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.algcomparison.sweep
edu.cmu.tetrad.algcomparison.utils
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calibration
edu.cmu.tetrad.data
edu.cmu.tetrad.data.missing
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.cdnod_pag
edu.cmu.tetrad.search.harness
edu.cmu.tetrad.search.harness.tsbi
edu.cmu.tetrad.search.harness.tsc
edu.cmu.tetrad.search.is
edu.cmu.tetrad.search.mimic
edu.cmu.tetrad.search.rlcd
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.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, Graph targetGraph, DataModel dataModel, long elapsedTime, List<Statistic> statistics)	As CompareTwoGraphs.getStatsListTable(Graph, Graph, DataModel, long), but with the statistics to show given explicitly, in display order.
static String	CompareTwoGraphs.getStatsListTable(Graph trueDag, Graph trueGraph, Graph targetGraph, DataModel dataModel, long elapsedTime)	As CompareTwoGraphs.getStatsListTable(Graph, Graph, DataModel, long), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List).
static String	CompareTwoGraphs.getStatsListTable(Graph trueDag, Graph trueGraph, Graph targetGraph, DataModel dataModel, long elapsedTime, List<Statistic> statistics)	As CompareTwoGraphs.getStatsListTable(Graph, Graph, DataModel, long, List), but with the true DAG given separately from the comparison graph.
static String	CompareTwoGraphs.getStatsListTable(Graph trueDag, Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes)	As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List).
static String	CompareTwoGraphs.getStatsListTable(Graph trueDag, Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes, List<Statistic> statistics)	As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List, List), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List) for the division of labor between the two.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes)	Returns a string representing a table of statistics for several target graphs against a single true graph, one column per target.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes, List<Statistic> statistics)	As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List), but with the statistics to show given explicitly, in display order.

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

Modifier and Type	Method	Description
static String	CompareTwoGraphs.getStatsListTable(Graph trueDag, Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes)	As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List).
static String	CompareTwoGraphs.getStatsListTable(Graph trueDag, Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes, List<Statistic> statistics)	As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List, List), but with the true DAG given separately from the comparison graph; see CompareTwoGraphs.getStatsListTable(Graph, Graph, Graph, DataModel, long, List) for the division of labor between the two.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes)	Returns a string representing a table of statistics for several target graphs against a single true graph, one column per target.
static String	CompareTwoGraphs.getStatsListTable(Graph trueGraph, List<Graph> targetGraphs, List<String> targetNames, DataModel dataModel, List<Long> elapsedTimes, List<Statistic> statistics)	As CompareTwoGraphs.getStatsListTable(Graph, List, List, DataModel, List), but with the statistics to show given explicitly, in display order.

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	AbstractMultiBootstrapAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the multi-dataset search, bootstrapping if numberResampling is greater than zero.
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)	Deprecated. Returns a comparison graph based on the true directed graph, if there is one.
Graph	IcaLingam.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	IcaLingD.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Lingam.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	LingD.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)	Deprecated. Runs the Fask search algorithm on the given data model with the specified parameters.
Graph	IcaLingam.runSearch(DataModel dataSet, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	IcaLingD.runSearch(DataModel dataSet, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	Lingam.runSearch(DataModel dataSet, Parameters parameters)	Searches for a graph structure based on the given data set and parameters.
Graph	LingD.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()	Returns the stable graphs found by the most recent search.
List<Graph>	LingD.getStableGraphs()	Retrieves the list of stable graphs generated by the algorithm.
List<Graph>	IcaLingD.getUnstableGraphs()	Returns the unstable graphs found by the most recent search.
List<Graph>	LingD.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)	Deprecated. Returns a comparison graph based on the true directed graph, if there is one.
Graph	IcaLingam.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	IcaLingD.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Lingam.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	LingD.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	FaskPool.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	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 that graph that the result should be compared to.
Graph	ImagesBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	ImagesFges.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	Ion.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	FaskPool.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskPool.search(List<DataModel> dataSets, 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	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	ImagesBoss.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	ImagesBoss.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	Ion.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	Ion.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	FaskPool.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	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 that graph that the result should be compared to.
Graph	ImagesBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	ImagesFges.getComparisonGraph(Graph graph)	Returns a comparison graph based on the true directed graph, if there is one.
Graph	Ion.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	BossCdnodScore.getComparisonGraph(Graph graph)
Graph	BossTopK.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	BossTrekMimic.getComparisonGraph(Graph graph)	Deprecated. 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)	Returns a CPDAG that is the comparison graph for the given true directed graph.
Graph	CdnodBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	CdnodPag.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)	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	Flop.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
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	PcAr.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)	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	Tgin.getComparisonGraph(Graph graph)	Deprecated. Returns that graph that the result should be compared to.
Graph	TrekMimic.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	BossCdnodScore.getComparisonGraph(Graph graph)
Graph	BossTopK.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	BossTrekMimic.getComparisonGraph(Graph graph)	Deprecated. 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)	Returns a CPDAG that is the comparison graph for the given true directed graph.
Graph	CdnodBoss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	CdnodPag.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)	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	Flop.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
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	PcAr.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)	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	Tgin.getComparisonGraph(Graph graph)	Deprecated. Returns that graph that the result should be compared to.
Graph	TrekMimic.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	Bfci.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciBryan.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciBryanScore.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciC.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciD.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciScoreCheck.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BossCcd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph: the cyclic PAG that BOSS-CCD is meant to recover, not the true graph itself.
Graph	BossFci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	BossPod.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Ccd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph: the cyclic PAG that CCD is meant to recover, not the true graph itself.
Graph	Cfci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	Cpw.getComparisonGraph(Graph graph)	Generates a comparison graph for the given graph by transforming it into a partial ancestral graph (PAG) representation via dagToPag.
Graph	Dm.getComparisonGraph(Graph graph)	Returns the comparison graph for the supplied graph.
Graph	DmFcit.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	DmPc.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
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)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	Fcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitMagMarkov.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitMarkov.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitSl.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitSlScoreCheck.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitZm.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed 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	GfciOld.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	Gmas.getComparisonGraph(Graph graph)	Returns the comparison graph, the PAG of the true DAG.
Graph	GraspFci.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Icd.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph.
Graph	IsGfci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	LvBossExperimental.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a PAG.
Graph	LvHeuristic.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	PagSampleRfci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PagSamplingRfci.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	Bfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciBryan.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciBryanScore.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciC.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciD.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciScoreCheck.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BossCcd.runSearch(DataModel dataModel, Parameters parameters)	Runs the BOSS-CCD search algorithm on the given data set using the specified parameters.
Graph	BossFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	BossPod.runSearch(DataModel dataModel, Parameters parameters)	Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
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	Cpw.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm on a given data model and set of parameters, producing a partial ancestral graph (PAG) that represents the causal structure inferred from the data.
Graph	DmFcit.runSearch(DataModel dataModel, Parameters parameters)	Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
Graph	DmPc.runSearch(DataModel dataModel, Parameters parameters)	Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
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	Fcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitMagMarkov.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitMarkov.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitSl.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitSlScoreCheck.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitZm.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	Gfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	GfciOld.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	Gmas.runSearch(DataModel dataModel, Parameters parameters)	Runs the search.
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	Icd.runSearch(DataModel dataModel, Parameters parameters)	Runs the ICD search and returns the resulting PAG.
Graph	LvBossExperimental.runSearch(DataModel dataModel, Parameters parameters)	Runs LV-BOSS on the given data model.
Graph	LvHeuristic.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	PagSampleRfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	PagSamplingRfci.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	Bfci.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciBryan.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciBryanScore.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciC.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciD.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BfciScoreCheck.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	BossCcd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph: the cyclic PAG that BOSS-CCD is meant to recover, not the true graph itself.
Graph	BossFci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	BossPod.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Ccd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph: the cyclic PAG that CCD is meant to recover, not the true graph itself.
Graph	Cfci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	Cpw.getComparisonGraph(Graph graph)	Generates a comparison graph for the given graph by transforming it into a partial ancestral graph (PAG) representation via dagToPag.
Graph	Dm.getComparisonGraph(Graph graph)	Returns the comparison graph for the supplied graph.
Graph	DmFcit.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	DmPc.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
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)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	Fcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitMagMarkov.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitMarkov.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitSl.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitSlScoreCheck.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FcitZm.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed 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	GfciOld.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	Gmas.getComparisonGraph(Graph graph)	Returns the comparison graph, the PAG of the true DAG.
Graph	GraspFci.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Icd.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph.
Graph	IsGfci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	LvBossExperimental.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a PAG.
Graph	LvHeuristic.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	PagSampleRfci.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PagSamplingRfci.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	PythonGin.getComparisonGraph(Graph graph)	Deprecated. Returns a copy of the supplied graph for use as a comparison baseline.
Graph	Rlcd.getComparisonGraph(Graph graph)	The true graph itself is used for comparison, since the output includes latent nodes.
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.
Graph	PythonGin.search(DataModel dataModel, Parameters parameters)	Deprecated. Runs GIN on the supplied dataset and returns the resulting causal graph.
Graph	Rlcd.search(DataModel dataModel, Parameters parameters)	Runs RLCD on a continuous data set.
Graph	Rlcd.searchImputations(List<DataModel> imputations, Parameters parameters)	Runs ONE RLCD search over several imputations of a data set with missing values; see Rlcd(List).

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.
Graph	PythonGin.getComparisonGraph(Graph graph)	Deprecated. Returns a copy of the supplied graph for use as a comparison baseline.
Graph	Rlcd.getComparisonGraph(Graph graph)	The true graph itself is used for comparison, since the output includes latent nodes.

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	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	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	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	RandomMimic.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	ObservationalStudySimulation.getContemporaneousGraph(int index)	Returns the contemporaneous summary graph for the given run (equal to the true graph in non-serial mode).
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	DaoSimulation.getTrueGraph(int index)
Graph	DesignedExperimentSimulation.getTrueGraph(int index)	Returns the true (pre-selection) graph at the specified index.
Graph	GeneralAdditiveModel.getTrueGraph(int index)	Retrieves the true graph specified by the given index from the list of graphs.
Graph	GeneralNoiseSimulation.getTrueGraph(int index)	Returns the true graph at the specified 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	ObservationalStudySimulation.getTrueGraph(int index)	Returns the true graph at the specified index: a DAG, or a TimeLagGraph with maximum lag 1 in serial mode.
Graph	PostnonlinearSem.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.
Graph	TrainedDagModel.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.
static List<Node>	DaoSimulation.soficOrder(Graph dag)	Returns a source-first consistent topological ordering of the DAG nodes.

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

Modifier	Constructor	Description
	DaoSimulation(Graph trueGraph)	Convenience constructor that fixes a single, predetermined true DAG.

