Uses of Interface
edu.cmu.tetrad.graph.Graph

Packages that use Graph

Package	Description
edu.cmu.tetrad.algcomparison
edu.cmu.tetrad.algcomparison.algorithm
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.algcomparison.algorithm.multi
edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pag
edu.cmu.tetrad.algcomparison.algorithm.other
edu.cmu.tetrad.algcomparison.algorithm.pairwise
edu.cmu.tetrad.algcomparison.graph
edu.cmu.tetrad.algcomparison.independence
edu.cmu.tetrad.algcomparison.score
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.algcomparison.statistic
edu.cmu.tetrad.algcomparison.statistic.utils
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calibration
edu.cmu.tetrad.data
edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.graph
edu.cmu.tetrad.hybridcg
edu.cmu.tetrad.regression
edu.cmu.tetrad.search	This package contains classes for causal graph search algorithms.
edu.cmu.tetrad.search.score	This package contains classes for scoring causal graph models.
edu.cmu.tetrad.search.test	This package contains classes for testing causal graph search algorithms.
edu.cmu.tetrad.search.unmix
edu.cmu.tetrad.search.utils	This package contains utility classes for causal graph search algorithms.
edu.cmu.tetrad.search.work_in_progress	A package for algorithms that are not ready for prime time.
edu.cmu.tetrad.sem
edu.cmu.tetrad.simulation
edu.cmu.tetrad.study.performance
edu.cmu.tetrad.util
edu.pitt.csb.stability
edu.pitt.dbmi.algo.bayesian.constraint.search

Uses of Graph in edu.cmu.tetrad.algcomparison

Methods in edu.cmu.tetrad.algcomparison with parameters of type Graph

Modifier and Type	Method	Description
static @NotNull String	CompareTwoGraphs.getEdgewiseComparisonString(Graph trueGraph, Graph targetGraph)	Returns an edgewise comparison of two graphs.
static @NotNull String	CompareTwoGraphs.getMisclassificationTable(Graph trueGraph, Graph targetGraph)	Returns a misclassification comparison of two graphs.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, Graph targetGraph)	Returns a string representing a table of statistics that can be printed.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, Graph targetGraph, DataModel dataModel, long elapsedTime)	Returns a string representing a table of statistics that can be printed.

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm

Methods in edu.cmu.tetrad.algcomparison.algorithm that return Graph

Modifier and Type	Method	Description
Graph	Algorithm.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FirstInflection.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	StabilitySelection.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	StARS.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	AbstractBootstrapAlgorithm.search(DataModel dataModel, Parameters parameters)	Runs the search.
Graph	Algorithm.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FirstInflection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	MultiDataSetAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	StabilitySelection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	StARS.search(DataModel dataSet, Parameters parameters)	Runs the search.

Methods in edu.cmu.tetrad.algcomparison.algorithm that return types with arguments of type Graph

Modifier and Type	Method	Description
List<Graph>	AbstractBootstrapAlgorithm.getBootstrapGraphs()	Returns the bootstrap graphs.
List<Graph>	ReturnsBootstrapGraphs.getBootstrapGraphs()	Returns the bootstrap graphs.

Methods in edu.cmu.tetrad.algcomparison.algorithm with parameters of type Graph

Modifier and Type	Method	Description
static Algorithm	AlgorithmFactory.create(Class<? extends Algorithm> algoClass, IndependenceWrapper test, ScoreWrapper score, Graph externalGraph)	Creates an algorithm.
static Algorithm	AlgorithmFactory.create(Class<? extends Algorithm> algoClass, Class<? extends IndependenceWrapper> indTestClass, Class<? extends ScoreWrapper> scoreClass, Graph externalGraph)	Creates an algorithm.
Graph	Algorithm.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FirstInflection.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	StabilitySelection.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	StARS.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag

Methods in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag that return Graph

Modifier and Type	Method	Description
Graph	Dagma.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.
Graph	DirectLingam.getComparisonGraph(Graph graph)	Returns a comparison graph based on the given true directed graph.
Graph	Fask.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	FaskOrig.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	IcaLingam.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	IcaLingD.getComparisonGraph(Graph graph)	Retrieves the comparison graph of the provided true directed graph.
Graph	Dagma.runSearch(DataModel dataModel, Parameters parameters)	Runs the DAGMA algorithm to search for a directed acyclic graph (DAG) in the given data model with the specified parameters.
Graph	DirectLingam.runSearch(DataModel dataModel, Parameters parameters)	Runs the Direct LiNGAM search algorithm on the given data model with the specified parameters.
Graph	Fask.runSearch(DataModel dataModel, Parameters parameters)	Runs the Fask search algorithm on the given data model with the specified parameters.
Graph	FaskOrig.runSearch(DataModel dataModel, Parameters parameters)	Runs the Fask search algorithm on the given data model with the specified parameters.
Graph	IcaLingam.runSearch(DataModel dataSet, Parameters parameters)	Searches for a graph structure based on the given data set and parameters.
Graph	IcaLingD.runSearch(DataModel dataSet, Parameters parameters)	Runs a search on the provided data set using the given parameters.

Methods in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag that return types with arguments of type Graph

Modifier and Type	Method	Description
List<Graph>	IcaLingD.getStableGraphs()	Retrieves the list of stable graphs generated by the algorithm.
List<Graph>	IcaLingD.getUnstableGraphs()	Retrieves the list of unstable graphs generated by the algorithm.

Methods in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag with parameters of type Graph

Modifier and Type	Method	Description
Graph	Dagma.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.
Graph	DirectLingam.getComparisonGraph(Graph graph)	Returns a comparison graph based on the given true directed graph.
Graph	Fask.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	FaskOrig.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	IcaLingam.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	IcaLingD.getComparisonGraph(Graph graph)	Retrieves the comparison graph of the provided true directed graph.

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm.multi

Methods in edu.cmu.tetrad.algcomparison.algorithm.multi that return Graph

Modifier and Type	Method	Description
Graph	FaskConcatenated.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FaskLofsConcatenated.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FaskVote.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FasLofs.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FciIod.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FgesConcatenated.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Images.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	ImagesBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FaskConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskConcatenated.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FaskLofsConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskLofsConcatenated.search(List<DataModel> dataModels, Parameters parameters)	Runs the search.
Graph	FaskVote.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskVote.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FasLofs.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FciIod.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FciIod.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FgesConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FgesConcatenated.search(List<DataModel> dataModels, Parameters parameters)	Runs the search.
Graph	Images.search(DataModel dataSet, Parameters parameters)	Searches for a graph using the given data set and parameters.
Graph	Images.search(List<DataModel> dataSets, Parameters parameters)	Searches for a graph using the given data sets and parameters.
Graph	ImagesBoss.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	ImagesBoss.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.

Methods in edu.cmu.tetrad.algcomparison.algorithm.multi with parameters of type Graph

Modifier and Type	Method	Description
Graph	FaskConcatenated.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FaskLofsConcatenated.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FaskVote.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FasLofs.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FciIod.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FgesConcatenated.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Images.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	ImagesBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag

Methods in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag that return Graph

Modifier and Type	Method	Description
Graph	Boss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Cam.getComparisonGraph(Graph graph)	Constructs and returns a comparison graph in the form of a completed partially directed acyclic graph (CPDAG).
Graph	Cdnod.getComparisonGraph(Graph graph)	Generates a comparison graph by converting a given graph into its completed partially directed acyclic graph (CPDAG) form.
Graph	Cpc.getComparisonGraph(Graph graph)	Deprecated. Retrieves the comparison graph for the given true directed graph.
Graph	Cstar.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Fas.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Fges.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FgesMb.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Gin.getComparisonGraph(Graph graph)
Graph	Grasp.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	IsFges.getComparisonGraph(Graph graph)
Graph	Pc.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Pcd.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PcMax.getComparisonGraph(Graph graph)	Deprecated. Returns that graph that the result should be compared to.
Graph	PcMb.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Pcmci.getComparisonGraph(Graph graph)	Generates a comparison causal graph derived from the input graph by converting it to a completed partially directed acyclic graph (CPDAG).
Graph	RestrictedBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	SingleGraphAlg.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Sp.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	TwoStep.getComparisonGraph(Graph graph)	Deprecated. Returns the given graph for comparison purposes.
Graph	Cstar.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	Pcmci.search(DataModel dataModel, Parameters parameters)	Executes the PCMCI (Peter and Clark Momentary Conditional Independence) causal discovery algorithm on a given data model using specified parameters.
Graph	SingleGraphAlg.search(DataModel dataSet, Parameters parameters)	Runs the search.

Methods in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag with parameters of type Graph

Modifier and Type	Method	Description
Graph	Boss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Cam.getComparisonGraph(Graph graph)	Constructs and returns a comparison graph in the form of a completed partially directed acyclic graph (CPDAG).
Graph	Cdnod.getComparisonGraph(Graph graph)	Generates a comparison graph by converting a given graph into its completed partially directed acyclic graph (CPDAG) form.
Graph	Cpc.getComparisonGraph(Graph graph)	Deprecated. Retrieves the comparison graph for the given true directed graph.
Graph	Cstar.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Fas.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Fges.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	FgesMb.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Gin.getComparisonGraph(Graph graph)
Graph	Grasp.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	IsFges.getComparisonGraph(Graph graph)
Graph	Pc.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Pcd.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PcMax.getComparisonGraph(Graph graph)	Deprecated. Returns that graph that the result should be compared to.
Graph	PcMb.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Pcmci.getComparisonGraph(Graph graph)	Generates a comparison causal graph derived from the input graph by converting it to a completed partially directed acyclic graph (CPDAG).
Graph	RestrictedBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	SingleGraphAlg.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Sp.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	TwoStep.getComparisonGraph(Graph graph)	Deprecated. Returns the given graph for comparison purposes.

Constructors in edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag with parameters of type Graph

Modifier	Constructor	Description
	SingleGraphAlg(Graph graph)	Constructor for SingleGraphAlg.

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag

Methods in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag that return Graph

Modifier and Type	Method	Description
Graph	BossFci.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BossPod.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Ccd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.
Graph	Cfci.getComparisonGraph(Graph graph)	Deprecated. Retrieves the comparison graph by converting the given true directed graph into a partially directed graph (PAG) using the DAG to PAG transformation.
Graph	DmFcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	DmFciT2.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	DmPc.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Fci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	FciCyclicPw.getComparisonGraph(Graph graph)	Generates a comparison graph for the given graph by transforming it into a partially directed acyclic graph (PAG) representation.
Graph	FciMax.getComparisonGraph(Graph graph)	Deprecated. Returns the comparison graph transformed from the true directed graph.
Graph	Fcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FgesFci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
Graph	Gfci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
Graph	GraspFci.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	IsGfci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PagSampleRfci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph.
Graph	Rfci.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	RfciBsc.getComparisonGraph(Graph graph)	Retrieves the comparison graph from the true directed graph, if there is one.
Graph	SpFci.getComparisonGraph(Graph graph)	Returns the comparison graph created by converting a true directed graph into a partially directed acyclic graph (PAG).
Graph	BossFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BossPod.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	Ccd.runSearch(DataModel dataModel, Parameters parameters)	Runs the CCD (Cyclic Causal Discovery) search algorithm on the given data set using the specified parameters.
Graph	Cfci.runSearch(DataModel dataModel, Parameters parameters)	Deprecated. Runs the search algorithm to discover the causal graph.
Graph	DmFcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmFciT2.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmPc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	Fci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph based on the given data model and parameters.
Graph	FciCyclicPw.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm on a given data model and set of parameters, producing a partially directed acyclic graph (PAG) that represents the causal structure inferred from the data.
Graph	FciMax.runSearch(DataModel dataModel, Parameters parameters)	Deprecated. Runs a search algorithm to discover the causal graph structure.
Graph	Fcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FgesFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	Gfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	GraspFci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	PagSampleRfci.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm using the given data set and parameters.
Graph	Rfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model and parameters.
Graph	RfciBsc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm using a given dataset and parameters.
Graph	SpFci.runSearch(DataModel dataModel, Parameters parameters)	Executes a search algorithm to infer the causal graph structure from a given data model

Methods in edu.cmu.tetrad.algcomparison.algorithm.oracle.pag with parameters of type Graph

Modifier and Type	Method	Description
Graph	BossFci.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BossPod.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Ccd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.
Graph	Cfci.getComparisonGraph(Graph graph)	Deprecated. Retrieves the comparison graph by converting the given true directed graph into a partially directed graph (PAG) using the DAG to PAG transformation.
Graph	DmFcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	DmFciT2.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	DmPc.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Fci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	FciCyclicPw.getComparisonGraph(Graph graph)	Generates a comparison graph for the given graph by transforming it into a partially directed acyclic graph (PAG) representation.
Graph	FciMax.getComparisonGraph(Graph graph)	Deprecated. Returns the comparison graph transformed from the true directed graph.
Graph	Fcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FgesFci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
Graph	Gfci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
Graph	GraspFci.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	IsGfci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PagSampleRfci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph.
Graph	Rfci.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	RfciBsc.getComparisonGraph(Graph graph)	Retrieves the comparison graph from the true directed graph, if there is one.
Graph	SpFci.getComparisonGraph(Graph graph)	Returns the comparison graph created by converting a true directed graph into a partially directed acyclic graph (PAG).

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm.other

Methods in edu.cmu.tetrad.algcomparison.algorithm.other that return Graph

Modifier and Type	Method	Description
Graph	FactorAnalysis.getComparisonGraph(Graph graph)	Returns an undirected graph used for comparison.
Graph	Glasso.getComparisonGraph(Graph graph)	Retrieves a comparison graph for the given true directed graph.
Graph	MimbuildBollen.getComparisonGraph(Graph graph)	Returns an undirected graph used for comparison.
Graph	MimbuildPca.getComparisonGraph(Graph graph)	Returns an undirected graph used for comparison.
Graph	FactorAnalysis.runSearch(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.
Graph	Glasso.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to create a graph representation of the data.
Graph	MimbuildBollen.search(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.
Graph	MimbuildPca.search(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.

Methods in edu.cmu.tetrad.algcomparison.algorithm.other with parameters of type Graph

Modifier and Type	Method	Description
Graph	FactorAnalysis.getComparisonGraph(Graph graph)	Returns an undirected graph used for comparison.
Graph	Glasso.getComparisonGraph(Graph graph)	Retrieves a comparison graph for the given true directed graph.
Graph	MimbuildBollen.getComparisonGraph(Graph graph)	Returns an undirected graph used for comparison.
Graph	MimbuildPca.getComparisonGraph(Graph graph)	Returns an undirected graph used for comparison.

Uses of Graph in edu.cmu.tetrad.algcomparison.algorithm.pairwise

Methods in edu.cmu.tetrad.algcomparison.algorithm.pairwise that return Graph

Modifier and Type	Method	Description
Graph	Eb.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	FaskPw.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	R1.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	R2.getComparisonGraph(Graph graph)	Returns a comparison graph for the given true directed graph.
Graph	R3.getComparisonGraph(Graph graph)	Generates a comparison graph based on the provided true directed graph.
Graph	Rskew.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph.
Graph	RskewE.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	Skew.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	SkewE.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	Tanh.getComparisonGraph(Graph graph)	Returns a comparison graph for the given true directed graph, if there is one.
Graph	Eb.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to orient the edges in a graph using the given data and parameters.
Graph	FaskPw.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given data model and parameters.
Graph	R1.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model with the provided parameters.
Graph	R2.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the provided data model and parameters.
Graph	R3.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to orient edges in the input graph using the provided data.
Graph	Rskew.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the provided data model and parameters.
Graph	RskewE.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find the orientation of edges in a graph using the given data model and parameters.
Graph	Skew.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to orient edges in the input graph using the given data model and parameters.
Graph	SkewE.runSearch(DataModel dataModel, Parameters parameters)	Executes the SkewE search algorithm.
Graph	Tanh.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given data model and parameters.

