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

Packages that use DataSet

Package	Description
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calculator
edu.cmu.tetrad.calibration
edu.cmu.tetrad.classify
edu.cmu.tetrad.data
edu.cmu.tetrad.graph
edu.cmu.tetrad.hybridcg
edu.cmu.tetrad.regression
edu.cmu.tetrad.search	This package contains classes for causal graph search algorithms.
edu.cmu.tetrad.search.blocks
edu.cmu.tetrad.search.cdnod_pag
edu.cmu.tetrad.search.is
edu.cmu.tetrad.search.rlcd
edu.cmu.tetrad.search.score	This package contains classes for scoring causal graph models.
edu.cmu.tetrad.search.test	This package contains classes for testing causal graph search algorithms.
edu.cmu.tetrad.search.unmix
edu.cmu.tetrad.search.utils	This package contains utility classes for causal graph search algorithms.
edu.cmu.tetrad.search.work_in_progress	A package for algorithms that are not ready for prime time.
edu.cmu.tetrad.sem
edu.cmu.tetrad.simulation
edu.cmu.tetrad.util
edu.pitt.csb.mgm
edu.pitt.csb.stability
edu.pitt.dbmi.algo.bayesian.constraint.search

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

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

Modifier and Type	Method	Description
static DataSet	LeeHastieSimulation.makeMixedData(DataSet dsCont, Map<String,Integer> nodeDists)	makeMixedData.
static DataSet	LeeHastieSimulation.makeMixedData(DataSet dsCont, Map<String,String> nodeDists, int numCategories)	makeMixedData.

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

Modifier and Type	Method	Description
static DataSet	LeeHastieSimulation.makeMixedData(DataSet dsCont, Map<String,Integer> nodeDists)	makeMixedData.
static DataSet	LeeHastieSimulation.makeMixedData(DataSet dsCont, Map<String,String> nodeDists, int numCategories)	makeMixedData.

Constructors in edu.cmu.tetrad.algcomparison.simulation with parameters of type DataSet

Modifier	Constructor	Description
	SingleDatasetSimulation(DataSet dataSet)	A Simulation implementation that returns a single supplied data set.

Uses of DataSet in edu.cmu.tetrad.bayes

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

Modifier and Type	Method	Description
DataSet	ExtraCategoryInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	ModeInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	CellTableProbs.getDataSet()	Getter for the field dataSet.
DataSet	DataSetProbs.getDataSet()	Getter for the field dataSet.
DataSet	DirichletDataSetProbs.getDataSet()	Getter for the field dataSet.
DataSet	FactoredBayesStructuralEM.getDataSet()	Getter for the field dataSet.
DataSet	IntAveDataSetProbs.getDataSet()	Getter for the field dataSet.
DataSet	EmBayesEstimator.getMixedDataSet()	getMixedDataSet.
DataSet	BayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	BayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	DirichletBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	DirichletBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	MlBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set.
DataSet	MlBayesIm.simulateData(int sampleSize, boolean latentDataSaved, int[] tiers)	Simulates a sample with the given sample size.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data for the given data set.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved, int[] tiers)	simulateData.
DataSet	MlBayesImObs.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	MlBayesImObs.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	UpdatedBayesIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates a data set with the specified number of rows.
DataSet	UpdatedBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.

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

Modifier and Type	Method	Description
static DirichletBayesIm	DirichletEstimator.estimate(DirichletBayesIm prior, DataSet dataSet)	estimate.
BayesIm	EmBayesProperties.Estimator.estimate(BayesPm bayesPm, DataSet dataSet)	estimate.
BayesIm	MlBayesEstimator.estimate(BayesPm bayesPm, DataSet dataSet)	Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
BayesIm	MlBayesEstimatorOld.estimate(BayesPm bayesPm, DataSet dataSet)	33 Estimates a Bayes IM using the variables, graph, and parameters in the given Bayes PM and the data columns in the given data set.
DataSet	ExtraCategoryInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	ModeInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	BayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	DirichletBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data for the given data set.
DataSet	MlBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved, int[] tiers)	simulateData.
DataSet	MlBayesImObs.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.
DataSet	UpdatedBayesIm.simulateData(DataSet dataSet, boolean latentDataSaved)	Simulates data based on the provided data set and saves the latent data if specified.

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

Modifier	Constructor	Description
	BayesProperties(DataSet dataSet)	Constructs a new BayesProperties object for the given data set.
	BdeMetricCache(DataSet dataSet, BayesPm bayesPm)	Constructs a BdeMetricCache object for a given dataset and BayesPm.
	CellTableProbs(DataSet dataSet)	Creates a cell count table for the given data set.
	CptMapCounts(DataSet data)	Constructs a new CptMap based on counts from a given dataset.
	DataSetProbs(DataSet dataSet)	Creates a cell count table for the given data set.
	DirichletDataSetProbs(DataSet dataSet, double symmValue)	Creates a cell count table for the given data set.
	EmBayesEstimator(BayesIm inputBayesIm, DataSet dataSet)	Constructor for EmBayesEstimator.
	EmBayesEstimator(BayesPm bayesPm, DataSet dataSet)	Provides methods for estimating a Bayes IM from an existing BayesIM and a discrete dataset using EM (Expectation Maximization).
	EmBayesProperties(DataSet dataSet, Graph graph)	Constructor for EmBayesProperties.
	FactoredBayesStructuralEM(DataSet dataSet, BayesPm bayesPmM0)	Constructor for FactoredBayesStructuralEM.
	IntAveDataSetProbs(DataSet dataSet)	Creates a cell count table for the given data set.

Uses of DataSet in edu.cmu.tetrad.calculator

Methods in edu.cmu.tetrad.calculator with parameters of type DataSet

Modifier and Type	Method	Description
static void	Transformation.transform(DataSet data, String... equations)	Transforms the given data using the given representations of transforming equations.

Uses of DataSet in edu.cmu.tetrad.calibration

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

Modifier and Type	Method	Description
DataSet	DataForCalibrationRfci.bootStrapSampling(DataSet data, int bootsrapSampleSize)	bootStrapSampling.

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

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

Uses of DataSet in edu.cmu.tetrad.classify

Methods in edu.cmu.tetrad.classify that return DataSet

Modifier and Type	Method	Description
DataSet	ClassifierBayesUpdaterDiscrete.getTestData()	Returns the test data being used.

Constructors in edu.cmu.tetrad.classify with parameters of type DataSet

Modifier	Constructor	Description
	ClassifierBayesUpdaterDiscrete(BayesIm bayesIm, DataSet testData)	The constructor sets the values of the private member variables.

Uses of DataSet in edu.cmu.tetrad.data

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

Modifier and Type	Class	Description
final class	BoxDataSet	Wraps a DataBox in such a way that mixed data sets can be stored.
final class	NumberObjectDataSet	Wraps a 2D array of Number objects in such a way that mixed data sets can be stored.

