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

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	AbstractMultiBootstrapAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the multi-dataset search, bootstrapping if numberResampling is greater than zero.
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)	Deprecated. Runs the Fask search algorithm on the given data model with the specified parameters.
Graph	IcaLingam.runSearch(DataModel dataSet, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	IcaLingD.runSearch(DataModel dataSet, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	Lingam.runSearch(DataModel dataSet, Parameters parameters)	Searches for a graph structure based on the given data set and parameters.
Graph	LingD.runSearch(DataModel dataSet, Parameters parameters)	Runs a search on the provided data set using the given parameters.

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	FaskPool.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskVote.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	ImagesBoss.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	Ion.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	FaskPool.search(List<DataModel> dataSets, 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	ImagesBoss.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	Ion.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	Bfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciBryan.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciBryanScore.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciC.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciD.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BfciScoreCheck.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	BossCcd.runSearch(DataModel dataModel, Parameters parameters)	Runs the BOSS-CCD search algorithm on the given data set using the specified parameters.
Graph	BossFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	BossPod.runSearch(DataModel dataModel, Parameters parameters)	Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
Graph	Ccd.runSearch(DataModel dataModel, Parameters parameters)	Runs the CCD (Cyclic Causal Discovery) search algorithm on the given data set using the specified parameters.
Graph	Cpw.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm on a given data model and set of parameters, producing a partial ancestral graph (PAG) that represents the causal structure inferred from the data.
Graph	DmFcit.runSearch(DataModel dataModel, Parameters parameters)	Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
Graph	DmPc.runSearch(DataModel dataModel, Parameters parameters)	Always throws, because the implementation this class wrapped was removed in Tetrad 8.0.0.
Graph	Fci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph based on the given data model and parameters.
Graph	FciCyclicPw.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm on a given data model and set of parameters, producing a partially directed acyclic graph (PAG) that represents the causal structure inferred from the data.
Graph	Fcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitMagMarkov.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitMarkov.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitSl.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitSlScoreCheck.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FcitZm.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	Gfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	GfciOld.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	Gmas.runSearch(DataModel dataModel, Parameters parameters)	Runs the search.
Graph	GraspFci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	Icd.runSearch(DataModel dataModel, Parameters parameters)	Runs the ICD search and returns the resulting PAG.
Graph	LvBossExperimental.runSearch(DataModel dataModel, Parameters parameters)	Runs LV-BOSS on the given data model.
Graph	LvHeuristic.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	PagSampleRfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search on the given data by delegating to the successor algorithm and returns the resulting graph.
Graph	PagSamplingRfci.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm using the given data set and parameters.
Graph	Rfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model and parameters.
Graph	RfciBsc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm using a given dataset and parameters.
Graph	SpFci.runSearch(DataModel dataModel, Parameters parameters)	Executes a search algorithm to infer the causal graph structure from a given data model

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.
Graph	PythonGin.search(DataModel dataModel, Parameters parameters)	Deprecated. Runs GIN on the supplied dataset and returns the resulting causal graph.
Graph	Rlcd.search(DataModel dataModel, Parameters parameters)	Runs RLCD on a continuous data set.

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

Modifier and Type	Method	Description
Graph	Rlcd.searchImputations(List<DataModel> imputations, Parameters parameters)	Runs ONE RLCD search over several imputations of a data set with missing values; see Rlcd(List).

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	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	BasisExpandedGcm.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
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	ClFisherZPython.getTest(DataModel dataModel, Parameters parameters)	Retrieves an independence test based on the provided data model and parameters.
IndependenceTest	ClKciPython.getTest(DataModel dataModel, Parameters parameters)	Creates and returns an IndependenceTest instance using the provided DataModel and Parameters.
IndependenceTest	ClRcitPython.getTest(DataModel dataModel, Parameters parameters)	Retrieves an independence test based on the provided data model and parameters.
IndependenceTest	ConditionalGaussianLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	Cord.getTest(DataModel dataModel, Parameters parameters)	Gets a CORD (Eric's engine) independence test for the given data model and parameters.
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	EquivalenceTest.getTest(DataModel dataModel, Parameters parameters)	Builds the equivalence test for the given data and parameters.
IndependenceTest	FfCi.getTest(DataModel dataSet, Parameters parameters)	Creates and configures an instance of the FfCi independence test with specified parameters derived from the provided dataset and parameter set.
IndependenceTest	FfCiContinuous.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	FisherZ.getTest(DataModel dataModel, Parameters parameters)	Gets an independence test based on the given data model and parameters.
IndependenceTest	Gcm.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
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	MinimaxCITest.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)	Deprecated. 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)	Returns true iff x and y are independent conditional on z for the given data set.
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.
IndependenceTest	StratifiedRank.getTest(DataModel dataModel, Parameters parameters)	Gets an independence test based on the given data model and parameters.
IndependenceTest	TrekSeparationBlocksIndependence.getTest(DataModel dataModel, Parameters parameters)	Gets an independence test based on the given data model and parameters.
IndependenceTest	TRffLrTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.

