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

Packages that use DataSet

Package	Description
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.algcomparison.sweep
edu.cmu.tetrad.bayes
edu.cmu.tetrad.calculator
edu.cmu.tetrad.calibration
edu.cmu.tetrad.classify
edu.cmu.tetrad.data
edu.cmu.tetrad.data.audit
edu.cmu.tetrad.data.missing
edu.cmu.tetrad.estimate.v1
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.harness
edu.cmu.tetrad.search.harness.tsbi
edu.cmu.tetrad.search.harness.tsc
edu.cmu.tetrad.search.is
edu.cmu.tetrad.search.mimic
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.study.missingness
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.
	TrainedDagModel(RandomGraph randomGraph, DataSet dataSet)	Constructs a TrainedDagModel using the given randomGraph and data set.

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

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

Modifier and Type	Method	Description
static List<DataSet>	ParameterSweep.drawBlockResamples(DataSet data, int[] groups, int numResamples, double percent, boolean withReplacement, long seed)	Draws seed-controlled block resamples: each resample is the concatenation of the rows of percent * G groups drawn from the G distinct groups, with or without replacement, where a group is the set of rows sharing a label in groups.
static List<DataSet>	ParameterSweep.drawResamples(DataSet data, int numResamples, double percent, boolean withReplacement, long seed)	Draws seed-controlled resamples of the given dataset: row-index samples of size percent * n, with or without replacement.

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

Modifier and Type	Method	Description
static List<DataSet>	ParameterSweep.drawBlockResamples(DataSet data, int[] groups, int numResamples, double percent, boolean withReplacement, long seed)	Draws seed-controlled block resamples: each resample is the concatenation of the rows of percent * G groups drawn from the G distinct groups, with or without replacement, where a group is the set of rows sharing a label in groups.
static List<DataSet>	ParameterSweep.drawResamples(DataSet data, int numResamples, double percent, boolean withReplacement, long seed)	Draws seed-controlled resamples of the given dataset: row-index samples of size percent * n, with or without replacement.
SweepReport	ParameterSweep.sweep(DataSet data, String parameter, List<?> values)	Sweeps a single parameter over the given values.
SweepReport	ParameterSweep.sweep(DataSet data, String parameter1, List<?> values1, String parameter2, List<?> values2)	Sweeps the cross product of two parameters over the given value lists, varying the second parameter fastest.
SweepReport	ParameterSweep.sweepSettings(DataSet data, List<Map<String,Object>> settings)	Sweeps an explicit list of settings, each a map from parameter name to value.

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

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.

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
	BayesPm(Graph graph, DataSet dataSet)	Constructs a new BayesPm using the provided DAG and dataset.
	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.
	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.
DataSet	MissingnessInjector.Result.data()	Returns the value of the data record component.
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()	Retrieves the current DataSet instance associated with this object.
static DataSet	SimpleDataLoader.getDiscreteDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is discrete.
static DataSet	DataTransforms.getLinearizedTransformed(DataSet dataSet)	Returns a linearized ("un-warped") version of the dataset: each variable is passed through a monotone Yeo-Johnson transform chosen to make its relations to the other variables as linear as possible (post-nonlinear model un-warping).
static DataSet	SimpleDataLoader.getMixedDataSet(DataModel dataSet)	Returns the datamodel case to DataSet if it is mixed.
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)	Returns a nonparanormal-transformed version of the dataset.
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	JoinDatasets.Result.joined()	Returns the value of the joined record component.
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.
DataSet	Linearizer.linearize()	Runs the transform and returns the linearized dataset (same variables, columns standardized).
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.logData(DataSet dataSet, Map<String, LogTransformSpec> specs)	Applies a logarithmic transform to selected variables of the given dataset, each with its own offset, base, and direction.
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.
DataSet	LongToWide.Result.wide()	Returns the value of the wide record component.

