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.cluster
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.algcomparison.algorithm.multi
edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pag
edu.cmu.tetrad.algcomparison.algorithm.other
edu.cmu.tetrad.algcomparison.algorithm.pairwise
edu.cmu.tetrad.algcomparison.graph
edu.cmu.tetrad.algcomparison.independence
edu.cmu.tetrad.algcomparison.score
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.algcomparison.statistic
edu.cmu.tetrad.algcomparison.statistic.utils
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calibration
edu.cmu.tetrad.data
edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.graph
edu.cmu.tetrad.regression
edu.cmu.tetrad.search	Contains classes for searching for (mostly structural) causal models given data.
edu.cmu.tetrad.search.score	Contains classes for various sorts of scores for running score-based algorithms.
edu.cmu.tetrad.search.test	Contains classes for running conditional independence tests for various sorts of data.
edu.cmu.tetrad.search.utils	Contains some utility classes for search algorithms.
edu.cmu.tetrad.search.work_in_progress	Contains some classes that aren't ready for prime time.
edu.cmu.tetrad.sem
edu.cmu.tetrad.simulation
edu.cmu.tetrad.study.performance
edu.cmu.tetrad.util
edu.pitt.csb.stability
edu.pitt.dbmi.algo.bayesian.constraint.search

Uses of Graph in edu.cmu.tetrad.algcomparison

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method	Description
Graph	Bpc.getComparisonGraph(Graph graph)	Returns the comparison graph for the given true directed graph.
Graph	Fofc.getComparisonGraph(Graph graph)	This method returns a comparison graph that is obtained from the given true directed graph.
Graph	Ftfc.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.
Graph	Fofc.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm and returns the resulting graph.
Graph	Ftfc.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm to find a causal graph.
Graph	Bpc.search(DataModel dataModel, Parameters parameters)	Runs the search algorithm to build a graph using the given data model and parameters.

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

Modifier and Type	Method	Description
Graph	Bpc.getComparisonGraph(Graph graph)	Returns the comparison graph for the given true directed graph.
Graph	Fofc.getComparisonGraph(Graph graph)	This method returns a comparison graph that is obtained from the given true directed graph.
Graph	Ftfc.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Modifier and Type	Method	Description
Graph	Boss.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	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	FgesMeasurement.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	Pc.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Pcd.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PcMax.getComparisonGraph(Graph graph)	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	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	Cstar.search(DataModel dataSet, Parameters parameters)	Runs the search.
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	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	FgesMeasurement.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	Pc.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	Pcd.getComparisonGraph(Graph graph)	Returns that graph that the result should be compared to.
Graph	PcMax.getComparisonGraph(Graph graph)	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	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.

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	BossDot.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	BossFci.getComparisonGraph(Graph graph)	Retrieves the comparison graph generated by applying the DAG-to-PAG transformation to the given true directed graph.
Graph	Ccd.getComparisonGraph(Graph graph)	Retrieves the comparison graph for the given true directed graph.
Graph	Cfci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by converting the given true directed graph into a partially directed graph (PAG) using the DAG to PAG transformation.
Graph	DmFcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	DmFciT2.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	DmPc.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	Fci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph, if there is one.
Graph	FciMax.getComparisonGraph(Graph graph)	Returns the comparison graph transformed from the true directed graph.
Graph	Fcit.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	FgesFci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
Graph	Gfci.getComparisonGraph(Graph graph)	Retrieves the comparison graph by transforming the true directed graph (if there is one) into a partially directed acyclic graph (PAG).
Graph	GraspFci.getComparisonGraph(Graph graph)	Retrieves a comparison graph by transforming a true directed graph into a partially directed graph (PAG).
Graph	PagSampleRfci.getComparisonGraph(Graph graph)	Returns the comparison graph based on the true directed graph.
Graph	Rfci.getComparisonGraph(Graph graph)	Returns a comparison graph based on the provided true directed graph.
Graph	RfciBsc.getComparisonGraph(Graph graph)	Retrieves the comparison graph from the true directed graph, if there is one.
Graph	SpFci.getComparisonGraph(Graph graph)	Returns the comparison graph created by converting a true directed graph into a partially directed acyclic graph (PAG).
Graph	SvarFci.getComparisonGraph(Graph graph)	Returns a comparison graph based on the given true directed graph.
Graph	SvarGfci.getComparisonGraph(Graph graph)	Returns a comparison graph based on the given true directed graph.
Graph	BossDot.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	BossFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	Ccd.runSearch(DataModel dataModel, Parameters parameters)	Runs the CCD (Cyclic Causal Discovery) search algorithm on the given data set using the specified parameters.
Graph	Cfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to discover the causal graph.
Graph	DmFcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmFciT2.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmPc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	Fci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph based on the given data model and parameters.
Graph	FciMax.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to discover the causal graph structure.
Graph	Fcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FgesFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	Gfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	GraspFci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	PagSampleRfci.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm using the given data set and parameters.
Graph	Rfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model and parameters.
Graph	RfciBsc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm using a given dataset and parameters.
Graph	SpFci.runSearch(DataModel dataModel, Parameters parameters)	Executes a search algorithm to infer the causal graph structure from a given data model
Graph	SvarFci.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm to find a graph structure that best fits the given dataset and parameters.
Graph	SvarGfci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm on the given data set using the specified parameters.

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

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

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	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.

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.

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

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

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

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

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

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

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

Modifier and Type	Method	Description
Graph	Cyclic.createGraph(Parameters parameters)	createGraph.
Graph	ErdosRenyi.createGraph(Parameters parameters)	createGraph.
Graph	RandomForward.createGraph(Parameters parameters)	Creates a random graph by adding forward edges.
Graph	RandomGraph.createGraph(Parameters parameters)	createGraph.
Graph	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	BayesNetSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	CausalPerceptronNetwork.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	ConditionalGaussianSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	GeneralSemSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	GeneralSemSimulationSpecial1.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	GpSemSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	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	NonlinearAdditiveNoiseModel.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	NonlinearFunctionsOfLinear.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	PostnonlinearCausalModel.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	SemSimulation.getTrueGraph(int index)	Returns the true graph at the specified index.
Graph	SemThenDiscretize.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	Simulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	SingleDatasetSimulation.getTrueGraph(int index)	Gets the true graph for the simulation at the specified index.
Graph	StandardizedSemSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
Graph	TimeSeriesSemSimulation.getTrueGraph(int index)	Returns the true graph at the given index.
static Graph	LeeHastieSimulation.makeMixedGraph(Graph g, Map<String,Integer> m)	makeMixedGraph.

