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.score
Methods in edu.cmu.tetrad.algcomparison.score that return NodeModifier and TypeMethodDescriptionBasisFunctionBicScore.getVariable(String name) Returns the variable with the given name.BdeuScore.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.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.GicScores.getVariable(String name) Returns the variable with the given name.InstanceAugmentedSemBicScoreWrapper.getVariable(String name) Retrieves a variable node by its name.IsBDeuScoreWrapper.getVariable(String name) Retrieves a variable from the model by its name.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) 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.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.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 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.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) Returns the variable with the given name (assumed the target).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.JunctionTreeAlgorithm.getNodes()getNodes.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()getTargetNode.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.intgetColumn.intgetColumn.intgetColumn.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.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.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.Methods in edu.cmu.tetrad.graph that return NodeModifier and TypeMethodDescriptionstatic 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.traverseReverseDirected(Node node, Edge edge) For A -> B, given B, returns A; otherwise returns null.static NodeEdges.traverseSemiDirected(Node node, Edge edge) For A --* B or A o-* B, given A, returns B.static NodeGraphUtils.traverseSemiDirected(Node node, Edge edge) Traverses a semi-directed edge to identify the next node in the traversal.Methods in edu.cmu.tetrad.graph that return types with arguments of type NodeModifier and TypeMethodDescriptionPaths.adjustmentSets(Node source, Node target, int maxNumSets, int maxDistanceFromEndpoint, int nearWhichEndpoint, int maxPathLength) An adjustment set for a pair of nodes <source, target> for a CPDAG is a set of nodes that blocks all paths from the source to the target that cannot contribute to a calculation for the total effect of the source on the target in any DAG in a CPDAG while not blocking any path from the source to the target that could be causal.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 allowSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int maxLength, Set<Node> conditionSet, boolean allowSelectionBias) 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 allowSelectionBias) Generates all paths out of a given node within a specified maximum length and conditional set.Paths.amenablePathsMpdagMag(Node node1, Node node2, int maxLength) Finds amenable paths from the given source node to the given destination node with a maximum length.Paths.amenablePathsPag(Node node1, Node node2, int maxLength) Finds amenable paths from the given source node to the given destination node with a maximum length, for a PAG.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.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.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.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 allowSelectionBias) 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 allowSelectionBias, 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.GraphUtils.markovBlanket(Node x, Graph G) Returns a Markov blanket of a node for a DAG, CPDAG, MAG, or PAG.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.Paths.possibleDsep(Node x, int maxPossibleDsepPathLength) Identifies and returns a list of possible d-separating nodes relative to a specified node in the graph.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).Paths.semidirectedPaths(Node node1, Node node2, int maxLength) Finds all semi-directed paths between two nodes up to a maximum length.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) Adds a bidirectional edge between two nodes.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 source, Node target, int maxNumSets, int maxDistanceFromEndpoint, int nearWhichEndpoint, int maxPathLength) An adjustment set for a pair of nodes <source, target> for a CPDAG is a set of nodes that blocks all paths from the source to the target that cannot contribute to a calculation for the total effect of the source on the target in any DAG in a CPDAG while not blocking any path from the source to the target that could be causal.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 allowSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int maxLength, Set<Node> conditionSet, boolean allowSelectionBias) 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 allowSelectionBias) Generates all paths out of a given node within a specified maximum length and conditional set.Paths.amenablePathsMpdagMag(Node node1, Node node2, int maxLength) Finds amenable paths from the given source node to the given destination node with a maximum length.Paths.amenablePathsPag(Node node1, Node node2, int maxLength) Finds amenable paths from the given source node to the given destination node with a maximum length, for a PAG.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 intReturns the hashcode for this node.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.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.existsSemidirectedPath(Node node1, Node node2) Determines whether one node is an ancestor of another.default booleanGraph.existsSemidirectedPath(Node node1, Node node2) Determines whether there is a semidirected path from node1 to node2.booleanPaths.existsSemiDirectedPath(Node from, Node to) existsSemiDirectedPath.booleanPaths.existsSemiDirectedPath(Node node1, Set<Node> nodes) existsSemiDirectedPath.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.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.