Uses of Interface
edu.cmu.tetrad.data.DataModel

Packages that use DataModel

Package	Description
edu.cmu.tetrad.algcomparison
edu.cmu.tetrad.algcomparison.algorithm
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.algcomparison.algorithm.multi
edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pag
edu.cmu.tetrad.algcomparison.algorithm.other
edu.cmu.tetrad.algcomparison.algorithm.pairwise
edu.cmu.tetrad.algcomparison.independence
edu.cmu.tetrad.algcomparison.score
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.algcomparison.statistic
edu.cmu.tetrad.bayes
edu.cmu.tetrad.data
edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.search.score	This package contains classes for scoring causal graph models.
edu.cmu.tetrad.search.test	This package contains classes for testing causal graph search algorithms.
edu.cmu.tetrad.search.utils	This package contains utility classes for causal graph search algorithms.
edu.cmu.tetrad.search.work_in_progress	A package for algorithms that are not ready for prime time.
edu.cmu.tetrad.util

Uses of DataModel in edu.cmu.tetrad.algcomparison

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

Modifier and Type	Method	Description
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 DataModel in edu.cmu.tetrad.algcomparison.algorithm

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

Modifier and Type	Method	Description
abstract long	ExternalAlgorithm.getElapsedTime(DataModel dataSet, Parameters parameters)	getElapsedTime.
int	ExternalAlgorithm.getIndex(DataModel dataSet)	getIndex.
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	StabilitySelection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	StARS.search(DataModel dataSet, Parameters parameters)	Runs the search.

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

Modifier and Type	Method	Description
Graph	MultiDataSetAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.

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

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

Modifier and Type	Method	Description
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.

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

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

Modifier and Type	Method	Description
Graph	FaskConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskLofsConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskVote.search(DataModel dataSet, 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	FgesConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	Images.search(DataModel dataSet, Parameters parameters)	Searches for a graph using the given data set and parameters.
Graph	ImagesBoss.search(DataModel dataSet, Parameters parameters)	Runs the search.

Method parameters in edu.cmu.tetrad.algcomparison.algorithm.multi with type arguments of type DataModel

Modifier and Type	Method	Description
Graph	FaskConcatenated.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FaskLofsConcatenated.search(List<DataModel> dataModels, Parameters parameters)	Runs the search.
Graph	FaskVote.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FciIod.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FgesConcatenated.search(List<DataModel> dataModels, Parameters parameters)	Runs the search.
Graph	Images.search(List<DataModel> dataSets, Parameters parameters)	Searches for a graph using the given data sets and parameters.
Graph	ImagesBoss.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.

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

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

Modifier and Type	Method	Description
Graph	Cstar.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	Pcmci.search(DataModel dataModel, Parameters parameters)	Executes the PCMCI (Peter and Clark Momentary Conditional Independence) causal discovery algorithm on a given data model using specified parameters.
Graph	SingleGraphAlg.search(DataModel dataSet, Parameters parameters)	Runs the search.

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

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

Modifier and Type	Method	Description
Graph	BossFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BossPod.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	Ccd.runSearch(DataModel dataModel, Parameters parameters)	Runs the CCD (Cyclic Causal Discovery) search algorithm on the given data set using the specified parameters.
Graph	Cfci.runSearch(DataModel dataModel, Parameters parameters)	Deprecated. Runs the search algorithm to discover the causal graph.
Graph	DmFcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmFciT2.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmPc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	Fci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph based on the given data model and parameters.
Graph	FciCyclicPw.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm on a given data model and set of parameters, producing a partially directed acyclic graph (PAG) that represents the causal structure inferred from the data.
Graph	FciMax.runSearch(DataModel dataModel, Parameters parameters)	Deprecated. Runs a search algorithm to discover the causal graph structure.
Graph	Fcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FgesFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	Gfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	GraspFci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	PagSampleRfci.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm using the given data set and parameters.
Graph	Rfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model and parameters.
Graph	RfciBsc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm using a given dataset and parameters.
Graph	SpFci.runSearch(DataModel dataModel, Parameters parameters)	Executes a search algorithm to infer the causal graph structure from a given data model

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

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

Modifier and Type	Method	Description
Graph	FactorAnalysis.runSearch(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.
Graph	Glasso.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to create a graph representation of the data.
Graph	MimbuildBollen.search(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.
Graph	MimbuildPca.search(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.

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

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

Modifier and Type	Method	Description
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.