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

Modifier and Type	Method	Description
static List<DataSet>	DataTransforms.alignVariableOrder(List<DataSet> dataSets, List<String> names)	Puts the variables of each of the given data sets in the given order of names.
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 int	DataTransforms.appendBlockIdColumn(DataSet dataSet, List<String> keyColumns, String columnName)	Appends a new block ID column to the given dataset.
static List<String>	DataTransforms.appendWithinBlockCenteredColumns(DataSet dataSet, String blockColumn, String suffix)	Appends within-block centered ("fixed effects" / within-transformed) copies of the data set's continuous columns, in place: for each continuous column that varies within blocks, a new column named name + suffix is appended holding the values minus their within-block observed means.
JoinDatasets.Result	JoinDatasets.apply(DataSet left, DataSet right)	Applies the join.
LongToWide.Result	LongToWide.apply(DataSet data)	Applies the transform.
static DataSet	DataTransforms.center(DataSet data)	Subtracts the mean of each column from each datum that column.
List<String>	LongToWide.columnNames(DataSet data)	Returns the names the wide columns would have, in order, without building the wide data set: the kept row keys (if any), then for each value variable one name per observed level of the column key.
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.covarianceGaussianCopula(DataSet dataSet)	Returns the Gaussian copula ("nonparanormal") correlation matrix of a continuous data set: for each pair of variables the sample Kendall's tau-b is computed and mapped to the latent Gaussian correlation by
static ICovarianceMatrix	DataTransforms.covarianceNonparanormalDrton(DataSet dataSet)	Deprecated. This does NOT compute the nonparanormal (Gaussian copula) correlation estimator, despite the name.
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.
static ICovarianceMatrix	EmCovarianceEstimator.estimateCovariance(DataSet dataSet)	Convenience method: estimates and returns the covariance matrix for the given dataset using default settings.
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.getLinearizedTransformed(DataSet dataSet)	Returns a linearized ("un-warped") version of the dataset: each variable is passed through a monotone Yeo-Johnson transform chosen to make its relations to the other variables as linear as possible (post-nonlinear model un-warping).
static DataSet	DataTransforms.getNonparanormalTransformed(DataSet dataSet)	Returns a nonparanormal-transformed version of the dataset.
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 MissingnessInjector.Result	MissingnessInjector.inject(DataSet data, MissingnessInjector.Spec spec)	Injects missing values into a copy of the data, MCAR.
static MissingnessInjector.Result	MissingnessInjector.inject(DataSet data, MissingnessInjector.Spec spec, MissingnessInjector.Mechanism mechanism)	Injects missing values into a copy of the data under the given mechanism.
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.logData(DataSet dataSet, Map<String, LogTransformSpec> specs)	Applies a logarithmic transform to selected variables of the given dataset, each with its own offset, base, and direction.
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.alignVariableOrder(List<DataSet> dataSets, List<String> names)	Puts the variables of each of the given data sets in the given order of names.
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.
	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.
	EmCovarianceEstimator(DataSet dataSet)	Constructs an estimator for the given continuous dataset, whose missing values are represented as Double.NaN.
	Histogram(DataSet dataSet, String target, boolean removeZeroPointsPerPlot)	Constructs a Histogram object for the given data set.
	Linearizer(DataSet dataSet)	Constructs a linearizer for the given continuous dataset.
	Result(DataSet joined, String report)	Creates an instance of a Result record class.
	Result(DataSet wide, List<List<String>> unitKeys, String report)	Creates an instance of a Result record class.
	Result(DataSet data, MissingnessInjector.Report report, MissingnessInjector.MechanismReport mechanism)	Creates an instance of a Result record class.

Uses of DataSet in edu.cmu.tetrad.data.audit

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

Modifier and Type	Method	Description
static DeterministicClusters.Constraint	DeterministicClusters.constraintFor(DataSet data, String varName, double threshold)	Fits the equation writing the given variable as a function of the other continuous variables of the dataset.
static List<DeterministicClusters.Constraint>	DeterministicClusters.find(DataSet data, double threshold)	Finds deterministic constraints among the continuous variables of the dataset: while some continuous variable has residual fraction below the threshold given the other active continuous variables, its (screened) support is pruned to a minimal set still meeting the threshold, the constraint is recorded, and the variable is removed from the active set -- so multiple and overlapping constraints are each reported once.
static int	DataAudit.recodeToMissing(DataSet dataSet, String variable, double value)	Sets every cell of the named continuous variable that holds the given value to missing (NaN), in place, and returns the number of cells changed.