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.final EndpointEdge.getEndpoint(Node node) getProximalEndpoint.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.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 allowSelectionBias, 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.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 zbooleanChecks 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.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.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.isMConnectedTo(Node x, Node y, Set<Node> z, boolean allowSelectionBias) 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 allowSelectionBias) 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) Returns a Markov blanket of a node for a DAG, CPDAG, MAG, or PAG.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.static voidLayoutUtil.positionLatentNode(Node latent, Set<Node> neighbors) Positions a latent node based on the average position of its measured neighbors.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.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.Paths.semidirectedPaths(Node node1, Node node2, int maxLength) Finds all semi-directed paths between two nodes up to a maximum length.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.booleanReplicatingGraph.setEndpoint(Node from, Node to, Endpoint ep) Mirrors endpoint changes across all policy-mirrored edges.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.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.traverseReverseDirected(Node node, Edge edge) For A -> B, given B, returns A; otherwise returns null.static NodeEdges.traverseSemiDirected(Node node, Edge edge) For A --* B or A o-* B, given A, returns B.static NodeGraphUtils.traverseSemiDirected(Node node, Edge edge) Traverses a semi-directed edge to identify the next node in the traversal.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 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.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 allowSelectionBias) 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 allowSelectionBias) 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 allowSelectionBias) Finds all paths between two nodes satisfying certain conditions.Paths.allPaths(Node node1, Node node2, int maxLength, Set<Node> conditionSet, boolean allowSelectionBias) 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 allowSelectionBias) 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.existsSemiDirectedPath(Node node1, Set<Node> nodes) existsSemiDirectedPath.static voidGraphUtils.fciOrientbk(Knowledge knowledge, Graph graph, List<Node> variables) Attempts to orient the edges in the graph based on the given knowledge.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 allowSelectionBias) 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.isMConnectedTo(Node x, Node y, Set<Node> z, boolean allowSelectionBias) 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 isPag) Checks if the given path is an m-connecting path.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, boolean isPag) Checks if the given path is an m-connecting path.booleanPaths.isMConnectingPath(List<Node> path, Set<Node> conditioningSet, Map<Node, Set<Node>> ancestors, boolean allowSelectionBias) 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 allowSelectionBias) 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 allowSelectionBias) 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 allowSelectionBias) 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 allowSelectionBias) Checks if two nodes are M-separated.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, Map<Node, Set<Node>> ancestors, boolean allowSelectionBias) Checks if two nodes are M-separated.booleanPaths.isMSeparatedFrom(Node node1, Node node2, Set<Node> z, Map<Node, Set<Node>> ancestors, boolean allowSelectionBias) 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 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.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 allowSelectionBias) 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 allowSelectionBias) Returns a string representation of the given path in the graph, with additional information about conditioning variables.static voidLayoutUtil.positionLatentNode(Node latent, Set<Node> neighbors) Positions a latent node based on the average position of its measured neighbors.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 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 NodeModifierConstructorDescriptionConstructor 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. -
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.xfinal NodePc.Triple.yfinal NodePc.Triple.zMethods in edu.cmu.tetrad.search that return NodeModifier and TypeMethodDescriptionCstar.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.IsScore.getVariable(String targetName) Retrieves a variable node by its target name.MarkovCheck.getVariable(String name) Returns the variable with the given name.FciOrientDijkstra.DijkstraEdge.gety()Returns the node.Methods in edu.cmu.tetrad.search that return types with arguments of type NodeModifier and TypeMethodDescriptionGiven an initial permutation, 'order,' of the variables, searches for a best permutation of the variables by rearranging the variables in 'order.'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 maxPathLength) Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Variant ofRecursiveBlocking.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.SepsetFinder.blockPathsWithMarkovBlanket(Node x, Graph G) Identifies the set of nodes that form the Markov Blanket for a given node in a graph.Returns a map from nodes in V \ {Y} to their minimum effects.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.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, FciOrient fciOrient, int maxBlockingPathLength, int maxDdpPathLength, PreserveMarkov preserveMarkovHelper, int depth) Finds the set of nodes (separator set) for the Recursive Discriminating Path rule in a graph.