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

Modifier and Type	Method	Description
static DataSet	DataTransforms.addMissingData(DataSet inData, double[] probs)	Adds missing data values to cases in accordance with probabilities specified in a double array which has as many elements as there are columns in the input dataset.
static DataSet	DataTransforms.center(DataSet data)	Subtracts the mean of each column from each datum that column.
static DataSet	DataUtils.choleskySimulation(CovarianceMatrix cov)	choleskySimulation.
static DataSet	DataTransforms.concatenate(DataSet... dataSets)	concatenate.
static DataSet	DataTransforms.concatenate(DataSet dataSet1, DataSet dataSet2)	concatenate.
static DataSet	DataTransforms.concatenate(List<DataSet> dataSets)	concatenate.
static DataSet	DataTransforms.convertNumericalDiscreteToContinuous(DataSet dataSet)	convertNumericalDiscreteToContinuous.
DataSet	BoxDataSet.copy()	Returns a copy of this dataset.
DataSet	DataSet.copy()	Returns a copy of this dataset.
DataSet	NumberObjectDataSet.copy()	Returns a copy of this dataset.
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 DataSet	DataUtils.discreteSerializableInstance()	A discrete data set used to construct some other serializable instances.
static DataSet	DataTransforms.discretize(DataSet dataSet, int numCategories, boolean variablesCopied)	discretize.
DataSet	Discretizer.discretize()	discretize.
DataSet	CaseExpander.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	DataFilter.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	MeanInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize)	getBootstrapSample.
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled with replacement.
static DataSet	SimpleDataLoader.getContinuousDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is continuous.
DataSet	Histogram.getDataSet()	Getter for the field dataSet.
static DataSet	SimpleDataLoader.getDiscreteDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is discrete.
static DataSet	SimpleDataLoader.getMixedDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is mixed.
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)	getNonparanormalTransformed.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize)	getResamplingDataset.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled without replacement.
DataSet	Knowledge.getTestingData()	Retrieves the testing dataset.
DataSet	BoxDataSet.like()	Returns a dataset with the same dimensions as this dataset, but with no data.
DataSet	DataSet.like()	Returns a dataset with the same dimensions as this dataset, but with no data.
DataSet	NumberObjectDataSet.like()	Returns a dataset with the same dimensions as this dataset, but with no data.
static @NotNull DataSet	SimpleDataLoader.loadContinuousData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, Delimiter delimiter, boolean excludeFirstColumn)	Loads a continuous dataset from a file.
static @NotNull DataSet	SimpleDataLoader.loadDiscreteData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, Delimiter delimiter, boolean excludeFirstColumn)	Loads a discrete dataset from a file.
static @NotNull DataSet	SimpleDataLoader.loadMixedData(File file, String commentMarker, char quoteCharacter, String missingValueMarker, boolean hasHeader, int maxNumCategories, Delimiter delimiter, boolean excludeFirstColumn)	Loads a mixed dataset from a file.
static DataSet	DataTransforms.logData(DataSet dataSet, double a, boolean isUnlog, int base)	Log or unlog data
static DataSet	DataTransforms.removeConstantColumns(DataSet dataSet)	removeConstantColumns.
static DataSet	DataTransforms.removeRandomColumns(DataSet dataSet, double aDouble)	removeRandomColumns.
static DataSet	DataTransforms.replaceMissingWithRandom(DataSet inData)	replaceMissingWithRandom.
static DataSet	DataTransforms.restrictToMeasured(DataSet fullDataSet)	restrictToMeasured.
DataSet	BootstrapSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	RandomSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	DataTransforms.scale(DataSet dataSet, double[] scales)	Scales the columns of the provided dataset based on the given scale factors.
static DataSet	DataTransforms.scale(DataSet dataSet, double scaleMin, double scaleMax)	Scales the continuous variables in the given DataSet to have values in the range [-1, 1].
static DataSet	DataTransforms.shuffleColumns(DataSet dataModel)	shuffleColumns.
DataSet	Simulator.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
static DataSet	DataTransforms.standardizeData(DataSet dataSet)	standardizeData.
DataSet	BoxDataSet.subsetColumns(int[] indices)	Creates a new DataSet object containing only the specified columns.
DataSet	BoxDataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	DataSet.subsetColumns(int[] columns)	subsetColumns.
DataSet	DataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	NumberObjectDataSet.subsetColumns(int[] indices)	subsetColumns.
DataSet	NumberObjectDataSet.subsetColumns(List<Node> vars)	Creates and returns a dataset consisting of those variables in the list vars.
DataSet	BoxDataSet.subsetRows(int[] rows)	Creates a subset of rows from the existing DataSet.
DataSet	BoxDataSet.subsetRows(List<Integer> rows)
DataSet	DataSet.subsetRows(int[] rows)	subsetRows.
DataSet	DataSet.subsetRows(List<Integer> rows)	Creates a new DataSet containing only the rows specified by the given list of row indices.
DataSet	NumberObjectDataSet.subsetRows(int[] rows)	subsetRows.
DataSet	NumberObjectDataSet.subsetRows(List<Integer> rows)
DataSet	BoxDataSet.subsetRowsColumns(int[] rows, int[] columns)	Generates a subset of the current DataSet by selecting specified rows and columns.
DataSet	DataSet.subsetRowsColumns(int[] rows, int[] columns)	Generates a subset of the current DataSet by selecting specified rows and columns.
DataSet	NumberObjectDataSet.subsetRowsColumns(int[] rows, int[] columns)	Generates a subset of the current DataSet by selecting specified rows and columns.

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

Modifier and Type	Method	Description
static List<DataSet>	DataTransforms.center(List<DataSet> dataList)	center.
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.
static List<DataSet>	DataTransforms.shuffleColumns2(List<DataSet> dataSets)	shuffleColumns2.
static List<DataSet>	DataTransforms.split(DataSet data, double percentTest)	split.
static List<DataSet>	DataTransforms.standardizeData(List<DataSet> dataSets)	standardizeData.

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

Modifier and Type	Method	Description
static DataSet	DataTransforms.addMissingData(DataSet inData, double[] probs)	Adds missing data values to cases in accordance with probabilities specified in a double array which has as many elements as there are columns in the input dataset.
static DataSet	DataTransforms.center(DataSet data)	Subtracts the mean of each column from each datum that column.
static DataSet	DataTransforms.concatenate(DataSet... dataSets)	concatenate.
static DataSet	DataTransforms.concatenate(DataSet dataSet1, DataSet dataSet2)	concatenate.
static boolean	DataUtils.containsMissingValue(DataSet data)	containsMissingValue.
static DataSet	DataTransforms.convertNumericalDiscreteToContinuous(DataSet dataSet)	convertNumericalDiscreteToContinuous.
static void	DataTransforms.copyColumn(Node node, DataSet source, DataSet dest)	copyColumn.
static ICovarianceMatrix	DataTransforms.covarianceNonparanormalDrton(DataSet dataSet)	covarianceNonparanormalDrton.
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.
static DataSet	DataTransforms.discretize(DataSet dataSet, int numCategories, boolean variablesCopied)	discretize.
DataSet	CaseExpander.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	DataFilter.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
DataSet	MeanInterpolator.filter(DataSet dataSet)	Interpolates the given data set, producing a data set with no missing values.
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize)	getBootstrapSample.
static DataSet	DataTransforms.getBootstrapSample(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled with replacement.
static List<Node>	DataTransforms.getConstantColumns(DataSet dataSet)	getConstantColumns.
static @NotNull ICovarianceMatrix	SimpleDataLoader.getCorrelationMatrix(DataSet dataSet)	getCorrelationMatrix.
static @NotNull ICovarianceMatrix	SimpleDataLoader.getCovarianceMatrix(DataSet dataSet, boolean precomputeCovariances)	getCovarianceMatrix.
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)	getNonparanormalTransformed.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize)	getResamplingDataset.
static DataSet	DataTransforms.getResamplingDataset(DataSet data, int sampleSize, org.apache.commons.math3.random.RandomGenerator randomGenerator)	Get dataset sampled without replacement.
static boolean	DataUtils.isBinary(DataSet data, int column)	States whether the given column of the given data set is binary.
static DataSet	DataTransforms.logData(DataSet dataSet, double a, boolean isUnlog, int base)	Log or unlog data
static DataSet	DataTransforms.removeConstantColumns(DataSet dataSet)	removeConstantColumns.
static DataSet	DataTransforms.removeRandomColumns(DataSet dataSet, double aDouble)	removeRandomColumns.
static DataSet	DataTransforms.replaceMissingWithRandom(DataSet inData)	replaceMissingWithRandom.
static DataSet	DataTransforms.restrictToMeasured(DataSet fullDataSet)	restrictToMeasured.
DataSet	BootstrapSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	RandomSampler.sample(DataSet dataSet, int newSampleSize)	This method takes a dataset and a sample size and creates a new dataset containing that number of samples by drawing with replacement from the original dataset.
static DataSet	DataTransforms.scale(DataSet dataSet, double[] scales)	Scales the columns of the provided dataset based on the given scale factors.
static DataSet	DataTransforms.scale(DataSet dataSet, double scaleMin, double scaleMax)	Scales the continuous variables in the given DataSet to have values in the range [-1, 1].
static void	DataTransforms.scale(DataSet dataSet, double scaleMin, double scaleMax, Node node)	Scales the values of a specified node in the given dataset to a specified range [scaleMin, scaleMax].
void	Knowledge.setTestingData(DataSet ds)	Sets the testing data for this instance.
static DataSet	DataTransforms.shuffleColumns(DataSet dataModel)	shuffleColumns.
static List<DataSet>	DataTransforms.split(DataSet data, double percentTest)	split.
static DataSet	DataTransforms.standardizeData(DataSet dataSet)	standardizeData.
static void	DataWriter.writeRectangularData(DataSet dataSet, Writer out, char separator)	Writes a dataset to file.

