Uses of Class
edu.cmu.tetrad.util.Parameters

Packages that use Parameters

Package	Description
edu.cmu.tetrad.algcomparison
edu.cmu.tetrad.algcomparison.algorithm
edu.cmu.tetrad.algcomparison.algorithm.cluster
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.algcomparison.algorithm.multi
edu.cmu.tetrad.algcomparison.algorithm.oracle.cpdag
edu.cmu.tetrad.algcomparison.algorithm.oracle.pag
edu.cmu.tetrad.algcomparison.algorithm.other
edu.cmu.tetrad.algcomparison.algorithm.pairwise
edu.cmu.tetrad.algcomparison.graph
edu.cmu.tetrad.algcomparison.independence
edu.cmu.tetrad.algcomparison.score
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.algcomparison.statistic
edu.cmu.tetrad.algcomparison.utils
edu.cmu.tetrad.data
edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.graph
edu.cmu.tetrad.search	Contains classes for searching for (mostly structural) causal models given data.
edu.cmu.tetrad.search.work_in_progress	Contains some classes that aren't ready for prime time.
edu.cmu.tetrad.sem
edu.cmu.tetrad.study.gene.tetrad.gene.graph	Contains an editable display graph for (small) lag graphs.
edu.cmu.tetrad.study.gene.tetrad.gene.simulation	Contains classes for generating simulations of expression levels over a collection of genes.
edu.cmu.tetrad.study.gene.tetradapp.model
edu.cmu.tetrad.util

Uses of Parameters in edu.cmu.tetrad.algcomparison

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

Modifier and Type	Method	Description
void	Comparison.compareFromFiles(String filePath, Algorithms algorithms, Statistics statistics, Parameters parameters)	compareFromFiles.
void	Comparison.compareFromFiles(String dataPath, String resultsPath, Algorithms algorithms, Statistics statistics, Parameters parameters)	Compares algorithms.
void	TimeoutComparison.compareFromFiles(String filePath, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit)	compareFromFiles.
void	TimeoutComparison.compareFromFiles(String dataPath, String resultsPath, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit)	Compares algorithms.
void	Comparison.compareFromSimulations(String resultsPath, Simulations simulations, Algorithms algorithms, Statistics statistics, Parameters parameters)	Compare simulation results using the provided parameters and write the comparison results to a file.
void	Comparison.compareFromSimulations(String resultsPath, Simulations simulations, String outputFileName, Algorithms algorithms, Statistics statistics, Parameters parameters)	Compares the results obtained from simulations.
void	Comparison.compareFromSimulations(String resultsPath, Simulations simulations, String outputFileName, PrintStream localOut, Algorithms algorithms, Statistics statistics, Parameters parameters)	Compares the results of simulations and generates an output file.
void	Comparison.compareFromSimulations(String resultsPath, Simulations simulations, String outputFileName, PrintStream localOut, PrintStream localOut2, Algorithms algorithms, Statistics statistics, Parameters parameters)	Compares the results of different simulations and algorithms.
void	TimeoutComparison.compareFromSimulations(String resultsPath, Simulations simulations, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit)	compareFromSimulations.
void	TimeoutComparison.compareFromSimulations(String resultsPath, Simulations simulations, String outputFileName, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit)	Compares algorithms.
void	Comparison.generateReportFromExternalAlgorithms(String dataPath, String resultsPath, Algorithms algorithms, Statistics statistics, Parameters parameters)	Generates a report from external algorithms.
void	Comparison.generateReportFromExternalAlgorithms(String dataPath, String resultsPath, String outputFileName, Algorithms algorithms, Statistics statistics, Parameters parameters)	Generates a report from external algorithms based on the given parameters.
void	TimeoutComparison.generateReportFromExternalAlgorithms(String dataPath, String resultsPath, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit)	generateReportFromExternalAlgorithms.
void	TimeoutComparison.generateReportFromExternalAlgorithms(String dataPath, String resultsPath, String outputFileName, Algorithms algorithms, Statistics statistics, Parameters parameters, long timeout, TimeUnit unit)	generateReportFromExternalAlgorithms.
void	Comparison.saveToFiles(String dataPath, Simulation simulation, Parameters parameters)	Saves the simulation data to file in the specified data path.
void	TimeoutComparison.saveToFiles(String dataPath, Simulation simulation, Parameters parameters)	Saves simulationWrapper data.
void	Comparison.saveToFilesSingleSimulation(String dataPath, Simulation simulation, Parameters parameters)	Saves the results of a single simulation to files.

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

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

Modifier and Type	Method	Description
abstract long	ExternalAlgorithm.getElapsedTime(DataModel dataSet, Parameters parameters)	getElapsedTime.
Graph	AbstractBootstrapAlgorithm.search(DataModel dataModel, Parameters parameters)	Runs the search.
Graph	Algorithm.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FirstInflection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	MultiDataSetAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	StabilitySelection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	StARS.search(DataModel dataSet, Parameters parameters)	Runs the search.

