(List<Node> vars) Returns a new independence test object for the subset of variables provided.IndTestProbabilisticISBDeu.indTestSubset(List<Node> vars) Returns an independence test for a subset of variables.voidBDeuScoreWOprior.setVariables(List<Node> variables) Sets the list of variables for the scoring metric.voidIsBDeuScore2.setVariables(List<Node> variables) Sets the list of variables for the current instance.voidIsBicScore.setVariables(List<Node> variables) Sets the list of variables for this object. -
Uses of Node in edu.cmu.tetrad.search.mimic
Methods in edu.cmu.tetrad.search.mimic that return types with arguments of type NodeModifier and TypeMethodDescriptionLatentLatentEvaluator.Report.getEstimatedToTrueMatching()Returns the estimated-to-true latent matching.LatentLatentEvaluator.Report.getEstimatedToTrueMatching()Returns the estimated-to-true latent matching.MimicModel.getInputParents(Node latent) Returns the measured input parents of the given latent in the true graph.MimicModel.getInputs()Returns a defensive copy of the measured input list.MimicModel.getLatents()Returns a defensive copy of the latent list.TrekMimic.getLatents()Returns the current latent nodes after search, if available.MimicModel.getMeasuredNodes()Returns all measured variables, with inputs listed first and outputs second.MimicModel.getOutputChildren(Node latent) Returns the measured output children of the given latent in the true graph.MimicModel.getOutputs()Returns a defensive copy of the measured output list.TrekMeasurementModelBuilderBoss.MeasurementBuildResult.latents()Returns the value of thelatentsrecord component.TrekMeasurementModelBuilderPc.MeasurementBuildResult.latents()Returns the value of thelatentsrecord component.TrekMeasurementModelBuilderBoss.MeasurementBuildResult.observedChildren()Returns the value of theobservedChildrenrecord component.TrekMeasurementModelBuilderPc.MeasurementBuildResult.observedChildren()Returns the value of theobservedChildrenrecord component.TrekMeasurementModelBuilderBoss.MeasurementBuildResult.parentPool()Returns the value of theparentPoolrecord component.TrekMeasurementModelBuilderPc.MeasurementBuildResult.parentPool()Returns the value of theparentPoolrecord component.TrekMeasurementModelBuilderBoss.MeasurementBuildResult.variables()Returns the value of thevariablesrecord component.TrekMeasurementModelBuilderPc.MeasurementBuildResult.variables()Returns the value of thevariablesrecord component.Methods in edu.cmu.tetrad.search.mimic with parameters of type NodeModifier and TypeMethodDescriptionMimicModel.getInputParents(Node latent) Returns the measured input parents of the given latent in the true graph.MimicModel.getOutputChildren(Node latent) Returns the measured output children of the given latent in the true graph.Method parameters in edu.cmu.tetrad.search.mimic with type arguments of type NodeModifier and TypeMethodDescriptionBossTrekMimicRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters) Deprecated.Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.DmRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters) Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.MimicSearchRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters) Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.TrekMimicRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters) Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.voidDm.setMeasuredInputs(List<Node> inputs) Sets measured inputs explicitly.voidDm.setMeasuredOutputs(List<Node> outputs) Sets measured outputs explicitly.Constructor parameters in edu.cmu.tetrad.search.mimic with type arguments of type NodeModifierConstructorDescriptionLatentGraphRefinement(List<Node> variables, org.ejml.simple.SimpleMatrix s, DataSet dataSet, int sampleSize, double alpha) Constructs aLatentGraphRefinementwith the correlation context needed to run both operations.MeasurementBuildResult(BlockSpec spec, Graph graph, List<Node> latents, List<Node> observedChildren, List<Node> parentPool, List<Node> variables, org.ejml.simple.SimpleMatrix matrix, int sampleSize, double alpha) Creates an instance of aMeasurementBuildResultrecord class.MeasurementBuildResult(BlockSpec spec, Graph graph, List<Node> latents, List<Node> observedChildren, List<Node> parentPool, List<Node> variables, int sampleSize, double alpha) Creates an instance of aMeasurementBuildResultrecord class.Constructs a new MIMIC model container. -
Uses of Node in edu.cmu.tetrad.search.score
Methods in edu.cmu.tetrad.search.score that return NodeModifier and TypeMethodDescriptionHuangMarginalLikelihoodScore.getVariable(String targetName) default NodeScore.getVariable(String targetName) Returns the variable with the given name.Methods in edu.cmu.tetrad.search.score that return types with arguments of type NodeModifier and TypeMethodDescriptionBasisFunctionBgeScore.getVariables()The variables of the score.BasisFunctionBicScore.getVariables()Retrieves the list of nodes representing the variables in the basis function score.BasisFunctionBicScoreFullSample.getVariables()Retrieves the list of nodes representing the variables in the basis function score.BdeScore.getVariables()Returns the variables present in the DataSet associated with this method.BDeuScore.getVariables()Retrieves the list of variables used in the object.BgeScore.getVariables()The variables of the score.BlocksBicScore.getVariables()Retrieves the list of variables associated with this object.BlocksTrekBicScore.getVariables()Retrieves the list of variables associated with this instance.ConditionalGaussianScore.getVariables()Returns the list of variables.DegenerateGaussianScore.getVariables()The variables of the score.DiscreteBicScore.getVariables()The variables of the score.DiscreteBicScoreAdTree.getVariables()The variables of the score.EbicScore.getVariables()The variables of the score.EdgePriorScore.getVariables()Returns the variables of the score.FfCordContinuous.getVariables()Retrieves the list of variables in the current instance.FfDmlContinuous.getVariables()Retrieves the list of variables in the current instance.FfMl.getVariables()Retrieves a list of variable nodes.FfMlContinuous.getVariables()Retrieves the list of variables in the current instance.GicScores.getVariables()The variables of the score.GraphScore.getVariables()The variables of the score.HeavyTailSemBicScore.getVariables()Retrieves the list of variable nodes associated with the current score calculation.HuangMarginalLikelihoodScore.getVariables()Retrieves the list of variables used in the scoring process.ImagesScore.getVariables()The variables of the score.IndTestScore.getVariables()The variables of the score.InstanceAugmentedSemBicScore.getVariables()Retrieves the list of variables associated with the current instance score.KcvBicScore.getVariables()Retrieves the list of variables used in the computation or associated with this instance.KernelMarginalLikelihoodScore.getVariables()KernelMarginalLikelihoodScoreContinuous.getVariables()Retrieves the list of variables associated with this score object.LegendreBicScore.getVariables()Retrieves a list of variables as Node objects.MagRicfBicScorer.getVariables()The variables of the covariance matrix all scoring is done against.MvpScore.getVariables()The variables of the score.PoissonPriorScore.getVariables()The variables of the score.RffBicScore.getVariables()Retrieves the list of variable nodes.Score.getVariables()The variables of the score.SemBicScore.getVariables()Returns the variables of the covariance matrix.TRffBicScore.getVariables()Retrieves a list of nodes representing the variables used in the context of this scoring method.ZsbScore.getVariables()The variables of the score.Methods in edu.cmu.tetrad.search.score with parameters of type NodeModifier and TypeMethodDescriptionbooleanBDeuScore.determines(List<Node> z, Node y) Determines whether a set of nodes z determines a specific node y.booleanEbicScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanEdgePriorScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between the given nodes.booleanFfCordContinuous.determines(List<Node> z, Node y) Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.booleanFfDmlContinuous.determines(List<Node> z, Node y) Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.booleanFfMl.determines(List<Node> z, Node y) Determines whether a given node y is conditionally independent of other nodes z based on the local score calculation.booleanFfMlContinuous.determines(List<Node> z, Node y) Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.booleanGicScores.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanHeavyTailSemBicScore.determines(List<Node> z, Node yNode) Determines whether a specific node is conditionally independent of a target node given a set of parent nodes, based on the local score.booleanHuangMarginalLikelihoodScore.determines(List<Node> z, Node y) Determines whether the given variable y is conditionally independent of the set of variables z based on a computed score.booleanImagesScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanIndTestScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanKcvBicScore.determines(List<Node> z, Node yNode) Determines whether the given node yNode is influenced by the list of nodes z based on a calculated local score.booleanKernelMarginalLikelihoodScore.determines(List<Node> z, Node y) booleanKernelMarginalLikelihoodScoreContinuous.determines(List<Node> z, Node y) Determines if a node is determined by a set of nodes.booleanMvpScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanPoissonPriorScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanRffBicScore.determines(List<Node> z, Node yNode) Determines if an edge has an effect based on the given bump value.default booleanScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanSemBicScore.determines(List<Node> z, Node y) Returns true is the variables in z determine the variable y.booleanZsbScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.doubleAdditiveLocalScorer.localScore(Node y, Collection<Node> parents) Computes the local score for a target node given its set of parent nodes in a graphical model.doubleBlocksBicScore.localScore(Node y, List<Node> parents) Computes the local score for a given node and its potential parent nodes.doubleBlocksTrekBicScore.localScore(Node y, List<Node> parents) Computes the local score for a given node and its parent nodes in a directed structure.doubleCamAdditivePsplineBic.localScore(Node y, Collection<Node> parents) Local score: BIC(Y | parents) under additive P-splines, with λ_j by GCV via backfitting.Method parameters in edu.cmu.tetrad.search.score with type arguments of type NodeModifier and TypeMethodDescriptionbooleanBDeuScore.determines(List<Node> z, Node y) Determines whether a set of nodes z determines a specific node y.booleanEbicScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanEdgePriorScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between the given nodes.booleanFfCordContinuous.determines(List<Node> z, Node y) Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.booleanFfDmlContinuous.determines(List<Node> z, Node y) Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.booleanFfMl.determines(List<Node> z, Node y) Determines whether a given node y is conditionally independent of other nodes z based on the local score calculation.booleanFfMlContinuous.determines(List<Node> z, Node y) Determines whether a given node is conditionally independent of a set of nodes based on the local score calculation.booleanGicScores.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanHeavyTailSemBicScore.determines(List<Node> z, Node yNode) Determines whether a specific node is conditionally independent of a target node given a set of parent nodes, based on the local score.booleanHuangMarginalLikelihoodScore.determines(List<Node> z, Node y) Determines whether the given variable y is conditionally independent of the set of variables z based on a computed score.booleanImagesScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanIndTestScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanKcvBicScore.determines(List<Node> z, Node yNode) Determines whether the given node yNode is influenced by the list of nodes z based on a calculated local score.booleanKernelMarginalLikelihoodScore.determines(List<Node> z, Node y) booleanKernelMarginalLikelihoodScoreContinuous.determines(List<Node> z, Node y) Determines if a node is determined by a set of nodes.booleanMvpScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanPoissonPriorScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanRffBicScore.determines(List<Node> z, Node yNode) Determines if an edge has an effect based on the given bump value.default booleanScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.booleanSemBicScore.determines(List<Node> z, Node y) Returns true is the variables in z determine the variable y.booleanZsbScore.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.doubleAdditiveLocalScorer.localScore(Node y, Collection<Node> parents) Computes the local score for a target node given its set of parent nodes in a graphical model.doubleBlocksBicScore.localScore(Node y, List<Node> parents) Computes the local score for a given node and its potential parent nodes.doubleBlocksTrekBicScore.localScore(Node y, List<Node> parents) Computes the local score for a given node and its parent nodes in a directed structure.doubleCamAdditivePsplineBic.localScore(Node y, Collection<Node> parents) Local score: BIC(Y | parents) under additive P-splines, with λ_j by GCV via backfitting.voidDiscreteBicScore.setVariables(List<Node> variables) Sets the variables to a new list of the same size.voidDiscreteBicScoreAdTree.setVariables(List<Node> variables) Sets the variables to a new list of the same size.voidGicScores.setVariables(List<Node> variables) Sets the variables of the dataset.voidSemBicScore.setVariables(List<Node> variables) Sets the variables of the covariance matrix.Returns a SEM BIC score for the given subset of variables. -