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

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

Modifier and Type	Method	Description
static Graph	Statistic.alignNodes(Graph trueGraph, Graph estGraph)	Returns the estimated graph with its nodes replaced, by name, with the true graph's node objects.
static Graph	Statistic.trueDagOrGraph(Graph trueDag, Graph trueGraph)	Returns the graph a statistic should use for DAG-level questions--ancestry, latent confounding, or converting the truth to a CPDAG or PAG.

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

Modifier and Type	Method	Description
static Graph	Statistic.alignNodes(Graph trueGraph, Graph estGraph)	Returns the estimated graph with its nodes replaced, by name, with the true graph's node objects.
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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyFp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyFpr.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyTn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyTp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyTpr.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestorF1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestorPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestorRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestralPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestralRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadFn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadFp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadFpr.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadPrecisionCommonEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadRecallCommonEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadTn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadTp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AverageDegreeEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AverageDegreeTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicDiff.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicDiffPerRecord.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedFP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedLatentExistsLatentCommonCause.getValue(Graph trueDag, 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	BidirectedLatentPrecision.getValue(Graph trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedTP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CirclePrecision.getValue(Graph trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CommonAncestorFalsePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CommonAncestorTruePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CommonMeasuredAncestorRecallBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CorrectSkeleton.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DefiniteDirectedPathPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DefiniteDirectedPathRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DensityEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DensityTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ElapsedCpuTime.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Adj.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1All.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Arrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Circle.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Tail.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FalseNegativesAdjacencies.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FalsePositiveAdjacencies.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FBetaAdj.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FractionDependentUnderAlternative.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FractionDependentUnderNull.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	GraphExactlyRight.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	IdaAverageSquaredDistance.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the IDA Average Squared Distance statistic.
double	IdaCheckAvgMaxSquaredDiffEstTrue.getValue(Graph trueDag, 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 trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the statistic "IDA Average Maximum Squared Difference".
double	IdaMinimumSquaredDifference.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the statistic "IDA Average Minimum Squared Difference".
double	ImpliedArrowOrientationRatioEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ImpliedArrowOrientationRatioEst2.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ImpliedOrientationRatioEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ImpliesLegalMag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	KnowledgeSatisfied.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorFalseNegativeBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorFalsePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorRecallBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorTruePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LegalCpdag.getValue(Graph trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LocalGraphPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	This method calculates the Local Graph Precision.
double	LocalGraphRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)
double	MagCgScore.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MagDgScore.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MagSemScore.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MarkovCheckAdPasses.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling p-value > 0.05.
double	MarkovCheckAdPassesBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling p-value > 0.05.
double	MarkovCheckAndersonDarlingP.getValue(Graph trueDag, 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 trueDag, 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 trueDag, 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 trueDag, 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	MarkovCheckFdrCutoffP.getValue(Graph trueDag, 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	MarkovCheckFractionDependentH0.getValue(Graph trueDag, 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 trueDag, 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 trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates whether Kolmogorov-Smirnoff P > 0.05.
double	MarkovCheckKsPassesBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates whether Kolmogorov-Smirnoff P > 0.05.
double	MathewsCorrAdj.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MathewsCorrArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	Maximal.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Checks whether a PAG is maximal.
double	MaximalityCondition.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	McGetNumTestsH0.getValue(Graph trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoCyclicPathsCondition.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NodesInCyclesPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NodesInCyclesRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NonancestorPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NonancestorRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoPotentiallyDirectedF1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoSemidirectedPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoSemidirectedRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumAmbiguousTriples.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberArrowsEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberArrowsNotInUnshieldedCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesInCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesInUnshieldedCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberOfEdgesEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberOfEdgesTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberTailsEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberUnshieldedCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumBidirectedBothNonancestorAncestor.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumBidirectedEdgesEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumBidirectedEdgesTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredDD.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredNL.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredPD.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredPL.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCommonMeasuredAncestorBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleDefiniteDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleDirectedEdgeConfounded.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleDirectedEdgeNonAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePossiblyDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePossiblyDirectedEdgeNonAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePotentiallyDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePotentiallyDirectedEdgeNonAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleVisibleNonancestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCorrectBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the number of bidirected edges for which a latent confounder exists.
double	NumCorrectDDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCorrectPDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCorrectVisibleEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCoveringAdjacenciesInPag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDefinitelyDirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDefinitelyNotDirectedPaths.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeBnaMeasuredCounfounded.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeNoMeasureAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeNotAncNotRev.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeReversed.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedPathsEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedPathsTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedShouldBePartiallyDirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumEdgeInEstInTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumGenuineAdjacenciesInPag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumIncorrectDDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumIncorrectPDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumIncorrectVisibleAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumLatentCommonAncestorBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumNondirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumParametersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumPartiallyOrientedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumPotentiallyDirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumUndirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumVisibleEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumVisibleEdgeTrue.getValue(Graph trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	OrientationRecall.getValue(Graph trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the adjacency recall compared to the true PAG (Partial Ancestral Graph).
double	ParameterColumn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	PercentAmbiguous.getValue(Graph trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ProportionSemidirectedPathsNotReversedEst.getValue(Graph trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	PvalueUniformityUnderNull.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	SemidirectedPathF1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the F1 statistic for adjacencies.
double	SemidirectedPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the potentially directed precision value.
double	SemidirectedRecall.getValue(Graph trueDag, 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 potentially directed path in the true graph, then there is also a potentially directed 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.
default 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.
double	Statistic.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TailPrecision.getValue(Graph trueDag, 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 trueDag, 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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the number of False Negatives for Arrows in the estimated graph compared to the true DAG.
double	TrueDagFalseNegativesTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false negatives for tail endpoints compared to the true Directed Acyclic Graph (DAG).
double	TrueDagFalsePositiveArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positive arrows in the estimated graph compared to the true directed acyclic graph (DAG).
double	TrueDagFalsePositiveTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positives for tail endpoints in the estimated graph compared to the true graph.
double	TrueDagPrecisionArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the precision of arrow edges (X*->Y) in the estimated graph compared to the true DAG.
double	TrueDagPrecisionTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the precision of directed edges in the estimated graph (estGraph) relative to a true directed acyclic graph (trueDag).
double	TrueDagRecallArrows.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TrueDagRecallTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TrueDagTruePositiveArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the count of bidirected true positives for arrows in the estimated graph compared to the true graph.
double	TrueDagTruePositiveDirectedPathNonancestor.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the number of true positives for directed paths in the estimated graph that are consistent with the true graph, excluding cases where the estimated graph incorrectly identifies the reverse relationship.
double	TrueDagTruePositiveTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleFalseNegative.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleFalsePositive.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCyclePrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleTruePositive.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
static Graph	Statistic.trueDagOrGraph(Graph trueDag, Graph trueGraph)	Returns the graph a statistic should use for DAG-level questions--ancestry, latent confounding, or converting the truth to a CPDAG or PAG.

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.algcomparison.sweep

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

Modifier and Type	Method	Description
Graph	SweepResult.getEdgeProbabilityGraph()	Returns the probability-annotated graph aggregating the resample searches, or null if no resamples were run.
Graph	SweepResult.getPointGraph()	Returns the point-estimate graph from searching the full dataset at this setting.

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

Modifier and Type	Method	Description
static List<Graph>	ParameterSweep.searchOnResamples(Algorithm algorithm, Parameters parameters, List<DataSet> resamples, List<Node> variables, boolean parallelized)	Runs the algorithm at the given parameters on each resample and returns the graphs, with nodes replaced by the given variable list so graphs are comparable.

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

Modifier and Type	Method	Description
static double	ParameterSweep.adjacencyInstability(List<Graph> graphs, List<Node> variables)	Computes the StARS-style adjacency instability of a set of graphs over the given variables: the mean over unordered variable pairs of 2 * theta * (1 - theta), where theta is the fraction of graphs in which the pair is adjacent.

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

Modifier	Constructor	Description
	SweepResult(Map<String,Object> setting, Graph pointGraph, Graph edgeProbabilityGraph, double adjacencyInstability, SweepResult.MarkovStats markovStats, int numResamples, long elapsedMillis)	Constructs a result.

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

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

Modifier and Type	Method	Description
Graph	PoolsImputations.searchImputations(List<DataModel> imputations, Parameters parameters)	Runs one search over the imputations.

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	Identifiability.getManipulatedGraph()	getManipulatedGraph.
Graph	JunctionTreeUpdater.getManipulatedGraph()	Retrieves the manipulated Bayesian network representation associated with this updater.
Graph	ManipulatingBayesUpdater.getManipulatedGraph()	Returns the manipulated graph.
Graph	RowSummingExactUpdater.getManipulatedGraph()	getManipulatedGraph.
Graph	VariableEliminationUpdater.getManipulatedGraph()	Retrieves the manipulated Bayesian network representation associated with this updater.
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)	Likelihood-ratio p-value for BN vs.
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.
	BayesPm(Graph graph, DataSet dataSet)	Constructs a new BayesPm using the provided DAG and dataset.
	EmBayesProperties(DataSet dataSet, Graph graph)	Constructor for EmBayesProperties.

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.missing

Fields in edu.cmu.tetrad.data.missing declared as Graph

Modifier and Type	Field	Description
final Graph	ImputationSearch.Result.pooledGraph	The pooled graph.

Fields in edu.cmu.tetrad.data.missing with type parameters of type Graph

Modifier and Type	Field	Description
final List<Graph>	ImputationSearch.Result.imputationGraphs	The per-imputation graphs, in imputation order.

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.

Classes in edu.cmu.tetrad.graph that implement interfaces with type arguments of type Graph

Modifier and Type	Class	Description
final class	ConsistentDagIterator	Lazily enumerates the consistent DAG extensions of a DAG or CPDAG.
final class	ConsistentDagIterator	Lazily enumerates the consistent DAG extensions of a DAG or CPDAG.
final class	LegEnumerator	Lazily enumerates the Loyal Equivalent Graphs (LEGs) of a directed Markov equivalence class by walking the class transformationally, via Zhang & Spirtes' legitimate directed-edge reversals rather than by re-orienting the circle component from scratch.
final class	LegEnumerator	Lazily enumerates the Loyal Equivalent Graphs (LEGs) of a directed Markov equivalence class by walking the class transformationally, via Zhang & Spirtes' legitimate directed-edge reversals rather than by re-orienting the circle component from scratch.

