Uses of Interface
edu.cmu.tetrad.graph.Node
Packages that use Node
Package
Description
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Uses of Node in edu.cmu.tetrad.algcomparison.score
Methods in edu.cmu.tetrad.algcomparison.score that return NodeModifier and TypeMethodDescriptionBdeuScore.getVariable(String name) CciScore.getVariable(String name) ConditionalGaussianBicScore.getVariable(String name) ConditionalGaussianOtherBicScore.getVariable(String name) DegenerateGaussianBicScore.getVariable(String name) DiscreteBicScore.getVariable(String name) DiscreteMixedBicScore.getVariable(String name) DSeparationScore.getVariable(String name) EbicScore.getVariable(String name) FisherZScore.getVariable(String name) KimEtAlScores.getVariable(String name) MagSemBicScore.getVariable(String name) MNLRBicScore.getVariable(String name) MVPBicScore.getVariable(String name) PoissonPriorScore.getVariable(String name) PositiveCorrScore.getVariable(String name) ScoreWrapper.getVariable(String name) Returns the variable with the given name.SemBicScore.getVariable(String name) SemBicScoreDeterministic.getVariable(String name) ZhangShenBoundScore.getVariable(String name) -
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) -
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) BayesPm.getNode(int index) DirichletBayesIm.getNode(int nodeIndex) Evidence.getNode(int nodeIndex) MlBayesIm.getNode(int nodeIndex) MlBayesImObs.getNode(int nodeIndex) UpdatedBayesIm.getNode(int nodeIndex) BayesPm.getVariable(Node node) BayesProperties.getVariable(String targetName) 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) GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) BayesIm.getMeasuredNodes()BayesPm.getMeasuredNodes()DirichletBayesIm.getMeasuredNodes()MlBayesIm.getMeasuredNodes()MlBayesImObs.getMeasuredNodes()UpdatedBayesIm.getMeasuredNodes()JunctionTreeAlgorithm.getNodes()Calculate separator sets in clique tree.Calculate separator sets in clique tree.BayesIm.getVariables()BayesImProbs.getVariables()BayesPm.getVariables()CellTableProbs.getVariables()DataSetProbs.getVariables()DirichletBayesIm.getVariables()DirichletDataSetProbs.getVariables()IntAveDataSetProbs.getVariables()MlBayesIm.getVariables()MlBayesImObs.getVariables()StoredCellProbs.getVariables()StoredCellProbsObs.getVariables()UpdatedBayesIm.getVariables()Evidence.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) Evidence.getCategory(Node node, int j) intBayesPm.getCategoryIndex(Node node, String category) 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) intBayesIm.getNodeIndex(Node node) intDirichletBayesIm.getNodeIndex(Node node) intMlBayesIm.getNodeIndex(Node node) intMlBayesImObs.getNodeIndex(Node node) intUpdatedBayesIm.getNodeIndex(Node node) intBayesPm.getNumCategories(Node 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) 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) 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) GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) GraphTools.getCliqueTree(Node[] ordering, Map<Node, Set<Node>> cliques, Map<Node, Set<Node>> separators) 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 Node -
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()BoxDataSet.getVariable(int col) BoxDataSet.getVariable(String varName) CorrelationMatrixOnTheFly.getVariable(String name) CovarianceMatrix.getVariable(String name) CovarianceMatrixOnTheFly.getVariable(String name) DataModel.getVariable(String name) DataModelList.getVariable(String name) DataSet.getVariable(int column) DataSet.getVariable(String name) ICovarianceMatrix.getVariable(String name) IndependenceFacts.getVariable(String name) NumberObjectDataSet.getVariable(int col) NumberObjectDataSet.getVariable(String varName) TimeSeriesData.getVariable(String name) abstract NodeMethods in edu.cmu.tetrad.data that return types with arguments of type NodeModifier and TypeMethodDescriptionDataUtils.createContinuousVariables(String[] varNames) NumberObjectDataSet.getSelectedVariables()BoxDataSet.getVariables()CorrelationMatrixOnTheFly.getVariables()CovarianceMatrix.getVariables()CovarianceMatrixOnTheFly.getVariables()DataModelList.getVariables()DataSet.getVariables()ICovarianceMatrix.getVariables()IndependenceFacts.getVariables()NumberObjectDataSet.getVariables()TimeSeriesData.getVariables()VariableSource.getVariables()Methods in edu.cmu.tetrad.data with parameters of type NodeModifier and TypeMethodDescriptionvoidBoxDataSet.addVariable(int index, Node variable) Adds the given variable to the dataset, increasing the number of columns by one, moving columns i >=indexto column i + 1, and inserting a column of missing values at column i.voidBoxDataSet.addVariable(Node variable) Adds the given variable to the data set, increasing the number of columns by one, moving columns i >=indexto column i + 1, and inserting a column of missing values at column i.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) voidNumberObjectDataSet.addVariable(int index, Node variable) Adds the given variable to the dataset, increasing the number of columns by one, moving columns i >=indexto column i + 1, and inserting a column of missing values at column i.voidNumberObjectDataSet.addVariable(Node variable) Adds the given variable to the data set, increasing the number of columns by one, moving columns i >=indexto column i + 1, and inserting a column of missing values at column i.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.intstatic voidDataUtils.copyColumn(Node node, DataSet source, DataSet dest) 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.intintintbooleanIndependenceFacts.isIndependent(Node x, Node y, Node... z) booleanIndependenceFacts.isIndependent(Node x, Node y, List<Node> z) intKnowledge.isInWhichTier(Node node) Returns the index of the tier of node if it's in a tier, otherwise -1.booleanBoxDataSet.isSelected(Node variable) final booleanCorrelationMatrixOnTheFly.isSelected(Node variable) final booleanCovarianceMatrix.isSelected(Node variable) final booleanCovarianceMatrixOnTheFly.isSelected(Node variable) booleanDataSet.isSelected(Node variable) booleanICovarianceMatrix.isSelected(Node variable) booleanNumberObjectDataSet.isSelected(Node variable) voidBoxDataSet.removeColumn(Node variable) Removes the columns for the given variable from the dataset, reducing the number of columns by one.voidDataSet.removeColumn(Node variable) Removes the given variable, along with all of its data.voidNumberObjectDataSet.removeColumn(Node variable) Removes the columns for the given variable from the dataset, reducing the number of columns by one.final voidfinal voidfinal voidvoidvoidBoxDataSet.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.static Matrixstatic Matrixstatic Matrixstatic MatrixMethod parameters in edu.cmu.tetrad.data with type arguments of type NodeModifier and TypeMethodDescriptionbooleanIndependenceFacts.isIndependent(Node x, Node y, List<Node> z) voidvoidCorrelationMatrixOnTheFly.setVariables(List<Node> variables) voidCovarianceMatrix.setVariables(List<Node> variables) voidCovarianceMatrixOnTheFly.setVariables(List<Node> variables) voidICovarianceMatrix.setVariables(List<Node> variables) static Matrixstatic Matrixstatic Matrixstatic Matrixstatic Matrixstatic MatrixBoxDataSet.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) 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) 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) 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) -
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) 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) SemGraph.getExogenous(Node node) NodePair.getFirst()final NodeEdge.getNode1()final NodeEdge.getNode2()NodePair.getSecond()SemGraph.getVarNode(Node node) IndependenceFact.getX()Triple.getX()IndependenceFact.getY()Triple.getY()Triple.getZ()Creates a new node of the same type as this one with the given name.static Nodestatic NodeEdges.traverseDirected(Node node, Edge edge) For A -> 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) Methods in edu.cmu.tetrad.graph that return types with arguments of type NodeModifier and TypeMethodDescriptionPaths.allDirectedPathsFromTo(Node node1, Node node2, int maxLength) Paths.allPathsFromTo(Node node1, Node node2, int maxLength) Constructs a list of nodes from the givennodeslist at the given indices in that list.Paths.connectedComponents()Paths.directedPathsFromTo(Node node1, Node node2, int maxLength) Dag.getAdjacentNodes(Node node) EdgeListGraph.getAdjacentNodes(Node node) Graph.getAdjacentNodes(Node node) LagGraph.getAdjacentNodes(Node node) SemGraph.getAdjacentNodes(Node node) TimeLagGraph.getAdjacentNodes(Node node) Paths.getAncestors(List<Node> nodes) Dag.getChildren(Node node) EdgeListGraph.getChildren(Node node) Graph.getChildren(Node node) LagGraph.getChildren(Node node) SemGraph.getChildren(Node node) TimeLagGraph.getChildren(Node node) Paths.getDconnectedVars(Node y, List<Node> z) Paths.getDescendants(List<Node> nodes) SemGraph.getFullTierOrdering()Paths.getInducingPath(Node x, Node y) TimeLagGraph.getLag0Nodes()Dag.getNodes()EdgeListGraph.getNodes()Graph.getNodes()LagGraph.getNodes()SemGraph.getNodes()TimeLagGraph.getNodes()Dag.getNodesInTo(Node node, Endpoint n) 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) SemGraph.getNodesInTo(Node node, Endpoint endpoint) TimeLagGraph.getNodesInTo(Node node, Endpoint endpoint) Dag.getNodesOutTo(Node node, Endpoint n) 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) SemGraph.getNodesOutTo(Node node, Endpoint n) TimeLagGraph.getNodesOutTo(Node node, Endpoint endpoint) Dag.getParents(Node node) EdgeListGraph.getParents(Node node) Graph.getParents(Node node) LagGraph.getParents(Node node) SemGraph.getParents(Node node) TimeLagGraph.getParents(Node node) Paths.getValidOrder(List<Node> initialOrder, boolean forward) Returns a valid causal order for either a DAG or a CPDAG.IndependenceFact.getZ()GraphUtils.maximalCliques(Graph