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

Modifier	Constructor	Description
	DataAudit(DataSet dataSet)	Audits the given dataset with default thresholds.
	DataAudit(DataSet dataSet, DataAudit.Config config)	Audits the given dataset with the given thresholds.
	Fitter(DataSet data, double threshold)	Constructs the fitter, computing means, standard deviations, and the correlation matrix once.

Uses of DataSet in edu.cmu.tetrad.data.missing

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

Modifier and Type	Method	Description
static DataSet	MissingDataUtils.listwiseDelete(DataSet dataSet)	Returns a new dataset consisting of the rows of the given dataset that have no missing entries (listwise deletion).
static DataSet	MissingDataUtils.resolveDeletionPolicy(DataSet dataSet, MissingDataSpec spec, String caller)	Applies a missing-data specification to a dataset for a component that supports only the deletion policies natively: returns the dataset itself when it is complete or the policy is TESTWISE (the component then does its own per-family or per-test row selection), its complete cases under LISTWISE, and throws otherwise.

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

Modifier and Type	Method	Description
List<DataSet>	MiceLiteImputer.impute(DataSet dataSet, int m, long seed)	Produces m completed copies of the given dataset.
List<DataSet>	MultipleImputer.impute(DataSet dataSet, int m, long seed)	Produces m completed copies of the given dataset.
List<DataSet>	MvnImputer.impute(DataSet dataSet, int m, long seed)	Produces m completed copies of the given dataset.

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

Modifier and Type	Method	Description
static String	MissingDataUtils.briefSummary(DataSet dataSet)	A one-line missingness summary cheap enough to compute in a constructor: one O(n * p) pass giving the overall missing rate, the number of complete rows, and the worst variable.
static int	MissingDataUtils.effectiveSampleSize(DataSet dataSet, MissingDataSpec spec)	The effective sample size to use for penalized scores when the analysis runs on a covariance matrix estimated from incomplete data, per the spec's MissingDataSpec.EffectiveSampleSizeMode.
static MissingnessThreshold.Effect	MissingnessThreshold.effectOf(DataSet dataSet, MissingDataAudit audit, double threshold)	The effect of a single threshold.
static ICovarianceMatrix	PairwiseCovarianceEstimator.estimate(DataSet data)	Estimates the covariance matrix of the dataset's columns.
static ICovarianceMatrix	MissingDataUtils.estimateCovariance(DataSet dataSet, MissingDataSpec spec)	The covariance matrix of a dataset with missing values under a policy that estimates one: by EM with the spec's ridge, tolerance and iteration limit under EM_COVARIANCE, or by pairwise deletion (PairwiseCovarianceEstimator) under PAIRWISE_COVARIANCE.
static TestwiseRows	TestwiseRows.forDataSet(DataSet dataSet)	Returns the (shared, cached) instance for the given dataset, creating it if necessary.
List<DataSet>	MiceLiteImputer.impute(DataSet dataSet, int m, long seed)	Produces m completed copies of the given dataset.
List<DataSet>	MultipleImputer.impute(DataSet dataSet, int m, long seed)	Produces m completed copies of the given dataset.
List<DataSet>	MvnImputer.impute(DataSet dataSet, int m, long seed)	Produces m completed copies of the given dataset.
static void	TestwiseRows.invalidate(DataSet dataSet)	Discards any cached instance for the given dataset.
static boolean	MissingDataAudit.isMissing(DataSet dataSet, int row, int column)	Returns true just in case the given cell of the dataset is missing, using Tetrad's conventions: DiscreteVariable.MISSING_VALUE for discrete variables and Double.NaN otherwise.
static DataSet	MissingDataUtils.listwiseDelete(DataSet dataSet)	Returns a new dataset consisting of the rows of the given dataset that have no missing entries (listwise deletion).
static List<Node>	MissingnessThreshold.resolve(DataSet dataSet, List<String> names)	Resolves the dropped names to the dataset's variables, skipping any that are no longer present.
static DataSet	MissingDataUtils.resolveDeletionPolicy(DataSet dataSet, MissingDataSpec spec, String caller)	Applies a missing-data specification to a dataset for a component that supports only the deletion policies natively: returns the dataset itself when it is complete or the policy is TESTWISE (the component then does its own per-family or per-test row selection), its complete cases under LISTWISE, and throws otherwise.
static MissingDataPolicy	MissingDataUtils.resolveOrWarn(DataSet dataSet, MissingDataSpec spec, String caller)	Resolves a possibly-null spec to a policy for a component that historically switched silently to test-wise deletion on missing data.
static ImputationSearch.Result	ImputationSearch.search(DataSet dataSet, Algorithm algorithm, Parameters parameters, MultipleImputer imputer, MissingDataSpec spec)	Runs the multiple-imputation search.
static List<MissingnessThreshold.Effect>	MissingnessThreshold.sweep(DataSet dataSet, MissingDataAudit audit, double... thresholds)	The effect of each of a series of thresholds, for reading off where the complete-case count turns.
static double[]	CensoredMvnCovarianceEstimator.upperBoundsFromObservedMaxima(DataSet dataSet)	Upper bounds taken as each column's largest observed value, for data whose censored values were removed rather than recorded at the bound; columns with no missing values get no bound.