Method parameters in edu.cmu.tetrad.data with type arguments of type DataSet

Modifier and Type	Method	Description
static List<DataSet>	DataTransforms.center(List<DataSet> dataList)	center.
static DataSet	DataTransforms.concatenate(List<DataSet> dataSets)	concatenate.
static List<DataSet>	DataTransforms.shuffleColumns2(List<DataSet> dataSets)	shuffleColumns2.
static List<DataSet>	DataTransforms.standardizeData(List<DataSet> dataSets)	standardizeData.

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

Modifier	Constructor	Description
	CellTableAdTree(DataSet dataSet, int[] testIndices)	Constructs a new CellTableAdTree using the provided data set and test indices.
	CellTableAdTree(DataSet dataSet, int[] testIndices, List<Integer> rows)	Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
	CellTableCountSample(DataSet dataSet, int[] testIndices)	Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
	CellTableCountSample(DataSet dataSet, int[] testIndices, List<Integer> rows)	Constructs a new cell table using the given array for dimensions, initializing all cells in the table to zero.
	CorrelationMatrix(DataSet dataSet)	Constructs a new correlation matrix from the the given DataSet.
	CovarianceMatrix(DataSet dataSet)	Constructs a new covariance matrix from the given data set.
	CovarianceMatrix(DataSet dataSet, boolean biasCorrected)	Constructor for CovarianceMatrix.
	CovarianceMatrixOnTheFly(DataSet dataSet)	Constructs a new covariance matrix from the given data set.
	CovarianceMatrixOnTheFly(DataSet dataSet, boolean verbose)	Constructor for CovarianceMatrixOnTheFly.
	Discretizer(DataSet dataSet)	Constructs a new discretizer that discretizes every variable as binary, using evenly distributed values.
	Discretizer(DataSet dataSet, Map<Node, DiscretizationSpec> specs)	Constructor for Discretizer.
	Histogram(DataSet dataSet, String target, boolean removeZeroPointsPerPlot)	This histogram is for variables in a particular data set.

Uses of DataSet in edu.cmu.tetrad.graph

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

Modifier and Type	Method	Description
static Graph	GraphSaveLoadUtils.loadGraphBNTPcMatrix(List<Node> vars, DataSet dataSet)	loadGraphBNTPcMatrix.

Uses of DataSet in edu.cmu.tetrad.hybridcg

Methods in edu.cmu.tetrad.hybridcg that return DataSet

Modifier and Type	Method	Description
DataSet	HybridCgModel.HybridCgIm.toDataSet(HybridCgModel.HybridCgIm.Sample sample)	Convert a sampled matrix into a Tetrad DataSet with the provided node ordering.

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

Modifier and Type	Method	Description
void	HybridCgModel.HybridCgPm.autoCutpointsForDiscreteChild(Node child, DataSet data, int binsPerParent)	Populate cutpoints for each continuous parent of a DISCRETE child using equal-frequency binning.
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.
HybridCgModel.HybridCgIm	HybridCgModel.HybridCgIm.HybridEstimator.mle(HybridCgModel.HybridCgPm pm, DataSet data)	Estimates a HybridCgIm model from the given HybridCgPm and DataSet.
static int	HybridCgModel.HybridCgIm.rowIndexForCase(HybridCgModel.HybridCgPm pm, int nodeIndex, DataSet data, int row, int[] colIndex)	Computes the row index for a given data case in the hybrid causal graph model.
int	HybridCgModel.HybridCgPm.rowIndexForCase(int nodeIndex, DataSet data, int row)	Convenience overload that reads parent states/values from a DataSet row.
static void	HybridCgCutpoints.setAll(HybridCgModel.HybridCgPm pm, DataSet data, int bins, HybridCgCutpoints.Method method)	For every DISCRETE child Y with ≥1 continuous parent, computes and installs cutpoints for each continuous parent using the chosen method and desired number of bins.

Uses of DataSet in edu.cmu.tetrad.regression

Methods in edu.cmu.tetrad.regression that return DataSet

Modifier and Type	Method	Description
static DataSet	RegressionUtils.residuals(DataSet dataSet, Graph graph)	residuals.

Methods in edu.cmu.tetrad.regression with parameters of type DataSet

Modifier and Type	Method	Description
static DataSet	RegressionUtils.residuals(DataSet dataSet, Graph graph)	residuals.

Constructors in edu.cmu.tetrad.regression with parameters of type DataSet

Modifier	Constructor	Description
	LogisticRegression(DataSet dataSet)	A mixed data set.
	RegressionDataset(DataSet data)	Constructs a linear regression model for the given tabular data set.

Uses of DataSet in edu.cmu.tetrad.search

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

Modifier and Type	Method	Description
DataSet	IndTestIod.getData()

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

Modifier and Type	Method	Description
Cdnod.Builder	Cdnod.Builder.data(DataSet data)	Sets the data set for the builder.
CdnodBoss.Builder	CdnodBoss.Builder.data(DataSet dataWithC)	Provide a DataSet that ALREADY ends with C as the last column.
Cdnod.Builder	Cdnod.Builder.dataAndIndex(DataSet dataX, double[] cIndex, String cName)	Sets the data, index array, and potentially a custom name for the builder.
CdnodBoss.Builder	CdnodBoss.Builder.dataAndIndex(DataSet dataX, double[] cIndex, String cName)	Provide X and a continuous change index C to append as the last column.
static Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA)	Estimates the weight matrix W using the Fast Independent Component Analysis (FastICA) algorithm.
static Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA, boolean verbose)	Estimates the weight matrix (W) using the Fast Independent Component Analysis (FastICA) algorithm.
Matrix	IcaLingam.fit(DataSet D)	Fits a dataset to estimate a weight matrix using the ICA-LiNGAM algorithm.
List<Matrix>	IcaLingD.fit(DataSet D)	Convenience: estimate W via FastICA, then enumerate B̂ candidates.
double[][]	CpdagParentDistancesFromTrue.getDistances(Graph outputCpdag, double[][] trueEdgeStrengths, DataSet dataSet, CpdagParentDistancesFromTrue.DistanceType distanceType)	Calculates the distance matrix for the edges in the given CPDAG (outputCpdag).
LinkedList<LinkedList<Cstar.Record>>	Cstar.getRecords(DataSet dataSet, List<Node> possibleCauses, List<Node> possibleEffects, int topBracket, String path)	Returns records for a set of variables with expected number of false positives bounded by q.
static void	NtadExplorerHarness.runDemo(DataSet data, Cca ccaTest, int blockSize, int maxResults, double alpha)	Run the ntad explorer on the given dataset and CCA test, using ALL variables in the dataset as the candidate list.
static void	NtadExplorerHarness.runDemo(DataSet data, List<Node> vars, Cca ccaTest, int blockSize, int maxResults, double alpha)	Runs the NTAD Explorer demo using the specified dataset, variable list, CCA test, and parameters.
CyclicSemScorer.ScoreResult	CyclicSemScorer.score(DataSet data, Graph g)	Computes the score of a given data set and graph by evaluating the Bayesian Information Criterion (BIC), stability of the system, and the number of parameters used.
Graph	Gin.search(DataSet data)	Performs a search operation to generate a causal graph based on the provided dataset.
TwoStep.Result	TwoStep.search(DataSet data)	Deprecated. Performs a search operation over the given dataset to analyze dependencies, refine coefficients, and build a directed graph representation.
void	Cdnod.setData(DataSet data)	Sets the dataset to be used in this instance.
void	Cdnod.setDataAndIndex(DataSet dataX, double[] cIndex, String cName)	Sets the dataset with an additional continuous change-index column added as the last column.
void	CdnodBoss.setDataAndIndex(DataSet dataX, double[] cIndex, String cName)	Updates the internal dataset by appending a change index as the last column.
void	CdnodBoss.setDataWithC(DataSet dataWithC)	Sets the dataset to be used in this instance.