Methods in edu.cmu.tetrad.algcomparison.algorithm.pairwise with parameters of type Graph

Modifier and Type	Method	Description
Graph	Eb.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	FaskPw.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	R1.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	R2.getComparisonGraph(Graph graph)	Returns a comparison graph for the given true directed graph.
Graph	R3.getComparisonGraph(Graph graph)	Generates a comparison graph based on the provided true directed graph.
Graph	Rskew.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph.
Graph	RskewE.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	Skew.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	SkewE.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	Tanh.getComparisonGraph(Graph graph)	Returns a comparison graph for the given true directed graph, if there is one.

Uses of Graph in edu.cmu.tetrad.algcomparison.graph

Methods in edu.cmu.tetrad.algcomparison.graph that return Graph

Modifier and Type	Method	Description
Graph	Cyclic.createGraph(Parameters parameters)	createGraph.
Graph	ErdosRenyi.createGraph(Parameters parameters)	createGraph.
Graph	RandomForward.createGraph(Parameters parameters)	Creates a random graph by adding forward edges.
Graph	RandomGraph.createGraph(Parameters parameters)	createGraph.
Graph	RandomMim.createGraph(Parameters parameters)	createGraph.
Graph	RandomSingleFactorMim.createGraph(Parameters parameters)	createGraph.
Graph	RandomTwoFactorMim.createGraph(Parameters parameters)	createGraph.
Graph	ScaleFree.createGraph(Parameters parameters)	createGraph.
Graph	SingleGraph.createGraph(Parameters parameters)	createGraph.

Constructors in edu.cmu.tetrad.algcomparison.graph with parameters of type Graph

Modifier	Constructor	Description
	SingleGraph(Graph graph)	Constructor for SingleGraph.

Uses of Graph in edu.cmu.tetrad.algcomparison.independence

Methods in edu.cmu.tetrad.algcomparison.independence with parameters of type Graph

Modifier and Type	Method	Description
void	MSeparationTest.setGraph(Graph graph)	Setter for the field graph.
void	TakesGraph.setGraph(Graph graph)	setGraph.

Constructors in edu.cmu.tetrad.algcomparison.independence with parameters of type Graph

Modifier	Constructor	Description
	MSeparationTest(Graph graph)	Constructor for MSeparationTest.

Uses of Graph in edu.cmu.tetrad.algcomparison.score

Methods in edu.cmu.tetrad.algcomparison.score with parameters of type Graph

Modifier and Type	Method	Description
void	MSepScore.setGraph(Graph graph)	Setter for the field graph.

Constructors in edu.cmu.tetrad.algcomparison.score with parameters of type Graph

Modifier	Constructor	Description
	MSepScore(Graph graph)	Constructor for MSeparationScore.

Uses of Graph in edu.cmu.tetrad.algcomparison.simulation

Methods in edu.cmu.tetrad.algcomparison.simulation that return Graph

Modifier and Type	Method	Description
Graph	AdditiveAnmSimulator.getTrueGraph(int index)	Retrieves the true graph corresponding to the specified index.
Graph	AdditiveNoiseSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	BayesNetSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	ConditionalGaussianSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	GeneralSemSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	GeneralSemSimulationSpecial1.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	GpSemSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	HybridCgSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	LeeHastieSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LgMnarSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	LinearFisherModel.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LinearSineSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	NonlinearFunctionsOfLinear.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	PostnonlinearCausalModel.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	SemSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	SemThenDiscretize.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	Simulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	SingleDatasetSimulation.getTrueGraph(int index)	Gets the true graph for the simulation at the specified index.
Graph	StandardizedSemSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	TimeSeriesSemSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
static Graph	LeeHastieSimulation.makeMixedGraph(Graph g, Map<String,Integer> m)	makeMixedGraph.

Methods in edu.cmu.tetrad.algcomparison.simulation with parameters of type Graph

Modifier and Type	Method	Description
static GeneralizedSemPm	LeeHastieSimulation.GaussianCategoricalPm(Graph trueGraph, String paramTemplate)	GaussianCategoricalPm.
static Map<String,Integer>	LeeHastieSimulation.getNodeDists(Graph g)	getNodeDists.
static Graph	LeeHastieSimulation.makeMixedGraph(Graph g, Map<String,Integer> m)	makeMixedGraph.

Uses of Graph in edu.cmu.tetrad.algcomparison.statistic

Methods in edu.cmu.tetrad.algcomparison.statistic with parameters of type Graph

Modifier and Type	Method	Description
static boolean	CommonAncestorTruePositiveBidirected.existsCommonAncestor(Graph trueGraph, Edge edge)	Returns true if there is a common ancestor of X and Y in the graph.
static boolean	NumCommonMeasuredAncestorBidirected.existsCommonAncestor(Graph trueGraph, Edge edge)	existsCommonAncestor.
boolean	NumDirectedEdgeNoMeasureAncestors.existsDirectedPathFromTo(Graph graph, Node node1, Node node2)	existsDirectedPathFromTo.
static boolean	LatentCommonAncestorTruePositiveBidirected.existsLatentCommonAncestor(Graph trueGraph, Edge edge)	existsLatentCommonAncestor.
double	AdjacencyFn.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyFp.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyFpr.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyTn.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyTp.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AdjacencyTpr.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AncestorF1.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AncestorPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AncestorRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AncestralPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AncestralRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadFn.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadFp.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadFpr.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadPrecisionCommonEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadRecallCommonEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadTn.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ArrowheadTp.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AverageDegreeEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	AverageDegreeTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BicDiff.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BicDiffPerRecord.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BicEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BicTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BidirectedEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BidirectedFP.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BidirectedLatentPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the percentage of correctly identified bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
double	BidirectedPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BidirectedRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BidirectedTP.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	BidirectedTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	CirclePrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the circle precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
double	CircleRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the circle recall value for a given true graph, estimated graph, and data model.
double	CommonAncestorFalseNegativeBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	CommonAncestorFalsePositiveBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	CommonAncestorTruePositiveBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	CommonMeasuredAncestorRecallBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	CorrectSkeleton.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	DefiniteDirectedPathPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	DefiniteDirectedPathRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	DensityEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	DensityTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ElapsedCpuTime.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	F1Adj.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	F1All.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	F1Arrow.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	F1Circle.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	F1Tail.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	FalseNegativesAdjacencies.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	FalsePositiveAdjacencies.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	FBetaAdj.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	FractionDependentUnderAlternative.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	FractionDependentUnderNull.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	GraphExactlyRight.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	IdaAverageSquaredDistance.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the IDA Average Squared Distance statistic.
double	IdaCheckAvgMaxSquaredDiffEstTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.
double	IdaCheckAvgMinSquaredDiffEstTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.
double	IdaCheckAvgSquaredDifference.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Retrieves the value of the statistic, which is the average squared difference between the estimated and true values for a given data model and graphs.
double	IdaMaximumSquaredDifference.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the statistic "IDA Average Maximum Squared Difference".
double	IdaMinimumSquaredDifference.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the statistic "IDA Average Minimum Squared Difference".
double	ImpliedArrowOrientationRatioEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ImpliedArrowOrientationRatioEst2.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ImpliedOrientationRatioEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ImpliesLegalMag.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	KnowledgeSatisfied.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	LatentCommonAncestorFalseNegativeBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	LatentCommonAncestorFalsePositiveBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	LatentCommonAncestorRecallBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	LatentCommonAncestorTruePositiveBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	LegalCpdag.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value indicating whether the estimated graph is a Legal CPDAG (1.0) or not (0.0).
double	LegalPag.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	LocalGraphPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	This method calculates the Local Graph Precision.
double	LocalGraphRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)
double	MagCgScore.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	MagDgScore.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	MagSemScore.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	MarkovCheckAdPasses.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling p-value > 0.05.
double	MarkovCheckAdPassesBestOf10.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling p-value > 0.05.
double	MarkovCheckAndersonDarlingP.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckAndersonDarlingPBestOf10.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckBinomialP.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Binomial P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckBinomialPBestOf10.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Binomial P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckFractionDependentH0.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckFractionDependentH1.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckKolmogorovSmirnoffP.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckKolmogorovSmirnoffPBestOf10.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckKsPasses.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates whether Kolmogorov-Smirnoff P > 0.05.
double	MarkovCheckKsPassesBestOf10.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates whether Kolmogorov-Smirnoff P > 0.05.
double	MathewsCorrAdj.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	MathewsCorrArrow.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	Maximal.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Checks whether a PAG is maximal.
double	MaximalityCondition.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	McGetNumTestsH0.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of tests done under the null hypothesis of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	McGetNumTestsH1.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of tests done under the null hypothesis of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	NoAlmostCyclicPathsCondition.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NoCyclicPathsCondition.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NodesInCyclesPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NodesInCyclesRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NonancestorPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NonancestorRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NoSemidirectedF1.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NoSemidirectedPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NoSemidirectedRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumAmbiguousTriples.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberArrowsEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberArrowsNotInUnshieldedCollidersEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberCollidersEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberEdgesEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberEdgesInCollidersEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberEdgesInUnshieldedCollidersEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberEdgesTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberOfEdgesEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberOfEdgesTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberTailsEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumberUnshieldedCollidersEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumBidirectedBothNonancestorAncestor.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumBidirectedEdgesEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumBidirectedEdgesTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumColoredDD.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumColoredNL.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumColoredPD.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumColoredPL.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCommonMeasuredAncestorBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatibleDefiniteDirectedEdgeAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatibleDirectedEdgeConfounded.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatibleDirectedEdgeNonAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatibleEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatiblePossiblyDirectedEdgeAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatiblePossiblyDirectedEdgeNonAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCompatibleVisibleNonancestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCorrectBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the number of bidirected edges for which a latent confounder exists.
double	NumCorrectDDAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCorrectPDAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCorrectVisibleEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumCoveringAdjacenciesInPag.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDefinitelyDirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDefinitelyNotDirectedPaths.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedEdgeAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedEdgeBnaMeasuredCounfounded.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedEdgeNoMeasureAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedEdgeNotAncNotRev.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedEdgeReversed.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedPathsEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedPathsTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumDirectedShouldBePartiallyDirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumEdgeInEstInTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumGenuineAdjacenciesInPag.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumIncorrectDDAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumIncorrectPDAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumIncorrectVisibleAncestors.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumLatentCommonAncestorBidirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumNondirectedEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumParametersEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumPartiallyOrientedEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumPossiblyDirected.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumUndirectedEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumVisibleEdgeEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the number of X-->Y edges that are visible in the estimated PAG.
double	NumVisibleEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	NumVisibleEdgeTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Retrieves the number of X-->Y edges for which X-->Y is visible in the true PAG.
double	OrientationPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	OrientationRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Orientation Recall statistic, which measures the accuracy of the estimated orientation of edges in a graph compared to the true graph.
double	PagAdjacencyPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the adjacency precision of the estimated graph compared to the true graph.
double	PagAdjacencyRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the adjacency recall compared to the true PAG (Partial Ancestral Graph).
double	ParameterColumn.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	PercentAmbiguous.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the percentage of ambiguous triples in the estimated graph compared to the true graph.
double	PercentBidirectedEdges.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	ProportionSemidirectedPathsNotReversedEst.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the proportion of semi(X, Y) in the estimated graph for which there is no semi(Y, X) in the true graph.
double	ProportionSemidirectedPathsNotReversedTrue.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the proportion of semi(X, Y) paths in the true graph for which there is no semi(Y, Z) path in the estimated graph.
double	PvalueDistanceToAlpha.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	PvalueUniformityUnderNull.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	SemidirectedPathF1.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the F1 statistic for adjacencies.
double	SemidirectedPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the semi-directed precision value.
double	SemidirectedRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Semidirected-Rec statistic, which is the proportion of (X, Y) where if there is a semidirected path in the true graph, then there is also a semidirected path in the estimated graph.
default double	Statistic.getValue(Graph trueGraph, Graph estGraph)	Returns the value of this statistic, given the true graph and the estimated graph.
default double	Statistic.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel)	Returns the value of this statistic, given the true graph and the estimated graph.
double	Statistic.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
default double	Statistic.getValue(Graph trueGraph, Graph estGraph, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	StructuralHammingDistance.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TailPrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the tail precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
double	TailRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the tail recall value for a given true graph, estimated graph, and data model.
double	TrueDagFalseNegativesArrows.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false negatives for arrows compared to the true DAG.
double	TrueDagFalseNegativesTails.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false negatives for tails compared to the true DAG.
double	TrueDagFalsePositiveArrow.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the false positives for arrows compared to the true DAG.
double	TrueDagFalsePositiveTails.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positives for tails in the estimated graph compared to the true DAG.
double	TrueDagPrecisionArrow.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the proportion of X*->Y in the estimated graph for which there is no path Y~~>X in the true graph.
double	TrueDagPrecisionTails.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the proportion of X-->Y edges in the estimated graph for which there is a path X~~>Y in the true graph.
double	TrueDagRecallArrows.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TrueDagRecallTails.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TrueDagTruePositiveArrow.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of true positives for arrows compared to the true DAG.
double	TrueDagTruePositiveDirectedPathNonancestor.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the true positives for arrows compared to the true DAG.
double	TrueDagTruePositiveTails.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TwoCycleFalseNegative.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TwoCycleFalsePositive.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TwoCyclePrecision.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TwoCycleRecall.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	TwoCycleTruePositive.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.

Uses of Graph in edu.cmu.tetrad.algcomparison.statistic.utils

Constructors in edu.cmu.tetrad.algcomparison.statistic.utils with parameters of type Graph

Modifier	Constructor	Description
	AdjacencyConfusion(Graph truth, Graph est)	Constructs a new AdjacencyConfusion object from the given graphs.
	ArrowConfusion(Graph truth, Graph est)	Constructs a new ArrowConfusion object.
	ArrowConfusion(Graph truth, Graph est, boolean truthAdj)	Constructs a new ArrowConfusion object.
	BidirectedConfusion(Graph truth, Graph est)	Constructs a new confusion matrix for bidirected edges.
	CircleConfusion(Graph truth, Graph est)	Constructs a new CircleConfusion object.
	CircleConfusion(Graph truth, Graph est, boolean truthAdj)	Constructs a new CircleConfusion object.
	LocalGraphConfusion(Graph trueGraph, Graph estGraph)	Constructs a new LocalGraphConfusion object from the given graphs.
	OrientationConfusion(Graph truth, Graph est)	Constructor for OrientationConfusion.
	TailConfusion(Graph truth, Graph est)	Constructor for TailConfusion.