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

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

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

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

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

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

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

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

Uses of Graph in edu.cmu.tetrad.bayes

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

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

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

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

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

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

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

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

Uses of Graph in edu.cmu.tetrad.calibration

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

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

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

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

Uses of Graph in edu.cmu.tetrad.data

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

Modifier and Type	Method	Description
static Graph	DataGraphUtils.randomBifactorModel(int numStructuralNodes, int numStructuralEdges, int numMeasurementsPerLatent, int numLatentMeasuredImpureParents, int numMeasuredMeasuredImpureParents, int numMeasuredMeasuredImpureAssociations)	First a random single factor model is created with the specified number of latent nodes and latent edges, and impurity structure.
static Graph	DataGraphUtils.randomMim(Graph graph, int numMeasurementsPerLatent, int numLatentMeasuredImpureParents, int numMeasuredMeasuredImpureParents, int numMeasuredMeasuredImpureAssociations, boolean arrangeGraph)	randomMim.
static Graph	DataGraphUtils.randomSingleFactorModel(int numStructuralNodes, int numStructuralEdges, int numMeasurementsPerLatent, int numLatentMeasuredImpureParents, int numMeasuredMeasuredImpureParents, int numMeasuredMeasuredImpureAssociations)	randomSingleFactorModel.

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

Modifier and Type	Method	Description
boolean	Knowledge.isViolatedBy(Graph graph)	isViolatedBy.
static Graph	DataGraphUtils.randomMim(Graph graph, int numMeasurementsPerLatent, int numLatentMeasuredImpureParents, int numMeasuredMeasuredImpureParents, int numMeasuredMeasuredImpureAssociations, boolean arrangeGraph)	randomMim.

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

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

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

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

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

Uses of Graph in edu.cmu.tetrad.graph

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

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

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

Modifier and Type	Method	Description
static Graph	GraphUtils.bidirectedToUndirected(Graph graph)	Converts a bidirected graph to an undirected graph.
static Graph	GraphUtils.completeGraph(Graph graph)	completeGraph.
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.
static Graph	GraphTransforms.dagFromCpdag(Graph graph)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, boolean verbose)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, boolean meekPreventCycles, boolean verbose)	Converts a completed partially directed acyclic graph (CPDAG) into a directed acyclic graph (DAG).
static Graph	GraphTransforms.dagFromCpdag(Graph graph, Knowledge knowledge)	dagFromCpdag.
static Graph	GraphTransforms.dagFromCpdag(Graph cpdag, Knowledge knowledge, boolean verbose)	Returns a random DAG from the given CPDAG.
static Graph	GraphTransforms.dagToCpdag(Graph dag)	Returns the completed partially directed acyclic graph (CPDAG) for a given directed acyclic graph (DAG).
static @NotNull Graph	GraphTransforms.dagToMag(Graph dag)	Converts a Directed Acyclic Graph (DAG) to a Maximal Ancestral Graph (MAG) by adding arrows to the edges.
static @NotNull Graph	GraphTransforms.dagToPag(Graph graph)	Converts a Directed Acyclic Graph (DAG) to a Partial Ancestral Graph (PAG) using the DagToPag algorithm.
static Graph	GraphUtils.emptyGraph(int numNodes)	Creates an empty graph with the specified number of nodes.
static 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.guaranteePag(Graph pag, FciOrient fciOrient, Knowledge knowledge, Set<Triple> knownColliders, boolean verbose, Set<Node> selection)	Guarantees the correctness of a Partial Ancestral Graph (PAG) by repairing faulty structures such as cycles, violations of maximality, and incorrectly oriented edges.
static Graph	GraphSaveLoadUtils.loadGraph(File file)	loadGraph.
static Graph	GraphSaveLoadUtils.loadGraphAmatCpdag(File file)	Loads a CPDAG in the "amat.cpdag" format of PCALG.
static Graph	GraphSaveLoadUtils.loadGraphAmatPag(File file)	Loads a PAG in the "amat.pag" format of PCALG.
static Graph	GraphSaveLoadUtils.loadGraphBNTPcMatrix(List<Node> vars, DataSet dataSet)	loadGraphBNTPcMatrix.
static Graph	GraphSaveLoadUtils.loadGraphJson(File file)	loadGraphJson.
static Graph	GraphSaveLoadUtils.loadGraphRuben(File file)	loadGraphRuben.
static Graph	GraphSaveLoadUtils.loadGraphTxt(File file)	loadGraphTxt.
static Graph	GraphSaveLoadUtils.loadRSpecial(File file)	loadRSpecial.
static Graph	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	GraphUtils.nondirectedGraph(Graph graph)	undirectedGraph.
static Graph	GraphSaveLoadUtils.parseGraphXml(nu.xom.Element graphElement, Map<String,Node> nodes)	parseGraphXml.
static Graph	RandomGraph.randomCyclicGraph2(int numNodes, int numEdges, int maxDegree)	Makes a cyclic graph by repeatedly adding cycles of length of 3, 4, or 5 to the graph, then finally adding two cycles.
static Graph	RandomGraph.randomCyclicGraph3(int numNodes, int numEdges, int maxDegree, double probCycle, double probTwoCycle)	Makes a cyclic graph by repeatedly adding cycles of length of 3, 4, or 5 to the graph, then finally adding two cycles.
static Graph	RandomGraph.randomDag(int numNodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random Directed Acyclic Graph (DAG) with the specified parameters.
static Graph	RandomGraph.randomDag(int numNodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random Directed Acyclic Graph (DAG) based on specified constraints.
static Graph	RandomGraph.randomGraph(int numMeasures, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random graph based on the given parameters.
static Graph	RandomGraph.randomGraph(int numMeasures, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random graph based on the specified parameters.
static Graph	RandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random graph based on the given parameters.
static Graph	RandomGraph.randomGraph(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random graph with the specified parameters and properties.
static Graph	RandomGraph.randomGraphRandomForwardEdges(int numNodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random graph with the given parameters and random forward edges.
static Graph	RandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected)	Generates a random graph with forward edges.
static Graph	RandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, boolean layoutAsCircle, long seed)	Generates a random graph with forward edges.
static Graph	RandomGraph.randomGraphRandomForwardEdges(List<Node> nodes, int numLatentConfounders, int numEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, long seed)	Generates a random directed acyclic graph with specified properties, including constraints on maximum degree, indegree, and outdegree, with random forward edges.
static Graph	RandomGraph.randomGraphUniform(List<Node> nodes, int numLatentConfounders, int maxNumEdges, int maxDegree, int maxIndegree, int maxOutdegree, boolean connected, int numIterations)	Generates a random graph using UniformGraphGenerator with the specified parameters.
static Graph	RandomGraph.randomScaleFreeGraph(int numNodes, int numLatentConfounders, double alpha, double beta, double delta_in, double delta_out)	Generates a random scale-free graph.
static Graph	GraphSaveLoadUtils.readerToGraphJson(Reader reader)	readerToGraphJson.
static Graph	GraphSaveLoadUtils.readerToGraphRuben(Reader reader)	readerToGraphRuben.
static Graph	GraphSaveLoadUtils.readerToGraphTxt(Reader reader)	readerToGraphTxt.
static Graph	GraphSaveLoadUtils.readerToGraphTxt(String graphString)	readerToGraphTxt.
static Graph	GraphUtils.removeBidirectedOrientations(Graph estCpdag)	removeBidirectedOrientations.
static Graph	GraphUtils.replaceNodes(Graph originalGraph, List<Node> newVariables)	Converts the given graph, originalGraph, to use the new variables (with the same names as the old).
static Graph	GraphUtils.restrictToMeasured(Graph graph)	Removes all latent nodes from the graph and returns the modified graph.
Graph	Dag.subgraph(List<Node> nodes)	Returns a subgraph of the current graph consisting only of the specified nodes.
Graph	EdgeListGraph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	Graph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	LagGraph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	SemGraph.subgraph(List<Node> nodes)	Constructs and returns a subgraph consisting of a given subset of the nodes of this graph together with the edges between them.
Graph	TimeLagGraph.subgraph(List<Node> nodes)	Returns a subgraph of the current graph based on the provided nodes.
static Graph	GraphUtils.trimGraph(List<Node> targets, Graph graph, int trimmingStyle)	Trims the given graph based on the specified trimming style.
static Graph	GraphUtils.undirectedGraph(Graph graph)	undirectedGraph.
static Graph	GraphUtils.undirectedToBidirected(Graph graph)	Converts an undirected graph to a bidirected graph.
static Graph	GraphTransforms.zhangMagFromPag(Graph pag)	Transforms a partial ancestral graph (PAG) into a maximal ancestral graph (MAG) using Zhang's 2008 Theorem 2.