Uses of Node in edu.cmu.tetrad.search.test
Methods in edu.cmu.tetrad.search.test that return NodeModifier and TypeMethodDescriptiondefault NodeIndependenceTest.getVariable(String name) Returns The variable by the given name.IndTestFisherZ.getVariable(String name) Retrieves a variable by its name from the internal mapping of variable names to nodes.IndTestHsic.getVariable(String name) Deprecated.Returns the variable with the given name.IndTestIndependenceFacts.getVariable(String name) Retrieves a variable node based on its name.IndTestProbabilistic.getVariable(String name) Retrieves the Node object that matches the given name from the list of nodes.IndTestTrekSep.getVariable(String name) Deprecated.Gets the variable with the given name.MsepTest.getVariable(String name) Returns theNodeobject with the given name.ScoreIndTest.getVariable(String name) Retrieves the Node object with the specified name.Methods in edu.cmu.tetrad.search.test that return types with arguments of type NodeModifier and TypeMethodDescriptionCachedIndependenceQueries.getVariables()Retrieves a list of variables associated with the current object.CachingIndependenceTest.getVariables()Retrieves the list of variables from the underlying independence test.EdgePriorTest.getVariables()Returns the variables of the test.FfCi.getVariables()Retrieves a list of variable nodes.FfCi1.getVariables()Retrieves a list of variable nodes.FfCiContinuous.getVariables()Retrieves the list of variables (nodes) associated with this instance.Gcm.getVariables()IndependenceTest.getVariables()Retrieves the list of variables associated with this independence test.IndTestBasisExpandedGcm.getVariables()Returns the variables of this test (the caller's Node instances).IndTestBasisFunctionBlocks.getVariables()Retrieves the list of nodes (variables) associated with this instance.IndTestBasisFunctionLrt.getVariables()Deprecated.Retrieves the list of nodes (variables) associated with this instance.IndTestBasisFunctionLrtFullSample.getVariables()Deprecated.Retrieves the list of nodes (variables) associated with this instance.IndTestBlocksLemma10.getVariables()Deprecated.IndTestBlocksWilkes.getVariables()IndTestChiSquare.getVariables()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.IndTestConditionalCorrelation.getVariables()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.IndTestConditionalGaussianLrt.getVariables()Returns the list of variables over which this independence checker is capable of determining independence relations.IndTestCord.getVariables()Retrieves the list of variables associated with this test.IndTestCordEric.getVariables()Retrieves the list of variables associated with this test.IndTestDegenerateGaussianLrt.getVariables()Retrieves the list of variables involved in the independence test.IndTestDegenerateGaussianLrtFullSample.getVariables()Deprecated.Retrieves the list of nodes (variables) associated with this instance.IndTestEquivalence.getVariables()Returns the variables over which this test is defined.IndTestFdrWrapper.getVariables()Retrieves a list of variables involved in the independence tests.IndTestFisherZ.getVariables()Retrieves the list of variables.IndTestFisherZConcatenateResiduals.getVariables()Deprecated.Returns the list of variables used in this method.IndTestFisherZFisherPValue.getVariables()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.IndTestGin.getVariables()Returns the variables involved in the independence test.IndTestGSquare.getVariables()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.IndTestHsic.getVariables()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.IndTestIndependenceFacts.getVariables()Returns the list of variables for the facts.IndTestMulti.getVariables()IndTestMvpLrt.getVariables()Returns the list of searchVariables over which this independence checker is capable of determinining independence relations.IndTestProbabilistic.getVariables()Returns the list of variables used in this object.IndTestRcotCcaWilkes.getVariables()Retrieves the list of variables that are being used in the current context.IndTestRegression.getVariables()Deprecated.Returns the list of variables associated with this object.IndTestStratifiedRank.getVariables()Returns the variables of the test: the common variables of the strata, with the domain column removed.IndTestTrekSep.getVariables()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.Kci.getVariables()Retrieves the list of variables associated with the current instance.LegendreLrIndependenceTest.getVariables()Retrieves the list of variables used in the statistical independence test.MinimaxCITest.getVariables()Retrieves the list of variables associated with this object.MsepTest.getVariables()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.PythonKciIndependenceTest.getVariables()Retrieves the list of variables involved in the analysis.PythonRcitIndependenceTest.getVariables()Retrieves the list of variables involved in the analysis.Rcit.getVariables()Retrieves the list of variables represented as Node objects.ScoreIndTest.getVariables()Returns the list of variables over which this independence checker is capable of determinining independence relations.TrekSeparationBlocksIndependence.getVariables()Retrieves the list of variable nodes associated with this instance.TRffLrTest.getVariables()Retrieves the list of variables involved in the test.Methods in edu.cmu.tetrad.search.test with parameters of type NodeModifier and TypeMethodDescriptionCachedIndependenceQueries.checkIndependence(Node x, Node y, Set<Node> z) IndependenceTest API: returns an IndependenceResult.CachingIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the conditional independence between two nodes given a condition set of nodes.EdgePriorTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x and y given z, judging the weighted p-value against the base alpha.FfCi.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes, x and y, given a set of conditioning nodes, z.FfCi1.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes, x and y, given a set of conditioning nodes, z.FfCiContinuous.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the independence between two nodes given a set of conditioning nodes.Gcm.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a conditioning set of nodes.default IndependenceResultIndependenceTest.checkIndependence(Node x, Node y, Node... z) Checks the independence fact in question and returns and independence result.IndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two variables x and y given a conditioning set z.IndTestBasisExpandedGcm.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x and y given z using the basis-expanded GCM statistic.IndTestBasisFunctionBlocks.checkIndependence(Node x, Node y, Set<Node> z) Checks for statistical independence between two given variables (nodes), conditioned on a set of other variables.IndTestBasisFunctionLrt.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Tests for the conditional independence of two nodes given a set of conditioning nodes.IndTestBasisFunctionLrtFullSample.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Tests for the conditional independence of two nodes, x and y, given a set of conditioning nodes z.IndTestBlocksLemma10.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.IndTestBlocksWilkes.checkIndependence(Node x, Node y, Set<Node> z) IndTestChiSquare.checkIndependence(Node x, Node y, Set<Node> _z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestConditionalCorrelation.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x _||_ y | zIndTestConditionalGaussianLrt.checkIndependence(Node x, Node y, Set<Node> _z) Returns an independence result that states whether x _||_ y | z and what the p-value of the test is.IndTestCord.checkIndependence(Node x, Node y, Set<Node> z) Testsx _||_ y | zwith CORD and returns the result.IndTestCordEric.checkIndependence(Node x, Node y, Set<Node> z) Testsx _||_ y | zwith CORD and returns the result.IndTestDegenerateGaussianLrt.checkIndependence(Node x, Node y, Set<Node> z) IndTestDegenerateGaussianLrtFullSample.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Tests for the conditional independence of two nodes, x and y, given a set of conditioning nodes z.IndTestEquivalence.checkIndependence(Node x, Node y, Set<Node> z) 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.IndTestFdrWrapper.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of two nodes given a conditioning set of nodes.IndTestFisherZ.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of two nodes given a conditioning set and returns the result.IndTestFisherZConcatenateResiduals.checkIndependence(Node x, Node y, Set<Node> _z) Deprecated.Determines whether x _||_ y | z.IndTestFisherZFisherPValue.checkIndependence(Node x, Node y, Set<Node> _z) Checks for independence between two nodes given a set of conditioning nodes.IndTestGin.checkIndependence(Node x, Node y, Set<Node> cond) Checks the independence of two given nodes, conditioned on a set of other nodes.IndTestGSquare.checkIndependence(Node x, Node y, Set<Node> _z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestHsic.checkIndependence(Node y, Node x, Set<Node> _z) Deprecated.Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestIndependenceFacts.checkIndependence(Node x, Node y, Set<Node> __z) Checks independence by looking up facts in the list of facts supplied in the constructor.IndTestMulti.checkIndependence(Node x, Node y, Set<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestMvpLrt.checkIndependence(Node x, Node y, Set<Node> _z) Determines whether two nodes are independent given a set of conditioning nodes.IndTestProbabilistic.checkIndependence(Node x, Node y, Node... z) Checks the independence fact in question and returns and independence result.IndTestProbabilistic.checkIndependence(Node x, Node y, Set<Node> _z) Checks the independence between two variables x and y given a conditioning set z.IndTestRcotCcaWilkes.checkIndependence(Node x, Node y, Set<Node> z) Checks the statistical independence between two variables x and y given a set of conditioning variables z.IndTestRegression.checkIndependence(Node xVar, Node yVar, Set<Node> zList) Deprecated.Checks the independence between two variables, given a set of conditioning variables.IndTestStratifiedRank.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x and y given z, pooling over domains.IndTestTrekSep.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Determines independence between variables x and y, given the set of variables z.Kci.checkIndependence(Node x, Node y, Set<Node> z) 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.LegendreLrIndependenceTest.checkIndependence(Node x, Node y, Set<Node> _z) Checks whether two nodes, x and y, are statistically independent given a set of conditioning nodes z.MinimaxCITest.checkIndependence(Node x, Node y, Set<Node> z) Checks the conditional independence between two variables given a conditioning set of variables using statistical testing.MsepTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes with respect to a set of conditioning nodes.PythonKciIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Tests the conditional independence between two variables given a conditioning set, using a Python backend for statistical computations.PythonRcitIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Tests the conditional independence between two variables given a conditioning set, using a Python backend for statistical computations.Rcit.checkIndependence(Node x, Node y, Set<Node> z) Checks the statistical independence between two nodes x and y, optionally conditioned on a set of variables z.ScoreIndTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of additional nodes.TrekSeparationBlocksIndependence.checkIndependence(Node x, Node y, Set<Node> z) Evaluates whether two nodes (variables) are independent given a set of conditioning nodes using a block-based conditional independence test.TRffLrTest.checkIndependence(Node x, Node y, Set<Node> _z) Evaluates the statistical independence of two nodes given a conditioning set using nested TRFF likelihood-ratio fits.static IndependenceResultDiscreteIndependenceUtils.conditionalChiSquare(DataSet dataSet, List<Node> variables, List<Integer> rows, Node x, Node y, List<Node> z, IndependenceFact fact, double alpha) Conditional chi-square test for X _||_ Y | Z, stratifying on Z value tuples.booleanEdgePriorTest.determines(Set<Node> z, Node y) Returns true if y is determined by z, per the wrapped test.default booleanIndependenceTest.determines(Set<Node> z, Node y) Returns true if y is determined the variable in z.booleanIndTestChiSquare.determines(List<Node> z, Node x) Determines whether variable x is independent of variable y given a list of conditioning nodes.booleanIndTestChiSquare.determines(Set<Node> _z, Node x) Determines whether variable x is independent of a set of variables _z.booleanIndTestConditionalGaussianLrt.determines(List<Node> z, Node y) Determines whether a given list of nodes (z) determines a node (y).booleanIndTestFisherZ.determines(List<Node> z, Node x) Determines whether the given set of nodes, z, has a deterministic relationship with the specified node, x.booleanIndTestFisherZConcatenateResiduals.determines(List<Node> z, Node x) Deprecated.Determines whether the z nodes determine the x node.booleanIndTestFisherZFisherPValue.determines(List<Node> z, Node x) Determines if a given list of conditioning nodes (z) determines the value of a specific node (x).booleanIndTestGSquare.determines(Set<Node> _z, Node x) Determines whether variable x is independent of a set of variables _z.booleanIndTestHsic.determines(List<Node> z, Node x) Deprecated.Determines whether variable x is independent of variable y given