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

Modifier	Constructor	Description
	Builder(DataSet data, IndependenceTest test)	Constructs a Builder instance for configuring and creating a Pcmci object.
	Cam(DataSet data)	Constructs a Cam instance with the given data set.
	Dagma(DataSet dataset)	Constructor.
	DirectLingam(DataSet dataset, Score score)	Constructor.
	FactorAnalysis(DataSet dataSet)	Constructor.
	Fask(DataSet dataSet, Score score)	Constructs a new Fask instance with the specified data set and score.
	FaskOrig(DataSet dataSet, Score score, IndependenceTest test)	Deprecated. Constructor.
	Fofc(DataSet dataSet, double alpha, int ess)	Constructs an instance of the Fofc class using the given dataset, significance level, and equivalent sample size.
	Ftfc(DataSet dataSet, double alpha, int ess, SingleClusterPolicy policy)	Constructs an instance of Ftfc.
	Gffc(DataSet dataSet, double alpha, int rMax, int ess)	Constructs a new Gffc instance with the specified data set, alpha value, maximum rank (rMax), and effective sample size (ess).
	Ida(DataSet dataSet, Graph graph, List<Node> possibleCauses)	Constructor.
	IdaCheck(Graph graph, DataSet dataSet, SemIm trueSemIm)	Constructs a new IDA check for the given PDAG and data set.
	PdagPagIda(DataSet dataSet, Graph graph, List<Node> possibleCauses)	Constructs a new instance of the PdagPagIda class which is used for processing a dataset and graph combination while ensuring the provided graph is a valid DAG, CPDAG/PDAG, or PAG, and the dataset is continuous.
	ScoredClusterFinder(DataSet dataSet, Collection<Integer> candidateVarIndices)	Constructs a ScoredClusterFinder instance using the provided dataset and a collection of candidate variable indices.

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

Modifier	Constructor	Description
	FaskVote(List<DataSet> dataSets, ScoreWrapper score, IndependenceWrapper test)	Constructor.
	Pairwise(Graph graph, List<DataSet> dataSets)	Constructor.

Uses of DataSet in edu.cmu.tetrad.search.blocks

Methods in edu.cmu.tetrad.search.blocks that return DataSet

Modifier and Type	Method	Description
DataSet	BlockSpec.dataSet()	Returns the data set associated with this block specification.

Methods in edu.cmu.tetrad.search.blocks with parameters of type DataSet

Modifier and Type	Method	Description
static BlockDiscoverer	BlockDiscoverers.bpc(DataSet data, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Constructs a BpcBlockDiscoverer instance, which implements the BPC algorithm for discovering clusters or "blocks" of indices in a dataset.
static BlockDiscoverer	BlockDiscoverers.fofc(DataSet data, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Creates a new instance of a FofcBlockDiscoverer, which is an implementation of the BlockDiscoverer interface.
static BlockDiscoverer	BlockDiscoverers.ftfc(DataSet data, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Constructs a FtfcBlockDiscoverer instance, which implements the FTFC algorithm for discovering clusters or "blocks" of indices in a dataset.
static BlockDiscoverer	BlockDiscoverers.gffc(DataSet data, double alpha, int ess, int rMax, SingleClusterPolicy policy, boolean verbose)	Constructs a GffcBlockDiscoverer instance, which implements the GFFC algorithm for discovering clusters or "blocks" of indices in a dataset.
static List<Node>	BlocksUtil.makeBlockVariables(List<List<Integer>> blocks, DataSet dataSet)	Creates a list of block variables based on the provided list of blocks and the dataset.
static BlockSpec	BlocksUtil.makeDisjointSpec(DataSet ds, List<List<Integer>> blocks)	Constructs a BlockSpec object using the provided DataSet and block definitions, ensuring that the blocks are made disjoint by prioritizing larger blocks first.
static BlockSpecTextCodec.ParseResult	BlockSpecTextCodec.parse(String text, DataSet ds)	Parses an input textual representation of block specifications into a structured format.
static BlockSpec	BlocksUtil.toSpec(List<List<Integer>> blocks, DataSet dataSet)	Converts a list of block indices and a dataset into a BlockSpec object, ensuring the blocks are canonicalized and generating the appropriate block variables.
static BlockSpec	BlocksUtil.toSpec(List<List<Integer>> blocks, List<Integer> ranks, DataSet dataSet)	Converts a list of blocks, ranks, and a dataset into a BlockSpec object.
static BlockDiscoverer	BlockDiscoverers.tsc(DataSet data, double alpha, int ess, double ridge, int rMax, SingleClusterPolicy policy, int minRedundancy, boolean verbose)	Creates a new instance of a TscTestBlockDiscoverer, which is an implementation of the BlockDiscoverer interface.
static void	BlocksUtil.validateBlocks(List<List<Integer>> blocks, DataSet data)	Validates the provided list of blocks to ensure that all indices within each block are non-negative, within the range of columns in the given dataset, and not null.

Constructors in edu.cmu.tetrad.search.blocks with parameters of type DataSet

Modifier	Constructor	Description
	BlockSpec(DataSet dataSet, List<List<Integer>> blocks, List<Node> blockVariables)	Constructs an instance of BlockSpec with the specified data set, blocks, and block variables.
	BlockSpec(DataSet dataSet, List<List<Integer>> blocks, List<Node> blockVariables, List<Integer> ranks)	Constructs an instance of BlockSpec with the specified data set, blocks, block variables, and their corresponding ranks.
	BpcBlockDiscoverer(DataSet dataSet, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Constructor for the BpcBlockDiscoverer class, responsible for initiating the discovery of clusters or blocks of indices within a dataset using the BPC (Bayesian Partitioning for Causal Discovery) algorithm.
	FofcBlockDiscoverer(DataSet dataSet, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Constructs a new instance of FofcBlockDiscoverer, which is used to discover clusters or "blocks" of indices in a dataset based on the FOFC algorithm.
	FtfcBlockDiscoverer(DataSet dataSet, double alpha, int ess, SingleClusterPolicy policy, boolean verbose)	Constructs a new instance of FofcBlockDiscoverer, which is used to discover clusters or "blocks" of indices in a dataset based on the FOFC algorithm.
	GffcBlockDiscoverer(DataSet dataSet, double alpha, int ess, int rMax, SingleClusterPolicy policy, boolean verbose)	Constructs a new instance of FofcBlockDiscoverer, which is used to discover clusters or "blocks" of indices in a dataset based on the FOFC algorithm.
	TscTestBlockDiscoverer(DataSet dataSet, double alpha, int ess, double ridge, int rMax, SingleClusterPolicy policy, int minRedundancy, boolean verbose)	Constructs a TscTestBlockDiscoverer instance with the specified parameters for discovering and analyzing blocks of variables in a dataset using the TSC algorithm.

Uses of DataSet in edu.cmu.tetrad.search.cdnod_pag

Methods in edu.cmu.tetrad.search.cdnod_pag with parameters of type DataSet

Modifier and Type	Method	Description
boolean	CgLrtChangeTest.changes(DataSet data, Node y, Set<Node> Z, Node env, double alpha)	Determines whether changes occur based on a test of the dependence of residuals on an environmental variable using a conditional-Gaussian approach.
boolean	ChangeTest.changes(DataSet data, Node y, Set<Node> Z, Node env, double alpha)	Evaluates whether there is a change or instability with respect to the given context variable.
default boolean	ChangeTest.changesAny(DataSet data, Node y, Set<Node> Z, Collection<Node> contexts, double alpha)	Determines whether there is any change or instability with respect to any of the specified context variables.
static void	ChangeTest.requireNonNulls(DataSet data, Node y, Set<Node> Z)	Ensures that the specified objects are not null.
Graph	CdnodPag.PagBuilder.search(DataSet fullData)	Performs a search operation on the provided dataset to build a graph representing relationships or dependencies within the data.
default boolean	ChangeTest.stable(DataSet data, Node y, Set<Node> Z, Node env, double alpha)	Determines whether the data, with respect to a given context variable, is stable (i.e., there is no change or instability).
default boolean	ChangeTest.stableAll(DataSet data, Node y, Set<Node> Z, Collection<Node> contexts, double alpha)	Determines whether the data is stable with respect to all specified context variables, meaning no changes or instability are detected across any of the contexts provided.

Constructors in edu.cmu.tetrad.search.cdnod_pag with parameters of type DataSet

Modifier	Constructor	Description
	CdnodPag(DataSet dataAll, double alpha, ChangeTest changeTest, CdnodPag.PagBuilder pagBuilder, Function<Graph,Boolean> legalityCheck, Function<Graph,Graph> propagator, Knowledge knowledge)	Constructs an instance of the CdnodPag class with the specified parameters.
	ChangeOracle(DataSet data, Node env, double alpha, ChangeTest test)	Constructor for the ChangeOracle class using a single context.
	ChangeOracle(DataSet data, Collection<Node> contexts, double alpha, ChangeTest test)	Constructs a new instance of the ChangeOracle class with multiple contexts.