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

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

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

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

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

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

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

Modifier	Constructor	Description
	Dag(Graph graph)	Constructs a new directed acyclic graph from the given graph object.
	EdgeListGraph(Graph graph)	Constructs a EdgeListGraph using the nodes and edges of the given graph.
	FruchtermanReingoldLayout(Graph graph)	Constructs a new FruchtermanReingoldLayout for the given graph.
	KamadaKawaiLayout(Graph graph)	Constructs a new Kamada-Kawai layout for the given graph.
	Paths(Graph graph)	Constructor for Paths.
	SemGraph(Graph graph)	Constructs a new SemGraph from the nodes and edges of the given graph.
	Underlines(Graph graph)	Constructor for Underlines.

Uses of Graph in edu.cmu.tetrad.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

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

Modifier and Type	Method	Description
Graph	SvarFges.getAdjacencies()	Retrieves the adjacency graph.
Graph	SvarFges.getExternalGraph()	Getter for the field externalGraph.
Graph	Mimbuild.getFullGraph(List<Node> includeNodes)	The full graph inferred, including the edges from latents to measures.
Graph	MimbuildTrek.getFullGraph()	The full graph discovered.
Graph	Cpc.getGraph()	The graph that's constructed during the search.
@NotNull Graph	Grasp.getGraph(boolean cpDag)	Returns the graph implied by the discovered permutation.
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.
Graph	BossFci.getMarkovCpdag()	Executes the Markov CPDAG search algorithm using the BOSS (Best Order Score Search) method and returns the resulting graph.
Graph	FgesFci.getMarkovCpdag()
Graph	Gfci.getMarkovCpdag()	Executes the FGES algorithm to compute the Markov equivalence class in the form of a completed partially directed acyclic graph (CPDAG) based on the provided score and algorithm configuration.
@NotNull Graph	GraspFci.getMarkovCpdag()
Graph	SpFci.getMarkovCpdag()
abstract Graph	StarFci.getMarkovCpdag()	Returns a Markov CPDAG to use as the initial graph in the Star-FCI search.
Graph	Cstar.makeGraph(List<Cstar.Record> records)	Makes a graph of the estimated predictors to the effect.
static @NotNull Graph	IcaLingD.makeGraph(Matrix B, List<Node> variables)	Returns a graph given a coefficient matrix and a list of variables.
Graph	Lofs.orient()	Orients the graph and returns the oriented graph.
Graph	PcMb.resultGraph()	Returns the result graph.
Graph	BossDot.search()	Run the search and return s a PAG.
Graph	Bpc.search()	Runs the search and returns the graph, or null if there is no model.
Graph	Ccd.search()	The search method assumes that the IndependenceTest provided to the constructor is a conditional independence oracle for the SEM (or Bayes network) which describes the causal structure of the population.
Graph	Cfci.search()	Performs the search and returns the PAG.
Graph	Cpc.search()	Runs CPC starting with a fully connected graph over all the variables in the domain of the independence test.
Graph	Dagma.search()	Performs a search algorithm to find a graph representation.
Graph	DirectLingam.search()	Performs the search.
Graph	DmPc.search()	Executes the Directed Maximal PC (DmPc) algorithm to identify a causal graph structure that represents the relationships between observed and latent variables.
Graph	Fas.search()	Performs a conditional independence graph search using the default set of variables from the initialized independence test.
Graph	Fas.search(List<Node> nodes)	Searches for conditional independence relationships in a graph constructed from the given list of nodes.
Graph	Fasd.search()	Discovers all adjacencies in data.
Graph	Fask.search()	Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
Graph	FaskOrig.search()	Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
Graph	Fci.search()	Performs a search using the FCI algorithm.
Graph	FciMax.search()	Performs the search and returns the PAG.
Graph	Fcit.search()	Run the search and return a PAG.
Graph	Fges.search()	Greedy equivalence search: Start from the empty graph, add edges till 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	Fofc.search()	Runs the search and returns a graph of clusters with the ir respective latent parents.
Graph	Ftfc.search()	Runs the search and returns a graph of clusters, each of which has two common latent parents.
Graph	Gfci.search()	Runs the graph and returns the search PAG.
Graph	IGraphSearch.search()	Runs the search and returns a graph.
Graph	Mimbuild.search(int[][] clustering, String[] measureNames, String[] latentNames, double[][] measuresCov)	Conducts a search to infer a graph over latent variables based on the provided clustering, measure names, latent variable names, and measures covariance.
Graph	Mimbuild.search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measurescov)	Does a Mimbuild search.
Graph	MimbuildTrek.search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov)	Does the search and returns the graph.
Graph	Pc.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	Pc.search(Fas fas, Set<Node> nodes)	Runs the search using a particular implementation of the fast adjacency search (FAS), over the given sublist of nodes.
Graph	Pc.search(Set<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	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	PermutationSearch.search()	Performs a search for a graph using the default options.
Graph	PermutationSearch.search(boolean cpdag)	Performe the search and return a CPDAG.
Graph	Rfci.search()	Runs the search and returns the RFCI PAG.
Graph	Rfci.search(Fas fas, List<Node> nodes)	Runs the search and returns the RFCI PAG.
Graph	Rfci.search(List<Node> nodes)	Searches of a specific sublist of nodes.
Graph	StarFci.search()	Runs the graph and returns the search PAG.
Graph	SvarFas.search()	Discovers all adjacencies in data.
Graph	SvarFci.search()	Runs the search and returns the PAG.
Graph	SvarFci.search(IFas fas)	Runs the search using a particular implementation of FAS.
Graph	SvarFges.search()	Greedy equivalence search: Start from the empty graph, add edges till the model is significant.
Graph	SvarGfci.search()	Runs the search and returns a PAG.