a list of conditioning variables z.booleanIndTestIndependenceFacts.determines(List<Node> z, Node y) Determines if the given list of nodes (z) determines the specified node (y).booleanIndTestMulti.determines(List<Node> z, Node x) Determines whether variable x is independent of variable y given a list of conditioning variables z.booleanIndTestMvpLrt.determines(List<Node> z, Node y) Determines whether two nodes are independent given a set of conditioning nodes.booleanIndTestProbabilistic.determines(Set<Node> z, Node y) Determines whether a given set of nodes, z, determines another node, y.booleanIndTestRegression.determines(List<Node> zList, Node xVar) Deprecated.Determines if a variable xVar can be determined by a list of conditioning variables zList.booleanIndTestTrekSep.determines(List<Node> z, Node x) Deprecated.Determines the independence between a set of variables z and a variable x.booleanMsepTest.determines(List<Node> z, Node x1) Determines if a node is m-separated from a set of conditioning nodes.booleanScoreIndTest.determines(List<Node> z, Node y) Determines the result of an independence test between a set of variables and a target variable.doubleReturns the effective significance level for the unordered pair {x, y}, namely w_xy * alpha, capped at 1.doubleCalculates the p-value for testing conditional independence between two variables given a set of conditioning variables in the context of a statistical model.Returns the pvalue if the fact of X _||_ Y | Z is within the cache of results for independence fact.doubleCalculates the p-value for the partial correlation between two nodes conditioned on a set of other nodes.doubleComputes the p-value for testing the conditional independence between two variables (x and y) given a conditioning set of variables (z).Computes the full result (partial correlation, p-value, degrees of freedom, BIC, and Ledoit-Wolf delta) for a single conditional-independence test.CachedIndependenceQueries.ImpliedFactProvider.impliedFactsForVertex(Node vertex) Retrieves the list of impliedIndependenceFactobjects for the given vertex.static booleanDiscreteIndependenceUtils.isAllDiscrete(Node x, Node y, Set<Node> z) Returns true if x, y, and all nodes in z are discrete variables.doubleKci.isIndependenceConditional(Node x, Node y, List<Node> z) Tests for conditional independence between two variables given a set of conditioning variables.doubleConditionalCorrelationIndependence.isIndependent(Node x, Node y, Set<Node> _z) Determines whether two given nodes are independent given a set of conditioning nodes, and calculates a score.booleanMsepTest.isMSeparated(Node x, Node y, Set<Node> z) Auxiliary method to calculate msep(x, y | z) directly from nodes instead of from variables.doubleConditionalCorrelationIndependence.permutationTest(Node x, Node y, Set<Node> z, int numPermutations) Performs a permutation test to empirically determine the distribution of p-values under the null hypothesis.doubleIndTestProbabilistic.probConstraint(BCInference bci, BCInference.OP op, Node x, Node y, Node[] z, Map<Node, Integer> indices) Returns the probability of the constraint x op y | z.CachedIndependenceQueries.pValuesForVertex(CachedIndependenceQueries.ImpliedFactProvider provider, Node vertex) Computes and returns a list of p-values associated with the given vertex based on implied independence facts provided by the given provider.doubleReturns the weight for the unordered pair {x, y}.Method parameters in edu.cmu.tetrad.search.test with type arguments of type NodeModifier and TypeMethodDescriptionCachedIndependenceQueries.checkIndependence(Node x, Node y, Set<Node> z) IndependenceTest API: returns an IndependenceResult.CachingIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the conditional independence between two nodes given a condition set of nodes.EdgePriorTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x and y given z, judging the weighted p-value against the base alpha.FfCi.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes, x and y, given a set of conditioning nodes, z.FfCi1.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes, x and y, given a set of conditioning nodes, z.FfCiContinuous.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the independence between two nodes given a set of conditioning nodes.Gcm.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a conditioning set of nodes.IndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two variables x and y given a conditioning set z.IndTestBasisExpandedGcm.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x and y given z using the basis-expanded GCM statistic.IndTestBasisFunctionBlocks.checkIndependence(Node x, Node y, Set<Node> z) Checks for statistical independence between two given variables (nodes), conditioned on a set of other variables.IndTestBasisFunctionLrt.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Tests for the conditional independence of two nodes given a set of conditioning nodes.IndTestBasisFunctionLrtFullSample.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Tests for the conditional independence of two nodes, x and y, given a set of conditioning nodes z.IndTestBlocksLemma10.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.IndTestBlocksWilkes.checkIndependence(Node x, Node y, Set<Node> z) IndTestChiSquare.checkIndependence(Node x, Node y, Set<Node> _z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestConditionalCorrelation.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x _||_ y | zIndTestConditionalGaussianLrt.checkIndependence(Node x, Node y, Set<Node> _z) Returns an independence result that states whether x _||_ y | z and what the p-value of the test is.IndTestCord.checkIndependence(Node x, Node y, Set<Node> z) Testsx _||_ y | zwith CORD and returns the result.IndTestCordEric.checkIndependence(Node x, Node y, Set<Node> z) Testsx _||_ y | zwith CORD and returns the result.IndTestDegenerateGaussianLrt.checkIndependence(Node x, Node y, Set<Node> z) IndTestDegenerateGaussianLrtFullSample.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Tests for the conditional independence of two nodes, x and y, given a set of conditioning nodes z.IndTestEquivalence.checkIndependence(Node x, Node y, Set<Node> z) 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.IndTestFdrWrapper.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of two nodes given a conditioning set of nodes.IndTestFisherZ.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of two nodes given a conditioning set and returns the result.IndTestFisherZConcatenateResiduals.checkIndependence(Node x, Node y, Set<Node> _z) Deprecated.Determines whether x _||_ y | z.IndTestFisherZFisherPValue.checkIndependence(Node x, Node y, Set<Node> _z) Checks for independence between two nodes given a set of conditioning nodes.IndTestGin.checkIndependence(Node x, Node y, Set<Node> cond) Checks the independence of two given nodes, conditioned on a set of other nodes.IndTestGSquare.checkIndependence(Node x, Node y, Set<Node> _z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestHsic.checkIndependence(Node y, Node x, Set<Node> _z) Deprecated.Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestIndependenceFacts.checkIndependence(Node x, Node y, Set<Node> __z) Checks independence by looking up facts in the list of facts supplied in the constructor.IndTestMulti.checkIndependence(Node x, Node y, Set<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestMvpLrt.checkIndependence(Node x, Node y, Set<Node> _z) Determines whether two nodes are independent given a set of conditioning nodes.IndTestProbabilistic.checkIndependence(Node x, Node y, Set<Node> _z) Checks the independence between two variables x and y given a conditioning set z.IndTestRcotCcaWilkes.checkIndependence(Node x, Node y, Set<Node> z) Checks the statistical independence between two variables x and y given a set of conditioning variables z.IndTestRegression.checkIndependence(Node xVar, Node yVar, Set<Node> zList) Deprecated.Checks the independence between two variables, given a set of conditioning variables.IndTestStratifiedRank.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence of x and y given z, pooling over domains.IndTestTrekSep.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Determines independence between variables x and y, given the set of variables z.Kci.checkIndependence(Node x, Node y, Set<Node> z) 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.LegendreLrIndependenceTest.checkIndependence(Node x, Node y, Set<Node> _z) Checks whether two nodes, x and y, are statistically independent given a set of conditioning nodes z.MinimaxCITest.checkIndependence(Node x, Node y, Set<Node> z) Checks the conditional independence between two variables given a conditioning set of variables using statistical testing.MsepTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes with respect to a set of conditioning nodes.PythonKciIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Tests the conditional independence between two variables given a conditioning set, using a Python backend for statistical computations.PythonRcitIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Tests the conditional independence between two variables given a conditioning set, using a Python backend for statistical computations.Rcit.checkIndependence(Node x, Node y, Set<Node> z) Checks the statistical independence between two nodes x and y, optionally conditioned on a set of variables z.ScoreIndTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of additional nodes.TrekSeparationBlocksIndependence.checkIndependence(Node x, Node y, Set<Node> z) Evaluates whether two nodes (variables) are independent given a set of conditioning nodes using a block-based conditional independence test.TRffLrTest.checkIndependence(Node x, Node y, Set<Node> _z) Evaluates the statistical independence of two nodes given a conditioning set using nested TRFF likelihood-ratio fits.static IndependenceResultDiscreteIndependenceUtils.conditionalChiSquare(DataSet dataSet, List<Node> variables, List<Integer> rows, Node x, Node y, List<Node> z, IndependenceFact fact, double alpha) Conditional chi-square test for X _||_ Y | Z, stratifying on Z value tuples.booleanEdgePriorTest.determines(Set<Node> z, Node y) Returns true if y is determined by z, per the wrapped test.default booleanIndependenceTest.determines(Set<Node> z, Node y) Returns true if y is determined the variable in z.booleanIndTestChiSquare.determines(List<Node> z, Node x) Determines whether variable x is independent of variable y given a list of conditioning nodes.booleanIndTestChiSquare.determines(Set<Node> _z, Node x) Determines whether variable x is independent of a set of variables _z.booleanIndTestConditionalGaussianLrt.determines(List<Node> z, Node y) Determines whether a given list of nodes (z) determines a node (y).booleanIndTestFisherZ.determines(List<Node> z, Node x) Determines whether the given set of nodes, z, has a deterministic relationship with the specified node, x.booleanIndTestFisherZConcatenateResiduals.determines(List<Node> z, Node x) Deprecated.Determines whether the z nodes determine the x node.booleanIndTestFisherZFisherPValue.determines(List<Node> z, Node x) Determines if a given list of conditioning nodes (z) determines the value of a specific node (x).booleanIndTestGSquare.determines(Set<Node> _z, Node x) Determines whether variable x is independent of a set of variables _z.booleanIndTestHsic.determines(List<Node> z, Node x) Deprecated.Determines whether variable x is independent of variable y given a list of conditioning variables z.booleanIndTestIndependenceFacts.determines(List<Node> z, Node y) Determines if the given list of nodes (z) determines the specified node (y).booleanIndTestMulti.determines(List<Node> z, Node x) Determines whether variable x is independent of variable y given a list of conditioning variables z.booleanIndTestMvpLrt.determines(List<Node> z, Node y) Determines whether two nodes are independent given a set of conditioning nodes.booleanIndTestProbabilistic.determines(Set<Node> z, Node y) Determines whether a given set of nodes, z, determines another node, y.booleanIndTestRegression.determines(List<Node> zList, Node xVar) Deprecated.Determines if a variable xVar can be determined by a list of conditioning variables zList.booleanIndTestTrekSep.determines(List<Node> z, Node x) Deprecated.Determines the independence between a set of variables z and a variable x.booleanMsepTest.determines(List<Node> z, Node x1) Determines if a node is m-separated from a set of conditioning nodes.booleanScoreIndTest.determines(List<Node> z, Node y) Determines the result of an independence test between a set of variables and a target variable.doubleCalculates the p-value for testing conditional independence between two variables given a set of conditioning variables in the context of a statistical model.Returns the pvalue if the fact of X _||_ Y | Z is within the cache of results for independence fact.doubleCalculates the p-value for the partial correlation between two nodes conditioned on a set of other nodes.doubleComputes the p-value for testing the conditional independence between two variables (x and y) given a conditioning set of variables (z).Computes the full result (partial correlation, p-value, degrees of freedom, BIC, and Ledoit-Wolf delta) for a single conditional-independence test.Gcm.indTestSubset(List<Node> vars) Generates a subset independence test that is restricted to the provided variables.default IndependenceTestIndependenceTest.indTestSubset(List<Node> vars) Returns an Independence test for a sublist of the variables.IndTestChiSquare.indTestSubset(List<Node> nodes) Checks conditional independence between variables in a subset.IndTestConditionalCorrelation.indTestSubset(List<Node> vars) Constructs a new Independence test which checks independence facts based on the correlation