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.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.sweep
edu.cmu.tetrad.algcomparison.utils
edu.cmu.tetrad.data
edu.cmu.tetrad.data.missing
edu.cmu.tetrad.data.simulation
edu.cmu.tetrad.graph
edu.cmu.tetrad.hybridcg
edu.cmu.tetrad.search	This package contains classes for causal graph search algorithms.
edu.cmu.tetrad.search.harness
edu.cmu.tetrad.search.harness.tsc
edu.cmu.tetrad.search.mimic
edu.cmu.tetrad.search.test	This package contains classes for testing causal graph search algorithms.
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.compareFromSavedResults(String savedResultsPath, String newResultsPath, Simulations simulations, Algorithms algorithms, Statistics statistics, Parameters parameters, PrintStream ps1, PrintStream ps2)	Re-runs statistics computation against saved graphs from a previous run, without re-running any 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	AbstractMultiBootstrapAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the multi-dataset search, bootstrapping if numberResampling is greater than zero.
Graph	Algorithm.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FirstInflection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	MultiDataSetAlgorithm.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	StabilitySelection.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	StARS.search(DataModel dataSet, Parameters parameters)	Runs the search.

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)	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 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	FaskPool.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskPool.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FaskVote.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FaskVote.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FciIod.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FciIod.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	FgesConcatenated.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	FgesConcatenated.search(List<DataModel> dataModels, Parameters parameters)	Runs the search.
Graph	ImagesBoss.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	ImagesBoss.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.
Graph	Ion.search(DataModel dataSet, Parameters parameters)	Runs the search.
Graph	Ion.search(List<DataModel> dataSets, Parameters parameters)	Runs the search.

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	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 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	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 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.
Graph	MimbuildBollen.search(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.
Graph	MimbuildPca.search(DataModel dataModel, Parameters parameters)	Executes a factor analysis search on the given data model using the provided parameters.
Graph	PythonGin.search(DataModel dataModel, Parameters parameters)	Deprecated. Runs GIN on the supplied dataset and returns the resulting causal graph.
Graph	Rlcd.search(DataModel dataModel, Parameters parameters)	Runs RLCD on a continuous data set.
Graph	Rlcd.searchImputations(List<DataModel> imputations, Parameters parameters)	Runs ONE RLCD search over several imputations of a data set with missing values; see Rlcd(List).

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	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	RandomMim.createGraph(Parameters parameters)	createGraph.
Graph	RandomMimic.createGraph(Parameters parameters)	createGraph.
Graph	RandomSingleFactorMim.createGraph(Parameters parameters)	createGraph.
Graph	RandomTwoFactorMim.createGraph(Parameters parameters)	createGraph.
Graph	ScaleFree.createGraph(Parameters parameters)	createGraph.
Graph	SingleGraph.createGraph(Parameters parameters)	createGraph.

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	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 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	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.
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 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	AdditiveNoiseSimulation.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	BayesNetSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	ConditionalGaussianSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	DaoSimulation.createData(Parameters parameters, boolean newModel)	Creates a data set and simulates data.
void	DesignedExperimentSimulation.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	GeneralAdditiveModel.createData(Parameters parameters, boolean newModel)	Creates data models and their corresponding graphs based on the provided parameters.
void	GeneralNoiseSimulation.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
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	HybridCgSimulation.createData(Parameters parameters, boolean newModel)	Creates data sets for simulation based on the given parameters and model reuse preference.
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	NonlinearFunctionsOfLinear.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	ObservationalStudySimulation.createData(Parameters parameters, boolean newModel)	Creates simulated data and associated graphs based on the given parameters.
void	PostnonlinearSem.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.
void	TrainedDagModel.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 trueDag, Graph trueGraph, 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, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	Statistic.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
default double	Statistic.getValue(Graph trueGraph, Graph estGraph, Parameters parameters)	Returns the value of this statistic, given the true graph and the estimated graph.
double	StructuralHammingDistance.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TailPrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the tail precision, which is the ratio of true positive arrows to the sum of true positive arrows and false positive arrows.
double	TailRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the tail recall value for a given true graph, estimated graph, and data model.
double	TrueDagFalseNegativesArrows.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the number of False Negatives for Arrows in the estimated graph compared to the true DAG.
double	TrueDagFalseNegativesTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false negatives for tail endpoints compared to the true Directed Acyclic Graph (DAG).
double	TrueDagFalsePositiveArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positive arrows in the estimated graph compared to the true directed acyclic graph (DAG).
double	TrueDagFalsePositiveTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the number of false positives for tail endpoints in the estimated graph compared to the true graph.
double	TrueDagPrecisionArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the precision of arrow edges (X*->Y) in the estimated graph compared to the true DAG.
double	TrueDagPrecisionTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates the precision of directed edges in the estimated graph (estGraph) relative to a true directed acyclic graph (trueDag).
double	TrueDagRecallArrows.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TrueDagRecallTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TrueDagTruePositiveArrow.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the count of bidirected true positives for arrows in the estimated graph compared to the true graph.
double	TrueDagTruePositiveDirectedPathNonancestor.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Computes the number of true positives for directed paths in the estimated graph that are consistent with the true graph, excluding cases where the estimated graph incorrectly identifies the reverse relationship.
double	TrueDagTruePositiveTails.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleFalseNegative.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleFalsePositive.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCyclePrecision.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleRecall.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
double	TwoCycleTruePositive.getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.