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

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

Modifier and Type	Method	Description
Graph	Fofc.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm and returns the resulting graph.
Graph	Ftfc.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm to find a causal graph.
Graph	Bpc.search(DataModel dataModel, Parameters parameters)	Runs the search algorithm to build a graph using the given data model and parameters.

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

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

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

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

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

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

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

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

Modifier and Type	Method	Description
Graph	Cstar.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	SingleGraphAlg.search(DataModel dataSet, Parameters parameters)	Runs the search.

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

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

Modifier and Type	Method	Description
Graph	BossDot.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	BossFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given dataset and parameters and returns the resulting graph.
Graph	Ccd.runSearch(DataModel dataModel, Parameters parameters)	Runs the CCD (Cyclic Causal Discovery) search algorithm on the given data set using the specified parameters.
Graph	Cfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to discover the causal graph.
Graph	DmFcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmFciT2.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	DmPc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	Fci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph based on the given data model and parameters.
Graph	FciMax.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to discover the causal graph structure.
Graph	Fcit.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to find a graph structure based on a given data model and parameters.
Graph	FgesFci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	Gfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to infer the causal graph given a dataset and specified parameters.
Graph	GraspFci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find a graph structure based on a given data set and parameters.
Graph	PagSampleRfci.runSearch(DataModel dataSet, Parameters parameters)	Runs the search algorithm using the given data set and parameters.
Graph	Rfci.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model and parameters.
Graph	RfciBsc.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm using a given dataset and parameters.
Graph	SpFci.runSearch(DataModel dataModel, Parameters parameters)	Executes a search algorithm to infer the causal graph structure from a given data model
Graph	SvarFci.runSearch(DataModel dataModel, Parameters parameters)	Executes the search algorithm to find a graph structure that best fits the given dataset and parameters.
Graph	SvarGfci.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm on the given data set using the specified parameters.

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

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

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.

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

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

Modifier and Type	Method	Description
Graph	Eb.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to orient the edges in a graph using the given data and parameters.
Graph	FaskPw.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given data model and parameters.
Graph	R1.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm on the given data model with the provided parameters.
Graph	R2.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the provided data model and parameters.
Graph	R3.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to orient edges in the input graph using the provided data.
Graph	Rskew.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the provided data model and parameters.
Graph	RskewE.runSearch(DataModel dataModel, Parameters parameters)	Runs a search algorithm to find the orientation of edges in a graph using the given data model and parameters.
Graph	Skew.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm to orient edges in the input graph using the given data model and parameters.
Graph	SkewE.runSearch(DataModel dataModel, Parameters parameters)	Executes the SkewE search algorithm.
Graph	Tanh.runSearch(DataModel dataModel, Parameters parameters)	Runs the search algorithm using the given data model and parameters.

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

Methods in edu.cmu.tetrad.algcomparison.graph with parameters of type Parameters

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

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

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

Modifier and Type	Method	Description
IndependenceTest	BasisFunctionLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	CciTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	ChiSquare.getTest(DataModel dataSet, Parameters parameters)	Retrieves an instance of the IndependenceTest interface that performs a Chi Square Test for independence.
IndependenceTest	ConditionalGaussianLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	DegenerateGaussianLrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	FisherZ.getTest(DataModel dataModel, Parameters parameters)	Gets an independence test based on the given data model and parameters.
IndependenceTest	GSquare.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	IndependenceWrapper.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	Kci.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	MSeparationTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	MultinomialLogisticRegressionWald.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	Mvplrt.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	PoissonBicTest.getTest(DataModel dataSet, Parameters parameters)	Returns an instance of IndependenceTest for the SEM BIC test.
IndependenceTest	ProbabilisticTest.getTest(DataModel dataSet, Parameters parameters)	Returns true iff x and y are independent conditional on z for the given data set.
IndependenceTest	SemBicTest.getTest(DataModel dataSet, Parameters parameters)	Returns an instance of IndependenceTest for the SEM BIC test.

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

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

Modifier and Type	Method	Description
Score	BasisFunctionBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	BdeuScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	ConditionalGaussianBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	DegenerateGaussianBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	DiscreteBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	EbicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	GicScores.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	MagDgBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	MSepScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	MVPBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	PoissonPriorScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	ScoreWrapper.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	SemBicScore.getScore(DataModel dataSet, Parameters parameters)	Returns true, iff x and y are independent, conditional on z for the given data set.
Score	ZhangShenBoundScore.getScore(DataModel dataSet, Parameters parameters)	Calculates the score based on the given data set and parameters.