Uses of Graph in edu.cmu.tetrad.bayes

Methods in edu.cmu.tetrad.bayes that return Graph

Modifier and Type	Method	Description
Graph	BayesIm.getDag()	$Description
Graph	BayesPm.getDag()	Returns the DAG.
Graph	DirichletBayesIm.getDag()	getDag.
Graph	MlBayesIm.getDag()	getDag.
Graph	MlBayesImObs.getDag()	getDag.
Graph	UpdatedBayesIm.getDag()	getDag.
Graph	ApproximateUpdater.getManipulatedGraph()	getManipulatedGraph.
Graph	CptInvariantUpdater.getManipulatedGraph()	getManipulatedGraph.
Graph	Identifiability.getManipulatedGraph()	getManipulatedGraph.
Graph	JunctionTreeUpdater.getManipulatedGraph()	Returns the manipulated graph.
Graph	ManipulatingBayesUpdater.getManipulatedGraph()	Returns the manipulated graph.
Graph	RowSummingExactUpdater.getManipulatedGraph()	getManipulatedGraph.
static Graph	GraphTools.moralize(Graph graph)	Create a moral graph.

Methods in edu.cmu.tetrad.bayes that return types with arguments of type Graph

Modifier and Type	Method	Description
static List<Graph>	ModelGenerator.generate(Graph graph)	This method takes an acyclic graph as input and returns a list of graphs each of which is a modification of the original graph with either an edge deleted, added or reversed.

Methods in edu.cmu.tetrad.bayes with parameters of type Graph

Modifier and Type	Method	Description
static void	GraphTools.fillIn(Graph graph, Node[] ordering)	Apply Tarjan and Yannakakis (1984) fill in algorithm for graph triangulation.
static List<Graph>	ModelGenerator.generate(Graph graph)	This method takes an acyclic graph as input and returns a list of graphs each of which is a modification of the original graph with either an edge deleted, added or reversed.
static Map<Node,Set<Node>>	GraphTools.getCliques(Node[] ordering, Graph graph)	Get cliques in a decomposable graph.
BayesProperties.LikelihoodRet	BayesProperties.getLikelihoodRatioP(Graph graph0)	Calculates the p-value of the graph with respect to the given data, against the complete model as an alternative.
static Node[]	GraphTools.getMaximumCardinalityOrdering(Graph graph)	Perform Tarjan and Yannakakis (1984) maximum cardinality search (MCS) to get the maximum cardinality ordering.
static Graph	GraphTools.moralize(Graph graph)	Create a moral graph.
void	EmBayesProperties.setGraph(Graph graph)	Setter for the field graph.

Constructors in edu.cmu.tetrad.bayes with parameters of type Graph

Modifier	Constructor	Description
	BayesPm(Graph graph)	Construct a new BayesPm using the given DAG, assigning each variable two values named "value1" and "value2" unless nodes are discrete variables with categories already defined.
	BayesPm(Graph graph, int lowerBound, int upperBound)	Constructs a new BayesPm from the given DAG, assigning each variable a random number of values between lowerBound and upperBound.
	BayesPm(Graph graph, BayesPm oldBayesPm)	Constructs a new BayesPm using a given DAG, using as much information from the old BayesPm as possible.
	BayesPm(Graph graph, BayesPm oldBayesPm, int lowerBound, int upperBound)	Constructs a new BayesPm from the given DAG, using as much information from the old BayesPm as possible.
	EmBayesProperties(DataSet dataSet, Graph graph)	Constructor for EmBayesProperties.
	JunctionTreeAlgorithm(Graph graph, DataModel dataModel)	Constructor for JunctionTreeAlgorithm.

Uses of Graph in edu.cmu.tetrad.calibration

Methods in edu.cmu.tetrad.calibration that return Graph

Modifier and Type	Method	Description
Graph	DataForCalibrationRfci.learnBNRFCI(DataSet bootstrapSample, int depth, Graph truePag)	learnBNRFCI.
Graph	DataForCalibrationRfci.makeDAG(int numVars, double edgesPerNode, int numLatentConfounders)	makeDAG.

Methods in edu.cmu.tetrad.calibration with parameters of type Graph

Modifier and Type	Method	Description
Graph	DataForCalibrationRfci.learnBNRFCI(DataSet bootstrapSample, int depth, Graph truePag)	learnBNRFCI.

Uses of Graph in edu.cmu.tetrad.data

Methods in edu.cmu.tetrad.data with parameters of type Graph

Modifier and Type	Method	Description
boolean	Knowledge.isViolatedBy(Graph graph)	isViolatedBy.

Constructors in edu.cmu.tetrad.data with parameters of type Graph

Modifier	Constructor	Description
	IndependenceFacts(Graph graph)	Constructor for IndependenceFacts.

Uses of Graph in edu.cmu.tetrad.data.simulation

Methods in edu.cmu.tetrad.data.simulation that return Graph

Modifier and Type	Method	Description
Graph	LoadContinuousDataAndGraphs.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LoadContinuousDataAndSingleGraph.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LoadContinuousDataSmithSim.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LoadDataAndGraphs.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LoadDataFromFileWithoutGraph.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	LoadContinuousDataSmithSim.readGraph(File file)	readGraph.

Uses of Graph in edu.cmu.tetrad.graph

Classes in edu.cmu.tetrad.graph that implement Graph

Modifier and Type	Class	Description
final class	Dag	Represents a directed acyclic graph--that is, a graph containing only directed edges, with no cycles.
class	EdgeListGraph	Stores a graph a list of lists of edges adjacent to each node in the graph, with an additional list storing all of the edges in the graph.
class	LagGraph	Implements a graph allowing nodes in the getModel time lag to have parents taken from previous time lags.
class	ReplicatingGraph	EdgeListGraph subclass that applies an EdgeReplicationPolicy whenever edges are added/removed or endpoints are oriented.
final class	SemGraph	Represents the graphical structure of a structural equation model.
class	TimeLagGraph	Represents a time series graph--that is, a graph with a fixed number S of lags, with edges into initial lags only--that is, into nodes in the first R lags, for some R.

Methods in edu.cmu.tetrad.graph that return Graph

Modifier and Type	Method	Description
static Graph	GraphUtils.bidirectedToUndirected(Graph graph)	Converts a bidirected graph to an undirected graph.
static Graph	GraphTransforms.calcAdjacencyGraph(Graph dag)	Calculates the adjacency graph for the given Directed Acyclic Graph (DAG).
static Graph	GraphUtils.completeGraph(Graph graph)	completeGraph.
static Graph	RandomMim.constructRandomMim(List<RandomMim.LatentGroupSpec> specs, Integer metaEdgeCount, int numLatentMeasuredImpureParents, int numMeasuredMeasuredImpureParents, int numMeasuredMeasuredImpureAssociations, RandomMim.LatentLinkMode latentLinkMode, Random rng)	Constructs a random meta-graph as a Multiple Indicator Model (MIM) with specified structural constraints.
static Graph	GraphUtils.convert(String spec)	Converts a string spec of a graph--for example, "X1-->X2, X1---X3, X2o->X4, X3<->X4" to a Graph.
default Graph	Graph.copy()	Creates and returns a copy of the current graph.
static Graph	GraphTransforms.dagFromCpdag(Graph graph)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, boolean verbose)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, boolean meekPreventCycles, boolean verbose)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, Knowledge knowledge)	dagFromCpdag.
static Graph	GraphTransforms.dagFromCpdag(Graph cpdag, Knowledge knowledge, boolean verbose)	Returns a random DAG from the given CPDAG.
static Graph	GraphTransforms.dagToCpdag(Graph dag)	Returns the completed partially directed acyclic graph (CPDAG) for a given directed acyclic graph (DAG).
static @NotNull Graph	GraphTransforms.dagToMag(Graph dag)	Converts a Directed Acyclic Graph (DAG) to a Maximal Ancestral Graph (MAG) by adding arrows to the edges.
static @NotNull Graph	GraphTransforms.dagToPag(Graph graph)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the DagToPag algorithm.
static Graph	GraphUtils.emptyGraph(int numNodes)	Creates an empty graph with the specified number of nodes.
static @NotNull Graph	GraphUtils.fixDirections(Graph graph)	Processes the given graph by fixing the directions of edges to ensure consistency, flipping edges where necessary, and optionally preserving ancillary graph information.
static Graph	GraphUtils.getAdjacencySubgraphWithTargetNode(Graph graph, Node target)	Calculates the subgraph over the adjacency of a target node for a DAG, CPDAG, MAG, or PAG.
Graph	EdgeListGraph.getAncillaryGraph(String name)	Returns the ancillary graph with the given name, or null if no ancillary graph by that name has been set.
static Graph	GraphUtils.getComparisonGraph(Graph graph, Parameters params)	Returns a comparison graph based on the specified parameters.
static Graph	Paths.getDag(List<Node> pi, Graph g, boolean verbose)	Generates a directed acyclic graph (DAG) based on the given list of nodes using Raskutti and Uhler's method.
Graph	RandomGraph.UniformGraphGenerator.getDag()	Returns the parent matrix for the graph.
Graph	RandomGraph.UniformGraphGenerator.getDag(List<Node> nodes)	Returns the parent matrix for the graph.
static Graph	GraphUtils.getGraphWithoutXToY(Graph G, Node x, Node y, GraphUtils.GraphType graphType)	Returns a graph that is obtained by removing the edge from node x to node y from the input graph.
static Graph	GraphUtils.getMarkovBlanketSubgraphWithTargetNode(Graph graph, Node target)	Calculates the subgraph over the Markov blanket of a target node for a DAG, CPDAG, MAG, or PAG.
static Graph	GraphUtils.getParentsSubgraphWithTargetNode(Graph graph, Node target)	Calculates the subgraph over the parents of a target node for a DAG, CPDAG, MAG, or PAG.
static Graph	GraphUtils.guaranteePag(Graph pag, FciOrient fciOrient, Knowledge knowledge, Set<Triple> knownColliders, boolean verbose, Set<Node> selection)	Guarantees the correctness of a Partial Ancestral Graph (PAG) by repairing faulty structures such as cycles, violations of maximality, and incorrectly oriented edges.
static Graph	GraphSaveLoadUtils.loadGraph(File file)	loadGraph.
static Graph	GraphSaveLoadUtils.loadGraphAmatCpdag(File file)	Loads a CPDAG in the "amat.cpdag" format of PCALG.
static Graph	GraphSaveLoadUtils.loadGraphAmatPag(File file)	Loads a PAG in the "amat.pag" format of PCALG.
static Graph	GraphSaveLoadUtils.loadGraphBNTPcMatrix(List<Node> vars, DataSet dataSet)	loadGraphBNTPcMatrix.
static Graph	GraphSaveLoadUtils.loadGraphJson(File file)	loadGraphJson.
static Graph	GraphSaveLoadUtils.loadGraphRuben(File file)	loadGraphRuben.
static Graph	GraphSaveLoadUtils.loadGraphTxt(File file)	loadGraphTxt.
static Graph	GraphSaveLoadUtils.loadRSpecial(File file)	loadRSpecial.
static Graph	GraphTransforms.magToPag(Graph mag)	Converts a maximal ancestral graph (MAG) into a partial ancestral graph (PAG).
static Graph	GraphUtils.markovBlanketSubgraph(Node target, Graph graph)	Calculates the subgraph over the Markov blanket of a target node in a given DAG, CPDAG, MAG, or PAG.
static Graph	GraphFactoryUtil.newGraph(boolean replicating)	Creates a new instance of a Graph with the specified replication policy.
static Graph	GraphFactoryUtil.newGraph(Graph graph)	Creates a new instance of a Graph based on the given graph.
static Graph	GraphFactoryUtil.newGraph(List<Node> nodes, boolean replicating)	Creates a new instance of a Graph initialized with the provided list of nodes.
static Graph	GraphUtils.nondirectedGraph(Graph graph)	undirectedGraph.
static Graph	GraphSaveLoadUtils.parseGraphXml(nu.xom.Element graphElement, Map<String,Node> nodes)	parseGraphXml.
static Graph	RandomGraph.randomCyclicGraph2(int numNodes, int numEdges, int maxDegree)	Makes a cyclic graph by repeatedly adding cycles of length of 3, 4, or 5 to the graph, then finally adding two cycles.
static Graph	RandomGraph.randomCyclicGraph3(int numNodes, int numEdges, int maxDegree, double probCycle, double probTwoCycle)	Makes a cyclic graph by repeatedly adding cycles of length of 3, 4, or 5 to the graph, then finally adding two cycles.
static Graph	RandomGraph.randomDag(int numNodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random Directed Acyclic Graph (DAG) with the specified parameters.
static Graph	RandomGraph.randomDag(int numNodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random Directed Acyclic Graph (DAG) based on specified constraints.
static Graph	RandomGraph.randomGraph(int numMeasures, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random graph based on the given parameters.
static Graph	RandomGraph.randomGraph(int numMeasures, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random graph based on the specified parameters.
static Graph	RandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random graph based on the given parameters.
static Graph	RandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random graph with the specified parameters and properties.
static Graph	RandomGraph.randomGraphRandomForwardEdges(int numNodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random graph with the given parameters and random forward edges.
static Graph	RandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random graph with forward edges.
static Graph	RandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, boolean layoutAsCircle, long seed)	Generates a random graph with forward edges.
static Graph	RandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random directed acyclic graph with specified properties, including constraints on maximum degree, indegree, and outdegree, with random forward edges.
static Graph	RandomGraph.randomGraphUniform(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, int numIterations)	Generates a random graph using UniformGraphGenerator with the specified parameters.
static Graph	RandomGraph.randomScaleFreeGraph(int numNodes, int numLatentConfounders, double alpha, double beta, double delta_in, double delta_out)	Generates a random scale-free graph.
static Graph	GraphSaveLoadUtils.readerToGraphJson(Reader reader)	readerToGraphJson.
static Graph	GraphSaveLoadUtils.readerToGraphRuben(Reader reader)	readerToGraphRuben.
static Graph	GraphSaveLoadUtils.readerToGraphTxt(Reader reader)	readerToGraphTxt.
static Graph	GraphSaveLoadUtils.readerToGraphTxt(String graphString)	readerToGraphTxt.
static Graph	GraphUtils.removeBidirectedOrientations(Graph estCpdag)	removeBidirectedOrientations.
static Graph	GraphUtils.replaceNodes(Graph originalGraph, List<Node> newVariables)	Converts the given graph, originalGraph, to use the new variables (with the same names as the old).
static Graph	GraphUtils.restrictToMeasured(Graph graph)	Removes all latent nodes from the graph and returns the modified graph.
static Graph	GraphTransforms.revertToUnshieldedColliders(Graph graph, boolean circles)	Reverts the provided graph to its unshielded colliders, with other endpoints oriented either as circles (PAG case) or tails (CPDAG case).
Graph	Dag.subgraph(List<Node> nodes)	Returns a subgraph of the current graph consisting only of the specified nodes.
Graph	EdgeListGraph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	Graph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	LagGraph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	SemGraph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	TimeLagGraph.subgraph(List<Node> nodes)	Returns a subgraph of the current graph based on the provided nodes.
static Graph	GraphUtils.trimGraph(List<Node> targets, Graph graph, int trimmingStyle)	Trims the given graph based on the specified trimming style.
static Graph	GraphUtils.undirectedGraph(Graph graph)	undirectedGraph.
static Graph	GraphUtils.undirectedToBidirected(Graph graph)	Converts an undirected graph to a bidirected graph.
static Graph	GraphTransforms.zhangMagFromPag(Graph pag)	Transforms a partial ancestral graph (PAG) into a maximal ancestral graph (MAG) using Zhang's 2008 Theorem 2.