graph, List<Node> nodes) Paths.possibleDsep(Node x, Node y, int maxPathLength) 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.semidirectedPathsFromTo(Node node1, Node node2, int maxLength) Paths.treksIncludingBidirected(Node node1, Node node2) Methods in edu.cmu.tetrad.graph with parameters of type NodeModifier and TypeMethodDescriptionvoidUnderlines.addAmbiguousTriple(Node x, Node y, Node z) booleanDag.addBidirectedEdge(Node node1, Node node2) booleanEdgeListGraph.addBidirectedEdge(Node node1, Node node2) Adds a bidirected edge to the graph from node A to node B.booleanGraph.addBidirectedEdge(Node node1, Node node2) Adds a bidirected edges <-> to the graph.booleanLagGraph.addBidirectedEdge(Node node1, Node node2) booleanSemGraph.addBidirectedEdge(Node nodeA, Node nodeB) booleanTimeLagGraph.addBidirectedEdge(Node node1, Node node2) booleanDag.addDirectedEdge(Node node1, Node node2) booleanEdgeListGraph.addDirectedEdge(Node node1, Node node2) Adds a directed edge to the graph from node A to node B.booleanGraph.addDirectedEdge(Node node1, Node node2) Adds a directed edge --> to the graph.booleanLagGraph.addDirectedEdge(Node node1, Node node2) booleanSemGraph.addDirectedEdge(Node nodeA, Node nodeB) booleanTimeLagGraph.addDirectedEdge(Node node1, Node node2) voidUnderlines.addDottedUnderlineTriple(Node x, Node y, Node z) booleanbooleanAdds a node to the graph.booleanAdds a node to the graph.booleanbooleanbooleanNodes may be added into the getModel time step only.booleanDag.addNondirectedEdge(Node node1, Node node2) booleanEdgeListGraph.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edge to the graph from node A to node B.booleanGraph.addNondirectedEdge(Node node1, Node node2) Adds a nondirected edges o-o to the graph.booleanLagGraph.addNondirectedEdge(Node node1, Node node2) booleanSemGraph.addNondirectedEdge(Node nodeA, Node nodeB) booleanTimeLagGraph.addNondirectedEdge(Node node1, Node node2) booleanDag.addPartiallyOrientedEdge(Node node1, Node node2) booleanEdgeListGraph.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge to the graph from node A to node B.booleanGraph.addPartiallyOrientedEdge(Node node1, Node node2) Adds a partially oriented edge o-> to the graph.booleanLagGraph.addPartiallyOrientedEdge(Node node1, Node node2) booleanSemGraph.addPartiallyOrientedEdge(Node nodeA, Node nodeB) booleanTimeLagGraph.addPartiallyOrientedEdge(Node node1, Node node2) voidUnderlines.addUnderlineTriple(Node x, Node y, Node z) booleanDag.addUndirectedEdge(Node node1, Node node2) booleanEdgeListGraph.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge to the graph from node A to node B.booleanGraph.addUndirectedEdge(Node node1, Node node2) Adds an undirected edge --- to the graph.booleanLagGraph.addUndirectedEdge(Node node1, Node node2) booleanSemGraph.addUndirectedEdge(Node nodeA, Node nodeB) booleanTimeLagGraph.addUndirectedEdge(Node node1, Node node2) static booleanGraphUtils.allAdjacenciesAreDirected(Node node, Graph graph) Paths.allDirectedPathsFromTo(Node node1, Node node2, int maxLength) Paths.allPathsFromTo(Node node1, Node node2, int maxLength) static EdgeEdges.bidirectedEdge(Node nodeA, Node nodeB) Constructs a new bidirected edge from nodeA to nodeB (<->).intintAlphabetical order.booleanDag.containsNode(Node node) booleanEdgeListGraph.containsNode(Node node) Determines whether the graph contains a particular node.booleanGraph.containsNode(Node node) Determines whether this graph contains the given node.booleanLagGraph.containsNode(Node node) booleanSemGraph.containsNode(Node node) booleanTimeLagGraph.containsNode(Node node) booleanPaths.defNonDescendent(Node node1, Node node2) added by ekorber, 2004/06/12static EdgeEdges.directedEdge(Node nodeA, Node nodeB) Constructs a new directed edge from nodeA to nodeB (-->).Paths.directedPathsFromTo(Node node1, Node node2, int maxLength) booleanPaths.existsDirectedPathFromTo(Node node1, Node node2) booleanPaths.existsDirectedPathFromTo(Node node1, Node node2, int depth) booleanPaths.existsInducingPath(Node x, Node y) 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, LinkedList<Node> path) booleanPaths.existsSemidirectedPath(Node from, Node to) booleanPaths.existsSemiDirectedPath(Node from, Node to) booleanPaths.existsSemiDirectedPathFromTo(Node node1, Node node2) booleanPaths.existsSemiDirectedPathFromTo(Node node1, Set<Node> nodes) booleanPaths.existsTrek(Node node1, Node node2) Determines whether a trek exists between two nodes in the graph.Dag.getAdjacentNodes(Node node) EdgeListGraph.getAdjacentNodes(Node node) Graph.getAdjacentNodes(Node node) LagGraph.getAdjacentNodes(Node node) SemGraph.getAdjacentNodes(Node node) TimeLagGraph.getAdjacentNodes(Node node) GraphUtils.getAmbiguousTriplesFromGraph(Node node, Graph graph) static NodeGraphUtils.getAssociatedNode(Node errorNode, Graph graph) Dag.getChildren(Node node) EdgeListGraph.getChildren(Node node) Graph.getChildren(Node node) LagGraph.getChildren(Node node) SemGraph.getChildren(Node node) TimeLagGraph.getChildren(Node node) GraphPersistence.getCollidersFromGraph(Node node, Graph graph) Paths.getDconnectedVars(Node y, List<Node> z) intintintintintintDag.getDirectedEdge(Node node1, Node node2) EdgeListGraph.getDirectedEdge(Node node1, Node node2) Graph.getDirectedEdge(Node node1, Node node2) LagGraph.getDirectedEdge(Node node1, Node node2) SemGraph.getDirectedEdge(Node node1, Node node2) TimeLagGraph.getDirectedEdge(Node node1, Node node2) final EndpointEdge.getDistalEndpoint(Node node) 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) Dag.getEndpoint(Node node1, Node node2) EdgeListGraph.getEndpoint(Node node1, Node node2) Graph.getEndpoint(Node node1, Node node2) LagGraph.getEndpoint(Node node1, Node node2) SemGraph.getEndpoint(Node node1, Node node2) TimeLagGraph.getEndpoint(Node node1, Node node2) SemGraph.getErrorNode(Node node) SemGraph.getExogenous(Node node) intDag.getIndegree(Node node) intEdgeListGraph.getIndegree(Node node) intGraph.getIndegree(Node node) intLagGraph.getIndegree(Node node) intSemGraph.getIndegree(Node node) intTimeLagGraph.getIndegree(Node node) Paths.getInducingPath(Node x, Node y) Dag.getNodesInTo(Node node, Endpoint n) 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) SemGraph.getNodesInTo(Node node, Endpoint endpoint) TimeLagGraph.getNodesInTo(Node node, Endpoint endpoint) Dag.getNodesOutTo(Node node, Endpoint n) 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) SemGraph.getNodesOutTo(Node node, Endpoint n) TimeLagGraph.getNodesOutTo(Node node, Endpoint endpoint) GraphUtils.getNoncollidersFromGraph(Node node, Graph graph) intDag.getNumEdges(Node node) intEdgeListGraph.getNumEdges(Node node) intGraph.getNumEdges(Node node) intLagGraph.getNumEdges(Node node) intSemGraph.getNumEdges(Node node) intTimeLagGraph.getNumEdges(Node node) intDag.getOutdegree(Node node) intEdgeListGraph.getOutdegree(Node node) intGraph.getOutdegree(Node node) intLagGraph.getOutdegree(Node node) intSemGraph.getOutdegree(Node node) intTimeLagGraph.getOutdegree(Node node) Dag.getParents(Node node) EdgeListGraph.getParents(Node node) Graph.getParents(Node node) LagGraph.getParents(Node node) SemGraph.getParents(Node node) TimeLagGraph.getParents(Node node) final EndpointEdge.getProximalEndpoint(Node node) TripleClassifier.getTriplesLists(Node node) Underlines.getTriplesLists(Node node) GraphUtils.getUnderlinedTriplesFromGraph(Node node, Graph graph) SemGraph.getVarNode(Node node) booleanDag.isAdjacentTo(Node node1, Node node2) booleanEdgeListGraph.isAdjacentTo(Node node1, Node node2) Determines whether some edge or other exists between two nodes.booleanGraph.isAdjacentTo(Node node1, Node node2) booleanLagGraph.isAdjacentTo(Node node1, Node node2) booleanSemGraph.isAdjacentTo(Node nodeX, Node nodeY) booleanTimeLagGraph.isAdjacentTo(Node node1, Node node2) booleanUnderlines.isAmbiguousTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanLagGraph.isAncestorOf(Node node1, Node node2) booleanPaths.isAncestorOf(Node node1, Node node2) Determines whether one node is an ancestor of another.booleanbooleanDetermines whether one node is a child of another.booleanbooleanbooleanbooleanbooleanPaths.isDConnectedTo(Node x, Node y, List<Node> z) booleanDag.isDefCollider(Node node1, Node node2, Node node3) booleanEdgeListGraph.isDefCollider(Node node1, Node node2, Node node3) booleanGraph.isDefCollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanLagGraph.isDefCollider(Node node1, Node node2, Node node3) booleanSemGraph.isDefCollider(Node node1, Node node2, Node node3) booleanTimeLagGraph.isDefCollider(Node node1, Node node2, Node node3) booleanDag.isDefNoncollider(Node node1, Node node2, Node node3) booleanEdgeListGraph.isDefNoncollider(Node node1, Node node2, Node node3) IllegalArgument exception raised (by isDirectedFromTo(getEndpoint) or by getEdge) if there are multiple edges between any of the node pairs.booleanGraph.isDefNoncollider(Node node1, Node node2, Node node3) Added by ekorber, 2004/6/9.booleanLagGraph.isDefNoncollider(Node node1, Node node2, Node node3) booleanSemGraph.isDefNoncollider(Node node1, Node node2, Node node3) booleanTimeLagGraph.isDefNoncollider(Node node1, Node node2, Node node3) booleanPaths.isDescendentOf(Node node1, Node node2) Determines whether one node is a descendent of another.booleanPaths.isDirectedFromTo(Node node1, Node node2) booleanEdgeListGraph.isDSeparatedFrom(Node x, Node y, List<Node> z) booleanPaths.isDSeparatedFrom(Node node1, Node node2, List<Node> z) Determines whether one n ode is d-separated from another.booleanDag.isExogenous(Node node) booleanEdgeListGraph.isExogenous(Node node) Determines whether a node in a graph is exogenous.booleanGraph.isExogenous(Node node) booleanLagGraph.isExogenous(Node node) booleanSemGraph.isExogenous(Node node) booleanTimeLagGraph.isExogenous(Node node) booleanDag.isParameterizable(Node node) booleanEdgeListGraph.isParameterizable(Node node) booleanGraph.isParameterizable(Node node) booleanLagGraph.isParameterizable(Node node) booleanSemGraph.isParameterizable(Node node) booleanTimeLagGraph.isParameterizable(Node node) booleanDag.isParentOf(Node node1, Node node2) booleanEdgeListGraph.isParentOf(Node node1, Node node2) Determines whether one node is a parent of another.booleanGraph.isParentOf(Node node1, Node node2) Determines whether node1 is a parent of node2.booleanLagGraph.isParentOf(Node node1, Node node2) booleanSemGraph.isParentOf(Node node1, Node node2) booleanTimeLagGraph.isParentOf(Node node1, Node node2) booleanCheck to see if a set of variables Z satisfies the back-door criterion relative to node x and node y.booleanUnderlines.isUnderlineTriple(Node x, Node y, Node z) States whether r-s-r is an underline triple or not.booleanPaths.isUndirectedFromTo(Node node1, Node node2) static GraphGraphUtils.markovBlanketDag(Node target, Graph dag) Calculates the Markov blanket of a target in a DAG.static EdgeEdges.nondirectedEdge(Node nodeA, Node nodeB) Constructs a new nondirected edge from nodeA to nodeB (o-o).