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

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

Modifier and Type	Method	Description
void	BayesNetSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	CausalPerceptronNetwork.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	ConditionalGaussianSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	GeneralSemSimulation.createData(Parameters parameters, boolean newModel)	Creates data sets for simulation based on the given parameters and model reuse preference.
void	GeneralSemSimulationSpecial1.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	GpSemSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LeeHastieSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LgMnarSimulation.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	LinearFisherModel.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LinearSineSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	NonlinearAdditiveNoiseModel.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	NonlinearFunctionsOfLinear.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	PostnonlinearCausalModel.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	SemSimulation.createData(Parameters parameters, boolean newModel)	Creates data sets for simulation based on the given parameters and model reuse preference.
void	SemThenDiscretize.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	Simulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	SingleDatasetSimulation.createData(Parameters parameters, boolean newModel)	Creates a new data model for the simulation.
void	StandardizedSemSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	TimeSeriesSemSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.

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

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

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

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

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

Modifier and Type	Method	Description
Parameters	HasParameterValues.getParameterValues()	getParameterValues.

Uses of Parameters in edu.cmu.tetrad.data

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

Modifier and Type	Method	Description
static DataSet	DataSampling.createDataSample(DataSet dataSet, org.apache.commons.math3.random.RandomGenerator randomGenerator, int[] selectedColumns, Parameters parameters)	Creates a resampled dataset from the given dataset based on the specified parameters.
static List<DataSet>	DataSampling.createDataSamples(DataSet dataSet, org.apache.commons.math3.random.RandomGenerator randomGenerator, Parameters parameters)	Create a list of dataset resampled from the given dataset.
static List<DataSet>	DataSampling.createDataSamples(org.apache.commons.math3.random.RandomGenerator randomGenerator, DataSet dataSet, Parameters parameters)	Create a list of dataset resampled from the given dataset.

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

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

Modifier and Type	Method	Description
Parameters	LoadContinuousDataAndSingleGraph.getParameterValues()	getParameterValues.
Parameters	LoadContinuousDataSmithSim.getParameterValues()	getParameterValues.

Methods in edu.cmu.tetrad.data.simulation with parameters of type Parameters

Modifier and Type	Method	Description
void	LoadContinuousDataAndGraphs.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LoadContinuousDataAndSingleGraph.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LoadContinuousDataSmithSim.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LoadDataAndGraphs.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	LoadDataFromFileWithoutGraph.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.

Uses of Parameters in edu.cmu.tetrad.graph

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

Modifier and Type	Method	Description
static Graph	GraphUtils.getComparisonGraph(Graph graph, Parameters params)	Returns a comparison graph based on the specified parameters.

Uses of Parameters in edu.cmu.tetrad.search

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

Modifier	Constructor	Description
	Cstar(IndependenceWrapper test, ScoreWrapper score, Parameters parameters)	Constructor.

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

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

Modifier and Type	Method	Description
Graph	FaskVote.search(Parameters parameters)	Does the search.

Uses of Parameters in edu.cmu.tetrad.sem

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

Modifier and Type	Method	Description
Parameters	SemIm.getParams()	Getter for the field params.

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

Modifier and Type	Method	Description
void	SemIm.setParams(Parameters params)	Setter for the field params.

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

Modifier	Constructor	Description
	SemIm(SemPm semPm, SemIm oldSemIm, Parameters parameters)	Constructs a new SEM IM from the given SEM PM, using the old SEM IM and params object to guide the choice of parameter values.
	SemIm(SemPm semPm, Parameters params)	Constructs a new SEM IM from the given SEM PM, using the given params object to guide the choice of parameter values.
	StandardizedSemIm(SemIm im, StandardizedSemIm.Initialization initialization, Parameters parameters)	Constructs a new standardized SEM IM from the freeParameters in the given SEM IM.
	StandardizedSemIm(SemIm im, Parameters parameters)	Constructs a new standardized SEM IM, initializing from the freeParameters in the given SEM IM.

Uses of Parameters in edu.cmu.tetrad.study.gene.tetrad.gene.graph

Constructors in edu.cmu.tetrad.study.gene.tetrad.gene.graph with parameters of type Parameters

Modifier	Constructor	Description
	LagGraphParams(Parameters parameters)	Constructor for LagGraphParams.

Uses of Parameters in edu.cmu.tetrad.study.gene.tetrad.gene.simulation

Constructors in edu.cmu.tetrad.study.gene.tetrad.gene.simulation with parameters of type Parameters

Modifier	Constructor	Description
	MeasurementSimulator(Parameters parameters)	Constructs a measurement simulator using the given history.

Uses of Parameters in edu.cmu.tetrad.study.gene.tetradapp.model

Constructors in edu.cmu.tetrad.study.gene.tetradapp.model with parameters of type Parameters

Modifier	Constructor	Description
	MeasurementSimulatorParams(Parameters parameters)	Constructs a measurement simulator using the given history.

Uses of Parameters in edu.cmu.tetrad.util

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

Modifier and Type	Method	Description
static Parameters	ParameterUtils.create(List<String> names)	Create parameters with their default values.
static Parameters	Parameters.serializableInstance()	serializableInstance.

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

Modifier and Type	Method	Description
void	Parameters.putAll(Parameters parameters)	putAll.

Constructors in edu.cmu.tetrad.util with parameters of type Parameters

Modifier	Constructor	Description
	Parameters(Parameters parameters)	Constructor for Parameters.