data implied by the given data set (must be continuous).IndTestConditionalGaussianLrt.indTestSubset(List<Node> vars) This method returns an instance of the IndependenceTest interface that can test the independence of a subset of variables.IndTestDegenerateGaussianLrt.indTestSubset(List<Node> vars) IndTestFisherZ.indTestSubset(List<Node> vars) Returns a new IndependenceTest instance for a subset of variables.IndTestFisherZConcatenateResiduals.indTestSubset(List<Node> vars) Deprecated.Returns an Independence test for a sublist of the variables.IndTestFisherZFisherPValue.indTestSubset(List<Node> vars) Returns an Independence test for a sublist of the variables.IndTestGSquare.indTestSubset(List<Node> vars) Performs an independence test on a subset of variables.IndTestHsic.indTestSubset(List<Node> vars) Deprecated.Subset of variables for independence testing.IndTestIndependenceFacts.indTestSubset(List<Node> vars) Returns anIndependenceTestobject for a sublist of variables.IndTestMulti.indTestSubset(List<Node> vars) Returns an Independence test for a sublist of the variables.IndTestMvpLrt.indTestSubset(List<Node> vars) Returns an Independence test for a sublist of the variables.IndTestProbabilistic.indTestSubset(List<Node> vars) Returns an Independence test for a sublist of the variables.IndTestRegression.indTestSubset(List<Node> vars) Deprecated.Performs an independence test for a sublist of variables.IndTestTrekSep.indTestSubset(List<Node> vars) Deprecated.Determines independence between variables in a given subset.MinimaxCITest.indTestSubset(List<Node> vars) Performs an independence test on a subset of variables and returns the result.MsepTest.indTestSubset(List<Node> vars) Conducts an independence test on a subset of variables.ScoreIndTest.indTestSubset(List<Node> vars) Tests the independence between variables in a given sublist.voidProcessPythonCiService.initializeIfNeeded(DataSet data, List<Node> vars, Map<String, Object> kciParams) voidPrepares the service for operation if it has not been initialized yet.static booleanDiscreteIndependenceUtils.isAllDiscrete(Node x, Node y, Set<Node> z) Returns true if x, y, and all nodes in z are discrete variables.doubleKci.isIndependenceConditional(Node x, Node y, List<Node> z) Tests for conditional independence between two variables given a set of conditioning variables.doubleConditionalCorrelationIndependence.isIndependent(Node x, Node y, Set<Node> _z) Determines whether two given nodes are independent given a set of conditioning nodes, and calculates a score.booleanMsepTest.isMSeparated(Node x, Node y, Set<Node> z) Auxiliary method to calculate msep(x, y | z) directly from nodes instead of from variables.doubleConditionalCorrelationIndependence.permutationTest(Node x, Node y, Set<Node> z, int numPermutations) Performs a permutation test to empirically determine the distribution of p-values under the null hypothesis.doubleIndTestProbabilistic.probConstraint(BCInference bci, BCInference.OP op, Node x, Node y, Node[] z, Map<Node, Integer> indices) Returns the probability of the constraint x op y | z.CachedIndependenceQueries.pValuesForAllVertices(Collection<Node> vertices, CachedIndependenceQueries.ImpliedFactProvider provider) Computes and returns a list of p-values for all given vertices based on the independence facts provided by the specifiedImpliedFactProvider.voidIndTestFisherZ.setVariables(List<Node> variables) Sets the list of variables for the instance.voidIndTestTrekSep.setVariables(List<Node> variables) Deprecated.Sets the varialbe to this list (of the same length).Constructor parameters in edu.cmu.tetrad.search.test with type arguments of type NodeModifierConstructorDescriptionIndTestFisherZ(Matrix data, List<Node> variables, double alpha) Constructs an instance of the IndTestFisherZ class, which is a statistical test for conditional independence based on the Fisher Z-test.IndTestFisherZ(Matrix data, List<Node> variables, double alpha, double ridge) Constructor for the IndTestFisherZ class, which performs a Fisher Z independence test with ridge regularization applied to handle issues with covariance matrix inversion.IndTestHsic(Matrix data, List<Node> variables, double alpha) Deprecated.Constructs a new HSIC Independence test.IndTestTrekSep(ICovarianceMatrix covMatrix, double alpha, List<List<Node>> clustering, List<Node> latents) Deprecated.Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.Constructs a Kci instance using specified data, variable-to-row mapping, an optional hint matrix, and a list of row indices.MsepTest(IndependenceFacts facts, List<Node> variables) Constructor. -
Uses of Node in edu.cmu.tetrad.search.unmix
Methods in edu.cmu.tetrad.search.unmix that return types with arguments of type NodeModifier and TypeMethodDescriptionParentSupersetBuilder.build(DataSet data, ParentSupersetBuilder.Config cfg) Builds a map representing a superset of parent nodes for each variable node in the given data set.ParentSupersetBuilder.build(DataSet data, ParentSupersetBuilder.Config cfg) Builds a map representing a superset of parent nodes for each variable node in the given data set.Methods in edu.cmu.tetrad.search.unmix with parameters of type NodeModifier and TypeMethodDescriptionvoidFits the linear regression model to the given data.voidFits a local mechanism for the target variable using its parents from the provided dataset.double[]Predicts the regression output for the given dataset, target node, and parent nodes using the model parameters.double[]Predicts fitted values for all rows for the given target using the current fitted model.default double[]Convenience method computing residuals = y - yhat for all rows.Method parameters in edu.cmu.tetrad.search.unmix with type arguments of type NodeModifier and TypeMethodDescriptionvoidFits the linear regression model to the given data.voidFits a local mechanism for the target variable using its parents from the provided dataset.double[]Predicts the regression output for the given dataset, target node, and parent nodes using the model parameters.double[]Predicts fitted values for all rows for the given target using the current fitted model.static double[][]ResidualUtils.residualMatrix(DataSet data, Map<Node, List<Node>> parentsMap, ResidualRegressor reg) 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.static double[][]ResidualUtils.residualMatrix(DataSet data, Map<Node, List<Node>> parentsMap, ResidualRegressor reg) 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.default double[]Convenience method computing residuals = y - yhat for all rows. -
Uses of Node in edu.cmu.tetrad.search.utils
Methods in edu.cmu.tetrad.search.utils that return NodeModifier and TypeMethodDescriptionTeyssierScorer.get(int j) Returns the node at index j in pi.Bes.Arrow.getA()Returns the first node.Bes.Arrow.getB()Returns the second node.TetradNode.getI()Getter for the fieldi.TetradNode.getJ()Getter for the fieldj.TetradNode.getK()Getter for the fieldk.TetradNode.getL()Getter for the fieldl.GrowShrinkTree.getNode()Getter for the fieldnode.R5R9Dijkstra.DijkstraEdge.getToNode()Retrieves to-node represented by this DijkstraEdge.DiscriminatingPath.getV()Retrieves the middle node that forms a collider in the discriminating path.DiscriminatingPath.getW()Retrieves the node following the starting node in the discriminating path.DiscriminatingPath.getX()Retrieves the starting node of the discriminating path.DiscriminatingPath.getY()Retrieves the ending node of the discriminating path.static Nodetranslate.Methods in edu.cmu.tetrad.search.utils that return types with arguments of type NodeModifier and TypeMethodDescriptionLogUtilsSearch.buildIndexing(List<Node> nodes) buildIndexing.R5R9Dijkstra.distances(R5R9Dijkstra.Graph dijkstraGraph, boolean uncovered, Node x, Node y, boolean r9) Finds shortest distances from a x node to all other nodes in a dijkstraGraph, subject to the following constraints.R5R9Dijkstra.distances(R5R9Dijkstra.Graph dijkstraGraph, boolean uncovered, Node x, Node y, boolean r9) Finds shortest distances from a x node to all other nodes in a dijkstraGraph, subject to the following constraints.R5R9Dijkstra.distances(R5R9Dijkstra.Graph dijkstraGraph, boolean uncovered, Node x, Node y, boolean r9) Finds shortest distances from a x node to all other nodes in a dijkstraGraph, subject to the following constraints.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.Returns the parents of the node x.Retrieves the sepset previously set for {a, b}, or null if no such set was previously set.TeyssierScorer.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorer.getAncestors(Node node) getAncestors.Returns a map of nodes to bidirected edges for them.TeyssierScorer.getChildren(int p) Returns the children of a node v.TeyssierScorer.getChildren(Node v) Returns the children of a node v.DiscriminatingPath.getColliderPath()Retrieves the list of nodes that represents the collider path in the discriminating path.PossibleMConnectingPath.getConditions()Getter for the fieldconditions.TeyssierScorer.getEdges()Returns a list of edges for the current graph as a list of ordered pairs.GrowShrinkTree.getFirstLayer()getFirstLayer.GrowShrinkTree.getForbidden()Getter for the fieldforbidden.R5R9Dijkstra.Graph.getNodes()Retrieves the nodes in the graph.TetradNode.getNodes()getNodes.AdTree.getNodesHash()Returns the nodes hash, for sharing across bootstrap instances.AdTreeCount.getNodesHash()Returns the nodes hash, for sharing across bootstrap instances.TeyssierScorer.getOrderShallow()Returns the current permutation without making a copy.Bes.Arrow.getParents()Returns the set of nodes that are in TNeighbors.TeyssierScorer.getParents(int p) Returns the parents of the node at index p.TeyssierScorer.getParents(Node v) Returns the parents of a node v.PossibleMConnectingPath.getPath()Getter for the fieldpath.TeyssierScorer.getPi()Getter for the fieldpi.TeyssierScorer.getPrefix(int i) Retrieves a prefix of the size specified by the parameter.GraphSearchUtils.getReachableNodes(List<Node> initialNodes, LegalPairs legalPairs, List<Node> c, List<Node> d, Graph graph, int maxPathLength) getReachableNodes.GrowShrinkTree.getRequired()Getter for the fieldrequired.Retrieves the sepset, which is the set of common neighbors between two given nodes.PossibleDsepFci.getSepset(IndependenceTest test, Node node1, Node node2) Getter for the fieldsepset.Retrieves the sepset, which is the set of common neighbors between two given nodes.Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.Retrieves the separating set (sepset) between two nodes in the graph.Retrieves the sepset (separating set) between two nodes which contains a set of nodes.Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.Retrieves the separation set (sepset) between two nodes.Retrieves the sepset between two nodes.DagSepsets.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Returns the sepset containing nodes 'a' and 'b' that also contains all the nodes in the given set 's'.SepsetProducer.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Retrieves a sepset containing nodes in s from the given set of nodes.SepsetsGreedy.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsGreedyMb.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsMaxP.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsMinP.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsPossibleDsep.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) Retrieves the separation set (sepset) between two nodes i and k that contains a given set of nodes s.SepsetsSet.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Retrieves the sepset for a and b, where we are expecting this sepset to contain all the nodes in s.TeyssierScorer.getShuffledVariables()getShuffledVariables.TeyssierScorer.getSkeleton()getSkeleton.Bes.Arrow.getTNeighbors()Returns the set of nodes that are in TNeighbors.Bes.getVariables()Returns the variables being searched over.BesPermutation.getVariables()Returns the variables.DagSepsets.getVariables()Retrieves the list of variables.DeltaSextadTest.getVariables()Returns the variables of the data being used.DeltaTetradTest.getVariables()Returns the list of variables in the dataset.DeltaTetradTest2.getVariables()Returns the variables of the data being used.DeltaTetradTest3.getVariables()Returns the list of variables in the dataset.GrowShrinkTree.getVariables()getVariables.SepsetProducer.getVariables()Retrieves the list of variables.SepsetsGreedy.getVariables()Retrieves the variables used in the independence test.SepsetsGreedyMb.getVariables()Retrieves the variables used in the independence test.SepsetsMaxP.getVariables()Retrieves the variables used in the independence test.SepsetsMinP.getVariables()Retrieves the variables used in the independence test.SepsetsPossibleDsep.getVariables()Retrieves the list of variables.SepsetsSet.getVariables()Retrieves the list of variables.TetradTest.getVariables()getVariables.TetradTestContinuous.getVariables()Getter for the fieldvariables.TetradTestDiscrete.getVariables()getVariables.TetradTestPopulation.getVariables()getVariables.GraphoidAxioms.GraphoidIndFact.getX()Returns the set of nodes X.GraphoidAxioms.GraphoidIndFact.getY()Returns the set of nodes Y.GraphoidAxioms.GraphoidIndFact.getZ()Returns the set of nodes Z.FlopInitialOrder.initialOrder(DataSet dataSet) Computes the FLOP initial order for the variables in the given data set.FlopInitialOrder.initialOrder(ICovarianceMatrix covariances) Computes the FLOP initial order for the variables of the given covariance matrix.SepsetMap.keySet()Returns the set of all keys in the map.PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node, Node> withoutPair) Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node, Node> withoutPair) Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).MeekRules.orientImplied(Graph graph) Uses the Meek rules to do as many orientations in the given graph as