Methods in edu.cmu.tetrad.graph that return types with arguments of type Graph

Modifier and Type	Method	Description
static List<Graph>	GraphTransforms.generateCpdagDags(Graph cpdag, boolean orientBidirectedEdges)	Generates the list of DAGs in the given cpdag.
static List<Graph>	GraphTransforms.getAllGraphsByDirectingUndirectedEdges(Graph skeleton)	Returns a list of all possible graphs obtained by directing undirected edges in the given graph.
static List<Graph>	GraphTransforms.getDagsInCpdagMeek(Graph cpdag, Knowledge knowledge)	Retrieves a list of directed acyclic graphs (DAGs) within the given completed partially directed acyclic graph (CPDAG) using the Meek rules.

Methods in edu.cmu.tetrad.graph with parameters of type Graph

Modifier and Type	Method	Description
static void	GraphUtils.addEdgeSpecializationMarkup(Graph graph)	Adds markups for edge specializations for the edges in the given graph.
static void	GraphUtils.addForbiddenReverseEdgesForDirectedEdges(Graph graph, Knowledge knowledge)	Adds forbidden reverse edges for directed edges in the given graph based on the knowledge.
static void	RandomGraph.addTwoCycles(Graph graph, int numTwoCycles)	addTwoCycles.
boolean	Triple.alongPathIn(Graph graph)	alongPathIn.
static Set<Node>	GraphUtils.anteriority(Graph G, Node... x)	Computes the anteriority of the given nodes in a graph.
static void	LayoutUtil.arrangeByLayout(Graph graph, HashMap<String,PointXy> layout)	arrangeByLayout.
static boolean	LayoutUtil.arrangeBySourceGraph(Graph resultGraph, Graph sourceGraph)	Arranges the nodes in the result graph according to their positions in the source graph.
static Graph	GraphUtils.bidirectedToUndirected(Graph graph)	Converts a bidirected graph to an undirected graph.
static Graph	GraphTransforms.calcAdjacencyGraph(Graph dag)	Calculates the adjacency graph for the given Directed Acyclic Graph (DAG).
static void	LayoutUtil.circleLayout(Graph graph)	Arranges the nodes in the graph in a circle.
static boolean	GraphUtils.colliderAllowed(Graph pag, Node x, Node b, Node y, Knowledge knowledge)	Determines if the collider is allowed.
static Graph	GraphUtils.completeGraph(Graph graph)	completeGraph.
static boolean	GraphUtils.containsBidirectedEdge(Graph graph)	Checks if a given graph contains a bidirected edge.
static nu.xom.Element	GraphSaveLoadUtils.convertToXml(Graph graph)	convertToXml.
static int	GraphUtils.countAdjErrors(Graph graph1, Graph graph2)	Counts the adjacencies that are in graph1 but not in graph2.
static Graph	GraphTransforms.dagFromCpdag(Graph graph)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, boolean verbose)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, boolean meekPreventCycles, boolean verbose)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, Knowledge knowledge)	dagFromCpdag.
static Graph	GraphTransforms.dagFromCpdag(Graph cpdag, Knowledge knowledge, boolean verbose)	Returns a random DAG from the given CPDAG.
static Graph	GraphTransforms.dagToCpdag(Graph dag)	Returns the completed partially directed acyclic graph (CPDAG) for a given directed acyclic graph (DAG).
static @NotNull Graph	GraphTransforms.dagToMag(Graph dag)	Converts a Directed Acyclic Graph (DAG) to a Maximal Ancestral Graph (MAG) by adding arrows to the edges.
static @NotNull Graph	GraphTransforms.dagToPag(Graph graph)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the DagToPag algorithm.
static void	LayoutUtil.defaultLayout(Graph graph)	Arranges the nodes in the graph in a circle if there are 20 or fewer nodes, otherwise arranges them in a square.
static int	GraphUtils.degree(Graph graph)	Calculates the maximum degree of a graph.
static Set<Node>	GraphUtils.district(Node x, Graph G)	Calculates the district of a given node in a graph.
static Set<Node>	GraphUtils.dsep(Node x, Node y, Graph G)	Returns D-SEP(x, y) for a MAG G.
static Set<Node>	GraphUtils.dsep0(Node x, Node y, Graph G)	Returns D-SEP(x, y) for a MAG G (or inducing path graph G, as in Causation, Prediction and Search).
static Set<Node>	GraphUtils.dsep2(Node x, Node y, Graph G)	Returns D-SEP(x, y) for a MAG G.
static Set<Node>	GraphUtils.dsepReachability(Node x, Node y, Graph G)	Returns D-SEP(x, y) for a MAG G.
static int[][]	GraphUtils.edgeMisclassificationCounts(Graph leftGraph, Graph topGraph, boolean print)	Computes the misclassification counts for each edge in the given graphs.
static String	MisclassificationUtils.edgeMisclassifications(Graph estGraph, Graph refGraph)	edgeMisclassifications.
static String	MisclassificationUtils.endpointMisclassification(Graph estGraph, Graph refGraph)	endpointMisclassification.
static void	GraphUtils.fciOrientbk(Knowledge knowledge, Graph graph, List<Node> variables)	Attempts to orient the edges in the graph based on the given knowledge.
static @NotNull Graph	GraphUtils.fixDirections(Graph graph)	Processes the given graph by fixing the directions of edges to ensure consistency, flipping edges where necessary, and optionally preserving ancillary graph information.
static void	RandomGraph.fixLatents1(int numLatentConfounders, Graph graph)	fixLatents1.
static void	RandomGraph.fixLatents4(int numLatentConfounders, Graph graph)	fixLatents4.
static void	LayoutUtil.fruchtermanReingoldLayout(Graph graph)	fruchtermanReingoldLayout.
static List<Graph>	GraphTransforms.generateCpdagDags(Graph cpdag, boolean orientBidirectedEdges)	Generates the list of DAGs in the given cpdag.
static Graph	GraphUtils.getAdjacencySubgraphWithTargetNode(Graph graph, Node target)	Calculates the subgraph over the adjacency of a target node for a DAG, CPDAG, MAG, or PAG.
static List<Graph>	GraphTransforms.getAllGraphsByDirectingUndirectedEdges(Graph skeleton)	Returns a list of all possible graphs obtained by directing undirected edges in the given graph.
static List<Triple>	GraphUtils.getAmbiguousTriplesFromGraph(Node node, Graph graph)	Retrieves the list of ambiguous triples from the given graph for a given node.
static Node	GraphUtils.getAssociatedNode(Node errorNode, Graph graph)	Returns the associated node for the given error node in the specified graph.
static List<Triple>	GraphSaveLoadUtils.getCollidersFromGraph(Node node, Graph graph)	getCollidersFromGraph.
static Graph	GraphUtils.getComparisonGraph(Graph graph, Parameters params)	Returns a comparison graph based on the specified parameters.
static Graph	Paths.getDag(List<Node> pi, Graph g, boolean verbose)	Generates a directed acyclic graph (DAG) based on the given list of nodes using Raskutti and Uhler's method.
static List<Graph>	GraphTransforms.getDagsInCpdagMeek(Graph cpdag, Knowledge knowledge)	Retrieves a list of directed acyclic graphs (DAGs) within the given completed partially directed acyclic graph (CPDAG) using the Meek rules.
static int	GraphUtils.getDegree(Graph graph)	Returns the maximum degree of a graph.
static List<Triple>	GraphUtils.getDottedUnderlinedTriplesFromGraph(Node node, Graph graph)	Retrieves the list of dotted and underlined triples from the given graph, with the specified node as the middle node.
static Graph	GraphUtils.getGraphWithoutXToY(Graph G, Node x, Node y, GraphUtils.GraphType graphType)	Returns a graph that is obtained by removing the edge from node x to node y from the input graph.
static int	GraphUtils.getIndegree(Graph graph)	Calculates the maximum indegree in a given graph.
static Graph	GraphUtils.getMarkovBlanketSubgraphWithTargetNode(Graph graph, Node target)	Calculates the subgraph over the Markov blanket of a target node for a DAG, CPDAG, MAG, or PAG.
static int	GraphUtils.getNumCoveringAdjacenciesInPag(Graph trueGraph, Graph estGraph)	Returns the number of covering edges in the given estimated partial ancestral graph (PAG) with respect to the given true PAG.
static int	GraphUtils.getNumInducedAdjacenciesInPag(Graph trueGraph, Graph estGraph)	Calculates the number of induced adjacencies in the given estiamted Partial Ancestral (PAG) with respect to the given true PAG.
static Set<Node>	Paths.getParents(List<Node> pi, int p, Graph g, boolean verbose, boolean allowSelectionBias)	Returns the parents of the node at index p, calculated using Pearl's method.
static Graph	GraphUtils.getParentsSubgraphWithTargetNode(Graph graph, Node target)	Calculates the subgraph over the parents of a target node for a DAG, CPDAG, MAG, or PAG.
static Node	GraphUtils.getTrekSource(Graph graph, List<Node> trek)	This method returns the source node of a given trek in a graph.
static GraphUtils.TwoCycleErrors	GraphUtils.getTwoCycleErrors(Graph trueGraph, Graph estGraph)	Returns the TwoCycleErrors object that represents errors for direct feedback loops.
static List<Triple>	GraphUtils.getUnderlinedTriplesFromGraph(Node node, Graph graph)	Retrieves the underlined triples from the given graph that involve the specified node.
static String	GraphUtils.graphAttributesToText(Graph graph, String title)	Converts the attributes of a given graph into a text format.
static String	GraphUtils.graphEdgesToText(Graph graph, String title)	Converts the edges of a graph to text representation.
static String	GraphUtils.graphNodeAttributesToText(Graph graph, String title, char delimiter)	Converts the attributes of nodes in a graph to text format.
static String	GraphUtils.graphNodesToText(Graph graph, String title, char delimiter)	Converts the nodes of a graph to a formatted text representation.
static String	GraphSaveLoadUtils.graphRMatrixTxt(Graph graph)	graphRMatrixTxt.
static String	GraphSaveLoadUtils.graphToAmatCpag(Graph g)	Converts a given graph into an adjacency matrix in CPAG format.
static String	GraphSaveLoadUtils.graphToAmatPag(Graph g)	Saves a PAG in the "amat.pag" format of PCALG.
static String	GraphSaveLoadUtils.graphToDot(Graph graph)	Converts a graph to a Graphviz .dot file
static void	GraphSaveLoadUtils.graphToDot(Graph graph, File file)	graphToDot.
static String	GraphSaveLoadUtils.graphToLavaan(Graph g)	graphToLavaan.
static String	GraphSaveLoadUtils.graphToPcalg(Graph g)	graphToPcalg.
static String	GraphSaveLoadUtils.graphToXml(Graph graph)	graphToXml.
static Graph	GraphUtils.guaranteePag(Graph pag, FciOrient fciOrient, Knowledge knowledge, Set<Triple> knownColliders, boolean verbose, Set<Node> selection)	Guarantees the correctness of a Partial Ancestral Graph (PAG) by repairing faulty structures such as cycles, violations of maximality, and incorrectly oriented edges.
static boolean	GraphUtils.isClique(Collection<Node> set, Graph graph)	Checks if the given set of nodes forms a clique in the specified graph.
static boolean	GraphUtils.isConfoundingTrek(Graph trueGraph, List<Node> trek, Node x, Node y)	Checks if the given trek in a graph is a confounding trek.
static boolean	GraphUtils.isCorrectBidirectedEdge(Edge edge, Graph trueGraph)	Determines if the given bidirected edge has a latent confounder in the true graph--that is, whether for X <-> Y there is a latent node Z such that X <- (Z) -> Y.
static boolean	GraphUtils.isCoveringAdjacency(Graph trueGraph, Graph estGraph, Node x, Node y)	Determines whether an edge between two nodes in the estimated graph is covering a collider or noncollider in the true graph.
static boolean	GraphUtils.isDag(Graph graph)	Determines if the given graph is a directed acyclic graph (DAG).
boolean	Paths.isSatisfyBackDoorCriterion(Graph graph, Node x, Node y, Set<Node> z)	Check to see if a set of variables Z satisfies the back-door criterion relative to node x and node y.
static void	LayoutUtil.kamadaKawaiLayout(Graph graph, boolean randomlyInitialized, double naturalEdgeLength, double springConstant, double stopEnergy)	kamadaKawaiLayout.
static void	LayoutUtil.layoutByCausalOrder(Graph graph)	layoutByCausalOrder.
static LinkedList<Triple>	GraphUtils.listColliderTriples(Graph graph)	Generates a list of triples where a node acts as a collider in a given graph.
static String	GraphSaveLoadUtils.loadGraphRMatrix(Graph graph)	loadGraphRMatrix.
static String	GraphSaveLoadUtils.loadGraphTxt(Graph graph, boolean pagEdgeSpecializationMarked)	Converts a given graph to human-readable text format.
static double	GraphUtils.localMarkovInitializePValues(Graph dag, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node,Node>,Set<Double>> pValues)	Initializes and evaluates p-values for local Markov properties in a given graph.
static Graph	GraphTransforms.magToPag(Graph mag)	Converts a maximal ancestral graph (MAG) into a partial ancestral graph (PAG).
static Set<Node>	GraphUtils.markovBlanket(Node x, Graph G)	Returns a Markov blanket of a node for a DAG, CPDAG, MAG, or PAG.
static Graph	GraphUtils.markovBlanketSubgraph(Node target, Graph graph)	Calculates the subgraph over the Markov blanket of a target node in a given DAG, CPDAG, MAG, or PAG.
static Set<Set<Node>>	GraphUtils.maximalCliques(Graph graph, List<Node> nodes)	Finds all maximal cliques in a given graph.
static Graph	GraphFactoryUtil.newGraph(Graph graph)	Creates a new instance of a Graph based on the given graph.
static Graph	GraphUtils.nondirectedGraph(Graph graph)	undirectedGraph.
static void	GraphUtils.orientCollider(Graph g, Node x, Node z, Node y)	Orients the edges of the given graph by setting both specified nodes as arrow endpoints directed towards the specified target node.
static String	GraphUtils.pathString(Graph graph, Node... x)	Generates a string representation of a path in a given graph, starting from the specified nodes.
static String	GraphUtils.pathString(Graph graph, List<Node> path, boolean showBlocked)	Constructs a string representation of a path in a graph.
static String	GraphUtils.pathString(Graph graph, List<Node> path, Set<Node> conditioningVars)	Returns a string representation of the given path in the graph, considering the conditioning variables.
static String	GraphUtils.pathString(Graph graph, List<Node> path, Set<Node> conditioningVars, boolean showBlocked, boolean allowSelectionBias)	Returns a string representation of the given path in the graph, with additional information about conditioning variables.
static String	Triple.pathString(Graph graph, Node x, Node y, Node z)	pathString.
static void	GraphUtils.recallInitialColliders(Graph pag, Set<Triple> initialColliders, Knowledge knowledge)	Recall unshielded triples in a given graph.
static Graph	GraphUtils.removeBidirectedOrientations(Graph estCpdag)	removeBidirectedOrientations.
static void	GraphUtils.removeNonSkeletonEdges(Graph graph, Knowledge knowledge)	Removes non-skeleton edges from the given graph based on the provided knowledge.
static void	GraphUtils.reorientWithCircles(Graph pag, boolean verbose)	Reorients all edges in a Graph as o-o.
static boolean	GraphUtils.repairMaximality(Graph pag, boolean verbose, Set<Node> selection, FciOrient fciOrient, Knowledge knowledge, Set<Triple> knownColliders)	Repairs the maximality of a PAG (Partial Ancestral Graph) by ensuring that any inducing path between two nodes not currently adjacent in the graph results in an added non-directed edge.
static Graph	GraphUtils.replaceNodes(Graph originalGraph, List<Node> newVariables)	Converts the given graph, originalGraph, to use the new variables (with the same names as the old).
static List<Node>	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 given graph.
static void	LayoutUtil.repositionLatents(Graph graph)	Repositions latent nodes in the given graph based on their non-latent neighbors.
static Graph	GraphUtils.restrictToMeasured(Graph graph)	Removes all latent nodes from the graph and returns the modified graph.
static Graph	GraphTransforms.revertToUnshieldedColliders(Graph graph, boolean circles)	Reverts the provided graph to its unshielded colliders, with other endpoints oriented either as circles (PAG case) or tails (CPDAG case).
static void	GraphSaveLoadUtils.saveGraph(Graph graph, File file, boolean xml)	saveGraph.
void	EdgeListGraph.setAncillaryGraph(String name, Graph graph)	Sets an ancillary graph with the given name.
static void	LayoutUtil.squareLayout(Graph graph)	squareLayout.
static List<Set<Node>>	GraphUtils.stronglyConnectedComponents(Graph g)	Compute strongly connected components (SCCs) of a directed graph.
void	Dag.transferAttributes(Graph graph)	Transfers attributes from the given graph to the current graph.
void	EdgeListGraph.transferAttributes(Graph graph)	transferAttributes.
void	Graph.transferAttributes(Graph graph)	transferAttributes.
void	LagGraph.transferAttributes(Graph graph)	transferAttributes.
void	SemGraph.transferAttributes(Graph graph)	transferAttributes.
void	TimeLagGraph.transferAttributes(Graph graph)	Transfers attributes from the given graph to the current graph.
void	Dag.transferNodesAndEdges(Graph graph)	Transfers nodes and edges from the given graph to the current graph.
void	EdgeListGraph.transferNodesAndEdges(Graph graph)	Transfers nodes and edges from one graph to another.
void	Graph.transferNodesAndEdges(Graph graph)	Transfers nodes and edges from one graph to another.
void	LagGraph.transferNodesAndEdges(Graph graph)	Transfers nodes and edges from one graph to another.
void	SemGraph.transferNodesAndEdges(Graph graph)	Transfers nodes and edges from one graph to another.
void	TimeLagGraph.transferNodesAndEdges(Graph graph)	Transfers nodes and edges from the given graph to the current graph.
static void	GraphTransforms.transformCpdagIntoDag(Graph graph, Knowledge knowledge, boolean verbose)	Transforms a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG) by orienting the undirected edges in the CPDAG.
static Graph	GraphUtils.trimGraph(List<Node> targets, Graph graph, int trimmingStyle)	Trims the given graph based on the specified trimming style.
static boolean	GraphUtils.triple(Graph graph, Node a, Node b, Node c)	Checks if three nodes are connected in a graph.
static Graph	GraphUtils.undirectedGraph(Graph graph)	undirectedGraph.
static Graph	GraphUtils.undirectedToBidirected(Graph graph)	Converts an undirected graph to a bidirected graph.
static Set<Set<Node>>	GraphUtils.visibleEdgeAdjustments1(Graph G, Node x, Node y, int numSmallestSizes, GraphUtils.GraphType graphType)	Calculates visual-edge adjustments given graph G between two nodes x and y that are subsets of MB(X).
static Set<Set<Node>>	GraphUtils.visibleEdgeAdjustments3(Graph G, Node x, Node y, int numSmallestSizes, GraphUtils.GraphType graphType)	This method calculates visible-edge adjustments for a given graph, two nodes, a number of smallest sizes, and a graph type.
static Graph	GraphTransforms.zhangMagFromPag(Graph pag)	Transforms a partial ancestral graph (PAG) into a maximal ancestral graph (MAG) using Zhang's 2008 Theorem 2.