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

Modifier and Type	Method	Description
static Set<Node>	SepsetFinder.blockPathsLocalMarkov(Graph graph, Node x)	Returns a set of nodes that are the parents of the given node in the graph.
static Set<Node>	RecursiveBlocking.blockPathsRecursively(Graph graph, Node x, Node y, Set<Node> containing, Set<Node> notFollowed, int maxPathLength)	Retrieves set that blocks all blockable paths between x and y in the given graph, where this set contains the given nodes.
static Set<Node>	RecursiveBlockingChokePointA.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength)	Identifies and blocks paths between two nodes in a graph recursively, ensuring that all possible open paths between the nodes are restricted by adding blocking nodes to a set.
static Set<Node>	RecursiveBlockingChokePointB.blockPathsRecursively(Graph G, Node x, Node y, Set<Node> forbidden, int maxPathLength)	Identifies and blocks paths in the given graph by iteratively finding and addressing chokepoints and eligible non-collider nodes.
static Set<Node>	SepsetFinder.blockPathsWithMarkovBlanket(Node x, Graph G)	Identifies the set of nodes that form the Markov Blanket for a given node in a graph.
static Set<Node>	RecursiveDiscriminatingPathRule.findDdpSepsetRecursive(IndependenceTest test, Graph pag, Node x, Node y, FciOrient fciOrient, int maxBlockingPathLength, int maxDdpPathLength, PreserveMarkov preserveMarkovHelper, int depth)	Finds the set of nodes (separator set) for the Recursive Discriminating Path rule in a graph.
static RecursiveBlocking.Blockable	RecursiveBlocking.findPathToTarget(Graph graph, Node a, Node b, Node y, Set<Node> path, Set<Node> z, int maxPathLength, Set<Node> notFollowed, Map<Node,Set<Node>> ancestorMap)	Finds a path from node a to node b that can be blocked by conditioning on a set of nodes z.
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.
double[][]	CpdagParentDistancesFromTrue.getDistances(Graph outputCpdag, double[][] trueEdgeStrengths, DataSet dataSet, CpdagParentDistancesFromTrue.DistanceType distanceType)	Calculates the distance matrix for the edges in the given CPDAG (outputCpdag).
static Set<IndependenceFact>	OrderedLocalMarkovProperty.getModel(Graph mag)	Computes the ordered local Markov property for a maximal ancestral graph (MAG).
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.
double	SvarFges.getScore(Graph dag)	Returns the score of the given DAG.
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 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.
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).
double	SvarFges.scoreDag(Graph dag)	Calculates the score of a Directed Acyclic Graph (DAG).
static Set<Node>	Gfci.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
static Set<Node>	StarFci.sepsetSubsetOfAdjxOrAdjy(Graph graph, Node x, Node y, Set<Node> containing, IndependenceTest test, int depth, List<Node> order, boolean useMaxP)	Finds a separating set that is a subset of the adjacency of nodes x or y in the input graph.
void	SvarFges.setAdjacencies(Graph adjacencies)	Sets the set of preset adjacencies for the algorithm; edges not in this adjacencies graph will not be added.
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 initial graph for the FaskOrig class.
void	FaskOrig.setExternalGraph(Graph externalGraph)	Sets the external graph to use.
void	SvarFas.setExternalGraph(Graph externalGraph)	Sets an external graph.
void	SvarFges.setExternalGraph(Graph externalGraph)	Sets the initial graph.
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	SvarFges.setTrueGraph(Graph trueGraph)	Sets the true graph, which will result in some edges in output graphs being marked with asterisks.

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

Modifier	Constructor	Description
	Graph(Graph graph, boolean potentiallyDirected)	Represents a graph used in Dijkstra's algorithm.
	Ida(DataSet dataSet, Graph graph, List<Node> possibleCauses)	Constructor.
	IdaCheck(Graph graph, DataSet dataSet, SemIm trueSemIm)	Constructs a new IDA check for the given MPDAG and data set.
	Lofs(Graph graph, List<DataSet> dataSets)	Constructor.
	MarkovCheck(Graph graph, IndependenceTest independenceTest, ConditioningSetType setType)	Constructor.