possible.powerSet.purify.purify.FcitSepsets.SepsetResult.sepset()Returns the value of thesepsetrecord component.AlmostCycleRemover.tKeys()Returns the set of nodes that are keys in the map of triples.ClusterSignificance.variablesForIndices(List<Integer> cluster, List<Node> variables) Converts a list of indices into a list of Nodes representing a cluster.Converts a list of indices into a list of Nodes representing a cluster.Methods in edu.cmu.tetrad.search.utils with parameters of type NodeModifier and TypeMethodDescriptionvoidCorrectnessGates.addKnownSeparator(Node x, Node z, Set<Node> sep) Declares a test-confirmed separator for a pair, trusted by the I-map check whenever it m-separates the pair in a candidate MAG.voidCorrectnessGates.addRefutedSeed(Node a, Node b) 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.voidAdds a triple consisting of three given nodes to the data structure.booleanReturns True iff a is adjacent to b in the current graph.static voidGraphSearchUtils.basicCpdagRestricted2(Graph graph, Node node) basicCpdagRestricted2.booleanReturns true iff [a, b, c] is a collider.static StringLogUtilsSearch.colliderOrientedMsg(Node x, Node y, Node z) colliderOrientedMsg.static StringcolliderOrientedMsg.static StringLogUtilsSearch.colliderOrientedMsg(String note, Node x, Node y, Node z) colliderOrientedMsg.doubleCalculates the partial correlation of x and y conditional on the nodes in z recursively.booleanTeyssierScorer.coveredEdge(Node x, Node y) Returns true iff x->y or y->x is a covered edge.static StringLogUtilsSearch.dependenceFactMsg(Node x, Node y, Set<Node> condSet, double pValue) dependenceFactMsg.static StringLogUtilsSearch.determinismDetected(Set<Node> sepset, Node x) determinismDetected.R5R9Dijkstra.distances(R5R9Dijkstra.Graph dijkstraGraph, boolean uncovered, Node x, Node y, boolean r9) Finds shortest distances from a x node to all other nodes in a dijkstraGraph, subject to the following constraints.static intComputes the per-fact effective sample size for x _||_ y | z under the given block structure.static booleanTsDagToPag.existsInducingPathInto(Node x, Node y, Graph graph, Knowledge knowledge) existsInducingPathInto.static booleanTsDagToPag.existsInducingPathVisitts(Graph graph, Node a, Node b, Node x, Node y, LinkedList<Node> path, Knowledge knowledge) existsInducingPathVisitts.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.static List<PossibleMConnectingPath> PossibleMConnectingPath.findMConnectingPaths(Graph pag, Node x, Node y, Collection<Node> z) Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z.static List<PossibleMConnectingPath> PossibleMConnectingPath.findMConnectingPathsOfLength(Graph pag, Node x, Node y, Collection<Node> z, Integer length) 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.MbUtils.generateMbDags(Graph mbCPDAG, boolean orientBidirectedEdges, IndependenceTest test, int depth, Node target) Generates the list of MB DAGs consistent with the MB CPDAG returned by the previous search.Returns the parents of the node x.Retrieves the sepset previously set for {a, b}, or null if no such set was previously set.TeyssierScorer.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorer.getAncestors(Node node) getAncestors.TeyssierScorer.getChildren(Node v) Returns the children of a node v.GraphSearchUtils.getCpcTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph) getCpcTripleType.Getter for the fieldindex.R5R9Dijkstra.Graph.getNeighbors(Node node) Retrieves the filtered neighbors of a given node.TeyssierScorer.getParents(Node v) Returns the parents of a node v.doubledoubleLooks up the p-value for {x, y}doubleCalculates the p-value for a statistical test a _||_ b | sepset.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doubleRetrieves the p-value from the result of an independence test between two nodes, given a set of separating nodes.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doublestatic doubleResolveSepsets.getPValuePooled(ResolveSepsets.Method method, List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) 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.static doubleResolveSepsets.getPValuePooledFisher(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Returns the pooled p-value using Fisher's method.static doubleResolveSepsets.getPValuePooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Returns the pooled p-value using Fisher's method 2.static doubleResolveSepsets.getPValuePooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Returns the pooled p-value using Tippett's method.static StringLogUtilsSearch.getScoreFact(Node i, List<Node> parents) getScoreFact.Retrieves the sepset, which is the set of common neighbors between two given nodes.PossibleDsepFci.getSepset(IndependenceTest test, Node node1, Node node2) Getter for the fieldsepset.Retrieves the sepset, which is the set of common neighbors between two given nodes.Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.Retrieves the separating set (sepset) between two nodes in the graph.Retrieves the sepset (separating set) between two nodes which contains a set of nodes.Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.Retrieves the separation set (sepset) between two nodes.Retrieves the sepset between two nodes.DagSepsets.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Returns the sepset containing nodes 'a' and 'b' that also contains all the nodes in the given set 's'.SepsetProducer.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Retrieves a sepset containing nodes in s from the given set of nodes.SepsetsGreedy.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsGreedyMb.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsMaxP.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsMinP.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsPossibleDsep.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) Retrieves the separation set (sepset) between two nodes i and k that contains a given set of nodes s.SepsetsSet.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Retrieves the sepset for a and b, where we are expecting this sepset to contain all the nodes in s.Returns the set of triples for the given node.static StringLogUtilsSearch.independenceFact(Node x, Node y, Set<Node> condSet) independenceFact.static StringLogUtilsSearch.independenceFactMsg(Node x, Node y, Set<Node> condSet, double pValue) independenceFactMsg.intReturn the index of v in the current permutation.static booleanFciOrient.isArrowheadAllowed(Node x, Node y, Graph graph, Knowledge K) 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.booleanGrowShrinkTree.isForbidden(Node node) isForbidden.booleanDagSepsets.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetProducer.isIndependent(Node d, Node c, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsGreedy.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsGreedyMb.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsMaxP.isIndependent(Node a, Node b, Set<Node> sepset) Determines if two nodes are independent given a set of separating nodes.booleanSepsetsMinP.isIndependent(Node a, Node b, Set<Node> sepset) Determines if two nodes are independent given a set of separating nodes.booleanSepsetsPossibleDsep.isIndependent(Node d, Node c, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsSet.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.static booleanResolveSepsets.isIndependentPooled(ResolveSepsets.Method method, List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Tests for independence using one of the pooled methodsstatic booleanResolveSepsets.isIndependentPooledAverage(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average p valuestatic booleanResolveSepsets.isIndependentPooledAverageTest(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average test statistic CURRENTLY ONLY WORKS FOR CHISQUARE TESTstatic booleanResolveSepsets.isIndependentPooledFisher(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Fisher's method.static booleanResolveSepsets.isIndependentPooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Eliminates from considerations independence tests that cannot be evaluated (due to missing variables mainly).static booleanResolveSepsets.isIndependentPooledMudholkerGeorge(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Mudholker and George's methodstatic booleanResolveSepsets.isIndependentPooledMudholkerGeorge2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.static booleanResolveSepsets.isIndependentPooledRandom(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by randomly selecting a p valuestatic booleanResolveSepsets.isIndependentPooledStouffer(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Stouffer et al.'s methodstatic booleanResolveSepsets.isIndependentPooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Tippett's methodstatic booleanResolveSepsets.isIndependentPooledWilkinson(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet, int r) Checks independence from pooled samples using Wilkinson's methodstatic booleanResolveSepsets.isIndependentPooledWorsleyFriston(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Worsley and Friston's methodbooleanLegalPairs.isLegalFirstEdge(Node x, Node y) isLegalFirstEdge.booleanisLegalPair.booleanGrowShrinkTree.isRequired(Node node) isRequired.booleanDagSepsets.isUnshieldedCollider(Node i, Node j, Node k, int depth) isUnshieldedCollider.booleanR0R4Strategy.isUnshieldedCollider(Graph graph, Node a, Node b, Node c) Determines if a given triple is an unshielded collider based on an examination of the data.booleanR0R4StrategyTestBased.isUnshieldedCollider(Graph graph, Node i, Node j, Node k) Checks if a collider is unshielded or not.booleanSepsetProducer.isUnshieldedCollider(Node i, Node j, Node k, int depth) isUnshieldedCollider.booleanSepsetsGreedy.isUnshieldedCollider(Node i, Node j, Node k, int depth) isUnshieldedCollider.booleanSepsetsGreedyMb.isUnshieldedCollider(Node i, Node j, Node k, int depth) isUnshieldedCollider.booleanSepsetsMaxP.isUnshieldedCollider(Node i, Node j, Node k, int depth) Determines if a node is an unshielded collider between two other nodes.booleanSepsetsMinP.isUnshieldedCollider(Node i, Node j, Node k, int depth) Checks if a given collider node is unshielded between two other nodes.booleanSepsetsPossibleDsep.isUnshieldedCollider(Node i, Node j, Node k, int depth) isUnshieldedCollider.booleanSepsetsSet.isUnshieldedCollider(Node i, Node j, Node k, int depth) isUnshieldedCollider.booleanCorrectnessGates.judgedIndependent(Node x, Node z, Set<Node> cond) 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.static Set<DiscriminatingPath> FciOrient.listDiscriminatingPaths(Graph graph, Node w, Node y, int maxLen, boolean checkXyNonadjacency) Finds and returns the set of discriminating paths in the given graph, based on the specified parameters.booleanPreserveMarkov.markovIndependence(Node x, Node y, Set<Node> z) 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.voidMoves v to a new index.booleanparent.voidR1: If α *→ β o––* γ, and α and γ are not adjacent, then orient the triple as α *→ β → γ.voidR10 (Zhang 2008 FCI orientation rule).voidR2: If α → β ∘→ γ or α ∘→ β → γ, and α ∘–o γ, then orient α ∘–o γ as α ∘→ γ.booleanR8: If α → β → γ or α −∘ β → γ, and α ∘→ γ, then orient α ∘→ γ as α → γ.booleanR9: 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.voidSets the sepset for {x, y} to be z.voidSepsetMap.set(Node x, LinkedHashSet<Node> z) Sets the parents of x to the (ordered) set z.voidKernel.setDefaultBw(DataSet dataset, Node node) Sets bandwidth from data using default methodvoidKernelGaussian.setDefaultBw(DataSet dataset, Node node) Sets bandwidth from data using default methodvoidDefaultSetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint endpoint) Sets the endpoint of a graph given the two nodes and the desired endpoint.voidSetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint arrow) Sets the endpoint of a graph given the two nodes and the desired endpoint.voidKernelGaussian.setMedianBandwidth(DataSet dataset, Node node) Sets the bandwidth of the kernel to median distance between two points in the given vectorstatic FcitSepsets.SepsetResultFcitSepsets.spanningSepset(Graph graph, IndependenceTest test, Node x, Node y, int recursiveDepth, int depth, int rbRadius, long deadline, Runnable onTest) Searches for a separating set S such that x and y are conditionally independent given S within the specified constraints.static FcitSepsets.SepsetSearchFcitSepsets.spanningSepsetSearch(Graph graph, IndependenceTest test, Node x, Node y, int recursiveDepth, int depth, int rbRadius, long deadline, Runnable onTest) Searches for a set S with x _||_ y | S in the given graph.booleanSwaps m and n in the permutation.booleanReturns true iff [a, b, c] is a triangle.static voidMbUtils.trimEdgesAmongParents(Graph graph, Node target) Removes edges among the parents of the target.static voidMbUtils.trimEdgesAmongParentsOfChildren(Graph graph, Node target) Removes edges among the parents of children of the target.static voidMbUtils.trimToAdjacents(Graph graph, Node target) Trims the graph to just the adjacents of the target.static voidMbUtils.trimToMbNodes(Graph graph, Node target, boolean includeBidirected) Trims the graph to the target, the parents and children of the target, and the parents of the children of the target.booleanAlmostCycleRemover.tripleAllowed(Node x, Node b, Node z) Determines whether a triple consisting of three given nodes is allowed.booleanMoves j to before k and moves all the ancestors of j betwween k and j to before k.booleanMoves all j's to before k and moves all the ancestors of all ji's betwween k and ji to before k.booleanTeyssierScorer.unshieldedCollider(Node a, Node b, Node c) Returns true iff [a, b, c] is an unshielded collider.booleanTeyssierScorer.unshieldedTriple(Node a, Node b, Node c) Returns true iff [a, b, c] is an unshielded triple.Method parameters in edu.cmu.tetrad.search.utils with type arguments of type NodeModifier and TypeMethodDescriptionvoidCorrectnessGates.addKnownSeparator(Node x, Node z, Set<Node> sep) Declares a test-confirmed separator for a pair, trusted by the I-map check whenever it m-separates the pair in a candidate MAG.voidRuns BES for a graph over the given list of variablesvoidRuns BES.LogUtilsSearch.buildIndexing(List<Node> nodes) buildIndexing.booleanTrue iff the nodes in W form a clique in the current DAG.static StringcolliderOrientedMsg.doubleCalculates the partial correlation of x and y conditional on the nodes in z recursively.static StringLogUtilsSearch.dependenceFactMsg(Node x, Node y, Set<Node> condSet, double pValue) dependenceFactMsg.static StringLogUtilsSearch.determinismDetected(Set<Node> sepset, Node x) determinismDetected.org.apache.commons.lang3.tuple.Pair<DiscriminatingPath, Boolean> R0R4Strategy.