Method parameters in edu.cmu.tetrad.graph with type arguments of type Graph

Modifier and Type	Method	Description
static String	GraphUtils.getIntersectionComparisonString(List<Graph> graphs)	Generates a comparison string for the intersection of multiple graphs.

Constructors in edu.cmu.tetrad.graph with parameters of type Graph

Modifier	Constructor	Description
	Dag(Graph graph)	Constructs a new directed acyclic graph from the given graph object.
	EdgeListGraph(Graph graph)	Constructs a EdgeListGraph using the nodes and edges of the given graph.
	FruchtermanReingoldLayout(Graph graph)	Constructs a new FruchtermanReingoldLayout for the given graph.
	KamadaKawaiLayout(Graph graph)	Constructs a new Kamada-Kawai layout for the given graph.
	Paths(Graph graph)	Constructor for Paths.
	ReplicatingGraph(Graph g, EdgeReplicationPolicy policy)	Creates a new ReplicatingGraph by copying the structure and attributes from the provided graph and applying the specified edge replication policy.
	SemGraph(Graph graph)	Constructs a new SemGraph from the nodes and edges of the given graph.
	Underlines(Graph graph)	Constructor for Underlines.

Uses of Graph in edu.cmu.tetrad.hybridcg

Methods in edu.cmu.tetrad.hybridcg that return Graph

Modifier and Type	Method	Description
Graph	HybridCgModel.HybridCgPm.getGraph()	Retrieves the directed acyclic graph (DAG) associated with this model.

Constructors in edu.cmu.tetrad.hybridcg with parameters of type Graph

Modifier	Constructor	Description
	HybridCgPm(Graph dag, List<Node> nodeOrder, Map<Node,Boolean> discreteFlags, Map<Node,List<String>> categoryMap)	Constructs a HybridCgPm instance based on the provided directed acyclic graph (DAG), node ordering, discrete flags for nodes, and a mapping of node categories.

Uses of Graph in edu.cmu.tetrad.regression

Methods in edu.cmu.tetrad.regression that return Graph

Modifier and Type	Method	Description
Graph	Regression.getGraph()	getGraph.
Graph	RegressionCovariance.getGraph()	Getter for the field graph.
Graph	RegressionDataset.getGraph()	Getter for the field graph.

Methods in edu.cmu.tetrad.regression with parameters of type Graph

Modifier and Type	Method	Description
static DataSet	RegressionUtils.residuals(DataSet dataSet, Graph graph)	residuals.

Uses of Graph in edu.cmu.tetrad.search

Fields in edu.cmu.tetrad.search declared as Graph

Modifier and Type	Field	Description
final Graph	TwoStep.Result.graph	A directed graph representing the relationships between variables as inferred from the coefficient matrix after pruning.

Methods in edu.cmu.tetrad.search that return Graph

Modifier and Type	Method	Description
Graph	RlcdCore.applyGinOrientations(CovarianceMatrix cov, Graph cpdag, int N)	Applies GIN (General Independence Network) orientation rules to the given CPDAG (Completed Partially Directed Acyclic Graph) using the specified covariance matrix and sample size.
Graph	RlcdCore.buildCpdagFromConstraints(List<Node> vars, RlcdCore.Stage1Output s1)	Constructs a CPDAG (Completed Partially Directed Acyclic Graph) from the given variables and constraints.
Graph	MimbuildTrek.getFullGraph()	Deprecated. The full graph discovered.
@NotNull Graph	Grasp.getGraph(boolean cpDag)	Retrieves a graph based on specified parameters.
Graph	Grasp.getGraph(boolean cpDag, boolean replicating)	Returns the graph based on the specified parameters.
static Graph	PermutationSearch.getGraph(List<Node> nodes, Map<Node,Set<Node>> parents, boolean cpDag)	Construct a graph given a specification of the parents for each node.
static Graph	PermutationSearch.getGraph(List<Node> nodes, Map<Node,Set<Node>> parents, Knowledge knowledge, boolean cpDag)	Constructs a graph given a specification of the parents for each node.
static Graph	PermutationSearch.getGraph(List<Node> nodes, Map<Node,Set<Node>> parents, Knowledge knowledge, boolean cpDag, boolean replicating)	Constructs a graph given a specification of the parents for each node.
Graph	BossFci.getMarkovCpdag()	Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
Graph	FgesFci.getMarkovCpdag()
Graph	Gfci.getMarkovCpdag()	Executes the FGES algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration.
@NotNull Graph	GraspFci.getMarkovCpdag()
Graph	SpFci.getMarkovCpdag()
abstract Graph	StarFci.getMarkovCpdag()	Returns a Markov CPDAG to use as the initial graph in the Star-FCI search.
Graph	Cstar.makeGraph(List<Cstar.Record> records)	Makes a graph of the estimated predictors to the effect.
static @NotNull Graph	IcaLingD.makeGraph(Matrix B, List<Node> variables)	Constructs a directed graph based on the input binary adjacency matrix and a list of nodes.
Graph	Lofs.orient()	Orients the graph and returns the oriented graph.
Graph	LatentPurifier.purify()	Purifies the graph by ensuring that each measured variable has at most one latent parent and removes latent variables that have no measured children.
Graph	PcMb.resultGraph()	Returns the result graph.
Graph	BossPod.search()	Run the search and return s a PAG.
Graph	Cam.search()	Executes the CAM (Causal Additive Model) algorithm to search for an optimal directed acyclic graph (DAG) structure that best fits the data.
Graph	Ccd.search()	Executes the CCD search algorithm to infer a causal graph based on statistical independence tests.
Graph	Cdnod.search()
Graph	Cfci.search()	Deprecated. Performs the search and returns the PAG.
Graph	Dagma.search()	Performs a search algorithm to find a graph representation.
Graph	DirectLingam.search()	Performs the search.
Graph	DmPc.search()	Executes the Directed Maximal PC (DmPc) algorithm to identify a causal graph structure that represents the relationships between observed and latent variables.
Graph	Fas.search()	Performs a conditional independence graph search using the default set of variables from the initialized independence test.
Graph	Fas.search(List<Node> nodes)	Searches for conditional independence relationships in a graph constructed from the given list of nodes.
Graph	Fasd.search()	Discovers all adjacencies in data.
Graph	Fask.search()	Executes the FASK (Fast Adjacency Skewness) algorithm to search for a causal graph based on the provided dataset, knowledge, and configurations.
Graph	FaskOrig.search()	Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
Graph	Fci.search()	Searches and retrieves a graph using the specified algorithm.
Graph	Fci.search(IFas fas)	Executes the search process using the provided `IFas` implementation and performs various graph orientation and refinement steps based on the FCI algorithm.
Graph	FciMax.search()	Deprecated. Performs the search and returns the PAG.
Graph	Fcit.search()	Run the search and return a PAG.
Graph	Fges.search()	Greedy equivalence search: Start from the empty graph, add edges tillsetre the model is significant.
Graph	FgesMb.search(List<Node> targets)	Greedy equivalence search: Start from the empty graph, add edges till the model is significant.
Graph	Gfci.search()	Runs the graph and returns the search PAG.
Graph	Gin.search(DataSet data)	Searches and constructs a causal graph representation using the provided dataset.
Graph	IFas.search()	Run adjacency search and return the skeleton graph.
Graph	IGraphSearch.search()	Runs the search and returns a graph.
Graph	IsGFci.search()	Executes the FCI algorithm using the provided independence test, score, and population graph, and returns the resulting graph with edges oriented according to the algorithm's rules.
Graph	MimbuildBollen.search()	Executes a search operation to build and optimize a graphical structure based on the covariance of latent and measured variables.
Graph	MimbuildPca.search()	Executes a search algorithm to identify a structural dependency graph based on block-specific data and principal components analysis (PCA).
Graph	MimbuildTrek.search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov)	Deprecated. Does the search and returns the graph.
Graph	Pc.search()	Performs a search operation based on the test variables associated with the instance.
Graph	Pc.search(List<Node> nodes)	Performs a search to generate a graph structure based on the provided list of nodes.
Graph	Pcd.search()	Runs PC starting with a complete graph over all nodes of the given conditional independence test, using the given independence test and knowledge and returns the resultant graph.
Graph	Pcd.search(Fas fas, List<Node> nodes)	Searches for a graph using the given IFas instance and list of nodes.
Graph	Pcd.search(List<Node> nodes)	Runs PC starting with a complete graph over the given list of nodes, using the given independence test and knowledge and returns the resultant graph.
Graph	PcMb.search()	Searches for the Markov blanket CPDAG for the given targets.
Graph	PcMb.search(List<Node> targets)	Searches for the MB CPDAG for the given targets.
Graph	Pcmci.search()	Executes the PCMCI (Peter and Clark Momentary Conditional Independence) algorithm to discover causal relationships in time series data.
Graph	PermutationSearch.search()	Performs a search for a graph using the default options.
Graph	PermutationSearch.search(boolean cpdag)	Performe the search and return a CPDAG.
Graph	Rfci.search()	Runs the search and returns the RFCI PAG.
Graph	Rfci.search(Fas fas, List<Node> nodes)	Runs the search and returns the RFCI PAG.
Graph	Rfci.search(List<Node> nodes)	Searches of a specific sublist of nodes.
Graph	StarFci.search()	Runs the graph and returns the search PAG.