Uses of DataSet in edu.cmu.tetrad.search.is

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

Modifier and Type	Method	Description
DataSet	IsBDeuScore2.getDataSet()	Retrieves the required effect/gain threshold for determining an effect edge.
DataSet	IsBicScore.getDataSet()	Retrieves the training data set that is utilized within this instance.
DataSet	IsScore.getDataSet()	Retrieves the dataset associated with the scoring or inference process.

Methods in edu.cmu.tetrad.search.is that return types with arguments of type DataSet

Modifier and Type	Method	Description
List<DataSet>	IndTestProbabilisticBDeu.getDataSets()	Retrieves the list of data sets associated with this independence test.
List<DataSet>	IndTestProbabilisticISBDeu.getDataSets()	Retrieves the list of data sets used in the computation.

Constructors in edu.cmu.tetrad.search.is with parameters of type DataSet

Modifier	Constructor	Description
	BDeuScoreWOprior(DataSet dataSet)	Constructs a BDeuScoreWOprior object for scoring discrete data based on the BDeu scoring metric.
	IndTestProbabilisticBDeu(DataSet dataSet, double prior)	Constructs an independence test object using a probabilistic BDeu (Bayesian Dirichlet equivalent uniform) score.
	IndTestProbabilisticISBDeu(DataSet dataSet, DataSet test, double prior)	Constructs an IndTestProbabilisticISBDeu object for probabilistic independence testing based on BDeu scoring, using a specified prior probability.
	IsBDeuScore(DataSet train, DataSet instanceOneRow)	Constructs an instance of the IsBDeuScore class, which validates and compares the schema and data consistency between a training dataset and a single-row instance dataset, ensuring they meet the criteria for Bayesian scoring.
	IsBDeuScore2(DataSet dataSet, DataSet testCase)	Constructs an instance of the IsBDeuScore2 class with the specified training and test datasets.
	IsBicScore(DataSet dataSet, DataSet testCase)	Constructs an instance of the IsBicScore class.

Uses of DataSet in edu.cmu.tetrad.search.rlcd

Methods in edu.cmu.tetrad.search.rlcd that return DataSet

Modifier and Type	Method	Description
DataSet	RLCD.getDataSet()	Retrieves the dataset associated with the RLCD process.

Methods in edu.cmu.tetrad.search.rlcd with parameters of type DataSet

Modifier and Type	Method	Description
IndependenceTest	IndependenceTestFactory.create(DataSet dataSet, double alpha)	Creates an independence test based on the provided dataset and significance level.
RankTest	RankTestFactory.create(DataSet dataSet)	Creates a RankTest instance for the given DataSet.

Constructors in edu.cmu.tetrad.search.rlcd with parameters of type DataSet

Modifier	Constructor	Description
	RLCD(DataSet dataSet, RLCDParams params)	Constructs an instance of the RLCD class.

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

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

Modifier and Type	Method	Description
DataSet	BdeScore.getDataSet()	Returns the DataSet associated with this method.
DataSet	BDeuScore.getDataSet()	Retrieves the dataset associated with this BdeuScore object.
DataSet	DiscreteBicScore.getDataSet()	Returns the dataset.
DataSet	DiscreteBicScoreAdTree.getDataSet()	Returns the dataset.
DataSet	DiscreteScore.getDataSet()	Returns the dataset.
DataSet	GicScores.getDataSet()	Returns the dataset.
DataSet	IndTestScore.getDataSet()	Returns the data set.

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

Modifier and Type	Method	Description
static Matrix	SemBicScore.getCov(List<Integer> rows, int[] cols, int[] all, DataSet dataSet, Matrix cov)	Computes the covariance matrix for the given subset of rows and columns in the provided data set.

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

Modifier	Constructor	Description
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda)	Constructs a BasisFunctionBicScore object with the specified parameters.
	BasisFunctionBicScoreFullSample(DataSet dataSet, int truncationLimit, double lambda)	Constructs a BasisFunctionBicScore object with the specified parameters.
	BdeScore(DataSet dataSet)	Constructs a BDe score for the given dataset.
	BDeuScore(DataSet dataSet)	Constructs a BDe score for the given dataset.
	CamAdditivePsplineBic(DataSet raw)	Constructs an instance of CamAdditivePsplineBic.
	ConditionalGaussianLikelihood(DataSet dataSet)	Constructs the score using a covariance matrix.
	ConditionalGaussianScore(DataSet dataSet, double penaltyDiscount, boolean discretize)	Constructs the score.
	DegenerateGaussianScore(DataSet dataSet, boolean precomputeCovariances, double lambda)	Constructs the score using a dataset.
	DiscreteBicScore(DataSet dataSet)	Constructs the score using a dataset.
	DiscreteBicScoreAdTree(DataSet dataSet)	Constructs the score using a dataset.
	EbicScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	GicScores(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	KcvBicScore(DataSet dataSet)	Constructor for the KcvBicScore class.
	MvpLikelihood(DataSet dataSet, double structurePrior, int fDegree, boolean discretize)	Constructs the score using a data set.
	MvpScore(DataSet dataSet, double structurePrior, int fDegree, boolean discretize, int effectiveSampleSize)	Constructs an MvpScore instance with the given dataset and parameters.
	PoissonPriorScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	RffBicScore(DataSet dataSet)	Constructs an instance of the RffBicScore class.
	SemBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	SemBicScore(DataSet dataSet, double penaltyDiscount, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	ZsbScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.

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

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

Modifier and Type	Method	Description
DataSet	IndTestChiSquare.getData()	Returns the data being analyzed.
DataSet	IndTestConditionalCorrelation.getData()	Returns the data set being analyzed.
DataSet	IndTestConditionalGaussianLrt.getData()	Returns the data.
DataSet	IndTestDegenerateGaussianLrt.getData()	Retrieves the data set used for the independence test.
DataSet	IndTestFisherZ.getData()	Retrieves the data set associated with the current instance.
DataSet	IndTestFisherZConcatenateResiduals.getData()	Deprecated. Returns the concatenated data.
DataSet	IndTestFisherZFisherPValue.getData()	Returns the concatenated data.
DataSet	IndTestGin.getData()	Returns the current regressor being used for the independence test.
DataSet	IndTestGSquare.getData()	Returns the data.
DataSet	IndTestHsic.getData()	Deprecated. Returns the data set being analyzed.
DataSet	IndTestMulti.getData()	Retrieves the data set.
DataSet	IndTestMvpLrt.getData()	Returns the data.
DataSet	IndTestRcit.getData()	Returns the data used for the independence test.
DataSet	IndTestRcot.getData()	Retrieves the dataset associated with this instance.
DataSet	IndTestRegression.getData()	Deprecated. Returns the data used.
DataSet	IndTestTrekSep.getData()	Deprecated. Gets the data set used for the independence test.
DataSet	MsepTest.getData()	Returns the data set used for the test.