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, int maxBlockingPathLength, int maxDiscriminatingPathLength, Graph graph, Set<Node> vNodes) Does a discriminating path orientation based on an examination of the data.org.apache.commons.lang3.tuple.Pair<DiscriminatingPath, Boolean> R0R4StrategyTestBased.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, int recursiveDepth, int maxDiscriminatingPathLength, Graph graph, Set<Node> vNodes) Does a discriminating path orientation.static intComputes the per-fact effective sample size for x _||_ y | z under the given block structure.static booleanTsDagToPag.existsInducingPathVisitts(Graph graph, Node a, Node b, Node x, Node y, LinkedList<Node> path, Knowledge knowledge) existsInducingPathVisitts.voidFciOrient.fciOrientbk(Knowledge bk, Graph graph, List<Node> variables, boolean excludeSelectionBias) Orient the edges of a graph based on the given knowledge.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.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.static List<PossibleMConnectingPath> PossibleMConnectingPath.findMConnectingPaths(Graph pag, Node x, Node y, Collection<Node> z) Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z.static List<PossibleMConnectingPath> PossibleMConnectingPath.findMConnectingPathsOfLength(Graph pag, Node x, Node y, Collection<Node> z, Integer length) 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.static R0R4StrategyTestBasedMagToPag.getFinalStrategyUsingDsep(Graph mag, Knowledge knowledge, boolean verbose, Map<Node, Set<Node>> ancestorCache) Returns the final strategy for finding a PAG using D-SEP.static R0R4StrategyTestBasedMagToPag.getFinalStrategyUsingDsep(Graph mag, Knowledge knowledge, boolean verbose, Map<Node, Set<Node>> ancestorCache) Returns the final strategy for finding a PAG using D-SEP.doubledoubleCalculates the p-value for a statistical test a _||_ b | sepset.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doubleRetrieves the p-value from the result of an independence test between two nodes, given a set of separating nodes.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doubleReturns the p-value for the independence test between two nodes, given a set of separator nodes.doublestatic doubleResolveSepsets.getPValuePooled(ResolveSepsets.Method method, List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) 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.static doubleResolveSepsets.getPValuePooledFisher(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Returns the pooled p-value using Fisher's method.static doubleResolveSepsets.getPValuePooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Returns the pooled p-value using Fisher's method 2.static doubleResolveSepsets.getPValuePooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Returns the pooled p-value using Tippett's method.GraphSearchUtils.getReachableNodes(List<Node> initialNodes, LegalPairs legalPairs, List<Node> c, List<Node> d, Graph graph, int maxPathLength) getReachableNodes.static StringLogUtilsSearch.getScoreFact(int i, int[] parents, List<Node> variables) getScoreFact.static StringLogUtilsSearch.getScoreFact(Node i, List<Node> parents) getScoreFact.Retrieves the sepset, which is the set of common neighbors between two given nodes.Retrieves the sepset, which is the set of common neighbors between two given nodes.Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.Retrieves the separating set (sepset) between two nodes in the graph.Retrieves the sepset (separating set) between two nodes which contains a set of nodes.Retrieves the sepset (separating set) between two nodes, or null if no such sepset is found.Retrieves the separation set (sepset) between two nodes.Retrieves the sepset between two nodes.DagSepsets.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Returns the sepset containing nodes 'a' and 'b' that also contains all the nodes in the given set 's'.SepsetProducer.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Retrieves a sepset containing nodes in s from the given set of nodes.SepsetsGreedy.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsGreedyMb.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsMaxP.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsMinP.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) 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.SepsetsPossibleDsep.getSepsetContaining(Node i, Node k, Set<Node> s, int depth) Retrieves the separation set (sepset) between two nodes i and k that contains a given set of nodes s.SepsetsSet.getSepsetContaining(Node a, Node b, Set<Node> s, int depth) Retrieves the sepset for a and b, where we are expecting this sepset to contain all the nodes in s.static StringLogUtilsSearch.independenceFact(Node x, Node y, Set<Node> condSet) independenceFact.static StringLogUtilsSearch.independenceFactMsg(Node x, Node y, Set<Node> condSet, double pValue) independenceFactMsg.booleanDagSepsets.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetProducer.isIndependent(Node d, Node c, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsGreedy.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsGreedyMb.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsMaxP.isIndependent(Node a, Node b, Set<Node> sepset) Determines if two nodes are independent given a set of separating nodes.booleanSepsetsMinP.isIndependent(Node a, Node b, Set<Node> sepset) Determines if two nodes are independent given a set of separating nodes.booleanSepsetsPossibleDsep.isIndependent(Node d, Node c, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.booleanSepsetsSet.isIndependent(Node a, Node b, Set<Node> sepset) Checks if node d is independent of node c given the set of nodes in sepset.static booleanResolveSepsets.isIndependentPooled(ResolveSepsets.Method method, List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Tests for independence using one of the pooled methodsstatic booleanResolveSepsets.isIndependentPooledAverage(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average p valuestatic booleanResolveSepsets.isIndependentPooledAverageTest(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average test statistic CURRENTLY ONLY WORKS FOR CHISQUARE TESTstatic booleanResolveSepsets.isIndependentPooledFisher(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Fisher's method.static booleanResolveSepsets.isIndependentPooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Eliminates from considerations independence tests that cannot be evaluated (due to missing variables mainly).static booleanResolveSepsets.isIndependentPooledMudholkerGeorge(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Mudholker and George's methodstatic booleanResolveSepsets.isIndependentPooledMudholkerGeorge2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.static booleanResolveSepsets.isIndependentPooledRandom(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by randomly selecting a p valuestatic booleanResolveSepsets.isIndependentPooledStouffer(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Stouffer et al.'s methodstatic booleanResolveSepsets.isIndependentPooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Tippett's methodstatic booleanResolveSepsets.isIndependentPooledWilkinson(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet, int r) Checks independence from pooled samples using Wilkinson's methodstatic booleanResolveSepsets.isIndependentPooledWorsleyFriston(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Worsley and Friston's methodstatic PagLegalityCheck.LegalMagRetPagLegalityCheck.isLegalMag(Graph mag, Set<Node> selection) Determines whether the given graph is a legal Maximal Ancestral Graph (MAG).static PagLegalityCheck.LegalMagRetPagLegalityCheck.isLegalMag(Graph mag, Set<Node> selection, int timeoutSeconds) RunsPagLegalityCheck.isLegalMag(Graph, Set)with a timeout.static booleanPagLegalityCheck.isLegalMagQuiet(Graph mag, Set<Node> selection) Determines whether the given graph is a legal Maximal Ancestral Graph (MAG) without providing detailed error messages.static PagLegalityCheck.LegalPagRetPagLegalityCheck.isLegalPag(Graph pag, Graph mag, Set<Node> selection) The same certificate with the Zhang MAG ofpagsupplied 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.static PagLegalityCheck.LegalPagRetPagLegalityCheck.isLegalPag(Graph pag, Set<Node> selection) Checks if the provided Partial Ancestral Graph (PAG) is a legal PAG.static PagLegalityCheck.LegalPagRetPagLegalityCheck.isLegalPag(Graph pag, Set<Node> selection, int timeoutSeconds) RunsPagLegalityCheck.isLegalPag(Graph, Set)with a timeout.static PagLegalityCheck.LegalPagRetPagLegalityCheck.isLegalPagModuloKnowledge(Graph pag, Set<Node> selection, Knowledge knowledge, FciOrient orient, boolean excludeSelectionBias) Legality certificate for a PAG that carries background-knowledge endpoint marks: legal PAG *modulo background knowledge*.static booleanPagLegalityCheck.isLegalPagQuiet(Graph pag, Set<Node> selection) Determines whether the provided Partial Ancestral Graph (PAG) is a legal PAG without providing detailed error messages.static booleanPagLegalityCheck.isLegalPagQuiet(Graph pag, Set<Node> selection, int timeoutSeconds) RunsPagLegalityCheck.isLegalPagQuiet(Graph, Set)with a timeout.booleanisLegalPair.booleanCorrectnessGates.judgedIndependent(Node x, Node z, Set<Node> cond) 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.PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node, Node> withoutPair) Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node, Node> withoutPair) Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node, Node> withoutPair) Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node, Node>, Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node, Node> withoutPair) Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).booleanPreserveMarkov.markovIndependence(Node x, Node y, Set<Node> z) 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.static voidGraphSearchUtils.pcOrientbk(Knowledge bk, Graph graph, List<Node> nodes, boolean verbose) Orients according to background knowledge.powerSet.purify.purify.double[][]Resolves this store against a variable list into a dense symmetric matrix indexed by that list's order.EdgePriors.restrictTo(List<Node> variables) Returns a copy of this store keeping only the pairs both of whose variables occur among the given ones.doubleScores the given permutation.voidSets the sepset for {x, y} to be z.voidSepsetMap.set(Node x, LinkedHashSet<Node> z) Sets the parents of x to the (ordered) set z.voidGrowShrinkTree.setKnowledge(List<Node> required, List<Node> forbidden) setKnowledge.voidBes.Arrow.setTNeighbors(Set<Node> TNeighbors) Sets the set of nodes that are in TNeighbors.doubletrace.doubletrace.static Nodetranslate.EdgePriors.unmatchedNames(List<Node> variables) Returns the names mentioned by this store that do not occur among the given variables.Converts a list of indices into a list of Nodes representing a cluster.Constructors in edu.cmu.tetrad.search.utils with parameters of type NodeModifierConstructorDescriptionDijkstraEdge(Node y, int weight) Represents an edge connecting two nodes in Dijkstra's algorithm.DiscriminatingPath(Node x, Node w, Node v, Node y, LinkedList<Node> colliderPath, boolean checkXyNonadjacency) Constructs a DiscriminatingPath object with the specified parameters.Constructor for GrowShrinkTree.KernelGaussian(DataSet dataset, Node node) Creates a new Gaussian kernel using the median distance between points to set the bandwidthTetradNode(Node i, Node j, Node k, Node l) Constructor for Tetrad.TetradNode(Node i, Node j, Node k, Node l, double pValue) Constructor for Tetrad.Constructor parameters in edu.cmu.tetrad.search.utils with type arguments of type NodeModifierConstructorDescriptionConstructs an AdTreeCount using pre-initialized discrete data, avoiding re-scanning the dataset.Constructs an AdTreeCount using pre-initialized discrete data, avoiding re-scanning the dataset.ClusterSignificance(List<Node> variables, DataModel dataModel) Constructs a new cluster significance object.DiscriminatingPath(Node x, Node w, Node v, Node y, LinkedList<Node> colliderPath, boolean checkXyNonadjacency) Constructs a DiscriminatingPath object with the specified parameters.GraphoidAxioms(Set<GraphoidAxioms.GraphoidIndFact> facts, List<Node> nodes) Constructor.GraphoidAxioms(Set<GraphoidAxioms.GraphoidIndFact> facts, List<Node> nodes, Map<GraphoidAxioms.GraphoidIndFact, String> textSpecs) Constructor.Constructor.Constructor for GrowShrinkTree.PartialCorrelation(List<Node> nodes, Matrix cov, int sampleSize) Constructor.SepsetResult(Set<Node> sepset, Double pValue) Creates an instance of aSepsetResultrecord class. -
Uses of Node in edu.cmu.tetrad.search.work_in_progress