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)	Constructs a random meta-graph as a Multiple Indicator Model (MIM) with specified structural constraints.
static Graph	RandomMimic.constructRandomMimic(List<RandomMimic.MimicGroupSpec> specs, Integer metaEdgeCount, int numSharedParents, int numLatentMeasuredImpureParents, int numMeasuredMeasuredImpureParents, int numMeasuredMeasuredImpureAssociations)	Constructs a random MIMIC graph 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, boolean excludeSelectionBias)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the DagToPag algorithm.
static Graph	GraphTransforms.dagToPag(Graph graph, boolean excludeSelectionBias, int timeoutSeconds)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG).
static Graph	GraphTransforms.dagToPag(Graph graph, Knowledge knowledge, boolean excludeSelectionBias, int recursiveDepth)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the provided DAG and knowledge.
static Graph	GraphTransforms.dcgToCcdPag(Graph graph)	Returns the graph CCD is meant to recover from a directed cyclic graph (DCG): the cyclic PAG of its d-separation equivalence class, with CCD's underline and dotted-underline annotations.
static Graph	GraphUtils.detachNodes(Graph graph)	Returns a copy of the graph whose nodes are fresh objects (see GraphUtils.copyNode(Node)), equal to the originals and in the same positions, with the same edges (including their properties, edge type probabilities, and display attributes), underlines, ambiguous triples, and graph attributes.
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 @NotNull Graph	GraphUtils.getReplicatingGraph(Graph graph)	Creates a replicating graph based on the provided graph.
static Graph	GraphUtils.guaranteePag(Graph pag, FciOrient fciOrient, Knowledge knowledge, Set<Triple> knownColliders, boolean verbose, Set<Node> selection, boolean excludeSelectionBias, int recursiveDepth)	Guarantees the correctness of a Partial Ancestral Graph (PAG) by repairing faulty structures such as cycles, violations of maximality, and incorrectly oriented edges, using FCI orientation rules and knowledge constraints.
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, boolean excludeSelectionBias, int recursiveDepth)	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.
Graph	ConsistentDagIterator.next()
Graph	LegEnumerator.next()
static Graph	GraphUtils.nondirectedGraph(Graph graph)	undirectedGraph.
static Graph	GraphTransforms.noUnshieldedColliderDag(Graph und)	Orients an undirected chordal graph into a DAG with no unshielded colliders by directing every edge from the earlier to the later endpoint of a Maximum Cardinality Search order.
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	GraphTransforms.randomZhangMagFromPag(Graph pag)	Convenience overload with a fresh Random; use the seeded overload for reproducibility.
static Graph	GraphTransforms.randomZhangMagFromPag(Graph pag, Random random)	Returns a random Zhang MAG of a PAG.
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.
Iterator<Graph>	ConsistentDagIterator.iterator()
Iterator<Graph>	LegEnumerator.iterator()
static Iterable<Graph>	GraphTransforms.zhangMagsFromPag(Graph pag)	Lazily enumerates the Zhang MAGs of a PAG: one MAG per consistent DAG orientation of the PAG's unoriented component.

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 boolean	GraphUtils.applyForbiddenCircleResolution(Graph pag, Knowledge knowledge)	Resolves forbidden circles in the given partially oriented graph (PAG) based on the provided knowledge.
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 boolean	GraphUtils.bidirectedExistsLatentCommonCause(Edge edge, Graph trueGraph)	Determines whether a bidirected edge exists due to a latent common cause in the given graph.
static boolean	GraphUtils.bidirectedExistsLatentCommonCauseUncovered(Edge edge, Graph trueGraph, Graph estGraph)	Determines whether the given bidirected edge has a latent common cause in the true graph via a trek x <~~ L ~~> y whose source L is latent, but where at least one consecutive pair of nodes (w, r) along the trek is NOT adjacent in the estimated graph.
static boolean	GraphUtils.bidirectedExistsLatentConfounder(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 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, using an estimate of each node's rendered size.
static void	LayoutUtil.circleLayout(Graph graph, LayoutUtil.NodeSize size)	Arranges the non-latent nodes in a circle just large enough that no two node boxes overlap and, for a circle of a few nodes, an edge between any two of them would be at least MIN_EDGE_LENGTH long from box to box (see MIN_EDGE_LENGTH_MAX_RADIUS), places each latent near its adjacent nodes without overlapping anything, and shifts the result so the bounding box of all node boxes sits at (LAYOUT_MARGIN, LAYOUT_MARGIN) — i.e.
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 List<Set<Node>>	GraphUtils.connectedComponents(Graph g)	Identifies all connected components in a given graph.
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, boolean excludeSelectionBias)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the DagToPag algorithm.
static Graph	GraphTransforms.dagToPag(Graph graph, boolean excludeSelectionBias, int timeoutSeconds)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG).
static Graph	GraphTransforms.dagToPag(Graph graph, Knowledge knowledge, boolean excludeSelectionBias, int recursiveDepth)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the provided DAG and knowledge.
static Graph	GraphTransforms.dcgToCcdPag(Graph graph)	Returns the graph CCD is meant to recover from a directed cyclic graph (DCG): the cyclic PAG of its d-separation equivalence class, with CCD's underline and dotted-underline annotations.
static void	LayoutUtil.defaultLayout(Graph graph)	Arranges the nodes in the graph by the default layout (see LayoutUtil.setDefaultLayout(DefaultLayout)), with estimated node sizes.
static void	LayoutUtil.defaultLayout(Graph graph, LayoutUtil.NodeSize size)	Arranges the nodes in the graph by the default layout (see LayoutUtil.setDefaultLayout(DefaultLayout)), with the given node sizes for the layouts that use them.
static void	LayoutUtil.defaultLayoutConditional(Graph graph)	Arranges the nodes using the default layout unless they all have positions already.
static int	GraphUtils.degree(Graph graph)	Calculates the maximum degree of a graph.
static Graph	GraphUtils.detachNodes(Graph graph)	Returns a copy of the graph whose nodes are fresh objects (see GraphUtils.copyNode(Node)), equal to the originals and in the same positions, with the same edges (including their properties, edge type probabilities, and display attributes), underlines, ambiguous triples, and graph attributes.
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 @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)	Adjusts the latent nodes in the given graph based on the specified number of latent confounders.
static LegEnumerator	LegEnumerator.fromPag(Graph pag)	Seeds the enumeration from the canonical Zhang MAG of a PAG, which is a LEG.
static void	LayoutUtil.fruchtermanReingoldLayout(Graph graph)	fruchtermanReingoldLayout.
static void	LayoutUtil.fruchtermanReingoldLayout(Graph graph, double naturalEdgeLength)	Fruchterman-Reingold with a chosen natural edge length: the distance at which the textbook pull along an edge and push between its two endpoints balance.
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 excludeSelectionBias)	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 @NotNull Graph	GraphUtils.getReplicatingGraph(Graph graph)	Creates a replicating graph based on the provided graph.
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.graphToAmatCpdag(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.graphToJson(Graph graph)	Converts a graph object to a JSON-formatted string.
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, boolean excludeSelectionBias, int recursiveDepth)	Guarantees the correctness of a Partial Ancestral Graph (PAG) by repairing faulty structures such as cycles, violations of maximality, and incorrectly oriented edges, using FCI orientation rules and knowledge constraints.
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.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).
static boolean	LayoutUtil.isLaggedGraph(Graph graph)	Returns true if the graph is a lagged (time-series) graph, i.e., at least one node has a positive lag index in its name.
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.kamadaKawaiLayout(Graph graph, boolean randomlyInitialized, double naturalEdgeLength, double springConstant, double stopEnergy, LayoutUtil.LayoutProgressListener listener)	Overload that accepts an optional progress listener.
static void	LayoutUtil.layoutByCausalOrder(Graph graph)	Lays the graph out in rows by causal order, using an estimate of each node's rendered size.
static void	LayoutUtil.layoutByCausalOrder(Graph graph, LayoutUtil.NodeSize size)	Lays the graph out in rows by causal order: the nodes with no parents in the top row, then the nodes all of whose parents are above, and so on down.
static void	LayoutUtil.layoutByKnowledgeIndices(Graph graph)	Arranges the nodes of the given graph in tiers based on numerical indices derived from the node names, and positions them accordingly in a 2D layout.
static void	LayoutUtil.layoutByKnowledgeTiers(Graph graph, Knowledge knowledge)	Arranges the nodes of the given graph in tiers based on their grouping as defined in the provided knowledge object.
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, boolean excludeSelectionBias, int recursiveDepth)	Converts a maximal ancestral graph (MAG) into a partial ancestral graph (PAG).
static Set<Node>	GraphUtils.markovBlanket(Node x, Graph G)	Computes the Markov blanket for a given node in a graph.
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 Graph	GraphTransforms.noUnshieldedColliderDag(Graph und)	Orients an undirected chordal graph into a DAG with no unshielded colliders by directing every edge from the earlier to the later endpoint of a Maximum Cardinality Search order.
static List<List<Node>>	LayoutUtil.orderRowsToReduceCrossings(Graph graph, List<List<Node>> rows)	Orders the nodes within each of the given rows to reduce the crossings of the edges between rows, by the ordering step of Richard's layout (see LayoutUtil.orderTiers(Graph, List, List)).
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 excludeSelectionBias)	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 Graph	GraphTransforms.randomZhangMagFromPag(Graph pag)	Convenience overload with a fresh Random; use the seeded overload for reproducibility.
static Graph	GraphTransforms.randomZhangMagFromPag(Graph pag, Random random)	Returns a random Zhang MAG of a PAG.
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 void	LayoutUtil.repositionLatents(Graph graph, LayoutUtil.NodeSize size)	Places each latent near its adjacent nodes without overlapping any other node's box.
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	LayoutUtil.richardsLayout(Graph graph)	Arranges the graph the way Richard lays out a graph for presentation: layered by causal depth, with each layer shifted a bit further right than the one above it, so that the flow reads down and to the right, and with within-layer orderings chosen by barycenter sweeps to reduce edge crossings.
static void	LayoutUtil.richardsLayout(Graph graph, LayoutUtil.NodeSize size)	Richard's layout with the given node sizes and default spacing: a 30 px horizontal gap between node boxes, 90 px between layer centerlines, and a 50 px rightward shear per layer.
static void	LayoutUtil.richardsLayout(Graph graph, LayoutUtil.NodeSize size, double xGap, double yGap, double shearPerLayer)	Richard's layout, fully parameterized.
static void	LayoutUtil.richardsLayout(Graph graph, LayoutUtil.NodeSize size, double xGap, double yGap, double shearPerLayer, boolean nudge)	Richard's layout, with the nudge pass switchable.
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 boolean	GraphUtils.trekIsUncovered(Graph trueGraph, Graph estGraph, List<Node> trek)	Checks whether the given trek is "uncovered" relative to the estimated graph in the following sense: every consecutive pair along the trek is adjacent in the true graph, and among the consecutive pairs whose BOTH endpoints are measured, at least one is non-adjacent in the estimated graph.
static boolean	GraphUtils.trekSourceLatent(Graph trueGraph, List<Node> trek, Node x, Node y)	Checks if the souce L of the given trek in a graph (not equal to x or y) is a latent.
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.
static Iterable<Graph>	GraphTransforms.zhangMagsFromPag(Graph pag)	Lazily enumerates the Zhang MAGs of a PAG: one MAG per consistent DAG orientation of the PAG's unoriented component.