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

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

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

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

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

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

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

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

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

Modifier and Type	Method	Description
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.utils

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

Modifier and Type	Method	Description
static Graph	DagToPag.calcAdjacencyGraph(Graph dag)	Calculates the adjacency graph for the given Directed Acyclic Graph (DAG).
Graph	DagToPag.convert()	This method does the conversion of DAG to PAG.
Graph	TsDagToPag.convert()	convert.
static Graph	ClusterUtils.convertSearchGraph(List<int[]> clusters, String[] varNames)	Converts a list of indices into a list of Nodes representing a cluster.
static Graph	Purify.convertSearchGraph(SemGraph input)	convertSearchGraph.
Graph	FgesOrienter.getAdjacencies()	Getter for the field adjacencies.
Graph	TsUtils.VarResult.getCollapsedVarGraph()	Getter for the field collapsedVarGraph.
Graph	DagSepsets.getDag()	Returns the DAG being analyzed.
Graph	SepsetsGreedy.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
Graph	SepsetsGreedyMb.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
Graph	SepsetsMaxP.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepset algorithm.
Graph	SepsetsMinP.getDag()	Retrieves the Directed Acyclic Graph (DAG) produced by the Sepsets algorithm.
Graph	FgesOrienter.getExternalGraph()	Getter for the field externalGraph.
Graph	TeyssierScorer.getGraph(boolean cpDag)	Returns the DAG build for the current permutation, or its CPDAG.
static Graph	MbUtils.getOneMbDag(Graph mbCpdag)	Returns an example DAG from the given MB CPDAG.
Graph	PossibleMConnectingPath.getPag()	Getter for the field pag.
Graph	TsDagToPag.getTruePag()	Getter for the field truePag.
Graph	DagInCpcagIterator.next()	Successive calls to this method return successive DAGs in the CPDAG, in a more or less natural enumeration of them in which an arbitrary undirected edge is picked, oriented one way, Meek rules applied, then a remaining unoriented edge is picked, oriented one way, and so on, until a DAG is obtained, and then by backtracking the other orientation of each chosen edge is tried.
Graph	DagIterator.next()	Successive calls to this method return successive DAGs in the CPDAG, in a more or less natural enumeration of them in which an arbitrary undirected edge is picked, oriented one way, Meek rules applied, then a remaining unoriented edge is picked, oriented one way, and so on, until a DAG is obtained, and then by backtracking the other orientation of each chosen edge is tried.
Graph	FgesOrienter.search()	Greedy equivalence search: Start from the empty graph, add edges till model is significant.
Graph	PcCommon.search()	Runs the search and returns the search graph.
Graph	PcCommon.search(List<Node> nodes)	Runs the search over the given list of nodes only, returning the search graph.
Graph	Purify.search()	****************************************************** SEARCH INTERFACE *******************************************************

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

Modifier and Type	Method	Description
static List<Graph>	MbUtils.generateMbDags(Graph mbCPDAG, boolean orientBidirectedEdges, IndependenceTest test, int depth, Node target)	Generates the list of MB DAGs consistent with the MB CPDAG returned by the previous search.