Methods in edu.cmu.tetrad.search.test that return types with arguments of type DataSet

Modifier and Type	Method	Description
default List<DataSet>	IndependenceTest.getDataSets()	Returns the datasets for this test
List<DataSet>	IndTestFdrWrapper.getDataSets()	Retrieves the data sets used in the underlying independence test.
List<DataSet>	IndTestFisherZ.getDataSets()	Retrieves a list of data sets associated with this instance.
List<DataSet>	IndTestTrekSep.getDataSets()	Deprecated. Returns the data sets used for the independence test.
List<DataSet>	ScoreIndTest.getDataSets()

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

Modifier	Constructor	Description
	ChiSquareTest(DataSet dataSet, double alpha, ChiSquareTest.TestType testType, List<Integer> rows)	Constructs a test using the given data set and significance level.
	ConditionalCorrelationIndependence(DataSet dataSet, int basisType, double basisScale, int numFunctions)	Initializes a new instance of the ConditionalCorrelationIndependence class using the provided DataSet.
	IndTestBasisFunctionBlocks(DataSet dataSet, int truncationLimit, int basisType)	Constructs an instance of IndTestBasisFunctionBlocks.
	IndTestBasisFunctionLrt(DataSet dataSet, int truncationLimit, double lambda)	Deprecated. Constructs an instance of IndTestBasisFunctionLrtCovariance.
	IndTestBasisFunctionLrtFullSample(DataSet dataSet, int truncationLimit, double lambda)	Deprecated. Constructs an instance of the IndTestBasisFunctionLrt class.
	IndTestChiSquare(DataSet dataSet, double alpha)	Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.
	IndTestConditionalCorrelation(DataSet dataSet, double alpha, double scalingFactor, int basisType, double basisScale, int numFunctions)	Constructs a new IndTestConditionalCorrelation to check independence based on conditional correlations derived from the given continuous data set.
	IndTestConditionalGaussianLrt(DataSet data, double alpha, boolean discretize)	Constructor.
	IndTestDegenerateGaussianLrt(DataSet dataSet)	Constructs the test using the given (mixed) data set.
	IndTestDegenerateGaussianLrtFullSample(DataSet dataSet)	Deprecated. Constructs an instance of the IndTestBasisFunctionLrt class.
	IndTestFisherZ(DataSet dataSet, double alpha)	Constructs an independence test using the Fisher Z test statistic.
	IndTestFisherZ(DataSet dataSet, double alpha, double ridge)	Constructor for IndTestFisherZ which initializes the independence test using the Fisher Z test with specified parameters.
	IndTestGin(DataSet data)	Constructs an instance of IndTestGin with the specified dataset.
	IndTestGin(DataSet data, IndTestGin.Regressor regressor, IndTestGin.UncondIndTest backend)	Constructs an instance of IndTestGin with the specified dataset, regressor, and backend components.
	IndTestGSquare(DataSet dataSet, double alpha)	Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.
	IndTestHsic(DataSet dataSet, double alpha)	Deprecated. Constructs a new HSIC Independence test.
	IndTestMvpLrt(DataSet data, double alpha, int fDegree, boolean discretize)	Constructor.
	IndTestProbabilistic(DataSet dataSet)	Initializes the test using a discrete data sets.
	IndTestRcit(DataSet dataSet)	Constructs an instance of the IndTestRcit class, which initializes the test with the given data set and default parameters.
	IndTestRcit(DataSet dataSet, Parameters params)	Constructs an instance of the IndTestRcit class, which initializes the test with the given data set and parameters.
	IndTestRcot(DataSet dataSet)	Constructs an instance of IndTestRcot with default parameters.
	IndTestRcot(DataSet dataSet, Parameters params)	Constructs an instance of IndTestRcot with Parameters (legacy keys match IndTestRcit style).
	IndTestRegression(DataSet dataSet, double alpha)	Deprecated. Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	Kci(DataSet dataSet)	Constructs a Kci instance with the given DataSet.

Constructor parameters in edu.cmu.tetrad.search.test with type arguments of type DataSet

Modifier	Constructor	Description
	IndTestFisherZConcatenateResiduals(List<DataSet> dataSets, double alpha)	Deprecated. Constructor.
	IndTestFisherZFisherPValue(List<DataSet> dataSets, double alpha)	Constructor.

Uses of DataSet in edu.cmu.tetrad.search.unmix

Fields in edu.cmu.tetrad.search.unmix with type parameters of type DataSet

Modifier and Type	Field	Description
final List<DataSet>	UnmixResult.clusterData	A list of datasets corresponding to individual clusters obtained from a clustering algorithm.
Function<CausalUnmixer.Config, Function<DataSet,Graph>>	CausalUnmixer.Config.perClusterGraphFn	A function that generates a cluster-specific graph creation method.
Function<CausalUnmixer.Config, Function<DataSet,Graph>>	CausalUnmixer.Config.pooledGraphFn	A function that generates a pooled graph model based on a given configuration and dataset.
Function<DataSet,Graph>	ParentSupersetBuilder.Config.shallowSearch	If provided, called on each subsample to get a shallow graph (e.g., PC-Max depth 1)

Methods in edu.cmu.tetrad.search.unmix with parameters of type DataSet

Modifier and Type	Method	Description
static Map<Node, List<Node>>	ParentSupersetBuilder.build(DataSet data, ParentSupersetBuilder.Config cfg)	Builds a map representing a superset of parent nodes for each variable node in the given data set.
static UnmixDiagnostics.BicDelta	UnmixDiagnostics.computeBicDeltaK1K2(DataSet data, EmUnmix.Config baseCfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster)	Computes the Bayesian Information Criterion (BIC) for mixture models with K=1 and K=2 clusters, and calculates the BIC difference (delta) between them.
void	LinearQRRegressor.fit(DataSet data, Node target, List<Node> parents)	Fits the linear regression model to the given data.
void	ResidualRegressor.fit(DataSet data, Node target, List<Node> parents)	Fits a local mechanism for the target variable using its parents from the provided dataset.
static @NotNull UnmixResult	CausalUnmixer.getUnmixedResult(DataSet data)	Retrieves the unmixing result by processing the provided dataset using default configuration settings.
static @NotNull UnmixResult	CausalUnmixer.getUnmixedResult(DataSet data, @NotNull CausalUnmixer.Config cfg)	Retrieves the unmixing results from the provided dataset and configuration.
double[]	LinearQRRegressor.predict(DataSet data, Node target, List<Node> parents)	Predicts the regression output for the given dataset, target node, and parent nodes using the model parameters.
double[]	ResidualRegressor.predict(DataSet data, Node target, List<Node> parents)	Predicts fitted values for all rows for the given target using the current fitted model.
static double[][]	ResidualUtils.residualMatrix(DataSet data, Graph g, ResidualRegressor reg)	Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given graph.
static double[][]	ResidualUtils.residualMatrix(DataSet data, Map<Node, List<Node>> parentsMap, ResidualRegressor reg)	Constructs a residual matrix where each column corresponds to a variable in the dataset and contains the residuals obtained after regressing that variable on its parents as specified by the given parent map.
default double[]	ResidualRegressor.residuals(DataSet data, Node target, List<Node> parents)	Convenience method computing residuals = y - yhat for all rows.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor)	Runs the unmixing process on the provided dataset using the specified configuration and regressor.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch)	Executes the unmixing process on the given dataset using the specified configuration, residual regressor, and optional graph search functions.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing the provided dataset within the specified range.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing a dataset within the specified range [Kmin, Kmax].
static UnmixDiagnostics.StabilityResult	UnmixDiagnostics.stabilityAcrossRestarts(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster, int repeats, long seedBase)	Evaluates the stability of clustering results across multiple independent runs of the EM algorithm by calculating the average Adjusted Rand Index (ARI) and its standard deviation between all pairs of runs.

Method parameters in edu.cmu.tetrad.search.unmix with type arguments of type DataSet

Modifier and Type	Method	Description
static UnmixDiagnostics.BicDelta	UnmixDiagnostics.computeBicDeltaK1K2(DataSet data, EmUnmix.Config baseCfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster)	Computes the Bayesian Information Criterion (BIC) for mixture models with K=1 and K=2 clusters, and calculates the BIC difference (delta) between them.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch)	Executes the unmixing process on the given dataset using the specified configuration, residual regressor, and optional graph search functions.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing a dataset within the specified range [Kmin, Kmax].
static UnmixDiagnostics.StabilityResult	UnmixDiagnostics.stabilityAcrossRestarts(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster, int repeats, long seedBase)	Evaluates the stability of clustering results across multiple independent runs of the EM algorithm by calculating the average Adjusted Rand Index (ARI) and its standard deviation between all pairs of runs.

Constructor parameters in edu.cmu.tetrad.search.unmix with type arguments of type DataSet

Modifier	Constructor	Description
	UnmixResult(int[] labels, int K, List<DataSet> clusterData, GaussianMixtureEM.Model gmmModel)	Constructs an instance of UnmixResult containing essential clustering outcome information without graphical representations.
	UnmixResult(int[] labels, int K, List<DataSet> clusterData, List<Graph> clusterGraphs, GaussianMixtureEM.Model gmmModel)	Constructs an instance of UnmixResult containing information about the results of a clustering algorithm.