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) static StringTriple.pathString(Graph graph, Node x, Node y, Node z) booleanEdge.pointsTowards(Node node) booleanPaths.possibleAncestor(Node node1, Node node2) Paths.possibleDsep(Node x, Node y, int maxPathLength) voidUnderlines.removeAmbiguousTriple(Node x, Node y, Node z) voidUnderlines.removeDottedUnderlineTriple(Node x, Node y, Node z) booleanDag.removeEdge(Node node1, Node node2) booleanEdgeListGraph.removeEdge(Node node1, Node node2) Removes the edge connecting the two given nodes.booleanGraph.removeEdge(Node node1, Node node2) Removes the edge connecting the two given nodes, provided there is exactly one such edge.booleanLagGraph.removeEdge(Node node1, Node node2) booleanSemGraph.removeEdge(Node node1, Node node2) booleanTimeLagGraph.removeEdge(Node node1, Node node2) booleanDag.removeEdges(Node node1, Node node2) 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) booleanSemGraph.removeEdges(Node node1, Node node2) booleanTimeLagGraph.removeEdges(Node node1, Node node2) booleanDag.removeNode(Node node) booleanEdgeListGraph.removeNode(Node node) Removes a node from the graph.booleanGraph.removeNode(Node node) Removes a node from the graph.booleanLagGraph.removeNode(Node node) booleanSemGraph.removeNode(Node node) booleanTimeLagGraph.removeNode(Node node) voidUnderlines.removeUnderlineTriple(Node x, Node y, Node z) Paths.semidirectedPathsFromTo(Node node1, Node node2, int maxLength) booleanDag.setEndpoint(Node from, Node to, Endpoint endPoint) booleanEdgeListGraph.setEndpoint(Node from, Node to, Endpoint endPoint) If there is currently an edge from node1 to node2, sets the endpoint at node2 to the given endpoint; if there is no such edge, adds an edge --# where # is 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) booleanSemGraph.setEndpoint(Node node1, Node node2, Endpoint endpoint) booleanTimeLagGraph.setEndpoint(Node from, Node to, Endpoint endPoint) static Nodestatic NodeEdges.traverseDirected(Node node, Edge edge) For A -> 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) Paths.treksIncludingBidirected(Node node1, Node node2) static EdgeEdges.undirectedEdge(Node nodeA, Node nodeB) Constructs a new undirected edge from nodeA to nodeB (--).Method parameters in edu.cmu.tetrad.graph with type arguments of type NodeModifier and TypeMethodDescriptionConstructs a list of nodes from the givennodeslist at the given indices in that list.MisclassificationUtils.convertNodes(Set<Edge> edges, List<Node> newVariables) booleanPaths.existsInducingPathVisit(Node a, Node b, Node x, Node y, LinkedList<Node> path) booleanPaths.existsSemiDirectedPathFromTo(Node node1, Set<Node> nodes) Paths.getAncestors(List<Node> nodes) Paths.getDconnectedVars(Node y, List<Node> z) Paths.getDescendants(List<Node> nodes) Paths.getValidOrder(List<Node> initialOrder, boolean forward) Returns a valid causal order for either a DAG or a CPDAG.static voidGraphUtils.gfciExtraEdgeRemovalStep(Graph graph, Graph referenceCpdag, List<Node> nodes, SepsetProducer sepsets) The extra edge removal step for GFCI.GraphPersistence.grabLayout(List<Node> nodes) static booleanGraphUtils.isClique(Collection<Node> set, Graph graph) booleanPaths.isDConnectedTo(Node x, Node y, List<Node> z) booleanbooleanEdgeListGraph.isDSeparatedFrom(Node x, Node y, List<Node> z) booleanbooleanPaths.isDSeparatedFrom(Node node1, Node node2, List<Node> z) Determines whether one n ode is d-separated from another.booleanCheck to see if a set of variables Z satisfies the back-door criterion relative to node x and node y.static GraphGraphPersistence.loadGraphBNTPcMatrix(List<Node> vars, DataSet dataSet) voidPaths.makeValidOrder(List<Node> order) GraphUtils.maximalCliques(Graph graph, List<Node> nodes) static GraphGraphPersistence.parseGraphXml(nu.xom.Element graphElement, Map<String, Node> nodes) static StringGraphUtils.pathString(Graph graph, List<Node> path) static DagRandomGraph.randomDag(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected) static GraphRandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected) Defaults to random forward graphs.static GraphRandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected) static GraphRandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, boolean layoutAsCircle) static GraphRandomGraph.randomGraphUniform(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, int numIterations) booleanDag.removeNodes(List<Node> nodes) booleanEdgeListGraph.removeNodes(List<Node> newNodes) Removes any relevant node objects found in this collection.booleanGraph.removeNodes(List<Node> nodes) Iterates through the list and removes any permissible nodes found.booleanLagGraph.removeNodes(List<Node> nodes) booleanSemGraph.removeNodes(List<Node> nodes) booleanTimeLagGraph.removeNodes(List<Node> nodes) 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).voidvoidvoidvoidvoidvoidConstructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.Constructors in edu.cmu.tetrad.graph with parameters of type NodeModifierConstructorDescriptionConstructs a new edge by specifying the nodes it connects and the endpoint types.IndependenceFact(Node x, Node y, Node... z) IndependenceFact(Node x, Node y, List<Node> z) Constructs a triple of nodes.IndependenceFact(Node x, Node y, Set<Node> z) Constructs a triple of nodes.Constructor parameters in edu.cmu.tetrad.graph with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.performance
Method parameters in edu.cmu.tetrad.performance with type arguments of type Node -
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.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.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 Node -
Uses of Node in edu.cmu.tetrad.search
Methods in edu.cmu.tetrad.search that return NodeModifier and TypeMethodDescriptionTeyssierScorer.get(int j) Returns the node at index j in pi.TeyssierScorer2.get(int j) Returns the node at index j in pi.TeyssierScorerOpt.get(int j) Returns the node at index j in pi.Cstar.Record.getCauseNode()Cstar.Record.getEffectNode()Sextad.getI()Tetrad.getI()Sextad.getJ()Tetrad.getJ()Sextad.getK()Tetrad.getK()Sextad.getL()Tetrad.getL()Sextad.getM()Sextad.getN()GrowShrinkTree.getNode()Cefs.getTarget()default NodeIndependenceTest.getVariable(String name) IndTestDSep.getVariable(String name) IndTestFisherZ.getVariable(String name) IndTestFisherZRecursive.getVariable(String name) IndTestHsic.getVariable(String name) IndTestIndependenceFacts.getVariable(String name) IndTestPositiveCorr.getVariable(String name) IndTestProbabilistic.getVariable(String name) IndTestScore.getVariable(String name) IndTestSepset.getVariable(Node node) IndTestSepset.getVariable(String name) IndTestTrekSep.getVariable(String name) Kci.getVariable(String name) Returns the variable by the given name.ProbabilisticMAPIndependence.getVariable(String name) default NodeScore.getVariable(String targetName) static NodeMethods in edu.cmu.tetrad.search that return types with arguments of type NodeModifier and TypeMethodDescriptionBossMB.besOrder(TeyssierScorer2 scorer) BossMB2.besOrder(TeyssierScorer2 scorer) Ida.calculateMinimumEffectsOnY(Node y) Returns a map from nodes in V \ {Y} to their minimum effects.ClusterUtils.clustersToPartition(Clusters clusters, List<Node> variables) ClusterUtils.convertIntToList(List<int[]> partition, List<Node> nodes) MimUtils.convertToClusters2(Graph clusterGraph) OtherPermAlgs.esp(@NotNull TeyssierScorer scorer) Finds the Markov blanket of the given target.Given the target this returns all the nodes in the Markov Blanket.OtherPermAlgs.gasp(@NotNull TeyssierScorer scorer) Retrieves the sepset previously set for {a, b}, or null if no such set was previously set.Retrieves the sepset previously set for {x, y}, or null if no such set was previously set.TeyssierScorer.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorer2.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorerOpt.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorer.getAdjMap()TeyssierScorer.getAdjMap()TeyssierScorer.getAncestors(Node node) TeyssierScorer2.getAncestors(Node node) TeyssierScorerOpt.getAncestors(Node node) TeyssierScorer.getAncestralNodes(Node v) Vcfas.getApparentlyNonadjacencies()BayesUpdaterClassifier.getBayesImVars()TeyssierScorer.getChildMap()TeyssierScorer.getChildMap()TeyssierScorer.getChildren(int p) TeyssierScorer.getChildren(Node v) Mimbuild.getClustering()MimbuildTrek.getClustering()FindOneFactorClusters.getClusters()The clusters output by the algorithm from the last call to search().FindTwoFactorClusters.getClusters()The clusters output by the algorithm from the last call to search().PossibleDConnectingPath.getConditions()TeyssierScorer.getDescendants(Node node) TeyssierScorer.getEdges()Returns a list of edges for the current graph as a list of ordered pairs.TeyssierScorer2.getEdges()Returns a list of edges for the current graph as a list of ordered pairs.TeyssierScorerOpt.getEdges()Returns a list of edges for the current graph as a list of ordered pairs.DMSearch.LatentStructure.getLatentEffects(Node latent) DMSearch.LatentStructure.getLatents()Bridges.getNeighbors(Graph graph, Node node) Fas.getNodes()FasConcurrent.getNodes()FasDeterministic.getNodes()FasFdr.getNodes()FasStableConcurrentFdr.getNodes()Fasts.getNodes()Ida.NodeEffects.getNodes()IFas.getNodes()MeekRulesRestricted.getNodes()Pc.getNodes()Pcd.getNodes()PcMax.getNodes()PcStable.getNodes()PcStableMax.getNodes()Sextad.getNodes()Tetrad.getNodes()Vcfas.getNodes()MagSemBicScore.getOrder()TeyssierScorer.getOrderShallow()Returns the current permutation without making a copy.DMSearch.LatentStructure.getOutputs(Node latent) Boss.getParents()Boss.getParents()Sp.getParents()Sp.getParents()SuborderSearch.getParents()SuborderSearch.getParents()TeyssierScorer.getParents(int p) Returns the parents of the node at index p.TeyssierScorer.getParents(Node v) Returns the parents of a node v.TeyssierScorer2.getParents(int p) Returns the parents of the node at index p.TeyssierScorer2.getParents(Node v) Returns the parents of a node v.TeyssierScorer2.Pair.getParents()TeyssierScorerOpt.getParents(int p) Returns the parents of the node at index p.TeyssierScorerOpt.getParents(Node v) Returns the parents of a node v.PossibleDConnectingPath.getPath()TeyssierScorer.getPi()TeyssierScorer2.getPi()TeyssierScorerOpt.getPi()TeyssierScorer.getPrefix(int i) TeyssierScorer2.getPrefix(int i) SearchGraphUtils.getReachableNodes(List<Node> initialNodes, LegalPairs legalPairs, List<Node> c, List<Node> d, Graph graph, int maxPathLength) SepsetMapDci.getSeparatedPairs()PossibleDsepFci.getSepset(IndependenceTest test, Node node1, Node node2) Pick out the sepset from among adj(i) or adj(k) with the highest p value.Pick out the sepset from among adj(i) or adj(k) with the highest score value.Pick out the sepset from among adj(i) or adj(k) with the highest score value.Pick out the sepset from among adj(i) or adj(k) with the highest p value.SepsetsConservative.getSepsetsLists(Node x, Node y, Node z, IndependenceTest test, int depth, boolean verbose) Retrieves the set of all condioning sets for {x, y} or null if no such set was ever setTeyssierScorer.getShuffledVariables()TeyssierScorer.getSkeleton()TeyssierScorer2.getSkeleton()PcMb.getTargets()Bdce.getVariables()BDeScore.getVariables()BDeuScore.getVariables()BdeuScoreImages.getVariables()Bes.getVariables()Boss.getVariables()BossMB.getVariables()BossMB2.getVariables()BossOrig.getVariables()BridgesOld.getVariables()ConditionalGaussianOtherScore.getVariables()ConditionalGaussianScore.getVariables()ContinuousTetradTest.getVariables()DagSepsets.getVariables()DegenerateGaussianScore.getVariables()DegenerateGaussianScoreOld.getVariables()Deprecated.DeltaSextadTest.getVariables()DirichletScore.getVariables()DiscreteBicScore.getVariables()DiscreteMixedScore.getVariables()DiscreteTetradTest.getVariables()EbicScore.getVariables()GraphScore.getVariables()Grasp.getVariables()GraspTol.getVariables()GrowShrinkTree.getVariables()ImagesScore.getVariables()IndependenceTest.getVariables()IndTestChiSquare.getVariables()IndTestCodec.getVariables()IndTestConditionalCorrelation.getVariables()IndTestConditionalCorrelationLingam.getVariables()IndTestConditionalGaussianLRT.getVariables()IndTestCramerT.getVariables()IndTestDegenerateGaussianLRT.getVariables()IndTestDSep.getVariables()IndTestFisherZ.getVariables()IndTestFisherZConcatenateResiduals.getVariables()IndTestFisherZFisherPValue.getVariables()IndTestFisherZGeneralizedInverse.getVariables()IndTestFisherZPercentIndependent.getVariables()IndTestFisherZRecursive.getVariables()IndTestGSquare.getVariables()IndTestHsic.getVariables()IndTestIndependenceFacts.getVariables()IndTestMixedMultipleTTest.getVariables()IndTestMNLRLRT.getVariables()IndTestMulti.getVariables()IndTestMultinomialLogisticRegression.getVariables()IndTestMVPLRT.getVariables()IndTestPositiveCorr.getVariables()IndTestProbabilistic.getVariables()IndTestRegression.getVariables()IndTestScore.getVariables()IndTestSepset.getVariables()IndTestTrekSep.getVariables()Kci.getVariables()Returns the list of variables over which this independence checker is capable of determinining independence relations.KimEtAlScores.getVariables()LvBesJoe.getVariables()MagSemBicScore.getVariables()MNLRScore.getVariables()MVPScore.getVariables()OtherPermAlgs.getVariables()PermutationSearch.getVariables()PoissonPriorScore.getVariables()PopulationTetradTest.getVariables()ProbabilisticMAPIndependence.getVariables()Score.getVariables()ScoredIndTest.getVariables()SemBicScore.getVariables()SemBicScoreDeterministic.getVariables()SemBicScoreImages.getVariables()SemBicScoreMultiFas.getVariables()SepsetProducer.getVariables()SepsetsConservative.getVariables()SepsetsGreedy.getVariables()SepsetsGreedy2.getVariables()SepsetsPossibleDsep.getVariables()SepsetsSet.getVariables()Sp.getVariables()SuborderSearch.getVariables()TetradTest.getVariables()ZhangShenBoundScore.getVariables()ZhangShenBoundTest.getVariables()MeekRulesRestricted.getVisitedNodes()GraphoidAxioms.GraphoidIndFact.getX()GraphoidAxioms.GraphoidIndFact.getY()GraphoidAxioms.GraphoidIndFact.getZ()Grasp.grasp(@NotNull TeyssierScorer scorer) GraspTol.grasp(@NotNull TeyssierScorer scorer) ImpliedOrientation.orientImplied(Graph graph) Adds implied orientations.MeekRules.orientImplied(Graph graph) MeekRulesCpdag.orientImplied(Graph graph) MeekRulesRestricted.orientImplied(Graph graph) OtherPermAlgs.rcg(@NotNull TeyssierScorer scorer) Greedy equivalence search: Start from the empty graph, add edges till model is significant.Fasts.searchMapOnly()Fasts.searchMapOnly()OtherPermAlgs.sp(@NotNull TeyssierScorer scorer) ClusterSignificance.variablesForIndices(List<Integer> cluster, List<Node> variables) Methods in edu.cmu.tetrad.search with parameters of type NodeModifier and TypeMethodDescriptionvoidvoidAdd another orient operation to the GraphChange.voidDMSearch.LatentStructure.addRecord(Node latent, SortedSet<Node> inputs, SortedSet<Node> outputs, SortedSet<Node> latentEffects) voidTsFges2.addSimilarEdges(Node x, Node y) booleanReturns True iff a is adjacent to b in the current graph.booleanReturns True iff a is adjacent to b in the current graph.booleanReturns True iff a is adjacent to b in the current graph.booleanTeyssierScorer.ancestorAdjacent(Node a, Node b) booleanTeyssierScorer.ancestralCollider(Node a, Node b, Node c) static voidSearchGraphUtils.basicCpdagRestricted2(Graph graph, Node node) Ida.calculateMinimumEffectsOnY(Node y) Returns a map from nodes in V \ {Y} to their minimum effects.default IndependenceResultIndependenceTest.checkIndependence(Node x, Node y, Node... z) IndependenceTest.checkIndependence(Node x, Node y, List<Node> z) IndTestChiSquare.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestCodec.checkIndependence(Node y, Node z, List<Node> x) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestConditionalCorrelation.checkIndependence(Node x, Node y, List<Node> z) IndTestConditionalCorrelationLingam.checkIndependence(Node x, Node y, List<Node> z) IndTestConditionalGaussianLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestCramerT.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestDegenerateGaussianLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestDSep.checkIndependence(Node x, Node y, List<Node> z) Checks the indicated d-separation fact.IndTestFisherZ.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestFisherZConcatenateResiduals.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestFisherZFisherPValue.checkIndependence(Node x, Node y, List<Node> z) IndTestFisherZGeneralizedInverse.checkIndependence(Node xVar, Node yVar, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestFisherZPercentIndependent.checkIndependence(Node x, Node y, List<Node> z) IndTestFisherZRecursive.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestGSquare.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestHsic.checkIndependence(Node y, Node x, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestIndependenceFacts.checkIndependence(Node x, Node y, List<Node> z) IndTestMixedMultipleTTest.checkIndependence(Node x, Node y, List<Node> z) IndTestMNLRLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestMulti.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestMultinomialLogisticRegression.checkIndependence(Node x, Node y, List<Node> z) IndTestMVPLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestPositiveCorr.checkIndependence(Node x0, Node y0, List<Node> z0) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestProbabilistic.checkIndependence(Node x, Node y, Node... z) IndTestProbabilistic.checkIndependence(Node x, Node y, List<Node> z) IndTestRegression.checkIndependence(Node xVar, Node yVar, List<Node> zList) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestScore.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestSepset.checkIndependence(Node x, Node y, List<Node> z) Checks the indicated independence fact.IndTestTrekSep.