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

Modifier and Type	Method	Description
static void	GraphSearchUtils.arrangeByKnowledgeTiers(Graph graph)	arrangeByKnowledgeTiers.
static void	GraphSearchUtils.arrangeByKnowledgeTiers(Graph graph, Knowledge knowledge)	arrangeByKnowledgeTiers.
static void	GraphSearchUtils.basicCpdag(Graph graph)	Get a graph and direct only the unshielded colliders.
static void	GraphSearchUtils.basicCpdagRestricted2(Graph graph, Node node)	basicCpdagRestricted2.
void	Bes.bes(Graph graph, List<Node> variables)	Runs BES for a graph over the given list of variables
void	BesPermutation.bes(Graph graph, List<Node> order, List<Node> suborder)	Runs BES.
static Graph	DagToPag.calcAdjacencyGraph(Graph dag)	Calculates the adjacency graph for the given Directed Acyclic Graph (DAG).
static Clusters	MimUtils.convertToClusters(Graph clusterGraph)	convertToClusters.
static Clusters	MimUtils.convertToClusters(Graph clusterGraph, List<Node> measuredVariables)	Converts a disconnected multiple indicator model into a set of clusters.
static List<List<Node>>	MimUtils.convertToClusters2(Graph clusterGraph)	convertToClusters2.
static R0R4Strategy	R0R4StrategyTestBased.defaultConfiguration(Graph dag, Knowledge knowledge)	Returns a default configuration of the FciOrientDataExaminationStrategy object.
org.apache.commons.lang3.tuple.Pair<DiscriminatingPath,Boolean>	R0R4Strategy.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, Graph graph, Set<Node> vNodes)	Does a discriminating path orientation based on an examination of the data.
org.apache.commons.lang3.tuple.Pair<DiscriminatingPath,Boolean>	R0R4StrategyTestBased.doDiscriminatingPathOrientation(DiscriminatingPath discriminatingPath, Graph graph, Set<Node> vNodes)	Does a discriminating path orientation.
boolean	DiscriminatingPath.existsIn(Graph graph)	Checks this discriminating path construct to make sure it is a discriminating path in the given graph.
static boolean	TsDagToPag.existsInducingPathInto(Node x, Node y, Graph graph, Knowledge knowledge)	existsInducingPathInto.
static boolean	TsDagToPag.existsInducingPathVisitts(Graph graph, Node a, Node b, Node x, Node y, LinkedList<Node> path, Knowledge knowledge)	existsInducingPathVisitts.
void	FciOrient.fciOrientbk(Knowledge bk, Graph graph, List<Node> variables)	Orient the edges of a graph based on the given knowledge.
void	FciOrient.finalOrientation(Graph graph)	Orients the graph (in place) according to rules in the graph (FCI step D).
static List<PossibleMConnectingPath>	PossibleMConnectingPath.findMConnectingPaths(Graph pag, Node x, Node y, Collection<Node> z)	Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z.
static List<PossibleMConnectingPath>	PossibleMConnectingPath.findMConnectingPathsOfLength(Graph pag, Node x, Node y, Collection<Node> z, Integer length)	Finds all possible D-connection undirectedPaths as sub-graphs of the pag given at construction time from x to y given z for a particular path length.
static List<Graph>	MbUtils.generateMbDags(Graph mbCPDAG, boolean orientBidirectedEdges, IndependenceTest test, int depth, Node target)	Generates the list of MB DAGs consistent with the MB CPDAG returned by the previous search.
static @NotNull Map<Node,Set<Edge>>	AlmostCycleRemover.getBMap(Graph pag)	Returns a map of nodes to bidirected edges for them.
static GraphSearchUtils.CpcTripleType	GraphSearchUtils.getCpcTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph)	getCpcTripleType.
static String	GraphSearchUtils.getEdgewiseComparisonString(String trueGraphName, Graph trueGraph, String targetGraphName, Graph targetGraph)	getEdgewiseComparisonString.
static R0R4StrategyTestBased	DagToPag.getFinalStrategyUsingDsep(Graph mag, Knowledge knowledge, boolean verbose)	Returns the final strategy for finding a PAG using D-SEP.
static GraphUtils.GraphComparison	GraphSearchUtils.getGraphComparison(Graph trueGraph, Graph targetGraph)	Just counts arrowhead errors--for cyclic edges counts an arrowhead at each node.
static Knowledge	TsUtils.getKnowledge(Graph graph)	getKnowledge.
static @NotNull DataSet	LgMnarDataSimulator.getMnarData(Graph graph, int numVariablesWithMissing, int numExtraInfluences, double threshold, int numRows)	Generates a dataset with Missing Not At a Random (MNAR) data mechanism applied to specific variables in a graph.
static Graph	MbUtils.getOneMbDag(Graph mbCpdag)	Returns an example DAG from the given MB CPDAG.
static Set<Node>	GraphSearchUtils.getReachableNodes(List<Node> initialNodes, LegalPairs legalPairs, List<Node> c, List<Node> d, Graph graph, int maxPathLength)	getReachableNodes.
double	FgesOrienter.getScore(Graph dag)	getScore.
static int[][]	GraphSearchUtils.graphComparison(Graph trueCpdag, Graph estCpdag, PrintStream out)	graphComparison.
static boolean	GraphInPag.graphInPagStep0(Graph pag, Graph dag)	This method implements step (1) of the definition.
static boolean	GraphInPag.graphInPagStep1(Graph pag, Graph dag)	graphInPagStep1.
static boolean	GraphInPag.graphInPagStep2(Graph pag, Graph dag)	graphInPagStep2.
static boolean	GraphInPag.graphInPagStep3(Graph pag, Graph dag)	graphInPagStep3.
static boolean	GraphInPag.graphInPagStep4(Graph pag, Graph dag)	graphInPagStep4.
static boolean	GraphInPag.graphInPagStep5(Graph pag, Graph dag)	graphInPagStep5.
static TimeLagGraph	TsUtils.graphToLagGraph(Graph _graph, int numLags)	graphToLagGraph.
static boolean	FciOrient.isArrowheadAllowed(Node x, Node y, Graph graph, Knowledge knowledge)	Determines whether an arrowhead is allowed between two nodes in a graph, based on specific conditions.
static GraphSearchUtils.LegalMagRet	GraphSearchUtils.isLegalMag(Graph mag, Set<Node> selection)	Determines whether the given graph is a legal Mixed Ancestral Graph (MAG).
static GraphSearchUtils.LegalPagRet	GraphSearchUtils.isLegalPag(Graph pag, Set<Node> selection)	Checks if the provided Directed Acyclic Graph (PAG) is a legal PAG.
boolean	R0R4Strategy.isUnshieldedCollider(Graph graph, Node a, Node b, Node c)	Determines if a given triple is an unshielded collider based on an examination of the data.
boolean	R0R4StrategyTestBased.isUnshieldedCollider(Graph graph, Node i, Node j, Node k)	Checks if a collider is unshielded or not.
static Set<DiscriminatingPath>	FciOrient.listDiscriminatingPaths(Graph graph, int maxDiscriminatingPathLength, boolean checkXyNonadjacency)	Lists all the discriminating paths in the given graph.
static Set<DiscriminatingPath>	FciOrient.listDiscriminatingPaths(Graph graph, Node w, Node y, int maxDiscriminatingPathLength, boolean checkEcNonadjacency)	Lists the discriminating paths for <w, y> in the graph.