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

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

Modifier and Type	Method	Description
static DataSet	TsUtils.addIndex(DataSet data)	Creates new time series dataset from the given one with index variable (e.g., time)
static DataSet	MissingnessIndicatorAdder.addMissingnessIndicators(DataSet dataSet)	Adds missingness indicators to a dataset.
static DataSet	TsUtils.ar(DataSet timeSeries, int numLags)	ar.
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)	ar2.
static DataSet	TsUtils.createLagData(DataSet data, int numLags)	Creates new time series dataset from the given one (fixed to deal with mixed datasets)
static DataSet	TsUtils.createShiftedData(DataSet data, int[] shifts)	createShiftedData.
static DataSet	TsUtils.difference(DataSet data, int d)	Calculates the dth difference of the given data.
DataSet	Embedding.EmbeddedData.embeddedData()	Returns the value of the embeddedData record component.
DataSet	TetradTest.getDataSet()	getDataSet.
DataSet	TetradTestContinuous.getDataSet()	Getter for the field dataSet.
DataSet	TetradTestDiscrete.getDataSet()	Getter for the field dataSet.
DataSet	TetradTestPopulation.getDataSet()	getDataSet.
static @NotNull DataSet	LgMnarDataSimulator.getMnarData(Graph graph, int numVariablesWithMissing, int numExtraInfluences, double threshold, int numRows)	Generates a dataset with Missing Not At a Random (MNAR) data mechanism applied to specific variables in a graph.
DataSet	TsUtils.VarResult.getResiduals()	Getter for the field residuals.
DataSet	Embedding.EmbeddedData.originalData()	Returns the value of the originalData record component.

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

Modifier and Type	Method	Description
static DataSet	TsUtils.addIndex(DataSet data)	Creates new time series dataset from the given one with index variable (e.g., time)
static DataSet	MissingnessIndicatorAdder.addMissingnessIndicators(DataSet dataSet)	Adds missingness indicators to a dataset.
static DataSet	TsUtils.ar(DataSet timeSeries, int numLags)	ar.
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)	ar2.
static Matrix	KernelUtils.constructCentralizedGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes)	Constructs the centralized Gram matrix for a given vector valued sample.
static Matrix	KernelUtils.constructGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes)	Constructs Gram matrix for a given vector valued sample.
static DataSet	TsUtils.createLagData(DataSet data, int numLags)	Creates new time series dataset from the given one (fixed to deal with mixed datasets)
static DataSet	TsUtils.createShiftedData(DataSet data, int[] shifts)	createShiftedData.
static DataSet	TsUtils.difference(DataSet data, int d)	Calculates the dth difference of the given data.
static @NotNull Embedding.EmbeddedData	Embedding.getEmbeddedData(DataSet dataSet, int truncationLimit, int basisType, double basisScale)	Computes the embedded data representation based on the provided dataset and parameters.
static double[]	TsUtils.getSelfLoopCoefs(DataSet timeSeries)	getSelfLoopCoefs.
static Matrix	KernelUtils.incompleteCholeskyGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes, double precision)	Approximates Gram matrix using incomplete Cholesky factorization
void	Kernel.setDefaultBw(DataSet dataset, Node node)	Sets bandwidth from data using default method
void	KernelGaussian.setDefaultBw(DataSet dataset, Node node)	Sets bandwidth from data using default method
void	KernelGaussian.setMedianBandwidth(DataSet dataset, Node node)	Sets the bandwidth of the kernel to median distance between two points in the given vector
static TsUtils.VarResult	TsUtils.structuralVar(DataSet timeSeries, int numLags)	structuralVar.
static double	TsUtils.sumOfArCoefficients(DataSet timeSeries, int numLags)	sumOfArCoefficients.

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

Modifier	Constructor	Description
	AdTree(DataSet dataSet)	Constructs an AD Leaf Tree for the given dataset, without subsampling.
	AdTree(DataSet dataSet, List<Integer> rows)	Constructs an AD Leaf Tree for the given dataset.
	DeltaSextadTest(DataSet dataSet)	Constructs a test using a given data set.
	DeltaTetradTest(DataSet dataSet)	Constructor for Gaussian data.
	DeltaTetradTest2(DataSet dataSet)	Constructs a test using a given data set.
	DeltaTetradTest3(DataSet dataSet)	Constructor for Gaussian data.
	EmbeddedData(DataSet originalData, DataSet embeddedData, Map<Integer, List<Integer>> embedding)	Creates an instance of a EmbeddedData record class.
	FgesOrienter(DataSet dataSet)	The data set must either be all continuous or all discrete.
	KernelGaussian(DataSet dataset, Node node)	Creates a new Gaussian kernel using the median distance between points to set the bandwidth
	TetradTestContinuous(DataSet dataSet, BpcTestType sigTestType, double sig)	Constructor for TetradTestContinuous.
	TetradTestDiscrete(DataSet dataSet, double sig)	Constructor for TetradTestDiscrete.
	VarResult(DataSet dataSet, Graph collapsedVarGraph)	Constructs a new result.

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

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

Modifier and Type	Method	Description
DataSet	DMSearch.getData()	Getter for the field data.
DataSet	IndTestCramerT.getData()	Deprecated. Retrieves the dataset used in the independence test.
DataSet	IndTestFisherZPercentIndependent.getData()	Deprecated. Retrieves the data set from the method.
DataSet	IndTestFisherZRecursive.getData()	Deprecated. getData.
DataSet	IndTestMixedMultipleTTest.getData()	Deprecated. Returne the original data for the method.
DataSet	IndTestMultinomialLogisticRegression.getData()	Retrieves the original data used for the independence test.
DataSet	IndTestPositiveCorr.getData()	Deprecated. Retrieve the data set used in the independence test.
DataSet	IndTestSepsetDci.getData()	getData.
DataSet	SemBicScoreDeterministic.getDataSet()	getDataSet.
DataSet[]	MixtureModel.getDemixedData()	getDemixedData.

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

Modifier and Type	Method	Description
List<DataSet>	IndTestFisherZPercentIndependent.getDataSets()	Deprecated. Retrieves the list of data sets.
List<DataSet>	IndTestFisherZRecursive.getDataSets()	Deprecated. Returns the datasets for this test
List<DataSet>	IndTestPositiveCorr.getDataSets()	Deprecated. Retrieves the data sets used in the independence test.

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

Modifier and Type	Method	Description
void	DMSearch.setData(DataSet data)	Setter for the field data.

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

Modifier	Constructor	Description
	BpcTetradPurifyWashdown(DataSet dataSet, BpcTestType testType, double alpha)	Construct the algorithm using a data set.
	Demixer(DataSet data, int k)	Constructor.
	HbsmsBeam(Graph graph, DataSet data, Knowledge knowledge)	Constructor for HbsmsBeam.
	HbsmsGes(Graph graph, DataSet data)	Constructor for HbsmsGes.
	IndTestCramerT(DataSet dataSet, double alpha)	Deprecated. Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestFisherZRecursive(DataSet dataSet, double alpha)	Deprecated. Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	IndTestMixedMultipleTTest(DataSet data, double alpha)	Deprecated. Constructor for IndTestMixedMultipleTTest.
	IndTestMultinomialLogisticRegression(DataSet data, double alpha)	Constructor for IndTestMultinomialLogisticRegression.
	IndTestPositiveCorr(DataSet dataSet, double alpha)	Deprecated. Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
	InverseCorrelation(DataSet dataSet, double threshold)	Constructor for InverseCorrelation.
	Kpc(DataSet dataset, double alpha)	Deprecated. Constructs a new PC search using for the given dataset.
	MagCgBicScore(DataSet dataSet)	Constructor.
	MagCgBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	MagDgBicScore(DataSet dataSet)	Constructor.
	MagDgBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	MagSemBicScore(DataSet dataSet, boolean precomputeCovariances)	Constructor.
	MixtureModel(DataSet data, double[][] dataArray, double[][] meansArray, double[] weightsArray, Matrix[] variancesArray, double[][] gammaArray)	Constructs a mixture model from a mixed data set, a means matrix, a weights array, a variance matrix, and a gamma matrix.
	Mmhc(IndependenceTest test, DataSet dataSet)	Constructor for Mmhc.
	ProbabilisticMapIndependence(DataSet dataSet)	Initializes the test using a discrete data sets.
	Washdown(DataSet data, double alpha)	Constructor.

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

Modifier	Constructor	Description
	IndTestFisherZPercentIndependent(List<DataSet> dataSets, double alpha)	Deprecated. Initializes an object of the class IndTestFisherZPercentIndependent.