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.MimbuildTrek.getClustering()Deprecated.The clustering used.MarkovCheck.getConditioningNodes()Returns the nodes that are possible Z1,...,Zn for X _||_ Y | Z1,...,Zn.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.PermutationSearch.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.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.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.Boss.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.IsScore.getVariables()Retrieves a list of variables represented as nodes.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.Sp.getVariables()Returns the list of variables associated with this object.SuborderSearch.getVariables()The list of all variables, in order.Gfci.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.StarFci.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.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.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 maxPathLength) Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Variant ofRecursiveBlocking.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.SepsetFinder.blockPathsWithMarkovBlanket(Node x, Graph G) Identifies the set of nodes that form the Markov Blanket for a given node in a graph.Returns a map from nodes in V \ {Y} to their minimum effects.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.booleanIndTestIod.determines(List<Node> z, Node x) Determines whether the variables in z determine x.booleanIsScore.determines(List<Node> z, Node y) Determines whether the given list of nodes has a specific relationship with the specified node.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.RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, FciOrient fciOrient, int maxBlockingPathLength, int maxDdpPathLength, PreserveMarkov preserveMarkovHelper, int depth) Finds the set of nodes (separator set) for the Recursive Discriminating Path rule in a graph.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.static RecursiveBlocking.BlockableRecursiveBlocking.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) 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.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.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.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.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.IndTestIod.getVariable(Node node) Returns the variable associated with the given node in the graph.Gfci.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.StarFci.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.Method parameters in edu.cmu.tetrad.search with type arguments of type NodeModifier and TypeMethodDescriptionGiven 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 maxPathLength) Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge) Variant ofRecursiveBlocking.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.RlcdCore.buildCpdagFromConstraints(List<Node> vars, RlcdCore.Stage1Output s1) Constructs a CPDAG (Completed Partially Directed Acyclic Graph) from the given variables and constraints.IndTestIod.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of nodes.booleanIndTestIod.determines(List<Node> z, Node x) Determines whether the variables in z determine x.booleanIsScore.determines(List<Node> z, Node y) Determines whether the given list of nodes has a specific relationship with the specified node.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.static RecursiveBlocking.BlockableRecursiveBlocking.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) Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.static RecursiveBlocking.BlockableRecursiveBlocking.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) Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.static RecursiveBlocking.BlockableRecursiveBlocking.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) 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.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.IndTestIod.indTestSubset(List<Node> vars) Calculates the independence test for a subset of variables.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.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.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.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.Gfci.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.Gfci.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.StarFci.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.StarFci.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.voidSets the nodes.voidSets the order list for the search.voidPcMb.setVariables(List<Node> variables) Setter for the fieldvariables.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.Constructors in edu.cmu.tetrad.search with parameters of type NodeModifierConstructorDescriptionDijkstraEdge(Node y, int weight) Creates a new DijkstraEdge.Constructs a Triple instance representing a relationship or connection between three Node objects.Constructor parameters in edu.cmu.tetrad.search with type arguments of type NodeModifierConstructorDescriptionFci(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.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.Tsc(List<Node> variables, CovarianceMatrix cov) Constructs an instance of the TscScored class using the provided variables and covariance matrix. -
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.score