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	BasisFunctionBgeScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	BasisFunctionBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	BasisFunctionBicScoreTabular.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	BfBlocksBicScore.getScore(DataModel model, Parameters parameters)	Computes and returns a score based on the provided data model and parameter settings.
Score	BgeScore.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	DegenerateGaussianBgeScore.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	FfMl.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	FfMlContinuous.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	HeavyTailSemBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	HuangMarginalLikelihoodScore.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	KcvBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	KernelMarginalLikelihoodScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
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)	Deprecated. 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	RffBicScore.getScore(DataModel dataSet, 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	TRffBicScore.getScore(DataModel dataSet, Parameters parameters)	Calculates and returns a score for the given dataset based on the specified parameters.
Score	ZhangShenBoundScore.getScore(DataModel dataSet, Parameters parameters)	Calculates the score based on the given data set and parameters.

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

Modifier and Type	Method	Description
List<Score>	BasisFunctionBicScore.getScores(List<DataModel> dataModels, Parameters parameters)	Constructs one score per data model, coordinating data-dependent representation choices across the data models so that all scores share a common parameterization.

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	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	DaoSimulation.getDataModel(int index)
DataModel	DesignedExperimentSimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	GeneralAdditiveModel.getDataModel(int index)	Retrieves the data model located at the specified index within the collection of data models.
DataModel	GeneralNoiseSimulation.getDataModel(int index)	Returns the data model at the specified index.
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	ObservationalStudySimulation.getDataModel(int index)	Returns the data model at the specified index.
DataModel	PostnonlinearSem.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.
DataModel	TrainedDagModel.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 trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyFp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyFpr.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyTn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyTp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AdjacencyTpr.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestorF1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestorPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestorRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestralPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AncestralRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadFn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadFp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadFpr.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadPrecisionCommonEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadRecallCommonEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadTn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ArrowheadTp.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AverageDegreeEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	AverageDegreeTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicDiff.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicDiffPerRecord.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BicTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedFP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedLatentExistsLatentCommonCause.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the percentage of correctly identified bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
double	BidirectedLatentPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the percentage of correctly identified bidirected edges in an estimated graph for which a latent confounder exists in the true graph.
double	BidirectedPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedTP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	BidirectedTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CirclePrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the circle precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
double	CircleRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the circle recall value for a given true graph, estimated graph, and data model.
double	CommonAncestorFalseNegativeBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CommonAncestorFalsePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CommonAncestorTruePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CommonMeasuredAncestorRecallBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	CorrectSkeleton.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DefiniteDirectedPathPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DefiniteDirectedPathRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DensityEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	DensityTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ElapsedCpuTime.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Adj.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1All.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Arrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Circle.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	F1Tail.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FalseNegativesAdjacencies.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FalsePositiveAdjacencies.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FBetaAdj.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FractionDependentUnderAlternative.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	FractionDependentUnderNull.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	GraphExactlyRight.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	IdaAverageSquaredDistance.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the IDA Average Squared Distance statistic.
double	IdaCheckAvgMaxSquaredDiffEstTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the average maximum squared difference between the estimated and true values for a given data model and graphs.
double	IdaCheckAvgMinSquaredDiffEstTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the average minimum squared difference between the estimated and true values for a given data model and graphs.
double	IdaCheckAvgSquaredDifference.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Retrieves the value of the statistic, which is the average squared difference between the estimated and true values for a given data model and graphs.
double	IdaMaximumSquaredDifference.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the statistic "IDA Average Maximum Squared Difference".
double	IdaMinimumSquaredDifference.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value of the statistic "IDA Average Minimum Squared Difference".
double	ImpliedArrowOrientationRatioEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ImpliedArrowOrientationRatioEst2.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ImpliedOrientationRatioEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ImpliesLegalMag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	KnowledgeSatisfied.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorFalseNegativeBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorFalsePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorRecallBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LatentCommonAncestorTruePositiveBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LegalCpdag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the value indicating whether the estimated graph is a Legal CPDAG (1.0) or not (0.0).
double	LegalPag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	LocalGraphPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	This method calculates the Local Graph Precision.
double	LocalGraphRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)
double	MagCgScore.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MagDgScore.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MagSemScore.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MarkovCheckAdPasses.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling p-value > 0.05.
double	MarkovCheckAdPassesBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling p-value > 0.05.
double	MarkovCheckAndersonDarlingP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckAndersonDarlingPBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckBinomialP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Binomial P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckBinomialPBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Binomial P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckFdrCutoffP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckFractionDependentH0.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckFractionDependentH1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Anderson Darling P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckKolmogorovSmirnoffP.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckKolmogorovSmirnoffPBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Kolmogorov-Smirnoff P value for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	MarkovCheckKsPasses.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates whether Kolmogorov-Smirnoff P > 0.05.