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
	ConsistentDagIterator(Graph graph)	Enumerate distinct extensions, preferring the graph's own node order (forward).
	ConsistentDagIterator(Graph graph, List<Node> initialOrder, boolean forward, boolean distinct)	Iterates over DAGs consistent with a given CPDAG.
	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.
	EdgeListGraph(Graph graph, boolean copyNodes)	Constructs a copy of the given graph, optionally with its own node objects.
	FruchtermanReingoldLayout(Graph graph)	Constructs a new FruchtermanReingoldLayout for the given graph.
	KamadaKawaiLayout(Graph graph)	Constructs a new Kamada-Kawai layout for the given graph.
	LegEnumerator(Graph startLeg)	Enumerates over LEGs starting wiht the given LEG.
	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.
	ValidOrderIterator(Graph graph)	Enumerate preferring the graph's own node order (forward).
	ValidOrderIterator(Graph graph, List<Node> initialOrder, boolean forward)	Iterates over valid orders for a CPDAG.

Uses of Graph in edu.cmu.tetrad.hybridcg

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

Modifier and Type	Method	Description
static Graph	HybridCgEdgeStrengths.coloredGraph(HybridCgModel.HybridCgIm im)	Returns a copy of the IM's graph (sharing node objects, with new edge objects) whose edges carry a line color and a tooltip annotation reflecting their strength.
static Graph	HybridCgEdgeStrengths.coloredGraph(HybridCgModel.HybridCgIm im, boolean dark)	As HybridCgEdgeStrengths.coloredGraph(HybridCgIm), with the edge colors chosen for a light or a dark canvas; see EdgeShading.color(EdgeShading.Hue, double, boolean).
Graph	HybridCgModel.HybridCgPm.getGraph()	Retrieves the directed acyclic graph (DAG) associated with this model.
Graph	HybridCgPruneReport.getPrunedGraph()	The proposed graph: the input graph minus the deleted edges.

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

Modifier and Type	Method	Description
static HybridCgModel.HybridCgPm	HybridCgEdgeSignificance.pmForGraph(HybridCgModel.HybridCgPm src, Graph g)	Builds a PM over the given graph with the typing, categories, and node order of src, carrying over cutpoints for whichever continuous parents each discrete child retains.

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.
	HybridCgPruneReport(List<HybridCgPruneReport.Deletion> deletions, List<String> untested, Graph prunedGraph, double alpha, boolean shareVariance)	Constructs a prune report.

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	VertexRepairSearch.applyEdit(VertexRepairSearch.CandidateEdit cand)	Applies the given candidate edit to the working graph and returns the resulting graph (or null if the edit could not be applied).
Graph	VertexRepairSearch.CandidateEdit.applyTo(Graph g)	Applies the specified CandidateEdit operation to the given graph.
Graph	MarkovCheckNullReference.Result.getDag()	The DAG actually checked (after any CPDAG-to-DAG conversion).
Graph	MimbuildTrek.getFullGraph()	Deprecated. The full graph discovered.
Graph	GacTotalEffectElibility.getGraph()	Retrieves the causal graph associated with this instance.
Graph	Grasp.getGraph(boolean cpdag)	Returns the graph based on the specified parameters.
Graph	GRegression.getGraph()	Returns the MPDAG.
Graph	Icd.getGraph()	Retrieves the graph associated with the current instance.
Graph	IdaCheck.getGraph()	Returns a copy of the graph associated with this instance.
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.
static Graph	PermutationSearchTopK.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	PermutationSearchTopK.getGraph(List<Node> nodes, Map<Node, Set<Node>> parents, Knowledge knowledge, boolean cpDag)	Constructs a graph given a specification of the parents for each node, with graph replication disabled.
static Graph	PermutationSearchTopK.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	VertexRepairSearch.getGraph()	Returns the current working graph (may be updated during search).
Graph	GRegressionPag.getMag()	Returns the MAG that was fitted.
Graph	Bfci.getMarkovCpdag(boolean verbose)	Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
Graph	BfciC.getMarkovCpdag(boolean verbose)	Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
Graph	BfciD.getMarkovCpdag(boolean verbose)	Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
Graph	Gfci.getMarkovCpdag(boolean verbose)	Executes the FGES algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration (paper, Alg.
Graph	GfciOld.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(boolean verbose)
Graph	SpFci.getMarkovCpdag(boolean verbose)	Executes the SP algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration.
abstract Graph	StarFci.getMarkovCpdag(boolean verbose)	Returns the initial Markov model for the search: a CPDAG that is Markov to the data (paper, Alg.
abstract Graph	StarFciB.getMarkovCpdag(boolean verbose)	Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
abstract Graph	StarFciC.getMarkovCpdag(boolean verbose)	Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
abstract Graph	StarFciD.getMarkovCpdag(boolean verbose)	Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
final Graph	StarFciGuaranteePag.getMarkovCpdag(boolean verbose)	Deprecated. Bridges the renamed abstract method: delegates to StarFciGuaranteePag.getMarkovDag(boolean), the abstract method of this class before the rename, so existing subclasses keep working unchanged.
abstract Graph	StarFciOriginal.getMarkovCpdag(boolean verbose)	Returns the initial Markov model for the search: a CPDAG that is Markov to the data or the underlying generative model (paper, Alg.
Graph	BfciScoreCheck.getMarkovDag(boolean verbose)	Runs BOSS to obtain the Markov CPDAG (as in Bfci).
abstract Graph	StarFciGuaranteePag.getMarkovDag(boolean verbose)	Deprecated. Override StarFci.getMarkovCpdag(boolean) on StarFci instead.
abstract Graph	StarFciKeepKnowledgeOrientations.getMarkovDag(boolean verbose)	Deprecated. Constructs and returns the Markov Directed Acyclic Graph (DAG) representing the probabilistic relationships between variables.
Graph	PermutationSearchTopK.getModelGraph(int i)	Returns the graph of the i-th top permutation (0 = highest scoring), built with the same graph-construction method used for the top model, honoring the current cpdag and replicating-graph flags and the current knowledge.
Graph	GRegressionPag.getPag()	Returns the PAG.
Graph	Rlcd.getStage1Graph()	Returns the stage-1 graph.
Graph	Cstar.makeGraph(List<Cstar.Record> records)	Makes a graph of the estimated predictors to the effect.
static @NotNull Graph	LingD.makeGraph(Matrix B, List<Node> variables)	Constructs a directed graph based on the input binary adjacency matrix and a list of nodes.
Graph	Pairwise.orient()	Orients the graph and returns the oriented graph.
static Graph	MarkovCheck.prepareMagForVertexFacts(Graph graph, ConditioningSetType setType)	Precomputes the graph-level transform (the MAG) used by the ordered-local-Markov conditioning types, so that callers evaluating facts for many vertices of the same graph can run the graph-type legality checks and the CPDAG-to-DAG-to-MAG conversion once rather than once per vertex.
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	VertexRepairSearch.repairNode(Node node)	Repairs the graph at one vertex only: repeatedly applies the top-ranked candidate edit at that vertex until none improves on the current graph.
Graph	PcMb.resultGraph()	Returns the result graph.
Graph	BfciBryan.search()	Runs the search and returns the PAG.
Graph	BfciBryanScore.search()	Runs the search and returns the PAG.
Graph	BfciScoreCheck.search()	Runs the search and returns the PAG.
Graph	BossCcd.search()	Runs BOSS to obtain a CPDAG, extracts its skeleton, and runs CCD with that skeleton as an adjacency superstructure.
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	CdnodBoss.search()
Graph	Dagma.search()	Performs a search algorithm to find a graph representation.
Graph	DirectLingam.search()	Executes DirectLiNGAM and returns the learned graph.
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()	Deprecated. Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
Graph	FaskPool.search(Parameters parameters)	Runs the search and returns the composite graph.
Graph	FaskVote.search(Parameters parameters)	Runs the search and returns the composite graph.
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	Fcit.search()	Run the search and return a PAG.
Graph	Fcit2.search()	Run the search and return a PAG.
Graph	FcitKeepKnowledgeOrientations.search()	Run the search and return a PAG.
Graph	FcitMagMarkov.search()	Run the search and return a PAG.
Graph	FcitMarkov.search()	Run the search and return a PAG.
Graph	FcitSl.search()	Run the search and return a PAG.
Graph	FcitSl2.search()	Run the search and return a PAG.
Graph	FcitSlKeepKnowledgeOrientations.search()	Run the search and return a PAG.
Graph	FcitSlScoreCheck.search()	Run the search and return a PAG.
Graph	FcitZm.search()	Run the search and return a PAG.
Graph	FcitZm2.search()	Run the search and return a PAG.
Graph	FcitZmKeepKnowledgeOrientations.search()	Run the search and return a PAG.
Graph	Fges.search()	Greedy equivalence search: Start from the empty graph, add edges until 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	Flop.search()	Runs the search and returns the estimated CPDAG.
Graph	GfciOld.search()	Runs the graph and returns the search PAG.
Graph	Gmas.search()	Runs the search.
Graph	Icd.search()	Runs ICD to completion and returns the resulting PAG.
Graph	IFas.search()	Run adjacency search and return the skeleton graph.
Graph	IGraphSearch.search()	Runs the search and returns a graph.
Graph	LatentParentRecovery.search()	Runs latent-parent recovery and returns the augmented graph.
Graph	LvHeuristic.search()	Run the search and return s a PAG.
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	PcAR.search()	Performs a search operation based on the test variables associated with the instance.
Graph	PcAR.search(List<Node> nodes)	Performs the search over the given 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	PermutationSearchTopK.search()	Performs a search for a graph using the default options.
Graph	PermutationSearchTopK.search(boolean cpdag)	Performe the search and return a CPDAG (the top model).
Graph	PooledAdjacencySearch.search(List<DataSet> standardized, Parameters parameters)	Estimates the common adjacency structure over the given standardized datasets, discarding any orientations the method produces.
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	Rlcd.search()	Runs the search.
Graph	StarFci.search()	Runs the search and returns the PAG.
Graph	StarFciB.search()	Runs the graph and returns the search PAG.
Graph	StarFciC.search()	Runs the graph and returns the search PAG.
Graph	StarFciD.search()	Runs the graph and returns the search PAG.
Graph	StarFciKeepKnowledgeOrientations.search()	Deprecated. Runs the graph and returns the search PAG.
Graph	StarFciOriginal.search()	Runs the graph and returns the search PAG.
Graph	Tgin.search()	Deprecated.
Graph	VertexRepairSearch.search()	Runs the full repair sweep (using the configured strategy) and returns the repaired graph.
Graph	PooledAdjacencySearch.searchWithOrientations(List<DataSet> standardized, Parameters parameters)	Estimates the common adjacency structure over the given standardized datasets, retaining whatever orientations the method produces.