Methods in edu.cmu.tetrad.search with parameters of type Graph

Modifier and Type	Method	Description
Graph	RlcdCore.applyGinOrientations(CovarianceMatrix cov, Graph cpdag, int N)	Applies GIN (General Independence Network) orientation rules to the given CPDAG (Completed Partially Directed Acyclic Graph) using the specified covariance matrix and sample size.
static Set<Node>	SepsetFinder.blockPathsLocalMarkov(Graph graph, Node x)	Returns a set of nodes that are the parents of the given node in the graph.
static Set<Node>	RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength)	Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.
static Set<Node>	RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge)	Variant of RecursiveBlocking.blockPathsRecursively(Graph, Node, Node, Set, Set, int) that additionally accepts an optional Knowledge object.
static Set<Node>	RecursiveBlockingChokePointA.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength)	Identifies and blocks paths between two nodes in a graph recursively, ensuring that all possible open paths between the nodes are restricted by adding blocking nodes to a set.
static Set<Node>	RecursiveBlockingChokePointB.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength)	Identifies and blocks paths in the given graph by iteratively finding and addressing chokepoints and eligible non-collider nodes.
static Set<Node>	SepsetFinder.blockPathsWithMarkovBlanket(Node x, Graph G)	Identifies the set of nodes that form the Markov Blanket for a given node in a graph.
static Set<Node>	RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, FciOrient fciOrient, int maxBlockingPathLength, int maxDdpPathLength, PreserveMarkov preserveMarkovHelper, int depth)	Finds the set of nodes (separator set) for the Recursive Discriminating Path rule in a graph.
static RecursiveBlocking.Blockable	RecursiveBlocking.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node,Set<Node>> descendantsMap)	Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.
static Set<Node>	SepsetFinder.findSepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth)	Returns the sepset that contains the greedy test for variables x and y in the given graph.
static List<Edge>	CheckKnowledge.forbiddenViolations(Graph graph, Knowledge knowledge)	Returns a sorted list of edges that violate the given knowledge.
List<Pc.Triple>	Pc.getAmbiguousTriples(Graph g)	Identifies and returns a list of ambiguous triples from the given graph.
List<List<Node>>	MarkovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodes(IndependenceTest independenceTest, Graph graph, Double threshold, Double shuffleThreshold)	Calculates the Anderson-Darling test and classifies nodes as accepted or rejected based on the given threshold.
List<List<Node>>	MarkovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData(IndependenceTest independenceTest, Graph estimatedCpdag, Graph trueGraph, Double threshold, Double shuffleThreshold, Double lowRecallBound)	Get accepts and rejects nodes for all nodes from Anderson-Darling test and generate the plot data for confusion statistics.
List<List<Node>>	MarkovCheck.getAndersonDarlingTestAcceptsRejectsNodesForAllNodesPlotData2(IndependenceTest independenceTest, Graph estimatedCpdag, Graph trueGraph, Double threshold, Double shuffleThreshold, Double lowRecallBound)	Get accepts and rejects nodes for all nodes from Anderson-Darling test and generate the plot data for confusion statistics.
List<Pc.Triple>	Pc.getColliderTriples(Graph g)	Retrieves all colliders from the provided graph based on specific criteria.
double[][]	CpdagParentDistancesFromTrue.getDistances(Graph outputCpdag, double[][] trueEdgeStrengths, DataSet dataSet, CpdagParentDistancesFromTrue.DistanceType distanceType)	Calculates the distance matrix for the edges in the given CPDAG (outputCpdag).
List<Double>	MarkovCheck.getF1StatsForTargetNodeAdjacencySubgraph(Node x, Graph estimatedGraph, Graph trueGraph)	Calculates F1 statistics for a target node's adjacency subgraph in terms of adjacency, arrow types, circle types, and tail types.
List<Double>	MarkovCheck.getF1StatsForTargetNodeMBSubgraph(Node x, Graph estimatedGraph, Graph trueGraph)	Computes the F1 statistics for the Markov blanket subgraph with the target node in the given estimated graph and true graph.
List<Double>	MarkovCheck.getF1StatsForTargetNodeParentsSubgraph(Node x, Graph estimatedGraph, Graph trueGraph)	Computes the F1 statistics (F1-Adjacency, F1-Arrow, F1-Circle, F1-Tail) between the parents subgraph of a given target node in the estimated graph and the corresponding parents subgraph in the true graph.
List<Double>	MarkovCheck.getF1StatsForWholeGraph(Graph estimatedGraph, Graph trueGraph)	Calculates the F1 statistics for the entire graph by comparing an estimated graph to the true graph.
static Set<IndependenceFact>	OrderedLocalMarkovProperty.getModel(Graph mag)	Computes the ordered local Markov property for a maximal ancestral graph (MAG).
List<Pc.Triple>	Pc.getNoncolliderTriples(Graph g)	Identifies and retrieves all noncollider triples from the given graph.
List<Double>	MarkovCheck.getPrecisionAndRecallGraphPlotData(Node x, Graph estimatedGraph, Graph trueGraph, ConditioningSetType conditioningSetType, String subgraphFeature)	Computes the precision and recall values for a specified subgraph structure between an estimated graph and a true graph.
void	MarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraph(Node x, Graph estimatedGraph, Graph trueGraph)	Calculates the precision and recall on the Markov Blanket graph for a given node.
void	MarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraph2(Node x, Graph estimatedGraph, Graph trueGraph)	Calculates the precision and recall using LocalGraphConfusion (which calculates the combination of Adjacency and ArrowHead) on the Markov Blanket graph for a given node.
List<Double>	MarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraphPlotData(Node x, Graph estimatedGraph, Graph trueGraph)	Calculates the precision and recall on the markov blanket graph plot data.
List<Double>	MarkovCheck.getPrecisionAndRecallOnMarkovBlanketGraphPlotData2(Node x, Graph estimatedGraph, Graph trueGraph)	This method calculates the precision and recall of a target node's Markov Blanket in the given estimated graph.
void	MarkovCheck.getPrecisionAndRecallWholeGraph(Graph estimatedGraph, Graph trueGraph)	Calculates and prints precision and recall metrics for the whole graph, comparing an estimated graph to the true graph.
static Set<Node>	SepsetFinder.getSepsetContainingGreedySubsetMb(Graph graph, Graph cpdag, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth)	Identifies a separating set (sepset) containing a given subset of nodes between two nodes x and y in a graph using a greedy approach and subsets of (adj(x) U adu(y)) \ {x, y}.
static Set<Node>	SepsetFinder.getSepsetContainingMaxPHybrid(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth)	Returns the set of nodes that act as a separating set between two given nodes (x and y) in a graph.
static Set<Node>	SepsetFinder.getSepsetContainingMinPHybrid(Graph graph, Node x, Node y, IndependenceTest test, int depth)	Returns the sepset containing the minimum p-value for the given variables x and y.
static Set<Node>	SepsetFinder.getSmallestSubset(Node x, Node y, Set<Node> blocking, Set<Node> containing, Graph graph, boolean isPag)	Finds a smallest subset S of blocking that renders two nodes x and y conditionally d-separated conditional on S in the given graph.
static org.ejml.simple.SimpleMatrix	TwoStep.maskFromUndirected(Graph skeleton, List<Node> vars)	Deprecated. Constructs a mask matrix from an undirected graph, indicating the adjacency relationships between a given list of nodes.
void	IsGFci.modifiedR0(Graph fgesGraph)	Modifies the given FGES graph based on the FCI algorithm rules, reorienting edges and potentially identifying and orienting definite colliders.
static List<Edge>	CheckKnowledge.requiredViolations(Graph graph, Knowledge knowledge)	Returns a sorted list of edges that are required by knowledge but which do not appear in the graph.
CyclicSemScorer.ScoreResult	CyclicSemScorer.score(DataSet data, Graph g)	Computes the score of a given data set and graph by evaluating the Bayesian Information Criterion (BIC), stability of the system, and the number of parameters used.
double	Fges.scoreDag(Graph dag)	Scores a Directed Acyclic Graph (DAG) based on its structure.
double	FgesMb.scoreDag(Graph dag)	Scores the given directed acyclic graph (DAG).
static Set<Node>	Gfci.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
static Set<Node>	StarFci.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
void	Fges.setBoundGraph(Graph boundGraph)	If non-null, edges not adjacent in this graph will not be added.
void	FgesMb.setBoundGraph(Graph boundGraph)	If non-null, edges not adjacent in this graph will not be added.
void	Fasd.setExternalGraph(Graph externalGraph)	Sets the external graph.
void	Fask.setExternalGraph(Graph externalGraph)	Sets the external graph for the FASK algorithm.
void	FaskOrig.setExternalGraph(Graph externalGraph)	Sets the external graph to use.
void	Fges.setInitialGraph(Graph initialGraph)	Sets the initial graph for the application.
void	FgesMb.setInitialGraph(Graph initialGraph)	Sets the initial graph for the software.
void	IsFges.setPopulationGraph(Graph pop)	Sets the population graph for the current instance.

Constructors in edu.cmu.tetrad.search with parameters of type Graph

Modifier	Constructor	Description
	Graph(Graph graph, boolean potentiallyDirected)	Represents a graph used in Dijkstra's algorithm.
	Ida(DataSet dataSet, Graph graph, List<Node> possibleCauses)	Constructor.
	IdaCheck(Graph graph, DataSet dataSet, SemIm trueSemIm)	Constructs a new IDA check for the given MPDAG and data set.
	LatentPurifier(Graph modelGraph, List<Node> latentVariables, List<Node> measuredVariables)	Constructor for the LatentPurifier class, which implements the purification step of Silva et al.'s BuildPureClusters algorithm.
	Lofs(Graph graph, List<DataSet> dataSets)	Constructor.
	MarkovCheck(Graph graph, IndependenceTest independenceTest, ConditioningSetType setType)	Constructor.
	Result(org.ejml.simple.SimpleMatrix B, Graph g)	Constructs a Result object that encapsulates the output of the TwoStep algorithm.

Uses of Graph in edu.cmu.tetrad.search.score

Methods in edu.cmu.tetrad.search.score that return Graph

Modifier and Type	Method	Description
Graph	GraphScore.getDag()	Returns a copy of the DAG being searched over.
Graph	ScoredGraph.getGraph()	Returns the graph.

Methods in edu.cmu.tetrad.search.score with parameters of type Graph

Modifier and Type	Method	Description
static double	SemBicScorer.scoreDag(Graph dag, DataModel data, boolean precomputeCovariances)	Scores the given DAG using the given data model, usimg a BIC score.
static double	SemBicScorer.scoreDag(Graph dag, DataModel data, double penaltyDiscount, boolean precomputeCovariances)	Scores the given DAG using the given data model, usimg a BIC score.

Constructors in edu.cmu.tetrad.search.score with parameters of type Graph

Modifier	Constructor	Description
	GraphScore(Graph dag)	Constructs a GraphScore from a DAG.
	ScoredGraph(Graph graph, Double score)	Constructs a scored graph.

Uses of Graph in edu.cmu.tetrad.search.test

Methods in edu.cmu.tetrad.search.test that return Graph

Modifier and Type	Method	Description
static Graph	IndTestFdrWrapper.doFdrLoop(IGraphSearch search, boolean negativelyCorrelated, double alpha, double fdrQ, boolean verbose)	Executes a loop for controlling the false discovery rate (FDR) as part of a graph search process.
Graph	MsepTest.getGraph()	Returns the underlying graph that is being used to calculate d-separation relationships.

Constructors in edu.cmu.tetrad.search.test with parameters of type Graph

Modifier	Constructor	Description
	MsepTest(Graph graph)	Constructor.
	MsepTest(Graph graph, boolean keepLatents)	Constructor.

Uses of Graph in edu.cmu.tetrad.search.unmix

Fields in edu.cmu.tetrad.search.unmix with type parameters of type Graph

Modifier and Type	Field	Description
final List<Graph>	UnmixResult.clusterGraphs	A list of optional graphical representations of clusters, where each graph corresponds to a specific cluster.
Function<CausalUnmixer.Config,Function<DataSet,Graph>>	CausalUnmixer.Config.perClusterGraphFn	A function that generates a cluster-specific graph creation method.
Function<CausalUnmixer.Config,Function<DataSet,Graph>>	CausalUnmixer.Config.pooledGraphFn	A function that generates a pooled graph model based on a given configuration and dataset.
Function<DataSet,Graph>	ParentSupersetBuilder.Config.shallowSearch	If provided, called on each subsample to get a shallow graph (e.g., PC-Max depth 1)

Methods in edu.cmu.tetrad.search.unmix with parameters of type Graph

Modifier and Type	Method	Description
static UnmixDiagnostics.GraphDiff	UnmixDiagnostics.compareClusterGraphsCpdag(Graph g1, Graph g2)	Compares two clustering graphs by transforming them into CPDAGs (Completed Partially Directed Acyclic Graphs), and calculates the adjacency F1 score, arrow F1 score, and Structural Hamming Distance (SHD) between the graphs.
static double[][]	ResidualUtils.residualMatrix(DataSet data, Graph g, ResidualRegressor reg)	Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given graph.

Method parameters in edu.cmu.tetrad.search.unmix with type arguments of type Graph

Modifier and Type	Method	Description
static UnmixDiagnostics.BicDelta	UnmixDiagnostics.computeBicDeltaK1K2(DataSet data, EmUnmix.Config baseCfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster)	Computes the Bayesian Information Criterion (BIC) for mixture models with K=1 and K=2 clusters, and calculates the BIC difference (delta) between them.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch)	Executes the unmixing process on the given dataset using the specified configuration, residual regressor, and optional graph search functions.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing a dataset within the specified range [Kmin, Kmax].
static UnmixDiagnostics.StabilityResult	UnmixDiagnostics.stabilityAcrossRestarts(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster, int repeats, long seedBase)	Evaluates the stability of clustering results across multiple independent runs of the EM algorithm by calculating the average Adjusted Rand Index (ARI) and its standard deviation between all pairs of runs.

Uses of Graph in edu.cmu.tetrad.search.utils

Methods in edu.cmu.tetrad.search.utils that return Graph

Modifier and Type	Method	Description
Graph	MagToPag.convert(boolean checkMag)	This method does the conversion of MAG to PAG.
Graph	TsDagToPag.convert()	convert.
Graph	FgesOrienter.getAdjacencies()	Getter for the field adjacencies.
Graph	TsUtils.VarResult.getCollapsedVarGraph()	Getter for the field collapsedVarGraph.
Graph	DagSepsets.getDag()	Returns the DAG being analyzed.
Graph	SepsetsGreedy.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
Graph	SepsetsGreedyMb.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
Graph	SepsetsMaxP.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepset algorithm.
Graph	SepsetsMinP.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
Graph	FgesOrienter.getExternalGraph()	Getter for the field externalGraph.
Graph	TeyssierScorer.getGraph(boolean cpDag)	Returns the DAG build for the current permutation, or its CPDAG.
static Graph	MbUtils.getOneMbDag(Graph mbCpdag)	Returns an example DAG from the given MB CPDAG.
Graph	PossibleMConnectingPath.getPag()	Getter for the field pag.
Graph	TsDagToPag.getTruePag()	Getter for the field truePag.
Graph	DagInCpcagIterator.next()	Successive calls to this method return successive DAGs in the CPDAG, in a more or less natural enumeration of them in which an arbitrary undirected edge is picked, oriented one way, Meek rules applied, then a remaining unoriented edge is picked, oriented one way, and so on, until a DAG is obtained, and then by backtracking the other orientation of each chosen edge is tried.
Graph	DagIterator.next()	Successive calls to this method return successive DAGs in the CPDAG, in a more or less natural enumeration of them in which an arbitrary undirected edge is picked, oriented one way, Meek rules applied, then a remaining unoriented edge is picked, oriented one way, and so on, until a DAG is obtained, and then by backtracking the other orientation of each chosen edge is tried.
Graph	FgesOrienter.search()	Greedy equivalence search: Start from the empty graph, add edges till model is significant.

Methods in edu.cmu.tetrad.search.utils that return types with arguments of type Graph

Modifier and Type	Method	Description
static List<Graph>	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.