double	MarkovCheckKsPassesBestOf10.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates whether Kolmogorov-Smirnoff P > 0.05.
double	MathewsCorrAdj.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	MathewsCorrArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	Maximal.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Checks whether a PAG is maximal.
double	MaximalityCondition.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	McGetNumTestsH0.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of tests done under the null hypothesis of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	McGetNumTestsH1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of tests done under the null hypothesis of independence for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
double	NoAlmostCyclicPathsCondition.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoCyclicPathsCondition.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NodesInCyclesPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NodesInCyclesRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NonancestorPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NonancestorRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoPotentiallyDirectedF1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoSemidirectedPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NoSemidirectedRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumAmbiguousTriples.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberArrowsEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberArrowsNotInUnshieldedCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesInCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesInUnshieldedCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberEdgesTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberOfEdgesEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberOfEdgesTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberTailsEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumberUnshieldedCollidersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumBidirectedBothNonancestorAncestor.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumBidirectedEdgesEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumBidirectedEdgesTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredDD.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredNL.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredPD.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumColoredPL.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCommonMeasuredAncestorBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleDefiniteDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleDirectedEdgeConfounded.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleDirectedEdgeNonAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePossiblyDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePossiblyDirectedEdgeNonAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePotentiallyDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatiblePotentiallyDirectedEdgeNonAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCompatibleVisibleNonancestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCorrectBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the number of bidirected edges for which a latent confounder exists.
double	NumCorrectDDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCorrectPDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCorrectVisibleEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumCoveringAdjacenciesInPag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDefinitelyDirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDefinitelyNotDirectedPaths.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeBnaMeasuredCounfounded.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeNoMeasureAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeNotAncNotRev.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdgeReversed.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedPathsEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedPathsTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumDirectedShouldBePartiallyDirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumEdgeInEstInTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumGenuineAdjacenciesInPag.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumIncorrectDDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumIncorrectPDAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumIncorrectVisibleAncestors.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumLatentCommonAncestorBidirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumNondirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumParametersEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumPartiallyOrientedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumPotentiallyDirected.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumUndirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumVisibleEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	NumVisibleEdgeTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Retrieves the number of X-->Y edges for which X-->Y is visible in the true PAG.
double	OrientationPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	OrientationRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Orientation Recall statistic, which measures the accuracy of the estimated orientation of edges in a graph compared to the true graph.
double	PagAdjacencyPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the adjacency precision of the estimated graph compared to the true graph.
double	PagAdjacencyRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the adjacency recall compared to the true PAG (Partial Ancestral Graph).
double	ParameterColumn.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	PercentAmbiguous.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the percentage of ambiguous triples in the estimated graph compared to the true graph.
double	PercentBidirectedEdges.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	ProportionSemidirectedPathsNotReversedEst.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the proportion of semi(X, Y) in the estimated graph for which there is no semi(Y, X) in the true graph.
double	ProportionSemidirectedPathsNotReversedTrue.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the proportion of semi(X, Y) paths in the true graph for which there is no semi(Y, Z) path in the estimated graph.
double	PvalueDistanceToAlpha.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	PvalueUniformityUnderNull.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	SemidirectedPathF1.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the F1 statistic for adjacencies.
double	SemidirectedPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the potentially directed precision value.
double	SemidirectedRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the Semidirected-Rec statistic, which is the proportion of (X, Y) where if there is a potentially directed path in the true graph, then there is also a potentially directed path in the estimated graph.
default double	Statistic.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel)	Returns the value of this statistic, given the true graph and the estimated graph.
default double	Statistic.getValue(Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	Statistic.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	StructuralHammingDistance.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TailPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the tail precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
double	TailRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the tail recall value for a given true graph, estimated graph, and data model.
double	TrueDagFalseNegativesArrows.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the number of False Negatives for Arrows in the estimated graph compared to the true DAG.
double	TrueDagFalseNegativesTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false negatives for tail endpoints compared to the true Directed Acyclic Graph (DAG).
double	TrueDagFalsePositiveArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positive arrows in the estimated graph compared to the true directed acyclic graph (DAG).
double	TrueDagFalsePositiveTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positives for tail endpoints in the estimated graph compared to the true graph.
double	TrueDagPrecisionArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the precision of arrow edges (X*->Y) in the estimated graph compared to the true DAG.
double	TrueDagPrecisionTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the precision of directed edges in the estimated graph (estGraph) relative to a true directed acyclic graph (trueDag).
double	TrueDagRecallArrows.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TrueDagRecallTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TrueDagTruePositiveArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the count of bidirected true positives for arrows in the estimated graph compared to the true graph.
double	TrueDagTruePositiveDirectedPathNonancestor.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the number of true positives for directed paths in the estimated graph that are consistent with the true graph, excluding cases where the estimated graph incorrectly identifies the reverse relationship.
double	TrueDagTruePositiveTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleFalseNegative.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleFalsePositive.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCyclePrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleTruePositive.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.