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

Modifier	Constructor	Description
	MarkovCheckAdPasses(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckAdPassesBestOf10(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckAndersonDarlingP(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckAndersonDarlingPBestOf10(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckBinomialP(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckFdrCutoffP(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckFractionDependentH0(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckFractionDependentH1(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckKolmogorovSmirnoffP(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckKolmogorovSmirnoffPBestOf10(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckKsPasses(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	MarkovCheckKsPassesBestOf10(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	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).
	McGetNumTestsH0(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	Calculates the number of tests for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).
	McGetNumTestsH1(IndependenceWrapper independenceWrapper, ConditioningSetType conditioningSetType, Parameters mcParameters)	Calculates the number of tests for the Markov check of whether the p-values for the estimated graph are distributed as U(0, 1).

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

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

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

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

Modifier	Constructor	Description
	ParameterSweep(Algorithm algorithm, Parameters baseParameters)	Constructs a sweep harness for the given algorithm and base parameters.

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.

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

Modifier and Type	Method	Description
static ParameterSettingsText.ApplyResult	ParameterSettingsText.applySettingsText(String text, Parameters parameters)	Parses settings text in the format produced by the render methods (" name = value" lines under title lines) and applies the parameter values to the given Parameters object.
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.
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.
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).
static ResolveSepsets.Method	PooledIndependenceWrapper.method(Parameters parameters)	Returns the p-value combining method selected by Params.POOLED_TEST_METHOD.
static String	ParameterSettingsText.render(Algorithm algorithm, Parameters parameters, boolean sourceGraphSupplied)	Renders the settings for the given configured algorithm.
static String	ParameterSettingsText.render(String title, Collection<String> params, Parameters parameters)	Renders a single titled section of parameter settings as plain text, in the same format as the algorithm rendering: the title on its own line, then one " name = value" line per parameter, in the iteration order of the given collection.
Graph	PoolsImputations.searchImputations(List<DataModel> imputations, Parameters parameters)	Runs one search over the imputations.

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, double percentResamplingSize)	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.missing

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

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 MissingDataSpec	MissingDataUtils.fromParameters(Parameters parameters)	Builds a MissingDataSpec from algcomparison/GUI/py-tetrad parameters (see the MISSING_* constants in Params), or returns null--meaning "legacy default behavior"--if the policy parameter is absent or "default".
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).
static BlockSpec	MissingDataUtils.gate(BlockSpec blockSpec, Parameters parameters, String caller)	The block-spec variant of MissingDataUtils.gate(DataModel, Parameters, boolean, String), for wrappers that construct block tests and scores from a BlockSpec rather than from the data model handed to getTest/getScore.
static BlockSpec	MissingDataUtils.gate(BlockSpec blockSpec, Parameters parameters, Set<String> nativePolicies, String caller)	As MissingDataUtils.gate(BlockSpec, Parameters, String), for block components that implement some policies natively; see MissingDataUtils.gate(DataModel, Parameters, java.util.Set, String) for the meaning of the native set.
static ImputationSearch.Result	ImputationSearch.search(DataSet dataSet, Algorithm algorithm, Parameters parameters, MultipleImputer imputer, MissingDataSpec spec)	Runs the multiple-imputation search.

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.hybridcg

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

Modifier and Type	Method	Description
static HybridCgModel.HybridCgIm	HybridCgEstimator.estimate(HybridCgModel.HybridCgPm pm, DataSet data, Parameters params)	Estimates the parameters of a Hybrid Conditional Gaussian (HybridCG) Model using the given parameter model (pm), data set, and optional parameters.