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

Modifier and Type	Method	Description
List<Graph>	Ion.search()	Runs the ION search and returns a list of compatible graphs.

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

Modifier and Type	Method	Description
static Set<Node>	GRegression.ancestorsAvoiding(Graph graph, Node y, Set<Node> excluded)	Returns the ancestors of y in the subgraph of the graph induced by V \ excluded, i.e., the set of nodes from which y is reachable by a directed path all of whose vertices lie outside the excluded set.
Graph	VertexRepairSearch.CandidateEdit.applyTo(Graph g)	Applies the specified CandidateEdit operation to the given graph.
List<PcAR.MarkovAuditFailure>	PcAR.MarkovAuditor.audit(Graph g, IndependenceTest test)	Audits a finished graph: enumerates (some or all of) the conditional independencies the graph implies, tests each afresh against the data, and returns those the data reject.
static List<IndependenceResult>	MarkovAuditUtils.auditFailures(Graph graph, IndependenceTest test, ConditioningSetType setType, double q, boolean negativelyCorrelated)	Runs a Markov audit of graph against test and returns the implied independence facts rejected under Benjamini-Hochberg at level q -- NOT the raw per-test rejections.
static List<IndependenceResult>	MarkovAuditUtils.auditFailures(Graph graph, IndependenceTest test, ConditioningSetType setType, double q, boolean negativelyCorrelated, Fas.DeterminismGuard guard)	Audits a given graph for independence test failures and applies false discovery rate (FDR) correction at a specified level to return only significant failures.
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 RecursiveBlocking.BlockingResult	RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge)	Recursively blocks paths in a graph between two nodes based on specified parameters and constraints.
static RecursiveBlocking.BlockingResult	RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs)	Single canonical entry point for separating-set search.
static RecursiveBlocking.BlockingResult	RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs, RecursiveBlocking.Strategy strategy)	Blocks paths in a graph recursively based on the given parameters and strategy.
static Set<Node>	RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength)	Attempts to construct a candidate blocking set Z between nodes x and y under PAG semantics.
static Set<Node>	RecursiveBlocking1.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge)	Variant of RecursiveBlocking1.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>	RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength)	Blocks paths recursively between two nodes in a graph, based on specified constraints and conditions.
static Set<Node>	RecursiveBlockingVerbose.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge)	Recursively blocks paths in a graph between two nodes under specified constraints.
static RecursiveBlocking.BlockingResult	ShallowRecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge)	Shallow ("near-x") separating-set search between x and y, with no deadline.
static RecursiveBlocking.BlockingResult	ShallowRecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs)	Shallow ("near-x") separating-set search between x and y.
static RecursiveBlocking.BlockingResult	ShallowRecursiveBlockingMemo.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge)	Shallow ("near-x") separating-set search, no deadline.
static RecursiveBlocking.BlockingResult	ShallowRecursiveBlockingMemo.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs)	Shallow ("near-x") separating-set search.
static RecursiveBlocking.BlockingResult	ShallowRecursiveBlockingStack.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge)	Shallow ("near-x") separating-set search, no deadline.
static RecursiveBlocking.BlockingResult	ShallowRecursiveBlockingStack.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int recursiveDepth, int depth, int maxRadius, int nearWhichEndpoint, boolean ignoreDirectEdge, long deadlineMs)	Shallow ("near-x") separating-set search.
static Set<Node>	RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, Knowledge knowledge, PrintStream out)	Blocks paths recursively between two nodes in a graph with detailed verbose tracing output.
static Set<Node>	RecursiveBlockingVerbose.blockPathsRecursivelyVerbose(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength, PrintStream out)	Blocks paths recursively between two nodes in a graph while tracing the process.
static 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 GRegression.BootstrapResult	GRegression.bootstrap(Graph mpdag, DataSet data, List<Node> treatments, Node outcome, int numBootstraps)	Estimates a total effect by G-regression on the full data set and estimates its sampling covariance by the nonparametric bootstrap (resampling rows with replacement), as in the eff2 R package.
static GRegressionPag.BootstrapResult	GRegressionPag.bootstrap(Graph pag, DataSet data, Node x, Node y, int numBootstraps)	Estimates a total effect on the full data set and its standard error by the nonparametric bootstrap.
static List<Set<Node>>	GRegression.bucketDecomposition(Graph graph)	Returns the bucket decomposition of the graph: the maximal connected components of its undirected subgraph (Section 3.4.1 of Guo and Perković, 2022).
WildBootstrapMarkovCheck.Result	WildBootstrapMarkovCheck.checkMarkovBlanket(Graph graph)	Build the stacked product matrix from the data and graph, then run the joint check.
List<Set<Node>>	GacTotalEffectElibility.AdjustmentSetProvider.compute(Graph graph, Node x, Node y, String graphType, int maxPathLength)	Computes adjustment sets based on the provided graph, pair of nodes, graph type, and maximum path length.
List<Set<Node>>	GacTotalEffectElibility.JointAdjustmentSetProvider.compute(Graph graph, Set<Node> X, Node y, String graphType, int maxPathLength)	Computes a list of sets of nodes based on the provided graph, set of nodes, target node, graph type, and maximum path length.
static MarkovCheckNullReference.Result	MarkovCheckNullReference.compute(DataSet data, Graph graph, IndependenceWrapper testWrapper, Parameters parameters, ConditioningSetType conditioningSetType, MarkovCheckNullReference.SimulatorType simulator, int numDraws, int sampleSize, long baseSeed, TrainedDagSimulatorGNM.Params gnmParams)	Convenience overload taking an IndependenceWrapper and Parameters, matching how the Markov-check test is configured in algcomparison and in py-tetrad's TetradSearch.
static MarkovCheckNullReference.Result	MarkovCheckNullReference.compute(DataSet data, Graph graph, Function<DataSet, IndependenceTest> testFactory, ConditioningSetType conditioningSetType, MarkovCheckNullReference.SimulatorType simulator, int numDraws, int sampleSize, long baseSeed, TrainedDagSimulatorGNM.Params gnmParams)	Computes the null reference using a test factory.
static Set<IndependenceFact>	MarkovCheck.computeAllImpliedFacts(Graph g, ConditioningSetType setType)	Computes all implied independence facts for the given graph based on the specified conditioning set type.
static Map<String, List<IndependenceFact>>	MarkovCheck.computeImpliedFactsByVertex(Graph graph, ConditioningSetType setType)	Computes the implied independence facts for EVERY vertex of graph at once, keyed by vertex name.
static List<IndependenceFact>	MarkovCheck.computeImpliedFactsForVertex(Graph graph, Node x, ConditioningSetType conditioningSetType)	Computes the implied independence facts for a given vertex within the specified graph based on the provided conditioning set type.
static List<IndependenceFact>	MarkovCheck.computeImpliedFactsForVertex(Graph graph, Node x, ConditioningSetType conditioningSetType, Graph preparedMag)	Variant of MarkovCheck.computeImpliedFactsForVertex(Graph, Node, ConditioningSetType) accepting a MAG prepared once per graph by MarkovCheck.prepareMagForVertexFacts(Graph, ConditioningSetType).
static double[][]	Fask.covariates(Graph graph, Node xi, Node xj, List<Node> nodes, double[][] data, int maxPathLength)	Computes the covariates for a pair of nodes in a graph based on a set of orientation covariates determined up to a given maximum path length.
default void	VertexRepairSearch.RepairListener.editApplied(VertexRepairSearch.CandidateEdit edit, Graph currentGraph)	Called each time a candidate edit is successfully applied to the graph.
static boolean	VertexRepairSearch.exhibitsSelectionBias(Graph g, VertexRepairSearch.AdjustmentGraphType graphType)	Returns true if g exhibits selection bias under graphType, the static form of the predicate used internally by this search.
static Set<Node>	GRegression.externalParents(Graph graph, Collection<Node> set)	Returns the external parents of a set of nodes: the union of their parents, minus the set itself.
static List<IndependenceFact>	MarkovCheck.factsForUniformZ(Graph g, Node x, Set<Node> z)	Generates a list of independence facts for a given node with respect to a given graph and a conditioning set.
static Set<Node>	RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, long timeout)	Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate, with no independence-check counter and the given timeout.
static Set<Node>	RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter)	Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate, with an independence-check counter and no timeout.
static Set<Node>	RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter, long timeout)	Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, returning the first passing candidate.
static Set<Node>	RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, int recursiveDepth, int maxDdpPathLength, int depth, PreserveMarkov preserveMarkovHelper, IndependenceCheckCounter counter, long timeout, boolean useMaxP)	Finds a separating set for the non-adjacent nodes x and y by recursive blocking over discriminating paths, with control over how the set is chosen when more than one candidate renders x and y independent.
static RecursiveBlockingVerbose.Blockable	RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap)	Finds and returns a path from node 'a' to the target node 'b' in a given graph considering various constraints.
static RecursiveBlockingVerbose.Blockable	RecursiveBlockingVerbose.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap, RecursiveBlockingVerbose.Tracer tracer)	Recursive method to find and evaluate the path between two nodes in a graph.
static RecursiveBlocking1.Blockable	RecursiveBlocking1.findPathToTargetVisit(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node, Set<Node>> descendantsMap)	Evaluates whether all paths from a→b onward to y can be blocked by the current candidate set Z, possibly augmented with b.
static 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<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.
List<PcAR.Triple>	PcAR.getColliderTriples(Graph g)	Retrieves all colliders from the provided graph based on specific criteria.
double[][]	CpdagParentDistancesFromTrue.getDistances(Graph outputPdag, double[][] trueEdgeStrengths, DataSet dataSet, CpdagParentDistancesFromTrue.DistanceType distanceType)	Calculates the distance matrix for the edges in the given PDAG (outputPdag).
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 admg)	Computes the complete OLMP model for the given ADMG.
static Set<IndependenceFact>	OrderedLocalMarkovPropertySinkElimination.getModel(Graph mag)	Computes the ordered local Markov property for a maximal ancestral graph (MAG).
static Set<IndependenceFact>	PairwiseMarkovProperty.getModel(Graph admg)	Computes the pairwise Markov property for the given ADMG.
static Set<IndependenceFact>	OrderedLocalMarkovProperty.getModelForNode(Graph admg, Node x)	Returns the subset of OLMP independence facts that have x as one endpoint (i.e., facts of the form x ⊥ y | Z or y ⊥ x | Z), normalised so that x is always the left-hand (getX()) node.
static Set<IndependenceFact>	OrderedLocalMarkovPropertySinkElimination.getModelForNode(Graph mag, Node x)	Returns the subset of ordered-local-Markov independence facts whose "sink" is the given node x.
static Set<IndependenceFact>	PairwiseMarkovProperty.getModelForNode(Graph admg, Node x)	Returns the subset of pairwise independence facts that involve node x (as either the left or right endpoint), normalised so that x is always the left-hand (getX()) node.
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 boolean	GRegression.hasRestrictiveProperty(Graph graph, Set<Node> bucket)	Checks the restrictive property (Corollary 4 of Guo and Perković, 2022) for a bucket: every node in the bucket has the same set of external parents.
static Set<Node>	MarkovAuditUtils.implicatedVertices(Graph graph, IndependenceTest test, ConditioningSetType setType, double q, boolean negativelyCorrelated)	Identifies and returns the set of vertices implicated by audit failures of a graph based on the provided independence test, conditioning set type, false discovery rate (FDR) level, and correlation constraints.
static boolean	GRegression.isIdentified(Graph mpdag, Set<Node> treatments, Node outcome)	Determines whether the total effect of the treatments on the outcome is identified given the MPDAG, according to Theorem 2 of Guo and Perković (2022) (Perković, 2020, Proposition 3.2): the effect is identified if and only if there is no proper possibly causal path from the treatment set to the outcome that starts with an undirected edge.
boolean	VertexRepairSearch.isLegalPagGivenKnowledge(Graph g)	Returns true if g is acceptable to this search as a PAG, given the background knowledge currently set.
static boolean	GRegression.isMpdag(Graph graph)	Returns true if the graph is an MPDAG (only directed and undirected edges, acyclic, and closed under Meek's rules R1-R4).
static boolean	PdagPagIda.isRefinementOf(Graph coarse, Graph fine)	Determines whether one graph is a refinement of another.
static double	Fask.leftRightDiffResidualized(int ruleIndex, Graph graph, Node xi, Node xj, List<Node> nodes, double[][] data)	Computes the left-right difference residualized on covariates for the given nodes in a graph.
static 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.
static String	GRegression.mpdagProblem(Graph graph)	Returns null if the graph is an MPDAG, or else a description of why it is not.
static List<Node>	GRegression.nonIdentificationWitness(Graph mpdag, Set<Node> treatments, Node outcome)	Returns a witness to non-identification, if any: a proper possibly causal path from a treatment to the outcome that starts with an undirected edge (Theorem 2).
static Set<Node>	Fask.orientationCovariates(Graph graph, Node xi, Node xj, int maxPathLength)	Identifies a set of covariates (nodes) that help orient causal relationships between two given nodes xi and xj within a graph.
static Set<Node>	OSet.oSetCpdag(Graph cpdag, Node X, Node Y, int maxPathLength)	O_G(X -> Y) for an amenable CPDAG or maxPDAG.
static Set<Node>	OSet.oSetDag(Graph dag, Node X, Node Y)	O_G(X -> Y) for a DAG.
static String	GRegressionPag.pagProblem(Graph graph)	Returns null if the graph can be used by this estimator, or else a description of why it cannot.
static String	GRegression.pathString(Graph graph, List<Node> path)	Renders a path as a string using the graph's edge marks, e.g.
static Graph	MarkovCheck.prepareMagForVertexFacts(Graph graph, ConditioningSetType setType)	Precomputes the graph-level transform (the MAG) used by the ordered-local-Markov conditioning types, so that callers evaluating facts for many vertices of the same graph can run the graph-type legality checks and the CPDAG-to-DAG-to-MAG conversion once rather than once per vertex.
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).
Set<Node>	BfciScoreCheck.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
static Set<Node>	GfciOld.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
Set<Node>	StarFciB.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
Set<Node>	StarFciC.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
Set<Node>	StarFciD.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
Set<Node>	StarFciKeepKnowledgeOrientations.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP)	Deprecated. Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
Set<Node>	StarFciOriginal.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
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)	Deprecated. Sets the external graph to use.
void	FaskPool.setExternalGraph(Graph externalGraph)	Sets a single external graph, applied to every dataset.
void	VertexRepairSearch.setGraph(Graph graph)	Replaces the working graph used for repair.
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	Rlcd.setStage1Graph(Graph stage1Graph)	Supplies a stage-1 graph over the data variables directly, skipping the stage-1 search.
void	Ccd.setSuperstructure(Graph superstructure)	Sets an optional adjacency superstructure for Step A.
static void	MarkovAuditUtils.stampSeedAttribute(Graph graph, Collection<Node> seeds)	Writes seeds onto graph under MarkovAuditUtils.SEED_ATTRIBUTE as a comma-separated name list, the channel the Vertex Repair GUI reads to offer seeded repair.
static List<List<Node>>	Ion.treks(Graph graph, Node node1, Node node2)	treks.