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.Kci.checkIndependence(Node x, Node y, List<Node> z) Returns true if the given independence question is judged true, false if not.ProbabilisticMAPIndependence.checkIndependence(Node x, Node y, Node... z) ProbabilisticMAPIndependence.checkIndependence(Node x, Node y, List<Node> z) booleanReturns true iff [a, b, c] is a collider.booleanReturns true iff [a, b, c] is a collider.booleanReturns true iff [a, b, c] is a collider.static StringSearchLogUtils.colliderOrientedMsg(Node x, Node y, Node z) static Stringstatic StringSearchLogUtils.colliderOrientedMsg(String note, Node x, Node y, Node z) booleanDMSearch.LatentStructure.containsLatent(Node latent) doublebooleanTeyssierScorer.coveredEdge(Node x, Node y) Returns true iff x->y or y->x is a covered edge.booleanTeyssierScorer2.coveredEdge(Node x, Node y) Returns true iff x->y or y->x is a covered edge.voida method to search "back from a" to find a DDP.voida method to search "back from a" to find a DDP.voidstatic StringSearchLogUtils.dependenceFactMsg(Node x, Node y, List<Node> condSet, double pValue) booleanBDeScore.determines(List<Node> z, Node y) booleanBDeuScore.determines(List<Node> z, Node y) booleanBdeuScoreImages.determines(List<Node> z, Node y) booleanConditionalGaussianOtherScore.determines(List<Node> z, Node y) booleanConditionalGaussianScore.determines(List<Node> z, Node y) booleanDegenerateGaussianScore.determines(List<Node> z, Node y) booleanDegenerateGaussianScoreOld.determines(List<Node> z, Node y) Deprecated.booleanDirichletScore.determines(List<Node> z, Node y) booleanDiscreteBicScore.determines(List<Node> z, Node y) booleanDiscreteMixedScore.determines(List<Node> z, Node y) booleanEbicScore.determines(List<Node> z, Node y) booleanGraphScore.determines(List<Node> z, Node y) booleanImagesScore.determines(List<Node> z, Node y) default booleanIndependenceTest.determines(List<Node> z, Node y) booleanIndTestChiSquare.determines(List<Node> z, Node x1) booleanIndTestCodec.determines(List<Node> z, Node y) booleanIndTestConditionalCorrelation.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestConditionalCorrelationLingam.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestConditionalGaussianLRT.determines(List<Node> z, Node y) booleanIndTestCramerT.determines(List<Node> z, Node x) booleanIndTestDegenerateGaussianLRT.determines(List<Node> z, Node y) booleanIndTestDSep.determines(List<Node> z, Node x1) booleanIndTestFisherZ.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestFisherZConcatenateResiduals.determines(List z, Node x) booleanIndTestFisherZFisherPValue.determines(List<Node> z, Node x) booleanIndTestFisherZGeneralizedInverse.determines(List<Node> zList, Node xVar) booleanIndTestFisherZPercentIndependent.determines(List z, Node x) booleanIndTestFisherZRecursive.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestGSquare.determines(List<Node> z, Node x1) booleanIndTestHsic.determines(List<Node> z, Node x) booleanIndTestIndependenceFacts.determines(List<Node> z, Node y) booleanIndTestMixedMultipleTTest.determines(List<Node> z, Node y) booleanIndTestMNLRLRT.determines(List<Node> z, Node y) booleanIndTestMulti.determines(List<Node> z, Node x) booleanIndTestMultinomialLogisticRegression.determines(List<Node> z, Node y) booleanIndTestMVPLRT.determines(List<Node> z, Node y) booleanIndTestPositiveCorr.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestProbabilistic.determines(List<Node> z, Node y) booleanIndTestRegression.determines(List<Node> zList, Node xVar) booleanIndTestScore.determines(List<Node> z, Node y) booleanIndTestSepset.determines(List<Node> z, Node x1) booleanIndTestTrekSep.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanKci.determines(List<Node> z, Node y) Returns true if y is determined the variable in z.booleanKimEtAlScores.determines(List<Node> z, Node y) booleanMagSemBicScore.determines(List<Node> z, Node y) booleanMNLRScore.determines(List<Node> z, Node y) booleanMVPScore.determines(List<Node> z, Node y) booleanPoissonPriorScore.determines(List<Node> z, Node y) booleanProbabilisticMAPIndependence.determines(List<Node> z, Node y) default booleanScore.determines(List<Node> z, Node y) booleanScoredIndTest.determines(List<Node> z, Node y) booleanSemBicScore.determines(List<Node> z, Node y) booleanSemBicScoreDeterministic.determines(List<Node> z, Node y) booleanSemBicScoreImages.determines(List<Node> z, Node y) booleanSemBicScoreMultiFas.determines(List<Node> z, Node y) booleanZhangShenBoundScore.determines(List<Node> z, Node y) booleanZhangShenBoundTest.determines(List<Node> z, Node y) static StringSearchLogUtils.determinismDetected(List<Node> sepset, Node x) static booleanDagToPag.existsInducingPathInto(Node x, Node y, Graph graph) static booleanTsDagToPag.existsInducingPathInto(Node x, Node y, Graph graph, Knowledge knowledge) static booleanTsDagToPag.existsInducingPathVisitts(Graph graph, Node a, Node b, Node x, Node y, LinkedList<Node> path, Knowledge knowledge) static booleanSearchGraphUtils.existsLocalSepsetWithout(Node x, Node y, Node z, IndependenceTest test, Graph graph, int depth) static List<PossibleDConnectingPath>PossibleDConnectingPath.findDConnectingPaths(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<PossibleDConnectingPath>PossibleDConnectingPath.findDConnectingPathsOfLength(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.Finds the Markov blanket of the given target.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) static voidSampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) 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.Retrieves the sepset previously set for {a, b}, or null if no such set was previously set.Retrieves the sepset previously set for {x, y}, or null if no such set was previously set.TeyssierScorer.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorer2.getAdjacentNodes(Node v) Returns the nodes adjacent to v.TeyssierScorerOpt.getAdjacentNodes(Node v) Returns the nodes adjacent to v.Fas.getAmbiguousTriples(Node node) FasConcurrent.getAmbiguousTriples(Node node) FasDeterministic.getAmbiguousTriples(Node node) FasFdr.getAmbiguousTriples(Node node) FasStableConcurrentFdr.getAmbiguousTriples(Node node) Fasts.getAmbiguousTriples(Node node) IFas.getAmbiguousTriples(Node node) TeyssierScorer.getAncestors(Node node) TeyssierScorer2.getAncestors(Node node) TeyssierScorerOpt.getAncestors(Node node) TeyssierScorer.getAncestralNodes(Node v) TeyssierScorer.getChildren(Node v) SearchGraphUtils.getCpcTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph) TeyssierScorer.getDescendants(Node node) DMSearch.LatentStructure.getLatentEffects(Node latent) Bridges.getNeighbors(Graph graph, Node node) DMSearch.LatentStructure.getOutputs(Node latent) TeyssierScorer.getParents(Node v) Returns the parents of a node v.TeyssierScorer2.getParents(Node v) Returns the parents of a node v.TeyssierScorerOpt.getParents(Node v) Returns the parents of a node v.Vcpc.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose) VcpcFast.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose) doubledoublePossibleDsepFci.getSepset(IndependenceTest test, Node node1, Node node2) Pick out the sepset from among adj(i) or adj(k) with the highest p value.Pick out the sepset from among adj(i) or adj(k) with the highest score value.Pick out the sepset from among adj(i) or adj(k) with the highest score value.Pick out the sepset from among adj(i) or adj(k) with the highest p value.SepsetsConservative.getSepsetsLists(Node x, Node y, Node z, IndependenceTest test, int depth, boolean verbose) Retrieves the set of all condioning sets for {x, y} or null if no such set was ever setIda.getSortedMinEffects(Node y) Returns the minimum effects of X on Y for X in V \ {Y}, sorted downward by minimum effectIndTestSepset.getVariable(Node node) static StringSearchLogUtils.independenceFact(Node x, Node y, List<Node> condSet) static StringSearchLogUtils.independenceFactMsg(Node x, Node y, List<Node> condSet, double pValue) intReturn the index of v in the current permutation.intReturn the index of v in the current permutation.intReturn the index of v in the current permutation.static booleanFciOrient.isArrowpointAllowed(Node x, Node y, Graph graph, Knowledge knowledge) static booleanCpc.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanCpcStable.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanPcAll.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanSampleVcpc.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanSampleVcpcFast.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanSearchGraphUtils.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) Checks if an arrowpoint is allowed by background knowledge.static booleanVcpc.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanVcpcAlt.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) static booleanVcpcFast.isArrowpointAllowed1(Node from, Node to, Knowledge knowledge) booleanGraphScore.isDConnectedTo(Node x, Node y, List<Node> z) booleanIndTestDSep.isDSeparated(Node x, Node y, List<Node> z) Auxiliary method to calculate dseparation facts directly from nodes instead of from variables.booleanGraphScore.isDSeparatedFrom(Node x, Node y, List<Node> z) booleanGrowShrinkTree.isForbidden(Node node) doubleConditionalCorrelationIndependence.isIndependent(Node x, Node y, List<Node> z) doubleConditionalCorrelationIndependenceLingam.isIndependent(Node x, Node y, List<Node> z) booleanDagSepsets.isIndependent(Node a, Node b, List<Node> c) booleanSepsetProducer.isIndependent(Node d, Node c, List<Node> path) booleanSepsetsConservative.isIndependent(Node a, Node b, List<Node> c) booleanSepsetsGreedy.isIndependent(Node a, Node b, List<Node> c) booleanSepsetsGreedy2.isIndependent(Node a, Node b, List<Node> c) booleanSepsetsPossibleDsep.isIndependent(Node d, Node c, List<Node> path) booleanSepsetsSet.isIndependent(Node a, Node b, List<Node> c) static booleanResolveSepsets.isIndependentPooled(ResolveSepsets.Method method, List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) Tests for independence using one of the pooled methodsstatic booleanResolveSepsets.isIndependentPooledAverage(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) Checks independence from pooled samples by taking the average p valuestatic booleanResolveSepsets.isIndependentPooledAverageTest(List<IndependenceTest> independenceTests, Node x, Node y, List<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, List<Node> condSet) Checks independence from pooled samples using Fisher's method.static booleanResolveSepsets.isIndependentPooledFisher2(List<IndependenceTest> independenceTests, Node x, Node y, List<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, List<Node> condSet) Checks independence from pooled samples using Mudholker and George's methodstatic booleanResolveSepsets.isIndependentPooledMudholkerGeorge2(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) The