Uses of DataSet in edu.cmu.tetrad.sem

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

Modifier and Type	Method	Description
DataSet	SemIm.CyclicSimResult.dataSet()	Returns the value of the dataSet record component.
DataSet	SemIm.Result.dataSet()	Returns the value of the dataSet record component.
DataSet	AdditiveAnmSimulator.generate()	Generates a synthetic dataset based on the configured directed acyclic graph (DAG) structure using additive noise models.
DataSet	AdditiveNoiseDjl.generateData()	Generates synthetic data based on a directed acyclic graph (DAG) with causal relationships and post-nonlinear causal mechanisms.
DataSet	AdditiveNoiseSimulation.generateData()	Generates a synthetic dataset by simulating data propagation through a graph with additive noise.
DataSet	AdditiveNoiseSimulationOld.generateData()	Generates synthetic data based on a directed acyclic graph (DAG) with causal relationships and post-nonlinear causal mechanisms.
DataSet	GeneralNoiseSimulation.generateData()	Generates a dataset based on the causal structure defined by the network's graph.
DataSet	NonlinearFunctionOfLinear.generateData()	Generates synthetic data based on a directed acyclic graph (DAG) with causal relationships and post-nonlinear causal mechanisms.
DataSet	PostnonlinearSem.generateData()	Generates synthetic data based on the directed acyclic graph (DAG) with post-nonlinear causal mechanisms.
DataSet	DagScorer.getDataSet()	Getter for the field dataSet.
DataSet	Scorer.getDataSet()	getDataSet.
DataSet	SemEstimator.getDataSet()	Getter for the field dataSet.
DataSet	GeneralizedSemIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data based on the given parameters.
DataSet	SemIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
DataSet	StandardizedSemIm.simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
DataSet	GeneralizedSemIm.simulateDataAvoidInfinity(int sampleSize, boolean latentDataSaved)	Simulates data avoiding infinity values.
DataSet	SemIm.simulateDataCholesky(int sampleSize, boolean latentDataSaved)	Simulates data from this Sem using a Cholesky decomposition of the implied covariance matrix.
DataSet	GeneralizedSemIm.simulateDataFisher(int sampleSize)	Simulates data using the model of R.
DataSet	GeneralizedSemIm.simulateDataFisher(int sampleSize, int intervalBetweenShocks, double epsilon)	Simulates data using the model of R.
DataSet	LargeScaleSimulation.simulateDataFisher(double[][] shocks, int intervalBetweenShocks, double epsilon)	Simulates data using the model of R.
DataSet	LargeScaleSimulation.simulateDataFisher(int sampleSize)	Simulates data using the model of R.
DataSet	LargeScaleSimulation.simulateDataFisher(int intervalBetweenShocks, int intervalBetweenRecordings, int sampleSize, double epsilon, boolean saveLatentVars)	simulateDataFisher.
DataSet	GeneralizedSemIm.simulateDataMinimizeSurface(int sampleSize, boolean latentDataSaved)	Simulates data by minimizing the surface defined by the given sample size and whether latent data is saved.
DataSet	GeneralizedSemIm.simulateDataNSteps(int sampleSize, boolean latentDataSaved)	Simulates data for a given number of steps.
DataSet	GeneralizedSemIm.simulateDataRecursive(int sampleSize, boolean latentDataSaved)	This simulates data by picking random values for the exogenous terms and percolating this information down through the SEM, assuming it is acyclic.
DataSet	LargeScaleSimulation.simulateDataRecursive(int sampleSize)	This simulates data by picking random values for the exogenous terms and percolating this information down through the SEM, assuming it is acyclic.
DataSet	SemIm.simulateDataRecursive(int sampleSize, boolean latentDataSaved)	simulateDataRecursive.
DataSet	LargeScaleSimulation.simulateDataReducedForm(int sampleSize)	Simulates data using the model X = (I - B)Y^-1 * e.
DataSet	SemIm.simulateDataReducedForm(int sampleSize, boolean latentDataSaved)	simulateDataReducedForm.
DataSet	StandardizedSemIm.simulateDataReducedForm(int sampleSize, boolean latentDataSaved)	simulateDataReducedForm.

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

Modifier and Type	Method	Description
GeneralizedSemIm	GeneralizedSemEstimator.estimate(GeneralizedSemPm pm, DataSet data)	Maximizes likelihood equation by equation.
static List<String>	ReidentifyVariables.reidentifyVariables2(List<List<Node>> clusters, Graph trueGraph, DataSet data)	reidentifyVariables2.
void	SemIm.setDataSet(DataSet dataSet)	Calculates the covariance matrix of the given DataSet and sets the sample covariance matrix for this model to a subset of it.

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

Modifier	Constructor	Description
	CyclicSimResult(DataSet dataSet, SemIm semIm)	Creates an instance of a CyclicSimResult record class.
	DagScorer(DataSet dataSet)	Constructs a new SemEstimator that uses the specified optimizer.
	Result(IndTestFisherZ.ShrinkageMode shrinkageMode, DataSet dataSet, int N, SemIm im)	Creates an instance of a Result record class.
	SemEstimator(DataSet dataSet, SemPm semPm)	Constructs a Sem Estimator that does default estimation.
	SemEstimator(DataSet dataSet, SemPm semPm, SemOptimizer semOptimizer)	Constructs a new SemEstimator that uses the specified optimizer.

Uses of DataSet in edu.cmu.tetrad.simulation

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

Modifier and Type	Method	Description
DataSet	Hsim.hybridsimulate()	hybridsimulate.
DataSet	HsimContinuous.hybridsimulate()	hybridsimulate.

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

Modifier and Type	Method	Description
static VerticalIntDataBox	HsimUtils.makeVertIntBox(DataSet dataset)	makeVertIntBox.
void	GdistanceRandom.setLocationMap(DataSet map)	Setter for the field locationMap.

Constructors in edu.cmu.tetrad.simulation with parameters of type DataSet

Modifier	Constructor	Description
	Gdistance(DataSet locationMap, double xDist, double yDist, double zDist)	Constructor for Gdistance.
	GdistanceRandom(DataSet inMap)	Constructor for GdistanceRandom.
	Hsim(Dag thedag, Set<Node> thesimnodes, DataSet thedata)	Constructor for Hsim.
	HsimAutoC(DataSet indata)	Constructor for HsimAutoC.
	HsimAutoRun(DataSet indata)	Constructor for HsimAutoRun.
	HsimContinuous(Dag thedag, Set<Node> thesimnodes, DataSet thedata)	Constructor for HsimContinuous.
	HsimRepeatAC(DataSet indata)	Constructor for HsimRepeatAC.
	HsimRepeatAutoRun(DataSet indata)	Constructor for HsimRepeatAutoRun.
	Vicinity(List<Edge> edges, DataSet locationMap, int xLow, int xHigh, int yLow, int yHigh, int zLow, int zHigh, double xDist, double yDist, double zDist)	Constructor for Vicinity.

Uses of DataSet in edu.cmu.tetrad.util

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

Modifier and Type	Method	Description
static DataSet	DataSetHelper.fromR(List<Node> vars, int nrows, double[][] cont, int[][] disc)	Creates a new DataSet instance from the specified inputs, including continuous and discrete data along with variable definitions.

Uses of DataSet in edu.pitt.csb.mgm

Methods in edu.pitt.csb.mgm that return DataSet

Modifier and Type	Method	Description
DataSet	IndTestMultinomialLogisticRegressionWald.getData()	Retrieves the original dataset used for the independence test.

Constructors in edu.pitt.csb.mgm with parameters of type DataSet

Modifier	Constructor	Description
	IndTestMultinomialLogisticRegressionWald(DataSet data, double alpha, boolean preferLinear)	Constructs a new instance of IndTestMultinomialLogisticRegressionWald with the specified parameters.

Uses of DataSet in edu.pitt.csb.stability

Methods in edu.pitt.csb.stability with parameters of type DataSet

Modifier and Type	Method	Description
abstract Graph	DataGraphSearch.search(DataSet data)	search.

Uses of DataSet in edu.pitt.dbmi.algo.bayesian.constraint.search

Constructors in edu.pitt.dbmi.algo.bayesian.constraint.search with parameters of type DataSet

Modifier	Constructor	Description
	PagSamplingRfci(DataSet dataSet)	Constructor.