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

Modifier and Type	Method	Description
void	FaskPool.setExternalGraphs(List<Graph> externalGraphs)	Sets the external graphs, one per dataset, in the order of the datasets.
void	FaskVote.setExternalGraphs(List<Graph> externalGraphs)	Sets the external graphs, one per dataset (or a single graph, when the external-graph algorithm pools the datasets internally).

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

Modifier	Constructor	Description
	GacTotalEffectElibility(Graph graph, String graphType, int maxPathLength)	Constructs a new GacTotalEffectElibility instance with the specified graph, graph type, and maximum path length.
	Graph(Graph graph, boolean potentiallyDirected)	Represents a graph used in Dijkstra's algorithm.
	GRegression(Graph mpdag, ICovarianceMatrix cov)	Constructs a G-regression estimator for the given MPDAG and covariance matrix.
	GRegressionPag(Graph pag, ICovarianceMatrix cov)	Constructs the estimator with default RICF settings.
	GRegressionPag(Graph pag, ICovarianceMatrix cov, double tol, int maxIters)	Constructs the estimator, fitting the Gaussian ancestral graph model once.
	Ida(DataSet dataSet, Graph graph, List<Node> possibleCauses)	Constructor.
	IdaCheck(Graph graph, DataSet dataSet, SemIm trueSemIm)	Constructs a new IDA check for the given PDAG and data set.
	LatentParentRecovery(DataSet data, Graph latentGraph)	Constructs a latent-parent recovery procedure.
	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.
	MarkovCheck(Graph graph, IndependenceTest independenceTest, ConditioningSetType setType)	Constructor.
	Pairwise(Graph graph, List<DataSet> dataSets)	Constructor.
	PdagPagIda(DataSet dataSet, Graph graph, List<Node> possibleCauses)	Constructs a new instance of the PdagPagIda class which is used for processing a dataset and graph combination while ensuring the provided graph is a valid DAG, CPDAG/PDAG, or PAG, and the dataset is continuous.
	RecursiveAdjustment(Graph graph)	Constructs a RecursiveAdjustment instance with the specified causal graph.
	RecursiveAdjustmentMultiple(Graph graph)	Constructs an instance of AdjustmentMultiple with the provided graph.
	Result(org.ejml.simple.SimpleMatrix B, Graph g)	Constructs a Result object that encapsulates the output of the TwoStep algorithm.
	TginOrientationStages(Graph graph, List<Node> allLatents, Map<Node,Node> nodeMap, DataSet dataSet, double alpha, int sampleSize, RawMarginalIndependenceTest hsic)	Constructs a new instance of TginOrientationStages.
	VertexRepairSearch(Graph graph, IndependenceTest test, ConditioningSetType type)	Constructs a new instance of VertexRepairSearch.

Constructor parameters in edu.cmu.tetrad.search 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.search.cdnod_pag

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

Modifier and Type	Method	Description
Graph	CdnodPagOrienter.getPag()	Returns the current PAG (the final one, after CdnodPagOrienter.run() has completed).
Graph	CdnodPag.run()	Executes the pipeline and returns the resulting PAG.
Graph	CdnodPagOrienter.run()	Runs the orientation pass and returns the resulting PAG.
Graph	CdnodPag.PagBuilder.search(DataSet fullData)	Builds the baseline PAG from the given dataset.

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

Modifier and Type	Method	Description
static void	PagEdgeUtils.orientArrowheadAt(Graph pag, Node parentCand, Node child)	Orients the edge in the given PAG (Partial Ancestral Graph) such that there is an arrowhead at the specified child node coming from the specified parent candidate node.
static List<Edge>	PagEdgeUtils.partiallyOriented(Graph pag)	Retrieves a list of edges from the specified graph (PAG) that are partially oriented.

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

Modifier	Constructor	Description
	CdnodPagOrienter(Graph pag, ChangeOracle oracle, Function<Graph,Boolean> strongPagLegality, Function<Graph,Graph> propagator)	Constructs a CdnodPagOrienter.
	MsepOracleChangeTest(Graph trueGraph)	Constructs the oracle over the given true graph.

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

Modifier	Constructor	Description
	CdnodPag(DataSet dataAll, double alpha, ChangeTest changeTest, CdnodPag.PagBuilder pagBuilder, Function<Graph,Boolean> legalityCheck, Function<Graph,Graph> propagator, Knowledge knowledge)	Constructs an instance of the CdnodPag class with the specified parameters.
	CdnodPagOrienter(Graph pag, ChangeOracle oracle, Function<Graph,Boolean> strongPagLegality, Function<Graph,Graph> propagator)	Constructs a CdnodPagOrienter.

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

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

Modifier and Type	Method	Description
CiTestHarness.Result	CiTestHarness.run(Graph trueGraph, DataSet data, List<IndependenceWrapper> tests, Parameters params, CiTestHarness.Config cfg)	Executes a series of independence tests on a dataset using a given true graph and configuration, and evaluates the results against predefined criteria.
void	GraphoidClosureHarness.run(Graph graph, IndependenceTest test, ConditioningSetType conditioningSetType, GraphoidClosureHarness.ClosureType closureType)	Runs the harness.

Uses of Graph in edu.cmu.tetrad.search.harness.tsbi

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

Modifier and Type	Method	Description
static Graph	StructuralGraphScorer.extractTrueStructural(Graph mim)	Extracts the true structural graph (latent-to-latent edges only) from a full MIM graph produced by RandomMim.
Graph	TsbiRunner.run(DataSet data, List<Set<Node>> trueClusters, List<Node> trueLatentLeaders)	Runs PC with TSBI given a dataset and a pre-determined clustering.

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

Modifier and Type	Method	Description
static Graph	StructuralGraphScorer.extractTrueStructural(Graph mim)	Extracts the true structural graph (latent-to-latent edges only) from a full MIM graph produced by RandomMim.
static StructuralGraphScorer.GraphScore	StructuralGraphScorer.score(Graph trueGraph, Graph recoveredGraph)	Scores the recovered structural graph against the true structural graph.