same as isIndepenentPooledMudholkerGeoerge, except that only available independence tests are used.static booleanResolveSepsets.isIndependentPooledRandom(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) Checks independence from pooled samples by randomly selecting a p valuestatic booleanResolveSepsets.isIndependentPooledStouffer(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) Checks independence from pooled samples using Stouffer et al.'s methodstatic booleanResolveSepsets.isIndependentPooledTippett(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) Checks independence from pooled samples using Tippett's methodstatic booleanResolveSepsets.isIndependentPooledWilkinson(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet, int r) Checks independence from pooled samples using Wilkinson's methodstatic booleanResolveSepsets.isIndependentPooledWorsleyFriston(List<IndependenceTest> independenceTests, Node x, Node y, List<Node> condSet) Checks independence from pooled samples using Worsley and Friston's methodbooleanLegalPairs.isLegalFirstEdge(Node x, Node y) booleanbooleanGrowShrinkTree.isRequired(Node node) booleanDagSepsets.isUnshieldedCollider(Node i, Node j, Node k) booleanSepsetProducer.isUnshieldedCollider(Node i, Node j, Node k) booleanSepsetsConservative.isUnshieldedCollider(Node i, Node j, Node k) booleanSepsetsGreedy.isUnshieldedCollider(Node i, Node j, Node k) booleanSepsetsGreedy2.isUnshieldedCollider(Node i, Node j, Node k) booleanSepsetsPossibleDsep.isUnshieldedCollider(Node i, Node j, Node k) booleanSepsetsSet.isUnshieldedCollider(Node i, Node j, Node k) booleanDagSepsets.isUnshieldedNoncollider(Node i, Node j, Node k) booleanSepsetProducer.isUnshieldedNoncollider(Node a, Node b, Node c) booleanSepsetsConservative.isUnshieldedNoncollider(Node i, Node j, Node k) booleanSepsetsGreedy.isUnshieldedNoncollider(Node i, Node j, Node k) booleanSepsetsGreedy2.isUnshieldedNoncollider(Node i, Node j, Node k) booleanSepsetsPossibleDsep.isUnshieldedNoncollider(Node i, Node j, Node k) booleanSepsetsSet.isUnshieldedNoncollider(Node i, Node j, Node k) doublevoidMoves v to a new index.voidMoves v to a new index.voidMoves v to a new index.voidvoidTeyssierScorer2.moveToNoUpdate(Node v, int toIndex) static voidOrientCollidersMaxP.orientCollider(Node x, Node y, Node z, PcAll.ConflictRule conflictRule, Graph graph) booleanbooleandoubleIndTestProbabilistic.probConstraint(BCInference bci, BCInference.OP op, Node x, Node y, Node[] z, Map<Node, Integer> indices) doubleProbabilisticMAPIndependence.probConstraint(BCInference.OP op, Node x, Node y, Node[] z) doublevoidDMSearch.LatentStructure.removeLatent(Node latent) voidSvarGFci.removeSimilarEdges(Node x, Node y) voidTsFges2.removeSimilarEdges(Node x, Node y) double[]Calculates the residuals of x regressed nonparametrically onto z.double[]Calculates the residuals of x regressed nonparametrically onto z.Greedy equivalence search: Start from the empty graph, add edges till model is significant.voidSets the sepset for {x, y} to be z.voidSepsetMap.set(Node x, LinkedHashSet<Node> z) voidSets the sepset for {x, y} to be z.voidSepsetMapDci.set(Node x, LinkedHashSet<Node> z) voidbooleanSwaps m and n in the permutation.booleanSwaps m and n in the permutation.booleanSwaps m and n in the permutation.booleanbooleanPerforms a tuck operation.booleanReturns true iff [a, b, c] is a triangle.booleanReturns true iff [a, b, c] is a triangle.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) 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.doubleIda.trueEffect(Node x, Node y, Graph trueDag) booleanbooleanbooleanvoidTeyssierScorer.tuckWithoutMovingAncestors(Node x, Node y) Method parameters in edu.cmu.tetrad.search with type arguments of type NodeModifier and TypeMethodDescriptionvoidDMSearch.LatentStructure.addRecord(Node latent, SortedSet<Node> inputs, SortedSet<Node> outputs, SortedSet<Node> latentEffects) ResolveSepsets.allNodePairs(List<Node> nodes) Generates NodePairs of all possible pairs of nodes from given list of nodes.voidvoidvoidBossMB.betterMutationBoss(@NotNull TeyssierScorer2 scorer, List<Node> targets) voidBossMB2.betterMutationBossTuck(@NotNull TeyssierScorer2 scorer, List<Node> targets) IndependenceTest.checkIndependence(Node x, Node y, List<Node> z) IndTestChiSquare.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestCodec.checkIndependence(Node y, Node z, List<Node> x) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestConditionalCorrelation.checkIndependence(Node x, Node y, List<Node> z) IndTestConditionalCorrelationLingam.checkIndependence(Node x, Node y, List<Node> z) IndTestConditionalGaussianLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestCramerT.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestDegenerateGaussianLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestDSep.checkIndependence(Node x, Node y, List<Node> z) Checks the indicated d-separation fact.IndTestFisherZ.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestFisherZConcatenateResiduals.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestFisherZFisherPValue.checkIndependence(Node x, Node y, List<Node> z) IndTestFisherZGeneralizedInverse.checkIndependence(Node xVar, Node yVar, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestFisherZPercentIndependent.checkIndependence(Node x, Node y, List<Node> z) IndTestFisherZRecursive.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestGSquare.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning varNames z.IndTestHsic.checkIndependence(Node y, Node x, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestIndependenceFacts.checkIndependence(Node x, Node y, List<Node> z) IndTestMixedMultipleTTest.checkIndependence(Node x, Node y, List<Node> z) IndTestMNLRLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestMulti.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestMultinomialLogisticRegression.checkIndependence(Node x, Node y, List<Node> z) IndTestMVPLRT.checkIndependence(Node x, Node y, List<Node> z) IndTestPositiveCorr.checkIndependence(Node x0, Node y0, List<Node> z0) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestProbabilistic.checkIndependence(Node x, Node y, List<Node> z) IndTestRegression.checkIndependence(Node xVar, Node yVar, List<Node> zList) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestScore.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.IndTestSepset.checkIndependence(Node x, Node y, List<Node> z) Checks the indicated independence fact.IndTestTrekSep.checkIndependence(Node x, Node y, List<Node> z) Determines whether variable x is independent of variable y given a list of conditioning variables z.Kci.checkIndependence(Node x, Node y, List<Node> z) Returns true if the given independence question is judged true, false if not.ProbabilisticMAPIndependence.checkIndependence(Node x, Node y, List<Node> z) booleanTrue iff the nodes in W form a clique in the current DAG.booleanTrue iff the nodes in W form a clique in the current DAG.booleanTrue iff the nodes in W form a clique in the current DAG.ClusterUtils.clustersToPartition(Clusters clusters, List<Node> variables) static Stringstatic List<int[]>static ClustersMimUtils.convertToClusters(Graph clusterGraph, List<Node> measuredVariables) Converts a disconnected multiple indicator model into a set of clusters.doublestatic StringSearchLogUtils.dependenceFactMsg(Node x, Node y, List<Node> condSet, double pValue) booleanBDeScore.determines(List<Node> z, Node y) booleanBDeuScore.determines(List<Node> z, Node y) booleanBdeuScoreImages.determines(List<Node> z, Node y) booleanConditionalGaussianOtherScore.determines(List<Node> z, Node y) booleanConditionalGaussianScore.determines(List<Node> z, Node y) booleanDegenerateGaussianScore.determines(List<Node> z, Node y) booleanDegenerateGaussianScoreOld.determines(List<Node> z, Node y) Deprecated.booleanDirichletScore.determines(List<Node> z, Node y) booleanDiscreteBicScore.determines(List<Node> z, Node y) booleanDiscreteMixedScore.determines(List<Node> z, Node y) booleanEbicScore.determines(List<Node> z, Node y) booleanGraphScore.determines(List<Node> z, Node y) booleanImagesScore.determines(List<Node> z, Node y) default booleanIndependenceTest.determines(List<Node> z, Node y) booleanIndTestChiSquare.determines(List<Node> z, Node x1) booleanIndTestCodec.determines(List<Node> z, Node y) booleanIndTestConditionalCorrelation.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestConditionalCorrelationLingam.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestConditionalGaussianLRT.determines(List<Node> z, Node y) booleanIndTestCramerT.determines(List<Node> z, Node x) booleanIndTestDegenerateGaussianLRT.determines(List<Node> z, Node y) booleanIndTestDSep.determines(List<Node> z, Node x1) booleanIndTestFisherZ.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestFisherZFisherPValue.determines(List<Node> z, Node x) booleanIndTestFisherZGeneralizedInverse.determines(List<Node> zList, Node xVar) booleanIndTestFisherZRecursive.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestGSquare.determines(List<Node> z, Node x1) booleanIndTestHsic.determines(List<Node> z, Node x) booleanIndTestIndependenceFacts.determines(List<Node> z, Node y) booleanIndTestMixedMultipleTTest.determines(List<Node> z, Node y) booleanIndTestMNLRLRT.determines(List<Node> z, Node y) booleanIndTestMulti.determines(List<Node> z, Node x) booleanIndTestMultinomialLogisticRegression.determines(List<Node> z, Node y) booleanIndTestMVPLRT.determines(List<Node> z, Node y) booleanIndTestPositiveCorr.