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

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

Modifier and Type	Method	Description
List<DataModel>	PooledScoreWrapper.getDataSets()	The data sets being pooled for the calling thread: those registered by PooledScoreWrapper.setThreadDataSets(List), or the default list if none are registered.

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

Modifier and Type	Method	Description
Score	PooledScoreWrapper.getScore(DataModel ignored, Parameters parameters)	Builds one score per pooled data set and combines them into an ImagesScore, which sums the local scores across data sets.
IndependenceTest	PooledIndependenceWrapper.getTest(DataModel ignored, Parameters parameters)	Builds one test per pooled data set and combines them into an IndTestMulti, which pools the per-data-set p-values by the method selected with PooledIndependenceWrapper.method(Parameters) (Fisher's method by default, or Tippett's).

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

Modifier and Type	Method	Description
List<Score>	MultiDataSetScoreWrapper.getScores(List<DataModel> dataModels, Parameters parameters)	Constructs one score per data model, coordinating data-dependent representation choices across the data models so that all scores share a common parameterization.
Graph	PoolsImputations.searchImputations(List<DataModel> imputations, Parameters parameters)	Runs one search over the imputations.
void	PooledIndependenceWrapper.setThreadDataSets(List<DataModel> dataSets)	Registers the data sets to pool for tests subsequently built on the calling thread, overriding the default list.
void	PooledScoreWrapper.setThreadDataSets(List<DataModel> dataSets)	Registers the data sets to pool for scores subsequently built on the calling thread, overriding the default list.

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

Modifier	Constructor	Description
	PooledIndependenceWrapper(IndependenceWrapper inner, List<DataModel> defaultDataSets)	Constructs a pooled independence wrapper.
	PooledScoreWrapper(ScoreWrapper inner, List<DataModel> defaultDataSets)	Constructs a pooled score wrapper.

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.

Subclasses with type arguments of type DataModel in edu.cmu.tetrad.data

Modifier and Type	Class	Description
final class	DataModelList	Stores a list of data models and keeps track of which one is selected.

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.missing

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

Modifier and Type	Method	Description
static DataModel	MissingDataUtils.emCovarianceIfRequested(DataModel dataModel, Parameters parameters, String caller)	Implements the "em" missing-data policy at the wrapper level for covariance-consuming scores that have an ICovarianceMatrix constructor but no native MissingDataSpec handling (EBIC, GIC, Poisson prior, Zhang-Shen bound, and the like).
static DataModel	MissingDataUtils.gate(DataModel dataModel, Parameters parameters, boolean specAware, String caller)	The wrapper-level missing-data gate, called at the top of every algcomparison test and score wrapper's getTest/getScore method.
static DataModel	MissingDataUtils.gate(DataModel dataModel, Parameters parameters, Set<String> nativePolicies, String caller)	The finer-grained form of MissingDataUtils.gate(DataModel, Parameters, boolean, String), for components that support some native policies but not others (e.g., the Chi-Square test performs test-wise deletion natively but has no EM-covariance route, while Fisher Z supports both).