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

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

Modifier and Type	Method	Description
IndependenceTest	BasisFunctionBlocksIndTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	BasisFunctionLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	BlocksIndTest.getTest(DataModel dataSet, Parameters parameters)	Deprecated. Creates and returns an instance of the IndTestBlocksWilkes initialized with a specific block specification and a significance level extracted from the provided parameters.
IndependenceTest	BlocksIndTestLemma10.getTest(DataModel dataModel, Parameters parameters)	Deprecated. Creates and returns an instance of an independence test based on the specified block structure and parameters provided.
IndependenceTest	BlocksIndTestTs.getTest(DataModel dataModel, Parameters parameters)	Creates and returns an instance of an independence test based on the specified block structure and parameters provided.
IndependenceTest	CciTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	ChiSquare.getTest(DataModel dataSet, Parameters parameters)	Retrieves an instance of the IndependenceTest interface that performs a Chi Square Test for independence.
IndependenceTest	ConditionalGaussianLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	DegenerateGaussianLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	DgBicTest.getTest(DataModel dataSet, Parameters parameters)	Returns an instance of IndependenceTest for the SEM BIC test.
IndependenceTest	FisherZ.getTest(DataModel dataModel, Parameters parameters)	Gets an independence test based on the given data model and parameters.
IndependenceTest	Gin.getTest(DataModel dataModel, Parameters parameters)	Configures and returns an IndependenceTest object based on the provided data model and parameters.
IndependenceTest	GSquare.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	IndependenceWrapper.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	Kci.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	MSeparationTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	MultinomialLogisticRegressionWald.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	Mvplrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	PoissonBicTest.getTest(DataModel dataSet, Parameters parameters)	Returns an instance of IndependenceTest for the SEM BIC test.
IndependenceTest	ProbabilisticTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	RankIndependenceTestLemma10Singletons.getTest(DataModel dataModel, Parameters parameters)	Deprecated. Gets an independence test based on the given data model and parameters.
IndependenceTest	RankIndependenceTestTs.getTest(DataModel dataModel, Parameters parameters)	Gets an independence test based on the given data model and parameters.
IndependenceTest	RankIndependenceTestTsSingletons.getTest(DataModel dataModel, Parameters parameters)	Deprecated. Gets an independence test based on the given data model and parameters.
IndependenceTest	RankIndependenceTestWilkesSingletons.getTest(DataModel dataModel, Parameters parameters)	Deprecated. Gets an independence test based on the given data model and parameters.
IndependenceTest	Rcit.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	SemBicTest.getTest(DataModel dataSet, Parameters parameters)	Returns an instance of IndependenceTest for the SEM BIC test.

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

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

Modifier and Type	Method	Description
Score	BasisFunctionBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	BdeuScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	BlocksBicScore.getScore(DataModel model, Parameters parameters)	Computes and returns a score based on the provided data model and parameter settings.
Score	BlockScoreWrapper.getScore(DataModel model, Parameters parameters)	Computes and returns a score for the given data model and parameters.
Score	ConditionalGaussianBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	DegenerateGaussianBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	DiscreteBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	EbicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	GicScores.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	InstanceAugmentedSemBicScoreWrapper.getScore(DataModel dataModel, Parameters parameters)	Computes the score for a given data model and set of parameters.
Score	IsBDeuScoreWrapper.getScore(DataModel dataModel, Parameters parameters)	Calculates and returns a score for the given data model and associated parameters.
Score	MagDgBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	MSepScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	MVPBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	PoissonPriorScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	RankBicScore.getScore(DataModel dataModel, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	ScoreWrapper.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	SemBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	ZhangShenBoundScore.getScore(DataModel dataSet, Parameters parameters)	Calculates the score based on the given data set and parameters.

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

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

Modifier and Type	Method	Description
DataModel	AdditiveAnmSimulator.getDataModel(int index)	Retrieves the simulated data set at the specified index.
DataModel	AdditiveNoiseSimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	BayesNetSimulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	ConditionalGaussianSimulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	GeneralSemSimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	GeneralSemSimulationSpecial1.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	GpSemSimulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	HybridCgSimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	LeeHastieSimulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	LgMnarSimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	LinearFisherModel.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	LinearSineSimulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	NonlinearFunctionsOfLinear.getDataModel(int index)	Returns the data model at the specified index.
DataModel	PostnonlinearCausalModel.getDataModel(int index)	Returns the data model at the specified index.
DataModel	SemSimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	SemThenDiscretize.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	Simulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	SingleDatasetSimulation.getDataModel(int index)	Retrieves the data model at the specified index from this simulation.
DataModel	StandardizedSemSimulation.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	TimeSeriesSemSimulation.getDataModel(int index)	Returns the number of data sets to simulate.