static Map<org.apache.commons.lang3.tuple.Pair<Node,Node>,Set<Double>>	PreserveMarkov.markovAdjustPValues(Graph graph, boolean preserveMarkov, IndependenceTest test, Map<org.apache.commons.lang3.tuple.Pair<Node,Node>,Set<Double>> pValues, org.apache.commons.lang3.tuple.Pair<Node,Node> withoutPair)	Adjusts the p-values for a local Markov condition in a given constraint-based partially directed acyclic graph (CPDAG).
static Clusters	ClusterUtils.mimClusters(Graph mim)	Converts a list of indices into a list of Nodes representing a cluster.
void	FciOrient.orient(Graph graph, Set<Triple> unshieldedTriples)	Performs FCI orientation on the given graph, including R0 and either the Spirtes or Zhang final orientation rules.
void	FgesOrienter.orient(Graph graph)	orient.
void	MaxP.orient(Graph graph)	Adds colliders to the given graph using the max P rule.
static void	PcCommon.orientCollider(Node x, Node y, Node z, PcCommon.ConflictRule conflictRule, Graph graph, boolean verbose, boolean acyclic)	Orient a single unshielded triple, x*-*y*-*z, in a graph.
static void	GraphSearchUtils.orientCollidersUsingSepsets(SepsetMap set, Knowledge knowledge, Graph graph, boolean verbose, boolean enforceCpdag)	Step C of PC; orients colliders using specified sepset.
Set<Node>	MeekRules.orientImplied(Graph graph)	Uses the Meek rules to do as many orientations in the given graph as possible.
static void	GraphSearchUtils.pcdOrientC(IndependenceTest test, Knowledge knowledge, Graph graph)	Performs step C of the algorithm, as indicated on page xxx of CPS, with the modification that X--W--Y is oriented as X-->W<--Y if W is *determined by* the sepset of (X, Y), rather than W just being *in* the sepset of (X, Y).
static void	GraphSearchUtils.pcOrientbk(Knowledge bk, Graph graph, List<Node> nodes, boolean verbose)	Orients according to background knowledge.
void	AlmostCycleRemover.recallUnshieldedTriples(Graph pag)	Recalls unshielded triples in the given graph.
boolean	AlmostCycleRemover.removeAlmostCycles(Graph pag)	Removes almost cycles from the Graph.
boolean	AlmostCycleRemover.removeCycles(Graph pag)	Removes cycles from the Graph.
void	FciOrient.ruleR0(Graph graph, Set<Triple> unshieldedTriples)	Orients unshielded colliders in the graph.
void	FciOrient.ruleR1(Node a, Node b, Node c, Graph graph)	R1 If α ∗→ β o−−∗ γ, and α and γ are not adjacent, then orient the triple as α ∗→ β → γ.
void	FciOrient.ruleR10(Node alpha, Node gamma, Graph graph)	R10 Suppose α o→ γ, β → γ ← θ, p1 is an uncovered potentially directed (semidirected) path from α to β, and p2 is an uncovered p.d.
void	FciOrient.ruleR2(Node a, Node b, Node c, Graph graph)	R2 If α → β ∗→ γ or α ∗→ β → γ, and α ∗−o γ, then orient α ∗−o γ as α ∗→ γ.
void	FciOrient.ruleR3(Graph graph)	R3 If α ∗→ β ←∗ γ, α ∗−o θ o−∗ γ, α and γ are not adjacent, and θ ∗−o β, then orient θ ∗−o β as θ ∗→ β.
void	FciOrient.ruleR4(Graph graph)	R4 If u = <θ ,...,α,β,γ> is a discriminating path between θ and γ for β, and β o−−∗ γ; then if β ∈ Sepset(θ,γ), orient β o−−∗ γ as β → γ; otherwise orient the triple <α,β,γ> as α ↔ β ↔ γ.
void	FciOrient.ruleR5(Graph graph)	R5 For every (remaining) α o−−o β, if there is an uncovered circle path p = <α,γ,...,θ,β> between α and β s.t.
void	FciOrient.ruleR6(Graph graph)	R6 If α —- β o−−∗ γ (α and γ may or may not be adjacent), then orient β o−−∗ γ as β −−∗ γ.
void	FciOrient.ruleR7(Graph graph)	R7 If α −−o β o−−∗ γ, and α, γ are not adjacent, then orient β o−−∗ γ as β −−∗ γ.
boolean	FciOrient.ruleR8(Node a, Node c, Graph graph)	R8 If α → β → γ or α−−◦β → γ, and α o→ γ, orient α o→ γ as α → γ.
boolean	FciOrient.ruleR9(Node a, Node c, Graph graph)	R9 If α o→ γ, and p = <α,β,θ,...,γ> is an uncovered potentialy directed path from α to γ such that γ and β are not adjacent, then orient α o→ γ as α → γ.
void	FciOrient.rulesR1R2cycle(Graph graph)	Apply rules R1 and R2 in cycles for a given graph.
void	FciOrient.rulesR8R9R10(Graph graph)	Implements Zhang's rules R8, R9, R10, applies them over the graph once.
double	DagScorer.scoreDag(Graph dag)	scoreDag.
double	FgesOrienter.scoreDag(Graph dag)	scoreDag.
void	FgesOrienter.setAdjacencies(Graph adjacencies)	Sets the set of preset adjacenies for the algorithm; edges not in this adjacencies graph will not be added.
void	DefaultSetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint endpoint)	Sets the endpoint of a graph given the two nodes and the desired endpoint.
void	SetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint arrow)	Sets the endpoint of a graph given the two nodes and the desired endpoint.
void	SvarSetEndpointStrategy.setEndpoint(Graph graph, Node a, Node b, Endpoint endpoint)	Sets the endpoint of a graph given the two nodes and the desired endpoint.
void	FgesOrienter.setExternalGraph(Graph externalGraph)	Sets the initial graph.
void	DagSepsets.setGraph(Graph graph)
void	SepsetProducer.setGraph(Graph graph)	Sets the graph for the SepsetProducer object.
void	SepsetsGreedy.setGraph(Graph graph)	Sets the graph for the Sepsets object.
void	SepsetsGreedyMb.setGraph(Graph graph)	Sets the graph for the Sepsets object.
void	SepsetsMaxP.setGraph(Graph graph)	Sets the graph for the SepsetsMaxP object.
void	SepsetsMinP.setGraph(Graph graph)	Sets the graph for the Sepsets object.
void	SepsetsPossibleDsep.setGraph(Graph graph)
void	SepsetsSet.setGraph(Graph graph)
void	FgesOrienter.setTrueGraph(Graph trueGraph)	If the true graph is set, askterisks will be printed in log output for the true edges.
void	IPurify.setTrueGraph(Graph mim)	setTrueGraph.
void	PurifyScoreBased.setTrueGraph(Graph mim)	setTrueGraph.
void	PurifyTetradBased.setTrueGraph(Graph mim)	setTrueGraph.
void	TsDagToPag.setTruePag(Graph truePag)	Setter for the field truePag.
static void	LogUtilsSearch.stampWithBic(Graph graph, DataModel dataModel)	stampWithBic.
static void	LogUtilsSearch.stampWithScore(Graph graph, Score score)	stampWithScore.
static int	GraphSearchUtils.structuralhammingdistance(Graph trueGraph, Graph estGraph, boolean useTrueCpdag)	Originally, Tsamardinos, I., Brown, L.
static void	MbUtils.trimEdgesAmongParents(Graph graph, Node target)	Removes edges among the parents of the target.
static void	MbUtils.trimEdgesAmongParentsOfChildren(Graph graph, Node target)	Removes edges among the parents of children of the target.
static void	MbUtils.trimToAdjacents(Graph graph, Node target)	Trims the graph to just the adjacents of the target.
static void	MbUtils.trimToMbNodes(Graph graph, Node target, boolean includeBidirected)	Trims the graph to the target, the parents and children of the target, and the parents of the children of the target.