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

Modifier and Type	Method	Description
static DataModel	MissingDataUtils.emCovarianceIfRequested(DataModel dataModel, Parameters parameters, String caller)	Implements the "em" missing-data policy at the wrapper level for covariance-consuming scores that have an ICovarianceMatrix constructor but no native MissingDataSpec handling (EBIC, GIC, Poisson prior, Zhang-Shen bound, and the like).
static DataModel	MissingDataUtils.gate(DataModel dataModel, Parameters parameters, boolean specAware, String caller)	The wrapper-level missing-data gate, called at the top of every algcomparison test and score wrapper's getTest/getScore method.
static DataModel	MissingDataUtils.gate(DataModel dataModel, Parameters parameters, Set<String> nativePolicies, String caller)	The finer-grained form of MissingDataUtils.gate(DataModel, Parameters, boolean, String), for components that support some native policies but not others (e.g., the Chi-Square test performs test-wise deletion natively but has no EM-covariance route, while Fisher Z supports both).

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.is

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

Modifier and Type	Method	Description
DataModel	IndTestProbabilisticBDeu.getData()	Retrieves the data model associated with this independence test.
DataModel	IndTestProbabilisticISBDeu.getData()	Retrieves the data model used in the computation.

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	FfCordContinuous.getDataModel()	Retrieves the data model associated with the current instance.
DataModel	FfDmlContinuous.getDataModel()	Retrieves the data model associated with the current instance.
DataModel	FfMl.getDataModel()	Retrieves the data model associated with this instance, encapsulating the data used for computations or analysis in the current context.
DataModel	FfMlContinuous.getDataModel()	Retrieves the data model associated with the current instance.
DataModel	HeavyTailSemBicScore.getDataModel()	Retrieves the data model associated with the HeavyTailSemBicScore instance.
DataModel	HuangMarginalLikelihoodScore.getDataModel()	Retrieves the data model associated with this instance.
DataModel	KcvBicScore.getDataModel()	Retrieves the current data model associated with this instance.
DataModel	KernelMarginalLikelihoodScore.getDataModel()	Retrieves the data model used for scoring.
DataModel	KernelMarginalLikelihoodScoreContinuous.getDataModel()	Retrieves the data model used for scoring.
DataModel	LegendreBicScore.getDataModel()	Retrieves the current data model instance.
DataModel	RffBicScore.getDataModel()	Retrieves the data model used for the score calculation.
DataModel	SemBicScore.getDataModel()	Returns the data model.
DataModel	TRffBicScore.getDataModel()	Retrieves the dataset associated with this instance.

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	CachedIndependenceQueries.getData()	Retrieves the data model from the test object.
DataModel	CachingIndependenceTest.getData()	Retrieves the data model associated with the underlying independence test.
DataModel	EdgePriorTest.getData()	Returns the data model of the test.
DataModel	IndependenceTest.getData()	Retrieves the data model associated with this test.
DataModel	IndTestBasisExpandedGcm.getData()	Returns the original dataset.
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	IndTestBlocksWilkes.getData()
DataModel	IndTestCord.getData()	Retrieves the data model backing this test.
DataModel	IndTestCordEric.getData()	Retrieves the data model backing this test.
DataModel	IndTestDegenerateGaussianLrtFullSample.getData()	Deprecated. Retrieves the data model associated with this instance.
DataModel	IndTestEquivalence.getData()	Returns the data this test reads.
DataModel	IndTestFdrWrapper.getData()	Retrieves the data model associated with the wrapped IndependenceTest.
DataModel	IndTestFisherZ.getData()	Retrieves the data set associated with the current instance.
DataModel	IndTestIndependenceFacts.getData()	Returns the facts supplied in the constructor, which constutite a data model.
DataModel	IndTestMulti.getData()	Returns the data of the first component test; the pooled test has no single data set.
DataModel	IndTestProbabilistic.getData()	Returns the data model associated with this instance.
DataModel	IndTestStratifiedRank.getData()	Returns the first of the per-domain data sets, anchoring the test to a data model.
DataModel	Kci.getData()	Retrieves the data model associated with the current instance.
DataModel	LegendreLrIndependenceTest.getData()	Retrieves the current data model associated with the independence test.
DataModel	PythonKciIndependenceTest.getData()	Retrieves the data model associated with this instance.
DataModel	PythonRcitIndependenceTest.getData()	Retrieves the data model associated with this instance.
DataModel	ScoreIndTest.getData()	Retrieves the data model associated with this object.
DataModel	TrekSeparationBlocksIndependence.getData()	Retrieves the data model associated with the current block specification.
DataModel	TRffLrTest.getData()	Retrieves the data model associated with the current test instance.

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.