Methods in edu.cmu.tetrad.search.utils with parameters of type Graph

Modifier and Type	Method	Description
static void	GraphSearchUtils.arrangeByKnowledgeTiers(Graph graph)	arrangeByKnowledgeTiers.
static void	GraphSearchUtils.arrangeByKnowledgeTiers(Graph graph, Knowledge knowledge)	arrangeByKnowledgeTiers.
static void	GraphSearchUtils.basicCpdag(Graph graph)	Get a graph and direct only the unshielded colliders.
static void	GraphSearchUtils.basicCpdagRestricted2(Graph graph, Node node)	basicCpdagRestricted2.
void	Bes.bes(Graph graph, List<Node> variables)	Runs BES for a graph over the given list of variables
void	BesPermutation.bes(Graph graph, List<Node> order, List<Node> suborder)	Runs BES.
static R0R4Strategy	R0R4StrategyTestBased.defaultConfiguration(Graph dag, Knowledge knowledge)	Returns a default configuration of the FciOrientDataExaminationStrategy object.
org.apache.commons.lang3.tuple.Pair<DiscriminatingPath,Boolean>	R0R4Strategy.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, Graph graph, Set<Node> vNodes)	Does a discriminating path orientation based on an examination of the data.
org.apache.commons.lang3.tuple.Pair<DiscriminatingPath,Boolean>	R0R4StrategyTestBased.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, Graph graph, Set<Node> vNodes)	Does a discriminating path orientation.
boolean	DiscriminatingPath.existsIn(Graph graph)	Checks this discriminating path construct to make sure it is a discriminating path in the given graph.
static boolean	TsDagToPag.existsInducingPathInto(Node x, Node y, Graph graph, Knowledge knowledge)	existsInducingPathInto.
static boolean	TsDagToPag.existsInducingPathVisitts(Graph graph, Node a, Node b, Node x, Node y, LinkedList<Node> path, Knowledge knowledge)	existsInducingPathVisitts.
void	FciOrient.fciOrientbk(Knowledge bk, Graph graph, List<Node> variables)	Orient the edges of a graph based on the given knowledge.
void	FciOrient.finalOrientation(Graph graph)	Orients the graph (in place) according to rules in the graph (FCI step D).
static List<PossibleMConnectingPath>	PossibleMConnectingPath.findMConnectingPaths(Graph pag, Node x, Node y, Collection<Node> z)	Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z.
static List<PossibleMConnectingPath>	PossibleMConnectingPath.findMConnectingPathsOfLength(Graph pag, Node x, Node y, Collection<Node> z, Integer length)	Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z for a particular path length.
static List<Graph>	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.
static @NotNull Map<Node,Set<Edge>>	AlmostCycleRemover.getBMap(Graph pag)	Returns a map of nodes to bidirected edges for them.
static GraphSearchUtils.CpcTripleType	GraphSearchUtils.getCpcTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph)	getCpcTripleType.
static String	GraphSearchUtils.getEdgewiseComparisonString(String trueGraphName, Graph trueGraph, String targetGraphName, Graph targetGraph)	getEdgewiseComparisonString.
static R0R4StrategyTestBased	MagToPag.getFinalStrategyUsingDsep(Graph mag, Knowledge knowledge, boolean verbose)	Returns the final strategy for finding a PAG using D-SEP.
static GraphUtils.GraphComparison	GraphSearchUtils.getGraphComparison(Graph trueGraph, Graph targetGraph)	Just counts arrowhead errors--for cyclic edges counts an arrowhead at each node.
static Knowledge	TsUtils.getKnowledge(Graph graph)	getKnowledge.
static @NotNull DataSet	LgMnarDataSimulator.getMnarData(Graph graph, int numVariablesWithMissing, int numExtraInfluences, double threshold, int numRows)	Generates a dataset with Missing Not At a Random (MNAR) data mechanism applied to specific variables in a graph.
static Graph	MbUtils.getOneMbDag(Graph mbCpdag)	Returns an example DAG from the given MB CPDAG.
static Set<Node>	GraphSearchUtils.getReachableNodes(List<Node> initialNodes, LegalPairs legalPairs, List<Node> c, List<Node> d, Graph graph, int maxPathLength)	getReachableNodes.
double	FgesOrienter.getScore(Graph dag)	getScore.
static int[][]	GraphSearchUtils.graphComparison(Graph trueCpdag, Graph estCpdag, PrintStream out)	graphComparison.
static boolean	GraphInPag.graphInPagStep0(Graph pag, Graph dag)	This method implements step (1) of the definition.
static boolean	GraphInPag.graphInPagStep1(Graph pag, Graph dag)	graphInPagStep1.
static boolean	GraphInPag.graphInPagStep2(Graph pag, Graph dag)	graphInPagStep2.
static boolean	GraphInPag.graphInPagStep3(Graph pag, Graph dag)	graphInPagStep3.
static boolean	GraphInPag.graphInPagStep4(Graph pag, Graph dag)	graphInPagStep4.
static boolean	GraphInPag.graphInPagStep5(Graph pag, Graph dag)	graphInPagStep5.
static TimeLagGraph	TsUtils.graphToLagGraph(Graph _graph, int numLags)	graphToLagGraph.
static boolean	FciOrient.isArrowheadAllowed(Node x, Node y, Graph graph, Knowledge K)	Determines if an arrowhead can be placed at node Y in the given graph, based on the adjacency relationships, endpoint types, and any provided prior knowledge constraints.
static GraphLegalityCheck.LegalMagRet	GraphLegalityCheck.isLegalMag(Graph mag, Set<Node> selection)	Determines whether the given graph is a legal Mixed Ancestral Graph (MAG).
static boolean	GraphLegalityCheck.isLegalMagQuiet(Graph mag, Set<Node> selection)	Determines whether the given graph is a legal Mixed Ancestral Graph (MAG) without providing detailed error messages.
static GraphLegalityCheck.LegalPagRet	GraphLegalityCheck.isLegalPag(Graph pag, Set<Node> selection)	Checks if the provided Directed Acyclic Graph (PAG) is a legal PAG.
static boolean	GraphLegalityCheck.isLegalPagQuiet(Graph pag, Set<Node> selection)	Determines whether the provided Partial Ancestral Graph (PAG) is a legal PAG without providing detailed error messages.
boolean	R0R4Strategy.isUnshieldedCollider(Graph graph, Node a, Node b, Node c)	Determines if a given triple is an unshielded collider based on an examination of the data.
boolean	R0R4StrategyTestBased.isUnshieldedCollider(Graph graph, Node i, Node j, Node k)	Checks if a collider is unshielded or not.
static Set<DiscriminatingPath>	FciOrient.listDiscriminatingPaths(Graph graph, int maxDiscriminatingPathLength, boolean checkXyNonadjacency)	Lists all the discriminating paths in the given graph.
static Set<DiscriminatingPath>	FciOrient.listDiscriminatingPaths(Graph graph, Node w, Node y, int maxDiscriminatingPathLength, boolean checkEcNonadjacency)	Lists the discriminating paths for <w, y> in the graph.
static Map<org.apache.commons.lang3.tuple.Pair<Node,Node>,Set<Double>>	PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node,Node>,Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node,Node> withoutPair)	Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).
void	FciOrient.orient(Graph graph, Set<Triple> unshieldedTriples)	Performs FCI orientation on the given graph, including R0 and either the Spirtes or Zhang final orientation rules.
void	FgesOrienter.orient(Graph graph)	orient.
static void	GraphSearchUtils.orientCollidersUsingSepsets(SepsetMap set, Knowledge knowledge, Graph graph, boolean verbose, boolean enforceCpdag)	Step C of PC; orients colliders using specified sepset.
Set<Node>	MeekRules.orientImplied(Graph graph)	Uses the Meek rules to do as many orientations in the given graph as possible.
static void	GraphSearchUtils.pcdOrientC(IndependenceTest test, Knowledge knowledge, Graph graph)	Performs step C of the algorithm, as indicated on page xxx of CPS, with the modification that X--W--Y is oriented as X-->W<--Y if W is *determined by* the sepset of (X, Y), rather than W just being *in* the sepset of (X, Y).
static void	GraphSearchUtils.pcOrientbk(Knowledge bk, Graph graph, List<Node> nodes, boolean verbose)	Orients according to background knowledge.
void	AlmostCycleRemover.recallUnshieldedTriples(Graph pag)	Recalls unshielded triples in the given graph.
boolean	AlmostCycleRemover.removeAlmostCycles(Graph pag)	Removes almost cycles from the Graph.
boolean	AlmostCycleRemover.removeCycles(Graph pag)	Removes cycles from the Graph.
void	FciOrient.ruleR0(Graph graph, Set<Triple> unshieldedTriples)	Orients unshielded colliders in the graph.
void	FciOrient.ruleR1(Node a, Node b, Node c, Graph graph)	R1: If α *→ β o––* γ, and α and γ are not adjacent, then orient the triple as α *→ β → γ.
void	FciOrient.ruleR10(Node alpha, Node gamma, Graph graph)	R10 Suppose α o→ γ, β → γ ← θ, p1 is an uncovered potentially directed (semidirected) path from α to β, and p2 is an uncovered p.d.
void	FciOrient.ruleR2(Node a, Node b, Node c, Graph graph)	R2: If α → β ∘→ γ or α ∘→ β → γ, and α ∘–o γ, then orient α ∘–o γ as α ∘→ γ.
void	FciOrient.ruleR3(Graph graph)	R3: If α *→ β ←* γ, α *–o θ o–* γ, α and γ are not adjacent, and θ *–o β, then orient θ *–o β as θ *→ β.
void	FciOrient.ruleR4(Graph graph)	R4 If u = <θ ,...,α,β,γ> is a discriminating path between θ and γ for β, and β o−−∗ γ; then if β ∈ Sepset(θ,γ), orient β o−−∗ γ as β → γ; otherwise orient the triple <α,β,γ> as α ↔ β ↔ γ.
void	FciOrient.ruleR5(Graph graph)	R5 For every (remaining) α o−−o β, if there is an uncovered circle path p = <α,γ,...,θ,β> between α and β s.t.
void	FciOrient.ruleR6(Graph graph)	R6 If α —- β o−−∗ γ (α and γ may or may not be adjacent), then orient β o−−∗ γ as β −−∗ γ.
void	FciOrient.ruleR7(Graph graph)	R7 If α −−o β o−−∗ γ, and α, γ are not adjacent, then orient β o−−∗ γ as β −−∗ γ.
boolean	FciOrient.ruleR8(Node a, Node c, Graph graph)	R8 If α → β → γ or α−−◦β → γ, and α o→ γ, orient α o→ γ as α → γ.
boolean	FciOrient.ruleR9(Node a, Node c, Graph graph)	R9 If α o→ γ, and p = <α,β,θ,...,γ> is an uncovered potentialy directed path from α to γ such that γ and β are not adjacent, then orient α o→ γ as α → γ.
void	FciOrient.rulesR1R2cycle(Graph graph)	Apply rules R1 and R2 in cycles for a given graph.
void	FciOrient.rulesR8R9R10(Graph graph)	Implements Zhang's rules R8, R9, R10, applies them over the graph once.
double	DagScorer.scoreDag(Graph dag)	scoreDag.
double	FgesOrienter.scoreDag(Graph dag)	scoreDag.
void	FgesOrienter.setAdjacencies(Graph adjacencies)	Sets the set of preset adjacenies for the algorithm; edges not in this adjacencies graph will not be added.
void	DefaultSetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint endpoint)	Sets the endpoint of a graph given the two nodes and the desired endpoint.
void	SetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint arrow)	Sets the endpoint of a graph given the two nodes and the desired endpoint.
void	FgesOrienter.setExternalGraph(Graph externalGraph)	Sets the initial graph.
void	DagSepsets.setGraph(Graph graph)
void	SepsetProducer.setGraph(Graph graph)	Sets the graph for the SepsetProducer object.
void	SepsetsGreedy.setGraph(Graph graph)	Sets the graph for the Sepsets object.
void	SepsetsGreedyMb.setGraph(Graph graph)	Sets the graph for the Sepsets object.
void	SepsetsMaxP.setGraph(Graph graph)	Sets the graph for the SepsetsMaxP object.
void	SepsetsMinP.setGraph(Graph graph)	Sets the graph for the Sepsets object.
void	SepsetsPossibleDsep.setGraph(Graph graph)
void	SepsetsSet.setGraph(Graph graph)
void	FgesOrienter.setTrueGraph(Graph trueGraph)	If the true graph is set, askterisks will be printed in log output for the true edges.
void	IPurify.setTrueGraph(Graph mim)	setTrueGraph.
void	PurifyTetradBased.setTrueGraph(Graph mim)	setTrueGraph.
void	TsDagToPag.setTruePag(Graph truePag)	Setter for the field truePag.
static void	LogUtilsSearch.stampWithBic(Graph graph, DataModel dataModel)	stampWithBic.
static void	LogUtilsSearch.stampWithScore(Graph graph, Score score)	stampWithScore.
static int	GraphSearchUtils.structuralhammingdistance(Graph trueGraph, Graph estGraph, boolean useTrueCpdag)	Originally, Tsamardinos, I., Brown, L.
static void	MbUtils.trimEdgesAmongParents(Graph graph, Node target)	Removes edges among the parents of the target.
static void	MbUtils.trimEdgesAmongParentsOfChildren(Graph graph, Node target)	Removes edges among the parents of children of the target.
static void	MbUtils.trimToAdjacents(Graph graph, Node target)	Trims the graph to just the adjacents of the target.
static void	MbUtils.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.

Constructors in edu.cmu.tetrad.search.utils with parameters of type Graph

Modifier	Constructor	Description
	DagInCpcagIterator(Graph CPDAG)	The given CPDAG must be a CPDAG.
	DagInCpcagIterator(Graph CPDAG, Knowledge knowledge)	The given CPDAG must be a CPDAG.
	DagInCpcagIterator(Graph CPDAG, Knowledge knowledge, boolean allowArbitraryOrientations, boolean allowNewColliders)	The given CPDAG must be a CPDAG.
	DagIterator(Graph CPDAG)	The given CPDAG must be a CPDAG.
	DagSepsets(Graph dag)	Constructs a new DagSepsets object for the given DAG.
	Graph(Graph graph, R5R9Dijkstra.Rule rule)	Represents a graph for Dijkstra's algorithm.
	MagToPag(Graph mag)	Constructs a new FCI search for the given independence test and background knowledge.
	MsepVertexCutFinder(Graph graph)	Constructs an instance of the MsepVertexCutFinder class with the specified graph.
	PossibleDsepFci(Graph graph, IndependenceTest test)	Creates a new SepSet and assumes that none of the variables have yet been checked.
	PreserveMarkov(Graph graph, IndependenceTest test, boolean preserveMarkov)	Constructs an PreserveMarkov class for a given Markov graph.
	SepsetsGreedy(Graph graph, IndependenceTest independenceTest, int depth)	Constructor for Sepsets.
	SepsetsGreedyMb(Graph graph, Graph cpdag, IndependenceTest independenceTest, int depth)	Constructor for Sepsets.
	SepsetsMaxP(Graph graph, IndependenceTest independenceTest, int depth)	Constructs a SepsetsMaxP object with the given graph, independence test, and depth.
	SepsetsMinP(Graph graph, IndependenceTest independenceTest, int depth)	Initializes a new instance of the SepsetsMinP class.
	SepsetsPossibleDsep(Graph graph, IndependenceTest test, Knowledge knowledge, int depth, int maxDiscriminatingPathLength)	Constructor for SepsetsPossibleDsep.
	TsDagToPag(Graph dag)	Constructs a new FCI search for the given independence test and background knowledge.
	VarResult(DataSet dataSet, Graph collapsedVarGraph)	Constructs a new result.

Uses of Graph in edu.cmu.tetrad.search.work_in_progress

Methods in edu.cmu.tetrad.search.work_in_progress that return Graph

Modifier and Type	Method	Description
Graph	GraphChange.applyTo(Graph graph)	Outputs a new PAG, a copy of the input excepting the applied changes of this object.
@NotNull Graph	GraspTol.getGraph(boolean cpDag)	getGraph.
Graph	HbsmsBeam.getGraph()	Getter for the field graph.
Graph	HbsmsGes.getGraph()	Getter for the field graph.
Graph	HbsmsGes.GraphWithPValue.getGraph()	Getter for the field graph.
Graph	SampleVcpc.getGraph()	The graph that's constructed during the search.
Graph	SampleVcpcFast.getGraph()	The graph that's constructed during the search.
Graph	VcPc.getGraph()	The graph that's constructed during the search.
Graph	VcPcAlt.getGraph()	The graph that's constructed during the search.
Graph	VcPcFast.getGraph()	The graph that's constructed during the search.
Graph	MagCgBicScore.getMag()	Returns the wrapped MAG.
Graph	MagDgBicScore.getMag()	Returns the wrapped MAG.
Graph	MagSemBicScore.getMag()	Returns the wrapped MAG.
Graph	DMSearch.LatentStructure.latentStructToEdgeListGraph(DMSearch.LatentStructure structure)	latentStructToEdgeListGraph.
Graph	HbsmsBeam.removeZeroEdges(Graph bestGraph)	removeZeroEdges.
Graph	BpcTetradPurifyWashdown.search()	Runs the search and returns a graph.
Graph	DMSearch.search()	search.
Graph	FaskVote.search(Parameters parameters)	Does the search.
Graph	FasLofs.search()	Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
Graph	Hbsms.search()	search.
Graph	HbsmsBeam.search()	search.
Graph	HbsmsGes.search()	search.
Graph	InverseCorrelation.search()	search.
Graph	Kpc.search()	Deprecated. Runs PC starting with a complete graph over all nodes of the given conditional independence test, using the given independence test and knowledge and returns the resultant graph.
Graph	Kpc.search(List<Node> nodes)	Deprecated. Runs PC starting with a commplete graph over the given list of nodes, using the given independence test and knowledge and returns the resultant graph.
Graph	Mmhc.search()	Runs PC starting with a fully connected graph over all of the variables in the domain of the independence test.
Graph	SampleVcpc.search()	search.
Graph	SampleVcpcFast.search()	search.
Graph	VcFas.search()	Discovers all adjacencies in data.
Graph	VcPc.search()	search.
Graph	VcPcAlt.search()	search.
Graph	VcPcFast.search()	search.
Graph	Washdown.search()	Runs the Washdown algorithm and return a graph.