Constructor parameters in edu.cmu.tetrad.algcomparison.simulation with type arguments of type DataModel

Modifier	Constructor	Description
	LinearFisherModel(RandomGraph graph, List<DataModel> shocks)	Constructor for LinearFisherModel.

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

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

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

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

Modifier	Constructor	Description
	JunctionTreeAlgorithm(Graph graph, DataModel dataModel)	Constructor for JunctionTreeAlgorithm.

Uses of DataModel in edu.cmu.tetrad.data

Subinterfaces of DataModel in edu.cmu.tetrad.data

Modifier and Type	Interface	Description
interface	DataSet	Implements a rectangular data set, in the sense of being a dataset with a fixed number of columns and a fixed number of rows, the length of each column being constant.
interface	ICovarianceMatrix	Interface for covariance matrices.

Classes in edu.cmu.tetrad.data that implement DataModel

Modifier and Type	Class	Description
final class	BoxDataSet	Wraps a DataBox in such a way that mixed data sets can be stored.
final class	CorrelationMatrix	Stores a correlation matrix together with variable names and sample size; intended as a representation of a data set.
class	CorrelationMatrixOnTheFly	Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
class	CovarianceMatrix	Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
class	CovarianceMatrixOnTheFly	Stores a covariance matrix together with variable names and sample size, intended as a representation of a data set.
final class	DataModelList	Stores a list of data models and keeps track of which one is selected.
class	IndependenceFacts	Stores a list of independence facts.
final class	NumberObjectDataSet	Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.
final class	TimeSeriesData	Stores time series data as a list of continuous columns.

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

Modifier and Type	Method	Description
DataModel	CorrelationMatrixOnTheFly.copy()	copy.
DataModel	CovarianceMatrix.copy()	copy.
DataModel	CovarianceMatrixOnTheFly.copy()	copy.
DataModel	DataModel.copy()	copy.
DataModel	DataModelList.copy()	copy.
DataModel	IndependenceFacts.copy()	copy.
DataModel	TimeSeriesData.copy()	copy.
DataModel	DataModelList.get(int index)
DataModel	DataModelList.getSelectedModel()	Getter for the field selectedModel.
DataModel	DataModelList.remove(int index)

Methods in edu.cmu.tetrad.data that return types with arguments of type DataModel

Modifier and Type	Method	Description
List<DataModel>	DataModelList.getModelList()	Getter for the field modelList.

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

Modifier and Type	Method	Description
void	DataModelList.add(int index, DataModel element)	Adds the given DataModel to the list at the given index.
static DataSet	SimpleDataLoader.getContinuousDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is continuous.
static ICovarianceMatrix	SimpleDataLoader.getCovarianceMatrix(DataModel dataModel, boolean precomputeCovariances)	Returns the model cast to ICovarianceMatrix if already a covariance matric, or else returns the covariance matrix for a dataset.
static DataSet	SimpleDataLoader.getDiscreteDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is discrete.
static DataSet	SimpleDataLoader.getMixedDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is mixed.
void	DataModelList.setSelectedModel(DataModel model)	Setter for the field selectedModel.

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

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

Modifier and Type	Method	Description
DataModel	LoadContinuousDataAndGraphs.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	LoadContinuousDataAndSingleGraph.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	LoadContinuousDataSmithSim.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	LoadDataAndGraphs.getDataModel(int index)	Returns the number of data sets to simulate.
DataModel	LoadDataFromFileWithoutGraph.getDataModel(int index)	Returns the number of data sets to simulate.

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

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

Modifier and Type	Method	Description
DataModel	PoissonPriorScore.getData()	Returns the data set.
DataModel	SemBicScore.getData()	Returns the data model.
DataModel	SemBicScore.getDataModel()	Returns the data model.

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

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.