Methods in edu.cmu.tetrad.search.score that return NodeModifier and TypeMethodDescriptiondefault 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 TypeMethodDescriptionBasisFunctionBicScore.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.BlocksBicScore.getVariables()Deprecated.Retrieves the list of variables associated with the current 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.GicScores.getVariables()The variables of the score.GraphScore.getVariables()The variables of the score.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.IsBDeuScore.getVariables()Retrieves the list of variables (nodes) associated with the model or scoring process.MvpScore.getVariables()The variables of the score.PoissonPriorScore.getVariables()The variables of the score.Score.getVariables()The variables of the score.SemBicScore.getVariables()Returns the variables of the covariance matrix.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.booleanGicScores.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.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.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.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) Deprecated.Computes the local score of a target node given its parent nodes in a graph.doubleCamAdditivePsplineBic.localScore(Node y, Collection<Node> parents) Local score: BIC(Y | parents) under additive P-splines, with λ_j by GCV via backfitting.doubleBlocksBicScore.localScoreDelta(Node y, List<Node> oldParents, Node changedParent, boolean adding) Deprecated.Computes the difference in local score for a given node when a parenthood relationship is either added or removed.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.booleanGicScores.determines(List<Node> z, Node y) Returns true iff the score determines the edge between x and y.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.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.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) Deprecated.Computes the local score of a target node given its parent nodes in a graph.doubleCamAdditivePsplineBic.localScore(Node y, Collection<Node> parents) Local score: BIC(Y | parents) under additive P-splines, with λ_j by GCV via backfitting.doubleBlocksBicScore.localScoreDelta(Node y, List<Node> oldParents, Node changedParent, boolean adding) Deprecated.Computes the difference in local score for a given node when a parenthood relationship is either added or removed.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 TypeMethodDescriptionCachingIndependenceTest.getVariables()Retrieves the list of variables from the underlying independence test.IndependenceTest.getVariables()Retrieves the list of variables associated with this independence test.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.IndTestBlocksTs.getVariables()Retrieves the list of variable nodes associated with this instance.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.IndTestDegenerateGaussianLrt.getVariables()Returns the list of variables over which this independence checker is capable of determinining independence relations.IndTestDegenerateGaussianLrtFullSample.getVariables()Deprecated.Retrieves the list of nodes (variables) associated with this instance.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()Retrieves the list of variables associated with this object.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.IndTestRcit.getVariables()Returns the list of variables involved in the independence test.IndTestRegression.getVariables()Deprecated.Returns the list of variables associated with this object.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.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.ScoreIndTest.getVariables()Returns the list of variables over which this independence checker is capable of determinining independence relations.Methods in edu.cmu.tetrad.search.test with parameters of type NodeModifier and TypeMethodDescriptionCachingIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the conditional independence between two nodes given a condition 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.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.IndTestBlocksTs.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.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 and independence result that states whether x _||_y | z and what the p-value of the test is.IndTestDegenerateGaussianLrt.checkIndependence(Node x, Node y, Set<Node> z) Tests for the conditional independence of two nodes given a set of conditioning nodes.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.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.IndTestRcit.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the independence between two nodes x and y given a set of conditioning nodes z.IndTestRegression.checkIndependence(Node xVar, Node yVar, Set<Node> zList) Deprecated.Checks the independence between two variables, given a set of conditioning variables.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.MsepTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes with respect to a set of conditioning nodes.ScoreIndTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of additional nodes.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.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.doubleKci.isIndependenceConditional(Node x, Node y, List<Node> z, double alpha) 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.Method parameters in edu.cmu.tetrad.search.test with type arguments of type NodeModifier and TypeMethodDescriptionCachingIndependenceTest.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the conditional independence between two nodes given a condition 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.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.IndTestBlocksTs.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.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 and independence result that states whether x _||_y | z and what the p-value of the test is.IndTestDegenerateGaussianLrt.checkIndependence(Node x, Node y, Set<Node> z) Tests for the conditional independence of two nodes given a set of conditioning nodes.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.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.IndTestRcit.checkIndependence(Node x, Node y, Set<Node> z) Evaluates the independence between two nodes x and y given a set of conditioning nodes z.IndTestRegression.checkIndependence(Node xVar, Node yVar, Set<Node> zList) Deprecated.Checks the independence between two variables, given a set of conditioning variables.