Methods in edu.cmu.tetrad.search.work_in_progress that return types with arguments of type Graph

Modifier and Type	Method	Description
List<Graph>	Dci.search()	Begins the DCI search procedure, described at each step
List<Graph>	Ion.search()	Runs the ION search and returns a list of compatible graphs.

Methods in edu.cmu.tetrad.search.work_in_progress with parameters of type Graph

Modifier and Type	Method	Description
DMSearch.LatentStructure	DMSearch.applyDmSearch(Graph pattern, Set<String> inputString, double penalty)	applyDmSearch.
Graph	GraphChange.applyTo(Graph graph)	Outputs a new PAG, a copy of the input excepting the applied changes of this object.
static void	SampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	SampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	VcPc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	VcPcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	VcPcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
VcPc.CpcTripleType	VcPc.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose)	getPopulationTripleType.
VcPcFast.CpcTripleType	VcPcFast.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose)	getPopulationTripleType.
Graph	HbsmsBeam.removeZeroEdges(Graph bestGraph)	removeZeroEdges.
HbsmsGes.Score	HbsmsGes.scoreDag(Graph dag)	scoreDag.
HbsmsBeam.Score	HbsmsBeam.scoreGraph(Graph graph)	scoreGraph.
HbsmsGes.Score	HbsmsGes.scoreGraph(Graph graph)	scoreGraph.
void	VcFas.setExternalGraph(Graph externalGraph)	Setter for the field externalGraph.
void	SampleVcpc.setGraph(Graph graph)	Setter for the field graph.
void	SampleVcpcFast.setGraph(Graph graph)	Setter for the field graph.
void	VcPc.setGraph(Graph graph)	Setter for the field graph.
void	VcPcAlt.setGraph(Graph graph)	Setter for the field graph.
void	VcPcFast.setGraph(Graph graph)	Setter for the field graph.
void	MagCgBicScore.setMag(Graph mag)	Sets the MAG to wrap.
void	MagDgBicScore.setMag(Graph mag)	Sets the MAG to wrap.
void	MagSemBicScore.setMag(Graph mag)	Sets the MAG to wrap.
static List<List<Node>>	Ion.treks(Graph graph, Node node1, Node node2)	treks.

Constructors in edu.cmu.tetrad.search.work_in_progress with parameters of type Graph

Modifier	Constructor	Description
	FasDci(Graph graph, IndependenceTest independenceTest)	Constructs a new FastAdjacencySearch for DCI.
	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.
	GraphWithPValue(Graph graph, double pValue)	Constructor for GraphWithPValue.
	HbsmsBeam(Graph graph, CovarianceMatrix cov, Knowledge knowledge)	Constructor for HbsmsBeam.
	HbsmsBeam(Graph graph, DataSet data, Knowledge knowledge)	Constructor for HbsmsBeam.
	HbsmsGes(Graph graph, DataSet data)	Constructor for HbsmsGes.

Constructor parameters in edu.cmu.tetrad.search.work_in_progress with type arguments of type Graph

Modifier	Constructor	Description
	Ion(List<Graph> pags)	Constructs a new instance of the ION search from the input PAGs

Uses of Graph in edu.cmu.tetrad.sem

Methods in edu.cmu.tetrad.sem that return Graph

Modifier and Type	Method	Description
Graph	LargeScaleSimulation.getGraph()	Getter for the field graph.
Graph	SemUpdater.getManipulatedGraph()	getManipulatedGraph.

Methods in edu.cmu.tetrad.sem with parameters of type Graph

Modifier and Type	Method	Description
List<List<Node>>	Ricf.cliques(Graph graph)	cliques.
Knowledge	LargeScaleSimulation.getKnowledge(Graph graph)	getKnowledge.
static List<Node>	ReidentifyVariables.getLatents(Graph graph)	getLatents.
static void	CyclicStableUtils.initializeInternalEdgesRandom(SemIm im, Graph g, List<Node> scc, double low, double high)	Randomize existing internal edges (that already exist in the graph) within [low, high], positive.
static List<String>	ReidentifyVariables.reidentifyVariables1(List<List<Node>> partition, Graph trueGraph)	reidentifyVariables1.
static List<String>	ReidentifyVariables.reidentifyVariables2(List<List<Node>> clusters, Graph trueGraph, DataSet data)	reidentifyVariables2.
Ricf.RicfResult	Ricf.ricf2(Graph mag, ICovarianceMatrix covMatrix, double tolerance)	Same as above but takes a Graph instead of a SemGraph
static void	CyclicStableUtils.scaleInternalEdges(SemIm im, Graph g, List<Node> scc, double factor)	Scale all internal edges of an SCC by a factor.
double	DagScorer.score(Graph dag)	Scores the given DAG using the implemented algorithm.
double	Scorer.score(Graph dag)	score.
static @NotNull SemIm.Result	SemIm.simulatePossibleShrinkage(Parameters params, Graph g)	Simulates possible shrinkage scenarios for a given graph and parameters.
static SemIm.CyclicSimResult	CyclicStableUtils.simulateStableFixedRadius(Graph g, int n, double s, double coefLow, double coefHigh, long seed, Parameters params)	Simulate from an arbitrary graph with SCC-wise fixed spectral radius s.
static SemIm.CyclicSimResult	CyclicStableUtils.simulateStableProductCapped(Graph g, int n, double maxProd, double coefLow, double coefHigh, long seed, Parameters params)	Simulate from an arbitrary graph with SCC-wise radius capped by sqrt(maxProd).
static double	CyclicStableUtils.spectralRadiusAbs(SemIm im, Graph g, List<Node> scc)	Computes the spectral radius of the absolute value of the coefficient matrix of the given strongly connected component (SCC) in a graph.
static void	CyclicStableUtils.stabilizeAllSccsFixedRadius(SemIm im, Graph g, double s, double coefLow, double coefHigh)	Stabilize an existing SemIm in-place: enforce per-SCC spectral radius target s.
static void	CyclicStableUtils.stabilizeAllSccsFixedRadiusScaleOnly(SemIm im, Graph g, double s)	Stabilizes all strongly connected components (SCCs) of a given graph by scaling internal edges such that the spectral radius of each SCC does not exceed the given target value s.
static List<List<Node>>	CyclicStableUtils.stronglyConnectedComponents(Graph g)	Kosaraju's algorithm for strongly connected components.

Constructors in edu.cmu.tetrad.sem with parameters of type Graph

Modifier	Constructor	Description
	AdditiveAnmSimulator(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution)	Constructs an instance of the AdditiveAnmSimulator.
	AdditiveNoiseDjl(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution, double rescaleMin, double rescaleMax, List<Integer> hiddenDimensions, double inputScale, Function<Double,Double> activationFunction)	Constructs a AdditiveNoiseSimulation that operates on a directed acyclic graph (DAG) to model causal relationships with post-nonlinear causal mechanisms and custom activation functions.
	AdditiveNoiseSimulation(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution, double rescaleMin, double rescaleMax, int[] hiddenDimensions, double inputScale, Function<Double,Double> activationFunction)	Creates a AdditiveNoiseSimulation for generating data with a causal structure based on the provided graph.
	AdditiveNoiseSimulationOld(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution, double rescaleMin, double rescaleMax, int[] hiddenDimensions, double inputScale, Function<Double,Double> activationFunction)	Constructs an AdditiveNoiseSimulation that operates on a directed acyclic graph (DAG) to model causal relationships with post-nonlinear causal mechanisms and custom activation functions.
	GeneralizedSemPm(Graph graph)	Constructs a BayesPm from the given Graph, which must be convertible first into a ProtoSemGraph and then into a SemGraph.
	LargeScaleSimulation(Graph graph)	Constructor for LargeScaleSimulation.
	LargeScaleSimulation(Graph graph, List<Node> nodes, int[] tierIndices)	Constructor for LargeScaleSimulation.
	NonlinearFunctionOfLinear(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution, double rescaleMin, double rescaleMax, double coefLow, double coefHigh, boolean coefSymmetric, int hiddenDimension, double inputScale)	Constructs a NonlinearFunctionOfLinear instance, which generates synthetic data based on a directed acyclic graph (DAG) using post-nonlinear causal relationships and associated modeling parameters.
	PostnonlinearCausalModel(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution, double rescaleMin, double rescaleMax, int hiddenDimension, double inputScale, double coefLow, double coefHigh, boolean coefSymmetric)	Constructs a PostnonlinearCausalModel object.
	SemPm(Graph graph)	Constructs a BayesPm from the given Graph, which must be convertible first into a ProtoSemGraph and then into a SemGraph.

Uses of Graph in edu.cmu.tetrad.simulation

Methods in edu.cmu.tetrad.simulation that return Graph

Modifier and Type	Method	Description
static Graph	HsimUtils.evalEdges(Graph inputgraph, Set<Node> simnodes, Set<Node> realnodes)	evalEdges.
static Graph	HsimUtils.mkRandSEMDAG(int numVars, int numEdges)	mkRandSEMDAG.

Methods in edu.cmu.tetrad.simulation with parameters of type Graph

Modifier and Type	Method	Description
List<Double>	Gdistance.distances(Graph graph1, Graph graph2)	distances.
static double[]	HsimUtils.errorEval(Graph estCPDAG, Graph truePattern)	errorEval.
static Graph	HsimUtils.evalEdges(Graph inputgraph, Set<Node> simnodes, Set<Node> realnodes)	evalEdges.
static Set<Node>	HsimUtils.getAllParents(Graph inputgraph, Set<Node> inputnodes)	getAllParents.

Uses of Graph in edu.cmu.tetrad.study.performance

Methods in edu.cmu.tetrad.study.performance that return Graph

Modifier and Type	Method	Description
Graph	ComparisonResult.getCorrectResult()	Getter for the field correctResult.
Graph	ComparisonResult.getResultGraph()	Getter for the field resultGraph.
Graph	ComparisonResult.getTrueDag()	Getter for the field trueDag.

Methods in edu.cmu.tetrad.study.performance with parameters of type Graph

Modifier and Type	Method	Description
static String	PerformanceTests.endpointMisclassification(List<Node> _nodes, Graph estGraph, Graph refGraph)	endpointMisclassification.
static Knowledge	Comparison2.getKnowledge(Graph graph)	getKnowledge.
void	ComparisonResult.setCorrectResult(Graph correctResult)	Setter for the field correctResult.
void	ComparisonResult.setResultGraph(Graph graph)	Setter for the field resultGraph.
void	ComparisonResult.setTrueDag(Graph trueDag)	Setter for the field trueDag.

Uses of Graph in edu.cmu.tetrad.util

Methods in edu.cmu.tetrad.util that return Graph

Modifier and Type	Method	Description
static Graph	GraphSampling.createDisplayGraph(Graph graph, ResamplingEdgeEnsemble ensemble)	Create a graph for displaying and print out.
static Graph	GraphSampling.createGraphWithHighProbabilityEdges(List<Graph> graphs)	Combine all the edges from the list of graphs onto one graph with the edge type that has the highest frequency probability.
static Graph	GraphSampling.createGraphWithoutNullEdges(Graph graph)	Create a graph from the given graph that contains no null edges.
Graph	PagCache.getDag(Graph graph)	Returns the Directed Acyclic Graph (DAG) corresponding to the given graph if it is a PAG that has previously been converted from a DAG.
@NotNull Graph	PagCache.getPag(Graph graph)	Returns the PAG (Partial Ancestral Graph) corresponding to the given DAG (Directed Acyclic Graph).
Graph	PagCache.getPag(Graph graph, Knowledge knowledge, boolean verbose)	Returns the PAG (Partial Ancestral Graph) corresponding to the given DAG (Directed Acyclic Graph).
static Graph	JsonUtils.parseJSONObjectToTetradGraph(String jsonResponse)	parseJSONObjectToTetradGraph.
static Graph	JsonUtils.parseJSONObjectToTetradGraph(org.json.JSONObject jObj)	parseJSONObjectToTetradGraph.

Methods in edu.cmu.tetrad.util with parameters of type Graph

Modifier and Type	Method	Description
static Graph	GraphSampling.createDisplayGraph(Graph graph, ResamplingEdgeEnsemble ensemble)	Create a graph for displaying and print out.
static Graph	GraphSampling.createGraphWithoutNullEdges(Graph graph)	Create a graph from the given graph that contains no null edges.
Graph	PagCache.getDag(Graph graph)	Returns the Directed Acyclic Graph (DAG) corresponding to the given graph if it is a PAG that has previously been converted from a DAG.
@NotNull Graph	PagCache.getPag(Graph graph)	Returns the PAG (Partial Ancestral Graph) corresponding to the given DAG (Directed Acyclic Graph).
Graph	PagCache.getPag(Graph graph, Knowledge knowledge, boolean verbose)	Returns the PAG (Partial Ancestral Graph) corresponding to the given DAG (Directed Acyclic Graph).
static Set<Edge>	JsonUtils.parseJSONArrayToTetradEdges(Graph graph, org.json.JSONArray jArray)	parseJSONArrayToTetradEdges.
static Edge	JsonUtils.parseJSONObjectToTetradEdge(Graph graph, org.json.JSONObject jObj)	parseJSONObjectToTetradEdge.

Method parameters in edu.cmu.tetrad.util with type arguments of type Graph

Modifier and Type	Method	Description
static Graph	GraphSampling.createGraphWithHighProbabilityEdges(List<Graph> graphs)	Combine all the edges from the list of graphs onto one graph with the edge type that has the highest frequency probability.

Uses of Graph in edu.pitt.csb.stability

Methods in edu.pitt.csb.stability that return Graph

Modifier and Type	Method	Description
abstract Graph	DataGraphSearch.search(DataSet data)	search.

Uses of Graph in edu.pitt.dbmi.algo.bayesian.constraint.search

Methods in edu.pitt.dbmi.algo.bayesian.constraint.search that return Graph

Modifier and Type	Method	Description
Graph	RfciBsc.getGraphRBD()	Returns the graph that was learned using the BSC-D method.
Graph	RfciBsc.getGraphRBI()	Returns the graph that was learned using the BSC-I method.
Graph	PagSamplingRfci.search()	Runs the search and returns a graph.
Graph	RfciBsc.search()	Runs the search and returns a graph.