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

Modifier	Constructor	Description
	CensoredMvnCovarianceEstimator(DataSet dataSet, double[] upper, double[] lower)	Constructs the estimator.
	MissingDataAudit(DataSet dataSet)	Constructs the audit for the given dataset, computing all descriptive statistics.

Uses of DataSet in edu.cmu.tetrad.estimate.v1

Methods in edu.cmu.tetrad.estimate.v1 with parameters of type DataSet

Modifier and Type	Method	Description
static AdjustmentEffectEstimatorV1.EffectEstimateResultV1	AdjustmentEffectEstimatorV1.estimateAteBinaryVectorV1(DataSet data, String xName, int[] x01Full, Node y, Set<Node> z, AdjustmentEffectEstimatorV1.ConfigV1 cfg)	Estimates the Average Treatment Effect (ATE) for a binary treatment variable provided as a vector.
static AdjustmentEffectEstimatorV1.EffectEstimateResultV1	AdjustmentEffectEstimatorV1.estimateAteV1(DataSet data, Node x, Node y, Set<Node> z, AdjustmentEffectEstimatorV1.ConfigV1 cfg)	v1: Estimate ATE using a provided adjustment set (already found by RA, Perkovic criterion, etc.).

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 HybridCgPruneReport	HybridCgEdgeSignificance.backwardPrune(HybridCgModel.HybridCgPm pm, DataSet data, double alpha, boolean shareVariance)	Per-child backward elimination of edges by the per-edge LRT: for each child, while any parent's test on the child's current family is testable with p > alpha, the parent with the largest such p is removed, the family is refit without it, and the survivors are re-tested — so drop-one p-values are recomputed after every removal and a redundant pair loses at most one member.
static double[][][]	HybridCgEdgeSignificance.coefficientPValues(HybridCgModel.HybridCgIm im, DataSet data)	Classical OLS t-test p-values for the linear coefficients of continuous children, one per (child, discrete-parent stratum, continuous parent), for table display.
static Map<Edge, HybridCgEdgeSignificance.Result>	HybridCgEdgeSignificance.compute(HybridCgModel.HybridCgIm im, DataSet data)	Computes a per-edge LRT significance for every edge of the IM's graph, with per-stratum variances for continuous children (shareVariance = false).
static Map<Edge, HybridCgEdgeSignificance.Result>	HybridCgEdgeSignificance.compute(HybridCgModel.HybridCgIm im, DataSet data, boolean shareVariance)	Computes a per-edge LRT significance for every edge of the IM's graph.
static Map<Edge, HybridCgEdgeSignificance.Result>	HybridCgEdgeSignificance.compute(HybridCgModel.HybridCgPm pm, DataSet data, boolean shareVariance)	As HybridCgEdgeSignificance.compute(HybridCgIm, DataSet, boolean), from the parametric model alone.
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 HybridCgEdgeSignificance.ModelFit	HybridCgEdgeSignificance.modelFit(HybridCgModel.HybridCgIm im, DataSet data, boolean shareVariance)	Convenience overload of HybridCgEdgeSignificance.modelFit(HybridCgPm, DataSet, boolean) from an IM.
static HybridCgEdgeSignificance.ModelFit	HybridCgEdgeSignificance.modelFit(HybridCgModel.HybridCgPm pm, DataSet data, boolean shareVariance)	The BIC of the Hybrid CG model family over the PM's structure against the data, at the maximum-likelihood fit — the same MLE, available-case, and identifiable-parameter conventions as the per-edge tests in this class, extended to whole families and to parentless nodes (which contribute their marginal fit).
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
static DataSet	CdnodBoss.Builder.appendChangeIndexAsLastColumn(DataSet dataX, double[] cIndex, String cName)	Backwards-compatible helper: append a single continuous change-index column as the last column.
DataSet	IndTestIod.getData()
static DataSet	PooledAdjacencySearch.standardize(DataSet dataSet)	Standardizes a data set for the pooled FASK algorithms: each continuous variable to mean zero and unit variance; discrete variables left as they are, as discrete variables, so that a score for mixed data can still be computed from the result.