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

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

Modifier and Type	Method	Description
DataModel	CachingIndependenceTest.getData()	Retrieves the data model associated with the underlying independence test.
DataModel	IndependenceTest.getData()	Retrieves the data model associated with this test.
DataModel	IndTestBasisFunctionBlocks.getData()	Retrieves the original dataset represented by this instance.
DataModel	IndTestBasisFunctionLrt.getData()	Deprecated. Retrieves the data model associated with this instance.
DataModel	IndTestBasisFunctionLrtFullSample.getData()	Deprecated. Retrieves the data model associated with this instance.
DataModel	IndTestBlocksLemma10.getData()	Deprecated.
DataModel	IndTestBlocksTs.getData()	Retrieves the data model associated with the current block specification.
DataModel	IndTestBlocksWilkes.getData()
DataModel	IndTestDegenerateGaussianLrtFullSample.getData()	Deprecated. Retrieves the data model associated with this instance.
DataModel	IndTestFdrWrapper.getData()	Retrieves the data model associated with the wrapped IndependenceTest.
DataModel	IndTestIndependenceFacts.getData()	Returns the facts supplied in the constructor, which constutite a data model.
DataModel	IndTestProbabilistic.getData()	Returns the data model associated with this instance.
DataModel	Kci.getData()	Retrieves the data model associated with the current instance.
DataModel	ScoreIndTest.getData()	Retrieves the data model associated with this object.

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

Modifier	Constructor	Description
	ScoreIndTest(Score score, DataModel data)	Constructor for ScoreIndTest.

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

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

Modifier and Type	Method	Description
static void	LogUtilsSearch.stampWithBic(Graph graph, DataModel dataModel)	stampWithBic.

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

Modifier	Constructor	Description
	ClusterSignificance(List<Node> variables, DataModel dataModel)	Constructs a new cluster significance object.

Constructor parameters in edu.cmu.tetrad.search.utils with type arguments of type DataModel

Modifier	Constructor	Description
	ShiftSearch(List<DataModel> dataSets)	Constructor for ShiftSearch.

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

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

Modifier and Type	Method	Description
DataModel	ProbabilisticMapIndependence.getData()	Retrieves the data model associated with this test.

Uses of DataModel in edu.cmu.tetrad.util

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

Modifier and Type	Method	Description
static DataModel	MultidataUtils.combineDataset(List<DataModel> dataModels)	combineDataset.
static DataModel	DataConvertUtils.toContinuousDataModel(ContinuousData dataset)	toContinuousDataModel.
static DataModel	DataConvertUtils.toCovarianceMatrix(CovarianceData dataset)	toCovarianceMatrix.
static DataModel	DataConvertUtils.toDataModel(Data data)	toDataModel.
static DataModel	DataConvertUtils.toDataModel(Data data, Metadata metadata)	toDataModel.
static DataModel	DataConvertUtils.toMixedDataBox(MixedTabularData dataset)	toMixedDataBox.
static DataModel	DataConvertUtils.toMixedDataBox(MixedTabularData dataset, Metadata metadata)	Converting using metadata
static DataModel	DataConvertUtils.toVerticalDiscreteDataModel(VerticalDiscreteTabularData dataset)	toVerticalDiscreteDataModel.
static DataModel	DataConvertUtils.toVerticalDiscreteDataModel(VerticalDiscreteTabularData dataset, Metadata metatdata)	Converting using metadata

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

Modifier and Type	Method	Description
static int	MultidataUtils.getNumberOfColumns(DataModel dataModel)	getNumberOfColumns.

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

Modifier and Type	Method	Description
static void	MultidataUtils.combineContinuousData(List<DataModel> dataModels, double[][] combinedData)	combineContinuousData.
static DataModel	MultidataUtils.combineDataset(List<DataModel> dataModels)	combineDataset.
static void	MultidataUtils.combineDiscreteDataToDiscreteVerticalData(List<DataModel> dataModels, List<Node> variables, int[][] combinedData, int numOfRows, int numOfColumns)	combineDiscreteDataToDiscreteVerticalData.
static void	MultidataUtils.combineMixedContinuousData(List<DataModel> dataModels, List<Node> variables, double[][] combinedData, int numOfRows, int numOfColumns)	combineMixedContinuousData.
static void	MultidataUtils.combineMixedDiscreteData(List<DataModel> dataModels, List<Node> variables, int[][] combinedData, int numOfRows, int numOfColumns)	combineMixedDiscreteData.
static void	MultidataUtils.combineVariables(List<DataModel> dataModels, List<Node> variables)	Combine the list of variables from each of data model in the list into one variable list.
static int[]	MultidataUtils.getRowCounts(List<DataModel> dataModels)	getRowCounts.