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

Modifier	Constructor	Description
	DagInCpcagIterator(Graph CPDAG)	The given CPDAG must be a CPDAG.
	DagInCpcagIterator(Graph CPDAG, Knowledge knowledge)	The given CPDAG must be a CPDAG.
	DagInCpcagIterator(Graph CPDAG, Knowledge knowledge, boolean allowArbitraryOrientations, boolean allowNewColliders)	The given CPDAG must be a CPDAG.
	DagIterator(Graph CPDAG)	The given CPDAG must be a CPDAG.
	DagSepsets(Graph dag)	Constructs a new DagSepsets object for the given DAG.
	DagToPag(Graph dag)	Constructs a new FCI search for the given independence test and background knowledge.
	Graph(Graph graph, R5R9Dijkstra.Rule rule)	Represents a graph for Dijkstra's algorithm.
	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.
Graph	ISFges.getAdjacencies()	Retrieves the preset adjacencies graph.
@NotNull Graph	GraspTol.getGraph(boolean cpDag)	getGraph.
Graph	HbsmsBeam.getGraph()	Getter for the field graph.
Graph	HbsmsGes.getGraph()	Getter for the field graph.
Graph	HbsmsGes.GraphWithPValue.getGraph()	Getter for the field graph.
Graph	SampleVcpc.getGraph()	The graph that's constructed during the search.
Graph	SampleVcpcFast.getGraph()	The graph that's constructed during the search.
Graph	VcPc.getGraph()	The graph that's constructed during the search.
Graph	VcPcAlt.getGraph()	The graph that's constructed during the search.
Graph	VcPcFast.getGraph()	The graph that's constructed during the search.
Graph	MagCgBicScore.getMag()	Returns the wrapped MAG.
Graph	MagDgBicScore.getMag()	Returns the wrapped MAG.
Graph	MagSemBicScore.getMag()	Returns the wrapped MAG.
Graph	DMSearch.LatentStructure.latentStructToEdgeListGraph(DMSearch.LatentStructure structure)	latentStructToEdgeListGraph.
Graph	HbsmsBeam.removeZeroEdges(Graph bestGraph)	removeZeroEdges.
Graph	BpcTetradPurifyWashdown.search()	Runs the search and returns a graph.
Graph	DMSearch.search()	search.
Graph	FasFdr.search()	Discovers all adjacencies in data.
Graph	FaskVote.search(Parameters parameters)	Does the search.
Graph	FasLofs.search()	Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with two-cycles.
Graph	Hbsms.search()	search.
Graph	HbsmsBeam.search()	search.
Graph	HbsmsGes.search()	search.
Graph	IGFci.search()	Executes the FCI algorithm using the provided independence test, score, and population graph, and returns the resulting graph with edges oriented according to the algorithm's rules.
Graph	InverseCorrelation.search()	search.
Graph	ISFges.search()	Greedy equivalence search: Start from the empty graph, add edges till the model is significant.
Graph	Kpc.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	Kpc.search(List<Node> nodes)	Runs PC starting with a commplete graph over the given list of nodes, using the given independence test and knowledge and returns the resultant graph.
Graph	Mmhc.search()	Runs PC starting with a fully connected graph over all of the variables in the domain of the independence test.
Graph	SampleVcpc.search()	search.
Graph	SampleVcpcFast.search()	search.
Graph	VcFas.search()	Discovers all adjacencies in data.
Graph	VcPc.search()	search.
Graph	VcPcAlt.search()	search.
Graph	VcPcFast.search()	search.
Graph	Washdown.search()	Runs the Washdown algorithm and return a graph.

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

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

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

Modifier and Type	Method	Description
DMSearch.LatentStructure	DMSearch.applyDmSearch(Graph pattern, Set<String> inputString, double penalty)	applyDmSearch.
Graph	GraphChange.applyTo(Graph graph)	Outputs a new PAG, a copy of the input excepting the applied changes of this object.
static void	SampleVcpc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	SampleVcpcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	VcPc.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	VcPcAlt.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
static void	VcPcFast.futureNodeVisit(Graph graph, Node b, LinkedList<Node> path, Set<Node> futureNodes)	futureNodeVisit.
VcPc.CpcTripleType	VcPc.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose)	getPopulationTripleType.
VcPcFast.CpcTripleType	VcPcFast.getPopulationTripleType(Node x, Node y, Node z, IndependenceTest test, int depth, Graph graph, boolean verbose)	getPopulationTripleType.
double	ISFges.getScore(Graph dag)	Calculates and returns the score of the provided Directed Acyclic Graph (DAG).
void	IGFci.modifiedR0(Graph fgesGraph)	Modifies the given FGES graph based on the FCI algorithm rules, reorienting edges and potentially identifying and orienting definite colliders.
Graph	HbsmsBeam.removeZeroEdges(Graph bestGraph)	removeZeroEdges.
HbsmsGes.Score	HbsmsGes.scoreDag(Graph dag)	scoreDag.
double	ISFges.scoreDag(Graph dag)	Computes the score of a given directed acyclic graph (DAG).
double	ISFges.scoreDag(Graph dag, Graph pop)	Computes the score of a Directed Acyclic Graph (DAG) based on a given population graph.
HbsmsBeam.Score	HbsmsBeam.scoreGraph(Graph graph)	scoreGraph.
HbsmsGes.Score	HbsmsGes.scoreGraph(Graph graph)	scoreGraph.
void	ISFges.setAdjacencies(Graph adjacencies)	Sets the preset adjacency information for the graph.
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	ISFges.setInitialGraph(Graph initialGraph)	Sets the initial graph, ensuring the graph's nodes match the expected variables.
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.
void	ISFges.setPopulationGraph(Graph populationGraph)	Sets the population graph for the current instance.
void	ISFges.setTrueGraph(Graph trueGraph)	Sets the true graph.
static List<List<Node>>	Ion.treks(Graph graph, Node node1, Node node2)	treks.

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

Modifier	Constructor	Description
	FasDci(Graph graph, IndependenceTest independenceTest)	Constructs a new FastAdjacencySearch for DCI.
	FasDci(Graph graph, IndependenceTest independenceTest, ResolveSepsets.Method method, List<Set<Node>> marginalVars, List<IndependenceTest> independenceTests, SepsetMapDci knownIndependencies, SepsetMapDci knownAssociations)	Constructs a new FastAdjacencySearch for DCI with independence test pooling to resolve inconsistencies.
	GraphWithPValue(Graph graph, double pValue)	Constructor for GraphWithPValue.
	HbsmsBeam(Graph graph, CovarianceMatrix cov, Knowledge knowledge)	Constructor for HbsmsBeam.
	HbsmsBeam(Graph graph, DataSet data, Knowledge knowledge)	Constructor for HbsmsBeam.
	HbsmsGes(Graph graph, DataSet data)	Constructor for HbsmsGes.
	IGFci(IndependenceTest test, ISScore score, Graph populationGraph)	Constructs an instance of IGFci with the provided independence test, score, and population graph.

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

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

Uses of Graph in edu.cmu.tetrad.sem

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

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

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

Modifier and Type	Method	Description
List<List<Node>>	Ricf.cliques(Graph graph)	cliques.
Knowledge	LargeScaleSimulation.getKnowledge(Graph graph)	getKnowledge.
static List<Node>	ReidentifyVariables.getLatents(Graph graph)	getLatents.
static List<String>	ReidentifyVariables.reidentifyVariables1(List<List<Node>> partition, Graph trueGraph)	reidentifyVariables1.
static List<String>	ReidentifyVariables.reidentifyVariables2(List<List<Node>> clusters, Graph trueGraph, DataSet data)	reidentifyVariables2.
Ricf.RicfResult	Ricf.ricf2(Graph mag, ICovarianceMatrix covMatrix, double tolerance)	Same as above but takes a Graph instead of a SemGraph
double	DagScorer.score(Graph dag)	Scores the given DAG using the implemented algorithm.
double	Scorer.score(Graph dag)	score.

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

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

Uses of Graph in edu.cmu.tetrad.simulation

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

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

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

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

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

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

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

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

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

Uses of Graph in edu.cmu.tetrad.util

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

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

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

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

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

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

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.