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanIndTestProbabilistic.determines(List<Node> z, Node y) booleanIndTestRegression.determines(List<Node> zList, Node xVar) booleanIndTestScore.determines(List<Node> z, Node y) booleanIndTestSepset.determines(List<Node> z, Node x1) booleanIndTestTrekSep.determines(List<Node> z, Node x) IfisDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.booleanKci.determines(List<Node> z, Node y) Returns true if y is determined the variable in z.booleanKimEtAlScores.determines(List<Node> z, Node y) booleanMagSemBicScore.determines(List<Node> z, Node y) booleanMNLRScore.determines(List<Node> z, Node y) booleanMVPScore.determines(List<Node> z, Node y) booleanPoissonPriorScore.determines(List<Node> z, Node y) booleanProbabilisticMAPIndependence.determines(List<Node> z, Node y) default booleanScore.determines(List<Node> z, Node y) booleanScoredIndTest.determines(List<Node> z, Node y) booleanSemBicScore.determines(List<Node> z, Node y) booleanSemBicScoreDeterministic.determines(List<Node> z, Node y) booleanSemBicScoreImages.determines(List<Node> z, Node y) booleanSemBicScoreMultiFas.determines(List<Node> z, Node y) booleanZhangShenBoundScore.determines(List<Node> z, Node y) booleanZhangShenBoundTest.determines(List<Node> z, Node y) static StringSearchLogUtils.determinismDetected(List<Node> sepset, Node x) static booleanTsDagToPag.existsInducingPathVisitts(Graph graph, Node a, Node b, Node x, Node y, LinkedList<Node> path, Knowledge knowledge) voidFciOrient.fciOrientbk(Knowledge bk, Graph graph, List<Node> variables) Orients according to background knowledgestatic List<PossibleDConnectingPath>PossibleDConnectingPath.findDConnectingPaths(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<PossibleDConnectingPath>PossibleDConnectingPath.findDConnectingPathsOfLength(Graph pag, Node x, Node y, Collection<Node> z, Integer length) Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z for a particular path length.static voidSampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidSampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidSampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidSampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static voidVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes) static Graphstatic Graphstatic Graphstatic GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) static GraphPermutationSearch.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag) Mimbuild.getLatentNames(List<Node> latents) MimbuildTrek.getLatentNames(List<Node> latents) doubleSearchGraphUtils.getReachableNodes(List<Node> initialNodes, LegalPairs legalPairs, List<Node> c, List<Node> d, Graph graph, int maxPathLength) Cstar.getRecords(DataSet dataSet, List<Node> possibleCauses, List<Node> possibleEffects, IndependenceTest test) Returns records for a set of variables with expected number of false positives bounded by q.Cstar.getRecords(DataSet dataSet, List<Node> possibleCauses, List<Node> possibleEffects, IndependenceTest test, String path) Returns records for a set of variables with expected number of false positives bounded by q.static StringSearchLogUtils.independenceFact(Node x, Node y, List<Node> condSet) static StringSearchLogUtils.independenceFactMsg(Node x, Node y, List<Node> condSet, double pValue) default IndependenceTestIndependenceTest.indTestSubset(List<Node> vars) IndTestChiSquare.indTestSubset(List<Node> nodes) Creates a new IndTestChiSquare for a subset of the nodes.IndTestCodec.indTestSubset(List<Node> vars) Creates a new independence test instance for a subset of the variables.IndTestConditionalCorrelation.indTestSubset(List<Node> vars) Creates a new IndTestCramerT instance for a subset of the variables.IndTestConditionalCorrelationLingam.indTestSubset(List<Node> vars) Creates a new IndTestCramerT instance for a subset of the variables.IndTestConditionalGaussianLRT.indTestSubset(List<Node> vars) IndTestCramerT.indTestSubset(List<Node> vars) Creates a new IndTestCramerT instance for a subset of the variables.IndTestDegenerateGaussianLRT.indTestSubset(List<Node> vars) IndTestDSep.indTestSubset(List<Node> vars) Required by IndependenceTest.IndTestFisherZ.indTestSubset(List<Node> vars) Creates a new independence test instance for a subset of the variables.IndTestFisherZConcatenateResiduals.indTestSubset(List<Node> vars) IndTestFisherZFisherPValue.indTestSubset(List<Node> vars) IndTestFisherZGeneralizedInverse.indTestSubset(List<Node> vars) Creates a new IndTestCramerT instance for a subset of the variables.IndTestFisherZPercentIndependent.indTestSubset(List<Node> vars) IndTestFisherZRecursive.indTestSubset(List<Node> vars) Creates a new independence test instance for a subset of the variables.IndTestGSquare.indTestSubset(List<Node> vars) Creates a new IndTestGSquare for a subset of the variables.IndTestHsic.indTestSubset(List<Node> vars) Creates a new IndTestHsic instance for a subset of the variables.IndTestIndependenceFacts.indTestSubset(List<Node> vars) IndTestMixedMultipleTTest.indTestSubset(List<Node> vars) IndTestMNLRLRT.indTestSubset(List<Node> vars) IndTestMulti.indTestSubset(List<Node> vars) IndTestMultinomialLogisticRegression.indTestSubset(List<Node> vars) IndTestMVPLRT.indTestSubset(List<Node> vars) IndTestPositiveCorr.indTestSubset(List<Node> vars) Creates a new independence test instance for a subset of the variables.IndTestProbabilistic.indTestSubset(List<Node> vars) IndTestRegression.indTestSubset(List<Node> vars) Creates a new IndTestCramerT instance for a subset of the variables.IndTestScore.indTestSubset(List<Node> vars) IndTestSepset.indTestSubset(List<Node> vars) Required by IndependenceTest.IndTestTrekSep.indTestSubset(List<Node> vars) Creates a new independence test instance for a subset of the variables.Kci.indTestSubset(List<Node> vars) Returns an Independence test for a subset of the variables.ProbabilisticMAPIndependence.indTestSubset(List<Node> vars) booleanGraphScore.isDConnectedTo(Node x, Node y, List<Node> z) booleanIndTestDSep.isDSeparated(Node x, Node y, List<Node> z) Auxiliary method to calculate dseparation facts directly from nodes instead of from variables.booleanGraphScore.isDSeparatedFrom(Node x, Node y, List<Node> z) doubleConditionalCorrelationIndependence.isIndependent(Node x, Node y, List<Node> z) doubleConditionalCorrelationIndependenceLingam.isIndependent(Node x, Node y, List<Node> z) booleanDagSepsets.isIndependent(Node a, Node b, List<Node> c) booleanSepsetProducer.isIndependent(Node d, Node c, List<Node> path) booleanSepsetsConservative.isIndependent(Node a, Node b, List<Node> c) booleanSepsetsGreedy.isIndependent(Node a, Node b, List<Node> c) booleanSepsetsGreedy2.isIndependent(Node a, Node b, List<Node> c) booleanSepsetsPossibleDsep.isIndependent(Node d, Node c, List<Node> path) booleanSepsetsSet.isIndependent(Node a, Node b, List<Node> c) booleanbooleanDetermines whether one trek is a subtrek of another trekstatic voidvoidvoidvoidvoidMeekRulesRestricted.orientImplied(Graph graph, Set<Node> nodes) static ClustersClusterUtils.partitionToClusters(List<List<Node>> partition) static voidSearchGraphUtils.pcOrientbk(Knowledge bk, Graph graph, List<Node> nodes) Orients according to background knowledge.doubleIndTestProbabilistic.probConstraint(BCInference bci, BCInference.OP op, Node x, Node y, Node[] z, Map<Node, Integer> indices) double[]Calculates the residuals of x regressed nonparametrically onto z.double[]Calculates the residuals of x regressed nonparametrically onto z.voidvoiddoubleScores the given permutation.doublefloatScores the given permutation.floatScores the given permutation.Prints local graphs for all variables and returns the one of them.Discovers all adjacencies in data.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.Mimbuild.search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov) MimbuildTrek.search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov) 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.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.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.Searches for the MB CPDAG for the given targets.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.Runs PC search, returning the output CPDAG, over the given nodes.Runs PC on just the given variable, all of which must be in the domain of the independence test.Greedy equivalence search: Start from the empty graph, add edges till model is significant.voidBoss.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) voidBoss.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) voidSp.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) This is the method called by PermutationSearch per tier.voidSp.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) This is the method called by PermutationSearch per tier.voidSuborderSearch.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) voidSuborderSearch.searchSuborder(List<Node> prefix, List<Node> suborder, Map<Node, GrowShrinkTree> gsts) voidSets the sepset for {x, y} to be z.voidSepsetMap.set(Node x, LinkedHashSet<Node> z) voidSets the sepset for {x, y} to be z.voidSepsetMapDci.set(Node x, LinkedHashSet<Node> z) voidvoidvoidvoidBDeuScore.setVariables(List<Node> variables) voidDiscreteBicScore.setVariables(List<Node> variables) voidGrowShrink.setVariables(List<Node> variables) voidIndTestFisherZ.setVariables(List<Node> variables) voidIndTestFisherZRecursive.setVariables(List<Node> variables) voidIndTestPositiveCorr.setVariables(List<Node> variables) voidIndTestTrekSep.setVariables(List<Node> variables) voidKimEtAlScores.setVariables(List<Node> variables) voidPcMb.setVariables(List<Node> variables) voidSemBicScore.setVariables(List<Node> variables) voidSemBicScoreDeterministic.setVariables(List<Node> variables) doublestatic NodeConstructors in edu.cmu.tetrad.search with parameters of type NodeModifierConstructorDescriptionGrowShrinkTree(Score score, Map<Node, Integer> index, Node node, List<Node> required, List<Node> forbidden) Constructor parameters in edu.cmu.tetrad.search with type arguments of type NodeModifierConstructorDescriptionClusterSignificance(List<Node> variables, DataModel dataModel) FasDci(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.Fci(IndependenceTest independenceTest, List<Node> searchVars) Constructs a new FCI search for the given independence test and background