Methods in edu.cmu.tetrad.search.work_in_progress that return NodeModifier and TypeMethodDescriptionSextad.getI()Getter for the fieldi.Sextad.getJ()Getter for the fieldj.Sextad.getK()Getter for the fieldk.Sextad.getL()Getter for the fieldl.Sextad.getM()Getter for the fieldm.Sextad.getN()Getter for the fieldn.getNode.IndTestFisherZRecursive.getVariable(String name) Deprecated.Returns The variable by the given name.IndTestPositiveCorr.getVariable(String name) Deprecated.Retrieves the node associated with the given variable name.IndTestSepsetDci.getVariable(Node node) getVariable.IndTestSepsetDci.getVariable(String name) Retrieves the variable with the specified name.ProbabilisticMapIndependence.getVariable(String name) Returns The variable by the given name.Methods in edu.cmu.tetrad.search.work_in_progress that return types with arguments of type NodeModifier and TypeMethodDescriptionbestOrder.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.get.Retrieves the sepset previously set for {x, y}, or null if no such set was previously set.VcFas.getApparentlyNonadjacencies()Getter for the fieldapparentlyNonadjacencies.LvBossExperimental.getBestOrder()The best order found by the last search, or null before any search.getInputs.DMSearch.LatentStructure.getLatentEffects(Node latent) getLatentEffects.DMSearch.LatentStructure.getLatents()getLatents.Sextad.getNodes()getNodes.VcFas.getNodes()getNodes.MagCgBicScore.getOrder()Returns the order.MagDgBicScore.getOrder()Returns the order.MagSemBicScore.getOrder()Returns the order.DMSearch.LatentStructure.getOutputs(Node latent) getOutputs.Getter for the fieldpc.SepsetMapDci.getSeparatedPairs()getSeparatedPairs.Retrieves the set of all condioning sets for {x, y} or null if no such set was ever setGraspTol.getVariables()Getter for the fieldvariables.IndTestCramerT.getVariables()Deprecated.Retrieves the list of variables used in the independence test.IndTestFisherZPercentIndependent.getVariables()Deprecated.Getter for the fieldvariables.IndTestFisherZRecursive.getVariables()Deprecated.Getter for the fieldvariables.IndTestMixedMultipleTTest.getVariables()Deprecated.Retrieves the list of variables used in the original data set.IndTestMultinomialLogisticRegression.getVariables()getVariables.IndTestPositiveCorr.getVariables()Deprecated.Retrieves the list of variables used in the independence test.IndTestSepsetDci.getVariables()getVariables.MagCgBicScore.getVariables()The variables of the score.MagDgBicScore.getVariables()The variables of the score.MagSemBicScore.getVariables()The variables of the score.ProbabilisticMapIndependence.getVariables()Retrieves the list of variables associated with this independence test.SemBicScoreDeterministic.getVariables()The variables of the score.GraspTol.grasp(@NotNull TeyssierScorer scorer) grasp.Methods in edu.cmu.tetrad.search.work_in_progress with parameters of type NodeModifier and TypeMethodDescriptionvoidAdd another orient operation to the GraphChange.voidDMSearch.LatentStructure.addRecord(Node latent, SortedSet<Node> inputs, SortedSet<Node> outputs, SortedSet<Node> latentEffects) addRecord.IndTestCramerT.checkIndependence(Node x, Node y, Set<Node> _z) Deprecated.Checks the independence between two nodes given a set of conditioning nodes.IndTestFisherZPercentIndependent.checkIndependence(Node x, Node y, Set<Node> _z) Deprecated.Checks the independence between two nodes x and y given a set of conditioning nodes z.IndTestFisherZRecursive.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Checks the independence between two variables x and y given a conditioning set z.IndTestMixedMultipleTTest.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Checks for independence between two nodes.IndTestMultinomialLogisticRegression.checkIndependence(Node x, Node y, Set<Node> z) Checks for independence between two nodes, given a set of conditioning nodes.IndTestPositiveCorr.checkIndependence(Node x0, Node y0, Set<Node> _z0) Deprecated.Checks the independence between two nodes, given a set of conditioning nodes.IndTestSepsetDci.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes, given a set of conditioning nodes.ProbabilisticMapIndependence.checkIndependence(Node x, Node y, Node... z) Checks the independence fact in question and returns and independence result.ProbabilisticMapIndependence.checkIndependence(Node x, Node y, Set<Node> _z) Checks the independence between two variables x and y given a conditioning set z.booleanDMSearch.LatentStructure.containsLatent(Node latent) containsLatent.booleanIndTestCramerT.determines(List<Node> z, Node x) Deprecated.Determines whether the given variables are conditionally independent.booleanIndTestFisherZPercentIndependent.determines(List z, Node x) Deprecated.Determines the independence between a list of conditioning variables (z) and a target variable (x).booleanIndTestFisherZRecursive.determines(Set<Node> _z, Node x) Deprecated.Returns true if y is determined the variable in z.booleanIndTestMixedMultipleTTest.determines(List<Node> z, Node y) Deprecated.Determines if a given set of nodes z determines the node y.booleanIndTestMultinomialLogisticRegression.determines(List<Node> z, Node y) Determines if Node y is determined by the given list of Nodes z.booleanIndTestPositiveCorr.determines(List<Node> z, Node x) Deprecated.Determines if there exists a causal relationship between the nodes in z and node x.booleanIndTestSepsetDci.determines(List<Node> z, Node x1) Determines if a given Node is present in a List of Nodes.booleanProbabilisticMapIndependence.determines(Set<Node> z, Node y) Returns true if y is determined the variable in z.booleanSemBicScoreDeterministic.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.Given the target, this returns all the nodes in the Markov Blanket.static voidSampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidSampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.get.Retrieves the sepset previously set for {x, y}, or null if no such set was previously set.getInputs.DMSearch.LatentStructure.getLatentEffects(Node latent) getLatentEffects.getNode.DMSearch.LatentStructure.getOutputs(Node latent) getOutputs.Getter for the fieldpc.VcPc.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose) getPopulationTripleType.VcPcFast.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose) getPopulationTripleType.Retrieves the set of all condioning sets for {x, y} or null if no such set was ever setIndTestSepsetDci.getVariable(Node node) getVariable.static booleanSampleVcpcFast.isArrowheadAllowed1(Node from, Node to, Knowledge knowledge) isArrowheadAllowed1.static booleanVcPc.isArrowheadAllowed1(Node from, Node to, Knowledge knowledge) isArrowheadAllowed1.static booleanResolveSepsetsDci.isIndependentPooled(ResolveSepsetsDci.Method method, List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Tests for independence using one of the pooled methodsstatic booleanResolveSepsetsDci.isIndependentPooledAverage(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average p valuestatic booleanResolveSepsetsDci.isIndependentPooledAverageTest(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average test statistic CURRENTLY ONLY WORKS FOR CHISQUARE TESTstatic booleanResolveSepsetsDci.isIndependentPooledFisher(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Fisher's method.static booleanResolveSepsetsDci.isIndependentPooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Eliminates from considerations independence tests that cannot be evaluated (due to missing variables mainly).static booleanResolveSepsetsDci.isIndependentPooledMudholkerGeorge(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Mudholker and George's methodstatic booleanResolveSepsetsDci.isIndependentPooledMudholkerGeorge2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.static booleanResolveSepsetsDci.isIndependentPooledRandom(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by randomly selecting a p valuestatic booleanResolveSepsetsDci.isIndependentPooledStouffer(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Stouffer et al.'s methodstatic booleanResolveSepsetsDci.isIndependentPooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Tippett's methodstatic booleanResolveSepsetsDci.isIndependentPooledWilkinson(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet, int r) Checks independence from pooled samples using Wilkinson's methodstatic booleanResolveSepsetsDci.isIndependentPooledWorsleyFriston(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Worsley and Friston's methoddoubleProbabilisticMapIndependence.probConstraint(BCInference.OP op, Node x, Node y, Node[] z) probConstraint.voidDMSearch.LatentStructure.removeLatent(Node latent) removeLatent.voidSets the sepset for {x, y} to be z.voidSepsetMapDci.set(Node x, LinkedHashSet<Node> z) set.Method parameters in edu.cmu.tetrad.search.work_in_progress with type arguments of type NodeModifier and TypeMethodDescriptionvoidDMSearch.LatentStructure.addRecord(Node latent, SortedSet<Node> inputs, SortedSet<Node> outputs, SortedSet<Node> latentEffects) addRecord.ResolveSepsetsDci.allNodePairs(List<Node> nodes) Generates NodePairs of all possible pairs of nodes from given list of nodes.bestOrder.IndTestCramerT.checkIndependence(Node x, Node y, Set<Node> _z) Deprecated.Checks the independence between two nodes given a set of conditioning nodes.IndTestFisherZPercentIndependent.checkIndependence(Node x, Node y, Set<Node> _z) Deprecated.Checks the independence between two nodes x and y given a set of conditioning nodes z.IndTestFisherZRecursive.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Checks the independence between two variables x and y given a conditioning set z.IndTestMixedMultipleTTest.checkIndependence(Node x, Node y, Set<Node> z) Deprecated.Checks for independence between two nodes.IndTestMultinomialLogisticRegression.checkIndependence(Node x, Node y, Set<Node> z) Checks for independence between two nodes, given a set of conditioning nodes.IndTestPositiveCorr.checkIndependence(Node x0, Node y0, Set<Node> _z0) Deprecated.Checks the independence between two nodes, given a set of conditioning nodes.IndTestSepsetDci.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes, given a set of conditioning nodes.ProbabilisticMapIndependence.checkIndependence(Node x, Node y, Set<Node> _z) Checks the independence between two variables x and y given a conditioning set z.booleanIndTestCramerT.determines(List<Node> z, Node x) Deprecated.Determines whether the given variables are conditionally independent.booleanIndTestFisherZRecursive.determines(Set<Node> _z, Node x) Deprecated.Returns true if y is determined the variable in z.booleanIndTestMixedMultipleTTest.determines(List<Node> z, Node y) Deprecated.Determines if a given set of nodes z determines the node y.booleanIndTestMultinomialLogisticRegression.determines(List<Node> z, Node y) Determines if Node y is determined by the given list of Nodes z.booleanIndTestPositiveCorr.determines(List<Node> z, Node x) Deprecated.Determines if there exists a causal relationship between the nodes in z and node x.booleanIndTestSepsetDci.determines(List<Node> z, Node x1) Determines if a given Node is present in a List of Nodes.booleanProbabilisticMapIndependence.determines(Set<Node> z, Node y) Returns true if y is determined the variable in z.booleanSemBicScoreDeterministic.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.static voidSampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidSampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidSampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidSampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.static voidVcPcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) futureNodeVisit.IndTestCramerT.indTestSubset(List<Node> vars) Deprecated.This method performs an independence test based on a given sublist of variables.IndTestFisherZPercentIndependent.indTestSubset(List<Node> vars) Deprecated.Performs an independence test on a subset of variables.IndTestFisherZRecursive.indTestSubset(List<Node> vars) Deprecated.Returns an Independence test for a sublist of the variables.IndTestMixedMultipleTTest.indTestSubset(List<Node> vars) Deprecated.IndTestMultinomialLogisticRegression.indTestSubset(List<Node> vars) Performs an independence test for a sublist of variables.IndTestPositiveCorr.indTestSubset(List<Node> vars) Deprecated.Performs an independence test on a subset of variables.IndTestSepsetDci.indTestSubset(List<Node> vars) Determines independence between a subset of variables.ProbabilisticMapIndependence.indTestSubset(List<Node> vars) Returns an Independence test for a sublist of the variables.static booleanResolveSepsetsDci.isIndependentPooled(ResolveSepsetsDci.Method method, List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Tests for independence using one of the pooled methodsstatic booleanResolveSepsetsDci.isIndependentPooledAverage(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average p valuestatic booleanResolveSepsetsDci.isIndependentPooledAverageTest(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by taking the average test statistic CURRENTLY ONLY WORKS FOR CHISQUARE TESTstatic booleanResolveSepsetsDci.isIndependentPooledFisher(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Fisher's method.static booleanResolveSepsetsDci.isIndependentPooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Eliminates from considerations independence tests that cannot be evaluated (due to missing variables mainly).static booleanResolveSepsetsDci.isIndependentPooledMudholkerGeorge(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Mudholker and George's methodstatic booleanResolveSepsetsDci.isIndependentPooledMudholkerGeorge2(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.static booleanResolveSepsetsDci.isIndependentPooledRandom(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples by randomly selecting a p valuestatic booleanResolveSepsetsDci.isIndependentPooledStouffer(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Stouffer et al.'s methodstatic booleanResolveSepsetsDci.isIndependentPooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Tippett's methodstatic booleanResolveSepsetsDci.isIndependentPooledWilkinson(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet, int r) Checks independence from pooled samples using Wilkinson's methodstatic booleanResolveSepsetsDci.isIndependentPooledWorsleyFriston(List<IndependenceTest> independenceTests, Node x, Node y, Set<Node> condSet) Checks independence from pooled samples using Worsley and Friston's methodbooleanDetermines whether one trek is a subtrek of another trekProjects the order to a high-scoring ancestral graph consistent with it.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.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.voidSets the sepset for {x, y} to be z.voidSepsetMapDci.set(Node x, LinkedHashSet<Node> z) set.voidSets the order.voidSets the order.voidSets the order.voidIndTestFisherZRecursive.setVariables(List<Node> variables) Deprecated.Setter for the fieldvariables.voidIndTestPositiveCorr.setVariables(List<Node> variables) Deprecated.Sets the variables used in the independence test.voidSemBicScoreDeterministic.setVariables(List<Node> variables) Setter for the fieldvariables.Constructors in edu.cmu.tetrad.search.work_in_progress with parameters of type NodeModifierConstructorDescriptionConstructor for Sextad.Constructor for Sextad.Constructor parameters in edu.cmu.tetrad.search.work_in_progress with type arguments of type NodeModifierConstructorDescriptionFasDci(Graph graph, IndependenceTest independenceTest, ResolveSepsets.Method method, List<Set<Node>> marginalVars, List<IndependenceTest> independenceTests, SepsetMapDci knownIndependencies, SepsetMapDci knownAssociations) Constructs a new FastAdjacencySearch for DCI with independence test pooling to resolve inconsistencies.Constructor for GraspTol.IndTestFisherZRecursive(Matrix data, List<Node> variables, double alpha) Deprecated.Constructs a new Fisher Z independence test with the listed arguments.IndTestSepsetDci(SepsetMapDci sepset, List<Node> nodes) Constructs a new independence test that returns d-separation facts for the given graph as independence results. -