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.MsepTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes with respect to a set of conditioning nodes.ScoreIndTest.checkIndependence(Node x, Node y, Set<Node> z) Checks the independence between two nodes given a set of additional nodes.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.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.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) Subsets the variables used in the independence test.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.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.doubleKci.isIndependenceConditional(Node x, Node y, List<Node> z, double alpha) 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.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.Kci(org.ejml.simple.SimpleMatrix dataVxN, Map<Node, Integer> varToRow, org.ejml.simple.SimpleMatrix hHint, List<Integer> rows) 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()Returns the node V in the discriminating path.DiscriminatingPath.getW()Retrieves the node W in the discriminating path.DiscriminatingPath.getX()Returns the node X in the discriminating path.DiscriminatingPath.getY()Returns the node Y in 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()Returns the collider subpath of 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.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.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.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 TypeMethodDescriptionvoidAdds 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 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 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.static Set<DiscriminatingPath> FciOrient.listDiscriminatingPaths(Graph graph, Node w, Node y, int maxDiscriminatingPathLength, boolean checkEcNonadjacency) Lists the discriminating paths for <w, y> in the graph.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 Suppose α oâ γ, β â γ â θ, p1 is an uncovered potentially directed (semidirected) path from α to β, and p2 is an uncovered p.d.voidR2: If α → β ∘→ γ or α ∘→ β → γ, and α ∘–o γ, then orient α ∘–o γ as α ∘→ γ.booleanR8 If α â β â γ or αâââ¦Î² â γ, and α oâ γ, orient α oâ γ as α â γ.booleanR9 If α oâ γ, and p = <α,β,θ,...,γ> is an uncovered potentialy directed path from α to γ such that γ and β are not adjacent, then orient α oâ γ as α â γ.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 vectorbooleanSwaps 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 TypeMethodDescriptionvoidRuns 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, 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, Graph graph, Set<Node> vNodes) Does a discriminating path orientation.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) 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.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.doubleGraphSearchUtils.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 methodGraphLegalityCheck.isLegalMag(Graph mag, Set<Node> selection) Determines whether the given graph is a legal Mixed Ancestral Graph (MAG).static booleanGraphLegalityCheck.isLegalMagQuiet(Graph mag, Set<Node> selection) Determines whether the given graph is a legal Mixed Ancestral Graph (MAG) without providing detailed error messages.GraphLegalityCheck.isLegalPag(Graph pag, Set<Node> selection) Checks if the provided Directed Acyclic Graph (PAG) is a legal PAG.static booleanGraphLegalityCheck.isLegalPagQuiet(Graph pag, Set<Node> selection) Determines whether the provided Partial Ancestral Graph (PAG) is a legal PAG without providing detailed error messages.booleanisLegalPair.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.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.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 checkEcNonadjacency) Represents a discriminating path construct in a graph.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 NodeModifierConstructorDescriptionClusterSignificance(List<Node> variables, DataModel dataModel) Constructs a new cluster significance object.DiscriminatingPath(Node x, Node w, Node v, Node y, LinkedList<Node> colliderPath, boolean checkEcNonadjacency) Represents a discriminating path construct in a graph.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. -
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.IsBDeuScore.getVariable(String targetName) Retrieves a Node from the variables list that matches the specified target name.IsBicScore.getVariable(String targetName) 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.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.IsBDeuScore.getVariables()Retrieves the list of variables associated with this instance.IsBicScore.getVariables()Retrieves the list of variable nodes associated with this instance.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.treks.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.booleanIsBDeuScore.determines(List<Node> z, Node y) Determines whether a given node y is determined by a list of nodes z.booleanIsBicScore.determines(List<Node> z, Node y) 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.treks.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.booleanIsBDeuScore.determines(List<Node> z, Node y) Determines whether a given node y is determined by a list of nodes z.booleanIsBicScore.determines(List<Node> z, Node y) 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 trekDeprecated.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.voidIsBDeuScore.setVariables(List<Node> variables) Sets the list of variables after validating that each variable in the provided list has the same name as the corresponding variable in the existing list.voidIsBicScore.setVariables(List<Node> variables) Sets the list of variable nodes for this instance, ensuring that the names of the input list match the names of the existing variables in order and size.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
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 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.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.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.