knowledge and a list of variables to search over.FciMax(IndependenceTest independenceTest, List<Node> searchVars) Constructs a new FCI search for the given independence test and background knowledge and a list of variables to search over.GraphoidAxioms(Set<GraphoidAxioms.GraphoidIndFact> facts, List<Node> nodes) Constructor.GraphoidAxioms(Set<GraphoidAxioms.GraphoidIndFact> facts, List<Node> nodes, Map<GraphoidAxioms.GraphoidIndFact, String> textSpecs) GrowShrinkTree(Score score, Map<Node, Integer> index, Node node, List<Node> required, List<Node> forbidden) GrowShrinkTree(Score score, Map<Node, Integer> index, Node node, List<Node> required, List<Node> forbidden) IndTestDSep(IndependenceFacts facts, List<Node> variables) IndTestFisherZ(Matrix data, List<Node> variables, double alpha) Constructs a new Fisher Z independence test with the listed arguments.IndTestFisherZRecursive(Matrix data, List<Node> variables, double alpha) Constructs a new Fisher Z independence test with the listed arguments.IndTestHsic(Matrix data, List<Node> variables, double alpha) IndTestSepset(SepsetMapDci sepset, List<Node> nodes) Constructs a new independence test that returns d-separation facts for the given graph as independence results.IndTestTrekSep(ICovarianceMatrix covMatrix, double alpha, List<List<Node>> clustering, List<Node> latents) Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.RecursivePartialCorrelation(List<Node> nodes, Matrix cov, int sampleSize) Rfci(IndependenceTest independenceTest, List<Node> searchVars) Constructs a new FCI search for the given independence test and background knowledge and a list of variables to search over.SvarFci(IndependenceTest independenceTest, List<Node> searchVars) Constructs a new FCI search for the given independence test and background knowledge and a list of variables to search over. -
Uses of Node in edu.cmu.tetrad.search.kernel
Methods in edu.cmu.tetrad.search.kernel with parameters of type NodeModifier and TypeMethodDescriptionvoidKernel.setDefaultBw(DataSet dataset, Node node) Sets bandwidth from data using default methodvoidKernelGaussian.setDefaultBw(DataSet dataset, Node node) Default setting of bandwidth based on median distance heuristicvoidKernelGaussian.setMedianBandwidth(DataSet dataset, Node node) Sets the bandwidth of the kernel to median distance between two points in the given vectorConstructors in edu.cmu.tetrad.search.kernel with parameters of type NodeModifierConstructorDescriptionKernelGaussian(DataSet dataset, Node node) Creates a new Gaussian kernel using the median distance between points to set the bandwidth -
Uses of Node in edu.cmu.tetrad.search.mb
Methods in edu.cmu.tetrad.search.mb that return types with arguments of type NodeModifier and TypeMethodDescriptionSearches for the Markov blanket of the node by the given name.Methods in edu.cmu.tetrad.search.mb with parameters of type Node -
Uses of Node in edu.cmu.tetrad.sem
Methods in edu.cmu.tetrad.sem that return NodeModifier and TypeMethodDescriptionGeneralizedSemPm.getErrorNode(Node node) Node[]ConnectionFunction.getInputNodes()SemEvidence.getNode(int nodeIndex) SemManipulation.getNode(int nodeIndex) Parameter.getNodeA()Parameter.getNodeB()SemIm.getVariableNode(String name) Methods in edu.cmu.tetrad.sem that return types with arguments of type NodeModifier and TypeMethodDescriptionGeneralizedSemPm.getErrorNodes()Returns the list of exogenous variableNodes.StandardizedSemIm.getErrorNodes()SemPm.getLatentNodes()ReidentifyVariables.getLatents(Graph graph) DagScorer.getMeasuredNodes()GeneralizedSemPm.getMeasuredNodes()ISemIm.getMeasuredNodes()Scorer.getMeasuredNodes()SemIm.getMeasuredNodes()The list of measured nodes for the semPm.SemPm.getMeasuredNodes()StandardizedSemIm.getMeasuredNodes()The list of measured nodes for the semPm.GeneralizedSemPm.getNodes()SemEvidence.getNodesInEvidence()GeneralizedSemPm.getParents(Node node) GeneralizedSemPm.getReferencedNodes(Node node) GeneralizedSemPm.getReferencingNodes(Node node) GeneralizedSemPm.getReferencingNodes(String parameter) GeneralizedSemPm.getVariableNodes()Returns the list of variable nodes--that is, node that are not error nodes.ISemIm.getVariableNodes()LargeScaleSimulation.getVariableNodes()SemIm.getVariableNodes()The list of measured and latent nodes for the semPm.SemPm.getVariableNodes()StandardizedSemIm.getVariableNodes()DagScorer.getVariables()Scorer.getVariables()ISemIm.listUnmeasuredLatents()SemIm.listUnmeasuredLatents()Methods in edu.cmu.tetrad.sem with parameters of type NodeModifier and TypeMethodDescriptionbooleanSemIm.existsEdgeCoef(Node x, Node y) 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) StandardizedSemIm.getCoefficientRange(Node a, Node b) SemPm.getCovarianceParameter(Node nodeA, Node nodeB) StandardizedSemIm.getCovarianceRange(Node a, Node b) doubleSemIm.getEdgeCoef(Node x, Node y) doubleStandardizedSemIm.getEdgeCoef(Node a, Node b) doubleSemIm.getErrCovar(Node x, Node y) doubleStandardizedSemIm.getErrorCovariance(Node a, Node b) GeneralizedSemPm.getErrorNode(Node node) doubleStandardizedSemIm.getErrorVariance(Node error) doubledoubleISemIm.getIntercept(Node node) doubleSemIm.getIntercept(Node node) doubledoubleSemPm.getMeanParameter(Node node) doubleISemIm.getMeanStdDev(Node node) doubleSemIm.getMeanStdDev(Node node) GeneralizedSemPm.getNodeExpression(Node node) GeneralizedSemPm.getNodeExpressionString(Node node) intSemEvidence.getNodeIndex(Node node) GeneralizedSemIm.getNodeSubstitutedString(Node node) GeneralizedSemIm.getNodeSubstitutedString(Node node, Map<String, Double> substitutedValues) SemPm.getParameter(Node nodeA, Node nodeB) doubleISemIm.getParamValue(Node nodeA, Node nodeB) doubleSemIm.getParamValue(Node nodeA, Node nodeB) Gets the value of a single free parameter to the given value, where the free parameter is specified by the endpoint nodes of its edge in the w graph.GeneralizedSemPm.getParents(Node node) GeneralizedSemPm.getReferencedNodes(Node node) GeneralizedSemPm.getReferencedParameters(Node node) GeneralizedSemPm.getReferencingNodes(Node node) doubledoubledoubledoubleISemIm.getVariance(Node nodeA, Matrix implCovar) doubleSemIm.getVariance(Node node, Matrix implCovar) 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) voidSemIm.setEdgeCoef(Node x, Node y, double value) 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) voidSemIm.setErrCovar(Node x, Node y, double value) booleanStandardizedSemIm.setErrorCovariance(Node a, Node b, double covar) Sets the covariance for the a<->b edge to the given covariance, if within range.voidvoidvoidISemIm.setIntercept(Node y, double intercept) voidSemIm.setIntercept(Node node, double intercept) Sets the intercept.voidvoidSets the mean associated with the given node.voidSemIm.setMeanStandardDeviation(Node node, double mean) Sets the mean associated with the given node.voidGeneralizedSemPm.setNodeExpression(Node node, String expressionString) voidISemIm.setParamValue(Node nodeA, Node nodeB, double value) voidSemIm.setParamValue(Node nodeA, Node nodeB, double value) Sets the value of a single free parameter to the given value, where the free parameter is specified by the endpoint nodes of its edge in the graph.voidMethod parameters in edu.cmu.tetrad.sem with type arguments of type NodeModifier and TypeMethodDescriptionSemIm.getImplCovar(List<Node> nodes) ReidentifyVariables.reidentifyVariables1(List<List<Node>> partition, Graph trueGraph) Constructors in edu.cmu.tetrad.sem with parameters of type NodeModifierConstructorDescriptionEmpiricalDistributionForExpression(GeneralizedSemPm semPm, Node error, Context context) Constructor parameters in edu.cmu.tetrad.sem with type arguments of type Node -
Uses of Node in edu.cmu.tetrad.session
Classes in edu.cmu.tetrad.session that implement NodeModifier and TypeClassDescriptionclassRepresents a node in a session for a model in a particular class.Methods in edu.cmu.tetrad.session that return NodeMethods in edu.cmu.tetrad.session with parameters 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) Method parameters in edu.cmu.tetrad.simulation with type arguments of type NodeModifier and TypeMethodDescriptionstatic GraphHsimUtils.getAllParents(Graph inputgraph, Set<Node> inputnodes) Constructor parameters in edu.cmu.tetrad.simulation 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) Methods in edu.cmu.tetrad.util that return types with arguments of type NodeModifier and TypeMethodDescriptionJsonUtils.parseJSONArrayToTetradNodes(org.json.JSONArray jArray) DataConvertUtils.toNodes(edu.pitt.dbmi.data.reader.DataColumn[] columns) DataConvertUtils.toNodes(edu.pitt.dbmi.data.reader.DiscreteDataColumn[] columns) -
Uses of Node in edu.pitt.csb.mgm
Methods in edu.pitt.csb.mgm that return types with arguments of type NodeMethods in edu.pitt.csb.mgm with parameters of type NodeModifier and TypeMethodDescriptionIndTestMultinomialLogisticRegressionWald.checkIndependence(Node x, Node y, List<Node> z) booleanIndTestMultinomialLogisticRegressionWald.determines(List<Node> z, Node y) MixedUtils.getEdgeParams(Node n1, Node n2, GeneralizedSemPm pm) Method parameters in edu.pitt.csb.mgm with type arguments of type NodeModifier and TypeMethodDescriptionIndTestMultinomialLogisticRegressionWald.checkIndependence(Node x, Node y, List<Node> z) booleanIndTestMultinomialLogisticRegressionWald.determines(List<Node> z, Node y) static int[]MixedUtils.getContinuousInds(List<Node> nodes) static int[]MixedUtils.getDiscreteInds(List<Node> nodes) IndTestMultinomialLogisticRegressionWald.indTestSubset(List<Node> vars) Constructor parameters in edu.pitt.csb.mgm with type arguments of type Node -
Uses of Node in edu.pitt.csb.stability
Method parameters in edu.pitt.csb.stability with type arguments of type NodeModifier and TypeMethodDescriptionstatic double[]StabilityUtils.totalInstabilityDir(cern.colt.matrix.DoubleMatrix2D xi, List<Node> vars) static double[]StabilityUtils.totalInstabilityUndir(cern.colt.matrix.DoubleMatrix2D xi, List<Node> vars)