Uses of Graph in edu.cmu.tetrad.search.harness.tsc

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

Modifier and Type	Method	Description
static List<Set<String>>	TrueClusterExtractor.extractClusterNames(Graph graph)	Extracts true clusters and returns them as sets of variable names rather than Node objects.
static List<Set<Node>>	TrueClusterExtractor.extractClusters(Graph graph)	Extracts true latent clusters from graph.
static List<Node>	TrueClusterExtractor.extractLatentLeaders(Graph graph)	Extracts the primary latent node for each group in the same order as TrueClusterExtractor.extractClusters(Graph).
SemIm	SemParameterizer.parameterize(Graph graph)	Creates a SemIm from graph using the current parameter ranges.
DataSet	SemParameterizer.parameterizeAndSimulate(Graph graph, int sampleSize)	Parameterises graph and simulates sampleSize rows of linear Gaussian data in a single call.

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

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

Modifier and Type	Method	Description
Graph	IsGFci.search()	Executes IS-FGES, targeted-pruning, and FCI orientations to produce an instance-specific PAG.

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

Modifier and Type	Method	Description
void	IsGFci.modifiedR0(Graph fgesGraph)	Modifies the orientation of edges in the given graph based on a set of rules and conditions that respect definite colliders, adjacency, and separation sets derived from FGES analysis.
void	IndTestProbabilisticBDeu.setGoldStandard(Graph gs)	Sets the gold standard graph for the independence test.
void	IsFges.setPopulationGraph(Graph pop)	Sets the population graph by either assigning it directly or replacing its nodes with the corresponding search variables using the utility method.
void	IsGFci.setPopulationGraph(Graph populationGraph)	Sets the population graph used in the instance.

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

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

Modifier and Type	Method	Description
Graph	MimicTrialResult.getEstimatedGraph(String algorithm)	Returns the estimated graph for the given algorithm name, or null if none exists.
Graph	BossTrekMimic.getGraph()	Deprecated. Returns the current graph after search, if available.
Graph	MimicModel.getGraph()	Returns a defensive copy of the full graph.
Graph	TrekMimic.getGraph()	Returns the current graph after search, if available.
Graph	TrekMeasurementModelBuilderBoss.MeasurementBuildResult.graph()	Returns the value of the graph record component.
Graph	TrekMeasurementModelBuilderPc.MeasurementBuildResult.graph()	Returns the value of the graph record component.
Graph	BossTrekMimicRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Deprecated. Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	DmRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	MimicSearchRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	TrekMimicRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	BossTrekMimic.search()	Deprecated. Runs the full Boss-Trek-MIMIC search.
Graph	Dm.search()	Runs the DM search and returns the resulting graph.
Graph	DmPcHybrid.search()
Graph	DmPcRobust.search()
Graph	TrekMimic.search()	Runs the full Trek-MIMIC search.

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

Modifier and Type	Method	Description
Map<String,Graph>	MimicTrialResult.getEstimatedGraphs()	Returns the estimated graphs by algorithm name.
List<Graph>	LatentGraphRefinement.orientAndPruneEdges(Graph graph)	Orients and prunes the latent structure of the given graph.

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

Modifier and Type	Method	Description
void	LatentGraphRefinement.addTransitiveInputEdges(Graph graph)	Adds all transitive input-to-latent edges implied by the current directed latent structure.
LatentLatentEvaluator.Report	LatentLatentEvaluator.evaluate(Graph trueGraph, Graph estGraph)	Evaluates the estimated graph against the true graph and returns a report object.
MimicEvaluation	MimicEvaluator.evaluate(MimicModel trueModel, Graph estimatedGraph)	Evaluates an estimated graph against the true model.
List<Graph>	LatentGraphRefinement.orientAndPruneEdges(Graph graph)	Orients and prunes the latent structure of the given graph.
void	LatentGraphRefinement.orientLatentEdges(Graph graph)	Orients undirected latent-latent edges using asymmetric correlations between measured parents and measured children.
void	LatentGraphRefinement.orientLatentEdgesAnyNone(Graph graph)	Orients undirected latent-latent edges using asymmetric correlations between measured parents and measured children.
void	LatentGraphRefinement.orientLatentEdgesUsingRanks(Graph graph)	Orients latent edges in the given graph using statistical rank-based tests.
void	LatentGraphRefinement.pruneInputLatentEdges(Graph graph)	Removes "latent" edges from the provided graph based on specific conditions.
void	LatentGraphRefinement.pruneLatentLatentEdges(Graph graph)	Removes latent-latent edges whose cross-block dependence is fully explained by the recovered measured parents of the two endpoint latents.
void	LatentGraphRefinement.pruneTransitiveInputEdgesByLatentAncestry(Graph graph)	Optionally removes input edges X -> L that are explained by a directed latent path X -> La -> ...
void	LatentGraphRefinement.pruneTransitiveLatentEdges(Graph graph)	Removes transitive latent-latent edges after orientation.

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

Modifier	Constructor	Description
	MeasurementBuildResult(BlockSpec spec, Graph graph, List<Node> latents, List<Node> observedChildren, List<Node> parentPool, List<Node> variables, org.ejml.simple.SimpleMatrix matrix, int sampleSize, double alpha)	Creates an instance of a MeasurementBuildResult record class.
	MeasurementBuildResult(BlockSpec spec, Graph graph, List<Node> latents, List<Node> observedChildren, List<Node> parentPool, List<Node> variables, int sampleSize, double alpha)	Creates an instance of a MeasurementBuildResult record class.
	MimicModel(Graph graph, List<Node> inputs, List<Node> latents, List<Node> outputs)	Constructs a new MIMIC model container.

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

Modifier	Constructor	Description
	MimicTrialResult(MimicModel trueModel, DataSet measuredData, Knowledge knowledge, Map<String,Graph> estimatedGraphs, Map<String, MimicEvaluation> evaluations, Map<String, LatentLatentEvaluator.Report> latentLatentReports)	Constructs a new trial result.

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

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

Modifier	Constructor	Description
	RlcdStabilitySummary(Graph main, List<Graph> runs)	Tallies co-clustering, observed adjacencies and latent counts over the runs.

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

Modifier	Constructor	Description
	RlcdStabilitySummary(Graph main, List<Graph> runs)	Tallies co-clustering, observed adjacencies and latent counts over the runs.

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
int	MagRicfBicScorer.dof(Graph mag)	The number of penalized parameters of the graph: its number of edges.
double	MagRicfBicScorer.fullScore(Graph mag)	Scores the graph with a single RICF fit of the whole graph, no decomposition and no cache.
double	MagRicfBicScorer.logLikelihood(Graph mag)	The maximized Gaussian log-likelihood of the graph, constants included, computed district by district with caching.
double	MagRicfBicScorer.score(Graph mag)	Scores the given MAG/ADMG: 2 * logLik - penaltyDiscount * numEdges * log(n), higher is better.
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, boolean excludeSelectionBias)	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 PDAG.
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.
static Graph	CorrectnessGates.tryZhangMag(Graph pag)	The Zhang MAG of the given PAG, or null if the conversion fails (which both gates treat as a veto).

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
boolean	CorrectnessGates.accepts(Graph mag, SepsetMap trialMap, String what)	Whether a candidate that has passed the caller's legality check also passes the gates that are on.
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	CorrectnessGates.beginRun(Graph startPag, SepsetMap committedMap)	Begins a run: builds the RICF scorer and scores the start graph when the score step is on, and computes the start graph's uncertified pairs when the I-map gate is on.
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 SeedMarkovCheck.Result	SeedMarkovCheck.check(Graph seedPag, Graph trueDag, Knowledge knowledge, int recursiveDepth, int sepsetMaxDepth, boolean checkColliders)	Does the check.
void	CorrectnessGates.committed(Graph committedPag, SepsetMap committedMap)	Records a commit: recomputes the committed state's uncertified pairs when the I-map gate is on.
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, int maxBlockingPathLength, int maxDiscriminatingPathLength, Graph graph, Set<Node> vNodes)	Does a discriminating path orientation based on an examination of the data.
org.apache.commons.lang3.tuple.Pair<DiscriminatingPath, Boolean>	R0R4StrategyTestBased.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, int recursiveDepth, int maxDiscriminatingPathLength, Graph graph, Set<Node> vNodes)	Does a discriminating path orientation.
boolean	DiscriminatingPath.existsIn(Graph graph)	Determines whether a discriminating path exists 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, boolean excludeSelectionBias)	Orient the edges of a graph based on the given knowledge.
void	FciOrient.finalOrientation(Graph graph)	Determines the final orientation of the given graph based on the rule set being used.
void	FciOrient.finalOrientation(Graph graph, boolean excludeSelectionBias)	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, Map<Node, Set<Node>> ancestorCache)	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, int extraLaggedEdges)	Converts a directed graph into a time-lagged graph representation.
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 PagLegalityCheck.LegalMagRet	PagLegalityCheck.isLegalMag(Graph mag, Set<Node> selection)	Determines whether the given graph is a legal Maximal Ancestral Graph (MAG).
static PagLegalityCheck.LegalMagRet	PagLegalityCheck.isLegalMag(Graph mag, Set<Node> selection, int timeoutSeconds)	Runs PagLegalityCheck.isLegalMag(Graph, Set) with a timeout.
static boolean	PagLegalityCheck.isLegalMagQuiet(Graph mag, Set<Node> selection)	Determines whether the given graph is a legal Maximal Ancestral Graph (MAG) without providing detailed error messages.
static PagLegalityCheck.LegalPagRet	PagLegalityCheck.isLegalPag(Graph pag, Graph mag, Set<Node> selection)	The same certificate with the Zhang MAG of pag supplied by the caller, for searches that have already computed that MAG for another purpose (to score it, say) and would otherwise pay for the conversion twice per candidate.
static PagLegalityCheck.LegalPagRet	PagLegalityCheck.isLegalPag(Graph pag, Set<Node> selection)	Checks if the provided Partial Ancestral Graph (PAG) is a legal PAG.
static PagLegalityCheck.LegalPagRet	PagLegalityCheck.isLegalPag(Graph pag, Set<Node> selection, int timeoutSeconds)	Runs PagLegalityCheck.isLegalPag(Graph, Set) with a timeout.
static PagLegalityCheck.LegalPagRet	PagLegalityCheck.isLegalPagModuloKnowledge(Graph pag, Set<Node> selection, Knowledge knowledge, FciOrient orient, boolean excludeSelectionBias)	Legality certificate for a PAG that carries background-knowledge endpoint marks: legal PAG *modulo background knowledge*.
static boolean	PagLegalityCheck.isLegalPagQuiet(Graph pag, Set<Node> selection)	Determines whether the provided Partial Ancestral Graph (PAG) is a legal PAG without providing detailed error messages.
static boolean	PagLegalityCheck.isLegalPagQuiet(Graph pag, Set<Node> selection, int timeoutSeconds)	Runs PagLegalityCheck.isLegalPagQuiet(Graph, Set) with a timeout.
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 maxLen, boolean checkXyNonadjacency)	Finds and returns a set of discriminating paths in the given graph.
static Set<DiscriminatingPath>	FciOrient.listDiscriminatingPaths(Graph graph, Node w, Node y, int maxLen, boolean checkXyNonadjacency)	Finds and returns the set of discriminating paths in the given graph, based on the specified parameters.
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, boolean excludeSelectionBias)	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.
static void	GraphSearchUtils.repositionLatents(Graph graph)	Repositions each latent node to the average (x, y) location of its adjacent measured nodes (parents and children combined).
void	FciOrient.ruleR0(Graph graph, Set<Triple> unshieldedTriples, boolean excludeSelectionBias)	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 (Zhang 2008 FCI orientation rule).
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 exists an uncovered circle path p = ⟨α, γ, …, θ, β⟩ between α and β such that α and θ are not adjacent and β and γ are not adjacent, then orient α o−o β and every edge on p as undirected (−−).
void	FciOrient.ruleR6(Graph graph)	R6: If α — β o−∗ γ (where 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 −∘ β o−∗ γ, and α and 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 ∘→ γ, then orient α ∘→ γ as α → γ.
boolean	FciOrient.ruleR9(Node a, Node c, Graph graph)	R9: Suppose α ∘→ γ, and let p = ⟨α, β, θ, …, γ⟩ be an uncovered, potentially-directed path from α to γ such that γ and β are not adjacent.
void	FciOrient.rulesR1R2cycle(Graph graph)	Apply rules R1 and R2 in cycles for a given graph.
void	FciOrient.rulesR5R6R7(Graph graph)	Applies transformation rules R5, R6, and R7 to the provided 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	R0R4StrategyTestBased.setSepsetGraph(Graph sepsetGraph)	Sets the frozen reference graph (G_0) used to compute separators for endpoint pairs that have no recorded separator.
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 FcitSepsets.SepsetResult	FcitSepsets.spanningSepset(Graph graph, IndependenceTest test, Node x, Node y, int recursiveDepth, int depth, int rbRadius, long deadline, Runnable onTest)	Searches for a separating set S such that x and y are conditionally independent given S within the specified constraints.
static FcitSepsets.SepsetSearch	FcitSepsets.spanningSepsetSearch(Graph graph, IndependenceTest test, Node x, Node y, int recursiveDepth, int depth, int rbRadius, long deadline, Runnable onTest)	Searches for a set S with x _||_ y | S in the given graph.
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.
static Graph	CorrectnessGates.tryZhangMag(Graph pag)	The Zhang MAG of the given PAG, or null if the conversion fails (which both gates treat as a veto).