Uses of Parameters in edu.cmu.tetrad.search

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

Modifier and Type	Method	Description
static MarkovCheckNullReference.Result	MarkovCheckNullReference.compute(DataSet data, Graph graph, IndependenceWrapper testWrapper, Parameters parameters, ConditioningSetType conditioningSetType, MarkovCheckNullReference.SimulatorType simulator, int numDraws, int sampleSize, long baseSeed, TrainedDagSimulatorGNM.Params gnmParams)	Convenience overload taking an IndependenceWrapper and Parameters, matching how the Markov-check test is configured in algcomparison and in py-tetrad's TetradSearch.
Graph	FaskPool.search(Parameters parameters)	Runs the search and returns the composite graph.
Graph	FaskVote.search(Parameters parameters)	Runs the search and returns the composite graph.
Graph	PooledAdjacencySearch.search(List<DataSet> standardized, Parameters parameters)	Estimates the common adjacency structure over the given standardized datasets, discarding any orientations the method produces.
Graph	PooledAdjacencySearch.searchWithOrientations(List<DataSet> standardized, Parameters parameters)	Estimates the common adjacency structure over the given standardized datasets, retaining whatever orientations the method produces.
void	Tgin.setParameters(Parameters parameters)	Deprecated. Sets the parameters to be used for the analysis.

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

Modifier	Constructor	Description
	Cstar(IndependenceWrapper test, ScoreWrapper score, Parameters parameters)	Constructor.
	Tgin(DataSet dataSet, Parameters parameters)	Deprecated. Constructs a TGIN search with data and parameters.

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

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

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

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

Methods in edu.cmu.tetrad.search.harness.tsc that return Parameters

Modifier and Type	Method	Description
Parameters	SemParameterizer.buildParameters()	Builds a Parameters object reflecting the current configuration.

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

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

Modifier and Type	Method	Description
String	MimicBenchmarkReport.createReport(MimicBenchmarkResult result, Parameters parameters)	Creates a readable report for the supplied benchmark result.
String	MimicBenchmarkReportTdf.createReport(MimicBenchmarkResult result, Parameters parameters)	Creates a tab-delimited report for the supplied benchmark result and parameters.
MimicModel	RandomMimicGraphGenerator.generate(Parameters parameters)	Generates a random MIMIC model using values read from the given parameters object.
Graph	BossTrekMimicRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Deprecated. Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	DmRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	MimicSearchRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
Graph	TrekMimicRunner.run(DataSet data, Knowledge knowledge, List<Node> inputs, List<Node> outputs, Parameters parameters)	Runs the search algorithm on the supplied measured data using the supplied knowledge and parameters.
MimicTrialResult	MimicBenchmark.runTrial(Parameters parameters)	Runs one benchmark trial.
MimicBenchmarkResult	MimicBenchmark.runTrials(Parameters parameters, int numTrials)	Runs multiple benchmark trials.
void	BossTrekMimic.setParameters(Parameters parameters)	Deprecated. Sets the parameters.
void	TrekMimic.setParameters(Parameters parameters)	Sets the parameters.

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

Modifier	Constructor	Description
	BossTrekMimic(DataSet dataSet, Parameters parameters, Score score)	Deprecated. Constructs a BossTrekMimic search with data, parameters, and score.
	TrekMeasurementModelBuilderBoss(DataSet dataSet, Parameters parameters)	Constructs the builder.
	TrekMeasurementModelBuilderPc(DataSet dataSet, Parameters parameters)	Constructs the builder.
	TrekMimic(DataSet dataSet, Parameters parameters)	Constructs a TrekMimic search with data and parameters.

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

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

Modifier	Constructor	Description
	FfCi(DataSet dataSet, Parameters params)	Constructs a new FfCi instance for conducting conditional independence tests on a dataset that may contain both continuous and discrete variables.
	FfCi(DataSet dataSet, Parameters params, MissingDataSpec spec)	Constructs an instance with an explicit missing-data specification.
	FfCi1(DataSet dataSet, Parameters params)	Constructs a new FfCiMixed instance for conducting conditional independence tests on a dataset that may contain both continuous and discrete variables.
	IndTestRcotCcaWilkes(DataSet dataSet, Parameters params)	Constructs an instance of IndTestRcot with Parameters (legacy keys match IndTestRcit style).
	Rcit(DataSet dataSet, Parameters params)	Constructs an instance of the Rcit class using the provided DataSet and Parameters.
	Rcit(DataSet dataSet, Parameters params, MissingDataSpec spec)	Constructs an instance with an explicit missing-data specification.

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.
static @NotNull SemIm.Result	SemIm.simulatePossibleShrinkage(Parameters params, Graph g)	Simulates possible shrinkage scenarios for a given graph and parameters.
static SemIm.CyclicSimResult	CyclicStableUtils.simulateStableFixedRadius(Graph g, int n, double s, double coefLow, double coefHigh, Parameters params)	Simulate from an arbitrary graph with SCC-wise fixed spectral radius s.
static SemIm.CyclicSimResult	CyclicStableUtils.simulateStableProductCapped(Graph g, int n, double maxProd, double coefLow, double coefHigh, long seed, Parameters params)	Simulate from an arbitrary graph with SCC-wise radius capped by sqrt(maxProd).

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.