Uses of Node in edu.cmu.tetrad.sem
Fields in edu.cmu.tetrad.sem with type parameters of type NodeModifier and TypeFieldDescriptionTrainedDagSimulatorANM.SimResult.variablesA list of variables involved in the simulation, including both continuous and discrete variables.TrainedDagSimulatorGNM.SimResult.variablesList of variables in the simulation result.TrainedDagSimulatorGNM1.SimResult.variablesList of variables in the simulation result.TrainedDagSimulatorGNM2.SimResult.variablesList of variables in the simulation result.Methods in edu.cmu.tetrad.sem that return NodeModifier and TypeMethodDescriptionGeneralizedSemPm.getErrorNode(Node node) Retrieves the error node associated with the given node.Node[]ConnectionFunction.getInputNodes()getInputNodes.Returns the Node with the given name.SemEvidence.getNode(int nodeIndex) getNode.SemManipulation.getNode(int nodeIndex) getNode.Parameter.getNodeA()Getter for the fieldnodeA.Parameter.getNodeB()Getter for the fieldnodeB.SemIm.getVariableNode(String name) getVariableNode.Methods in edu.cmu.tetrad.sem that return types with arguments of type NodeModifier and TypeMethodDescriptioncliques.GeneralizedSemPm.getErrorNodes()Returns the list of exogenous variableNodes.StandardizedSemIm.getErrorNodes()getErrorNodes.SemPm.getLatentNodes()getLatentNodes.ReidentifyVariables.getLatents(Graph graph) getLatents.DagScorer.getMeasuredNodes()getMeasuredNodes.GeneralizedSemPm.getMeasuredNodes()Returns a list of measured nodes.ISemIm.getMeasuredNodes()getMeasuredNodes.Scorer.getMeasuredNodes()getMeasuredNodes.SemIm.getMeasuredNodes()The list of measured nodes for the semPm.SemPm.getMeasuredNodes()getMeasuredNodes.StandardizedSemIm.getMeasuredNodes()The list of measured nodes for the semPm.GeneralizedSemPm.getNodes()Retrieves a list of nodes.SemEvidence.getNodesInEvidence()getNodesInEvidence.GeneralizedSemPm.getParents(Node node) Retrieves the list of parent nodes for the given node.GeneralizedSemPm.getReferencedNodes(Node node) Retrieves a set of referenced nodes for the given node.GeneralizedSemPm.getReferencingNodes(Node node) Retrieves the set of referencing nodes for a given node.GeneralizedSemPm.getReferencingNodes(String parameter) Returns a set of nodes that reference the given parameter.GeneralizedSemPm.getVariableNodes()Returns the list of variable nodes--that is, node that is not error nodes.ISemIm.getVariableNodes()getVariableNodes.LargeScaleSimulation.getVariableNodes()Getter for the fieldvariableNodes.SemIm.getVariableNodes()The list of measured and latent nodes for the semPm.SemPm.getVariableNodes()Getter for the fieldvariableNodes.StandardizedSemIm.getVariableNodes()getVariableNodes.DagScorer.getVariables()Getter for the fieldvariables.Scorer.getVariables()getVariables.ISemIm.listUnmeasuredLatents()listUnmeasuredLatents.SemIm.listUnmeasuredLatents()listUnmeasuredLatents.CyclicStableUtils.stronglyConnectedComponents(Graph g) Kosaraju's algorithm for strongly connected components.Methods in edu.cmu.tetrad.sem with parameters of type NodeModifier and TypeMethodDescriptionbooleanSemIm.existsEdgeCoef(Node x, Node y) Determines whether an edge coefficient exists between two given nodes.TemplateExpander.expandTemplate(String template, GeneralizedSemPm semPm, Node node) Returns the expanded template, which needs to be checked to make sure it can be used.SemPm.getCoefficientParameter(Node nodeA, Node nodeB) getCoefficientParameter.StandardizedSemIm.getCoefficientRange(Node a, Node b) getCoefficientRange.SemPm.getCovarianceParameter(Node nodeA, Node nodeB) getCovarianceParameter.StandardizedSemIm.getCovarianceRange(Node a, Node b) getCovarianceRange.doubleSemIm.getEdgeCoef(Node x, Node y) Getter for the fieldedgeCoef.doubleStandardizedSemIm.getEdgeCoef(Node a, Node b) Getter for the fieldedgeCoef.doubleSemIm.getErrCovar(Node x, Node y) Getter for the fielderrCovar.doubleStandardizedSemIm.getErrorCovariance(Node a, Node b) getErrorCovariance.GeneralizedSemPm.getErrorNode(Node node) Retrieves the error node associated with the given node.doubleStandardizedSemIm.getErrorVariance(Node error) getErrorVariance.doublegetErrVar.doubleISemIm.getIntercept(Node node) getIntercept.doubleSemIm.getIntercept(Node node) Calculates the intercept for a given node.doublegetMean.doubleCalculates the mean value associated with a givenNode.SemPm.getMeanParameter(Node node) getMeanParameter.doubleISemIm.getMeanStdDev(Node node) getMeanStdDev.doubleSemIm.getMeanStdDev(Node node) Calculates the mean standard deviation for the given node.GeneralizedSemPm.getNodeExpression(Node node) getNodeExpression.GeneralizedSemPm.getNodeExpressionString(Node node) getNodeExpressionString.intSemEvidence.getNodeIndex(Node node) getNodeIndex.GeneralizedSemIm.getNodeSubstitutedString(Node node) Retrieves the substituted string representation of a given Node.GeneralizedSemIm.getNodeSubstitutedString(Node node, Map<String, Double> substitutedValues) Retrieves the substituted string representation of a given Node.SemPm.getParameter(Node nodeA, Node nodeB) getParameter.doubleISemIm.getParamValue(Node nodeA, Node nodeB) getParamValue.doubleSemIm.getParamValue(Node nodeA, Node nodeB) getParamValue.GeneralizedSemPm.getParents(Node node) Retrieves the list of parent nodes for the given node.GeneralizedSemPm.getReferencedNodes(Node node) Retrieves a set of referenced nodes for the given node.GeneralizedSemPm.getReferencedParameters(Node node) Retrieves the set of referenced parameters from a given node.GeneralizedSemPm.getReferencingNodes(Node node) Retrieves the set of referencing nodes for a given node.doublegetStdDev.doublegetStdDev.doubleSemIm.getTotalEffect(Node x, Node y) Calculates the total effect between two nodes.doubleRetrieves the value associated with the given node.doubleISemIm.getVariance(Node nodeA, Matrix implCovar) getVariance.doubleSemIm.getVariance(Node node, Matrix implCovar) Returns the variance for a given node.SemPm.getVarianceParameter(Node node) 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.voidISemIm.setEdgeCoef(Node x, Node y, double value) setEdgeCoef.voidSemIm.setEdgeCoef(Node x, Node y, double value) Sets the coefficient value for the edge between two nodes in the graph.booleanStandardizedSemIm.setEdgeCoefficient(Node a, Node b, double coef) Sets the coefficient for the a->b edge to the given coefficient, if within range.voidSemIm.setErrCovar(Node x, double value) Deprecated.Use setErrVar(x, value) for variances, or setErrCovar(x, y, value) for covariances.voidSemIm.setErrCovar(Node x, Node y, double value) Setter for the fielderrCovar.booleanStandardizedSemIm.setErrorCovariance(Node a, Node b, double covar) Sets the covariance for the a<->b edge to the given covariance, if within range.voidsetErrVar.voidSets the error variance value for a specific node in the model's structural equation.voidISemIm.setIntercept(Node y, double intercept) setIntercept.voidSemIm.setIntercept(Node node, double intercept) Sets the intercept for a specified node in the SEM model.voidsetMean.voidSets the mean value for a given node in the variableNodes list.voidSemIm.setMeanStandardDeviation(Node node, double stdDev) Sets the standard deviation value for the specified node.voidGeneralizedSemPm.setNodeExpression(Node node, String expressionString) Sets the expression for a given node.voidISemIm.setParamValue(Node nodeA, Node nodeB, double value) setParamValue.voidSemIm.setParamValue(Node nodeA, Node nodeB, double value) setParamValue.voidSets the value for a given node in the SemProposition object.Method parameters in edu.cmu.tetrad.sem with type arguments of type NodeModifier and TypeMethodDescriptionSemIm.getImplCovar(List<Node> nodes) Getter for the fieldimplCovar.static voidCyclicStableUtils.initializeInternalEdgesRandom(SemIm im, Graph g, List<Node> scc, double low, double high) Randomize existing internal edges (that already exist in the graph) within [low, high], positive.ReidentifyVariables.reidentifyVariables1(List<List<Node>> partition, Graph trueGraph) reidentifyVariables1.reidentifyVariables2.static MatrixResidualCovariance.ridgeIfIndefinite(Matrix m, List<Node> measured) 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.static voidCyclicStableUtils.scaleInternalEdges(SemIm im, Graph g, List<Node> scc, double factor) Scale all internal edges of an SCC by a factor.static doubleCyclicStableUtils.spectralRadiusAbs(SemIm im, Graph g, List<Node> scc) Computes the spectral radius of the absolute value of the coefficient matrix of the given strongly connected component (SCC) in a graph.Constructors in edu.cmu.tetrad.sem with parameters of type NodeModifierConstructorDescriptionEmpiricalDistributionForExpression(GeneralizedSemPm semPm, Node error, Context context) Constructor for EmpiricalDistributionForExpression.Constructor for Parameter.Constructor parameters in edu.cmu.tetrad.sem with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.simulation
Methods in edu.cmu.tetrad.simulation that return types with arguments of type NodeModifier and TypeMethodDescriptionHsimUtils.getAllParents(Graph inputgraph, Set<Node> inputnodes) getAllParents.Method parameters in edu.cmu.tetrad.simulation with type arguments of type NodeModifier and TypeMethodDescriptionstatic GraphevalEdges.HsimUtils.getAllParents(Graph inputgraph, Set<Node> inputnodes) getAllParents.Constructor parameters in edu.cmu.tetrad.simulation with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.study.missingness
Methods in edu.cmu.tetrad.study.missingness that return types with arguments of type NodeMethods in edu.cmu.tetrad.study.missingness with parameters of type NodeMethod parameters in edu.cmu.tetrad.study.missingness with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.study.performance
Method parameters in edu.cmu.tetrad.study.performance with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.util
Methods in edu.cmu.tetrad.util that return NodeModifier and TypeMethodDescriptionstatic NodeJsonUtils.parseJSONObjectToTetradNode(org.json.JSONObject jObj) parseJSONObjectToTetradNode.Methods in edu.cmu.tetrad.util that return types with arguments of type NodeModifier and TypeMethodDescriptionJsonUtils.parseJSONArrayToTetradNodes(org.json.JSONArray jArray) parseJSONArrayToTetradNodes.DataConvertUtils.toNodes(DataColumn[] columns) toNodes.DataConvertUtils.toNodes(DiscreteDataColumn[] columns) toNodes.toNodes.Methods in edu.cmu.tetrad.util with parameters of type NodeModifier and TypeMethodDescriptionSplits a data set into one data set per value of a discrete domain column, dropping that column.Method parameters in edu.cmu.tetrad.util with type arguments of type Node -
Uses of Node in edu.pitt.csb.mgm
Methods in edu.pitt.csb.mgm that return types with arguments of type NodeModifier and TypeMethodDescriptionIndTestMultinomialLogisticRegressionWald.getVariables()getVariables.Methods in edu.pitt.csb.mgm with parameters of type NodeModifier and TypeMethodDescriptionIndTestMultinomialLogisticRegressionWald.checkIndependence(Node x, Node y, Set<Node> z) Determines the independence between two variables given a set of conditioning variables.booleanIndTestMultinomialLogisticRegressionWald.determines(List<Node> z, Node y) Determines the independence between a set of variables and a target variable.Method parameters in edu.pitt.csb.mgm with type arguments of type NodeModifier and TypeMethodDescriptionIndTestMultinomialLogisticRegressionWald.checkIndependence(Node x, Node y, Set<Node> z) Determines the independence between two variables given a set of conditioning variables.booleanIndTestMultinomialLogisticRegressionWald.determines(List<Node> z, Node y) Determines the independence between a set of variables and a target variable.IndTestMultinomialLogisticRegressionWald.indTestSubset(List<Node> vars) Tests the conditional independence between two variables given a sublist of variables.