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	MagOrderProjection.project(List<Node> order)	Projects the order to a high-scoring ancestral graph consistent with it.
Graph	MagOrderProjection.project(List<Node> order, Graph warmStart)	Projects the order, warm-started from an earlier projection's graph: the warm start's order-consistent edges (directed edges whose tail precedes their head in the new order, and all bidirected edges) seed the state, the forward phases are skipped, and only the polish and plateau-escape phases run.
Graph	HbsmsBeam.removeZeroEdges(Graph bestGraph)	removeZeroEdges.
Graph	BpcTetradPurifyWashdown.search()	Runs the search and returns a graph.
Graph	DMSearch.search()	search.
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	LvBossExperimental.search()	Runs the search and returns the best graph found.
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

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	MagOrderProjection.project(List<Node> order, Graph warmStart)	Projects the order, warm-started from an earlier projection's graph: the warm start's order-consistent edges (directed edges whose tail precedes their head in the new order, and all bidirected edges) seed the state, the forward phases are skipped, and only the polish and plateau-escape phases run.
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.

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.

Uses of Graph in edu.cmu.tetrad.sem

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

Modifier and Type	Field	Description
final Graph	TrainedDagSimulatorANM.SimResult.trueDag	Represents the true Directed Acyclic Graph (DAG) associated with the simulation results.
final Graph	TrainedDagSimulatorGNM.SimResult.trueDag	Graph representing the true causal structure of the simulation.
final Graph	TrainedDagSimulatorGNM1.SimResult.trueDag	Graph representing the true causal structure of the simulation.
final Graph	TrainedDagSimulatorGNM2.SimResult.trueDag	Graph representing the true causal structure of the simulation.

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

Modifier and Type	Method	Description
Graph	NNEstimator.getDag()	Returns the DAG used for factorization.
Graph	LargeScaleSimulation.getGraph()	Getter for the field graph.
Graph	SemUpdater.getManipulatedGraph()	getManipulatedGraph.
Graph	PredictionPruneReport.getPrunedGraph()	A copy of the input DAG with the proposed deletions removed.

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

Modifier and Type	Method	Description
static double	DagScores.bicDiscrete(DataSet data, Graph dag)	Computes the Bayesian Information Criterion (BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of discrete variables.
static double	DagScores.bicMixedConditionalGaussian(DataSet data, Graph dag)	Computes the Bayesian Information Criterion (BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of mixed or conditional Gaussian relationships.
List<List<Node>>	Ricf.cliques(Graph graph)	cliques.
DagMetricResult	DagMetric.compute(DataSet data, Graph dag)	Computes and returns the result of a metric evaluation for a given dataset and directed acyclic graph (DAG).
double	DagMetric.MetricComputer.compute(DataSet data, Graph dag)	Computes a metric value based on the given dataset and directed acyclic graph (DAG).
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 double	DagScores.mmd2ExactRbf(DataSet data, Graph dag)	Computes the Maximum Mean Discrepancy (MMD) score using the Radial Basis Function (RBF) kernel between the real dataset and a simulated dataset.
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.
static DataSet	NNEstimator.restrictToGraph(DataSet data, Graph graph)	Restricts a dataset to the columns named by the nodes of a graph, in the dataset's column order, so that a graph over a subset of the dataset's variables can be estimated.
RicfEjml.RicfResult	RicfEjml.ricf(Graph admg, ICovarianceMatrix covMatrix)	Runs RICF with the default tolerance and iteration cap.
RicfEjml.RicfResult	RicfEjml.ricf(Graph admg, ICovarianceMatrix covMatrix, double tol, int maxIters)	Runs Residual Iterative Conditional Fitting for a Gaussian ADMG/MAG.
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 double	DagScores.semBicLinearGaussian(DataSet data, Graph dag)	Computes the structural equation model Bayesian Information Criterion (SEM-BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of linear Gaussian relationships.
static double	DagScores.semBicLinearGaussian(DataSet data, Graph dag, double penaltyDiscount)	Computes the structural equation model Bayesian Information Criterion (SEM-BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of linear Gaussian relationships, with a specified penalty discount factor.
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, 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
	AdditiveNoiseSimulation(Graph graph, int numSamples, Sampler sampler, int[] hiddenDimensions, double inputScale, Function<Double,Double> activationFunction)	Constructs a new AdditiveNoiseSimulation instance with the specified parameters.
	GeneralAdditiveModel(Graph graph, int numSamples, Sampler sampler)	Constructs a new generalized additive model simulator.
	GeneralizedSemPm(Graph graph)	Constructs a BayesPm from the given Graph, which must be convertible first into a ProtoSemGraph and then into a SemGraph.
	GeneralNoiseSimulation(Graph graph, int numSamples, Sampler sampler, int[] hiddenDimensions, double inputScale, Function<Double,Double> activationFunction)	Constructs a GeneralNoiseSimulation instance based on the provided parameters.
	GeneralNoiseSimulation(Graph graph, int numSamples, Sampler sampler, int[] hiddenDimensions, double inputScale, Function<Double,Double> activationFunction, boolean reportSaturation, double saturationAbsActivationThreshold)	Constructs a GeneralNoiseSimulation instance based on the provided parameters.
	LargeScaleSimulation(Graph graph)	Constructor for LargeScaleSimulation.
	LargeScaleSimulation(Graph graph, List<Node> nodes, int[] tierIndices)	Constructor for LargeScaleSimulation.
	NNEstimator(DataSet observedData, Graph dag)	Creates an estimator with default parameters.
	NNEstimator(DataSet observedData, Graph dag, NNEstimatorParams params)	Creates an estimator with explicit parameters.
	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.
	PostnonlinearSem(Graph graph, int numSamples, org.apache.commons.math3.distribution.RealDistribution noiseDistribution, int hiddenDimension, double inputScale)	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.
	TrainedDagSimulatorANM(DataSet data, Graph dag, TrainedDagSimulatorANM.Params params)	Constructs a TrainedDagSimulatorANM
	TrainedDagSimulatorGNM(DataSet data, Graph dag, TrainedDagSimulatorGNM.Params params)	Constructs a new instance of the TrainedDagSimulatorGNM class.
	TrainedDagSimulatorGNM1(DataSet data, Graph dag, TrainedDagSimulatorGNM1.Params params)	Constructs a new instance of the TrainedDagSimulatorGNM class.
	TrainedDagSimulatorGNM2(DataSet data, Graph dag, TrainedDagSimulatorGNM2.Params params)	Constructs a new instance of the TrainedDagSimulatorGNM class.

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.
@NotNull Graph	PagCache.getPag(Graph g, boolean excludeSelectionBias)	Retrieves or computes a Partial Ancestral Graph (PAG) from the given input graph.
@NotNull Graph	PagCache.getPag(Graph g, Knowledge knowledge, boolean excludeSelectionBias, int recursiveDepth)	Retrieves or computes a PAG (Partial Ancestral Graph) from the given input graph.
static Graph	JsonUtils.parseJSONObjectToTetradGraph(String jsonResponse)	parseJSONObjectToTetradGraph.
static Graph	JsonUtils.parseJSONObjectToTetradGraph(org.json.JSONObject jObj)	parseJSONObjectToTetradGraph.
static Graph	GraphSampling.selectMedianEnsembleGraph(List<Graph> graphs)	Selects, from among the given member graphs (the actual outputs of the bootstrap searches), the single graph whose edges best agree with the ensemble edge-type frequencies, and returns a copy of it annotated with those frequencies.

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.
@NotNull Graph	PagCache.getPag(Graph g, boolean excludeSelectionBias)	Retrieves or computes a Partial Ancestral Graph (PAG) from the given input graph.
@NotNull Graph	PagCache.getPag(Graph g, Knowledge knowledge, boolean excludeSelectionBias, int recursiveDepth)	Retrieves or computes a PAG (Partial Ancestral Graph) from the given input 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.
static Graph	GraphSampling.selectMedianEnsembleGraph(List<Graph> graphs)	Selects, from among the given member graphs (the actual outputs of the bootstrap searches), the single graph whose edges best agree with the ensemble edge-type frequencies, and returns a copy of it annotated with those frequencies.

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.