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

Modifier and Type	Method	Description
static DataSet	CdnodBoss.Builder.appendChangeIndexAsLastColumn(DataSet dataX, double[] cIndex, String cName)	Backwards-compatible helper: append a single continuous change-index column as the last column.
static GRegression.BootstrapResult	GRegression.bootstrap(Graph mpdag, DataSet data, List<Node> treatments, Node outcome, int numBootstraps)	Estimates a total effect by G-regression on the full data set and estimates its sampling covariance by the nonparametric bootstrap (resampling rows with replacement), as in the eff2 R package.
static GRegressionPag.BootstrapResult	GRegressionPag.bootstrap(Graph pag, DataSet data, Node x, Node y, int numBootstraps)	Estimates a total effect on the full data set and its standard error by the nonparametric bootstrap.
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.
static MarkovCheckNullReference.Result	MarkovCheckNullReference.compute(DataSet data, Graph graph, Function<DataSet, IndependenceTest> testFactory, ConditioningSetType conditioningSetType, MarkovCheckNullReference.SimulatorType simulator, int numDraws, int sampleSize, long baseSeed, TrainedDagSimulatorGNM.Params gnmParams)	Computes the null reference using a test factory.
Cdnod.Builder	Cdnod.Builder.data(DataSet data)	Sets the data set for the builder.
CdnodBoss.Builder	CdnodBoss.Builder.data(DataSet data)	Provide the DataSet on which the search will be run.
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	LingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA)	Estimates the weight matrix W using the Fast Independent Component Analysis (FastICA) algorithm.
static Matrix	LingD.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	Lingam.fit(DataSet D)	Fits a dataset to estimate a weight matrix using the LiNGAM algorithm.
List<Matrix>	LingD.fit(DataSet D)	Convenience: estimate W via FastICA, then enumerate B̂ candidates.
double[][]	CpdagParentDistancesFromTrue.getDistances(Graph outputPdag, double[][] trueEdgeStrengths, DataSet dataSet, CpdagParentDistancesFromTrue.DistanceType distanceType)	Calculates the distance matrix for the edges in the given PDAG (outputPdag).
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.
Matrix	Lingam.regressAndPrune(Matrix acyclicBHat, DataSet data)	The regression refit-and-prune step of Shimizu et al.
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.
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	CdnodBoss.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	Tgin.setDataSet(DataSet dataSet)	Deprecated. Sets the data set to be used for the analysis.
void	CdnodBoss.setDataWithC(DataSet dataWithC)	Deprecated. Contexts are now determined from Knowledge tier 0, not from column position.
static DataSet	PooledAdjacencySearch.standardize(DataSet dataSet)	Standardizes a data set for the pooled FASK algorithms: each continuous variable to mean zero and unit variance; discrete variables left as they are, as discrete variables, so that a score for mixed data can still be computed from the result.

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

Modifier and Type	Method	Description
static MarkovCheckNullReference.Result	MarkovCheckNullReference.compute(DataSet data, Graph graph, Function<DataSet, IndependenceTest> testFactory, ConditioningSetType conditioningSetType, MarkovCheckNullReference.SimulatorType simulator, int numDraws, int sampleSize, long baseSeed, TrainedDagSimulatorGNM.Params gnmParams)	Computes the null reference using a test factory.
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.

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)	Constructs a DirectLiNGAM search object from a data set and a score.
	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.
	Flop(DataSet dataSet)	Constructs a FLOP search over the given continuous dataset.
	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)	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.
	LatentParentRecovery(DataSet data, Graph latentGraph)	Constructs a latent-parent recovery procedure.
	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.
	Rlcd(DataSet dataSet)	Constructs the search over a continuous data set.
	ScoredClusterFinder(DataSet dataSet, Collection<Integer> candidateVarIndices)	Constructs a ScoredClusterFinder instance using the provided dataset and a collection of candidate variable indices.
	Tgin(DataSet dataSet, Parameters parameters)	Deprecated. Constructs a TGIN search with data and parameters.
	TginOrientationStages(Graph graph, List<Node> allLatents, Map<Node,Node> nodeMap, DataSet dataSet, double alpha, int sampleSize, RawMarginalIndependenceTest hsic)	Constructs a new instance of TginOrientationStages.
	Tsc(List<Node> variables, DataSet data)	Constructs an instance of the Tsc class by initializing variables, correlation matrix, sample size, and data array from the provided nodes and dataset.
	WildBootstrapMarkovCheck(DataSet data)	Constructs a check over the given data.

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

Modifier	Constructor	Description
	DirectLingam(List<DataSet> datasets, Score score)	Constructs a DirectLiNGAM search object from several data sets over the same variables and a score.
	FaskPool(List<DataSet> dataSets, ScoreWrapper score)	Constructs a pooled-FASK search from the supplied datasets and score wrapper.
	FaskVote(List<DataSet> dataSets, ScoreWrapper score)	Constructs a FASK-voting search from the supplied datasets, score wrapper, and independence wrapper.
	Pairwise(Graph graph, List<DataSet> dataSets)	Constructor.
	Rlcd(List<DataSet> imputations)	Constructs ONE search over several imputations of a data set with missing values.

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, SingletonClusterPolicy 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.bpc(DataSet data, CovarianceMatrix pooled, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	BPC over a pooled covariance matrix; see BpcBlockDiscoverer.
static BlockDiscoverer	BlockDiscoverers.fofc(DataSet data, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	Creates a new instance of a FofcBlockDiscoverer, which is an implementation of the BlockDiscoverer interface.
static BlockDiscoverer	BlockDiscoverers.fofc(DataSet data, CovarianceMatrix pooled, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	FOFC over a pooled covariance matrix; see FofcBlockDiscoverer.
static BlockDiscoverer	BlockDiscoverers.fofc(DataSet data, CovarianceMatrix pooled, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose, String domainColumn)	Creates a FOFC block discoverer that runs in stratified (domain-pooled) mode when a domain column is named; see FofcBlockDiscoverer(DataSet, CovarianceMatrix, double, int, SingletonClusterPolicy, boolean, String).
static BlockDiscoverer	BlockDiscoverers.ftfc(DataSet data, double alpha, int ess, SingletonClusterPolicy 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.ftfc(DataSet data, CovarianceMatrix pooled, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	FTFC over a pooled covariance matrix; see FtfcBlockDiscoverer.
static BlockDiscoverer	BlockDiscoverers.gffc(DataSet data, double alpha, int ess, int rMax, SingletonClusterPolicy policy, boolean verbose)	Constructs a GffcBlockDiscoverer instance, which implements the GFFC algorithm for discovering clusters or "blocks" of indices in a dataset.
static BlockDiscoverer	BlockDiscoverers.gffc(DataSet data, CovarianceMatrix pooled, double alpha, int ess, int rMax, SingletonClusterPolicy policy, boolean verbose)	GFFC over a pooled covariance matrix; see GffcBlockDiscoverer.
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, SingletonClusterPolicy policy, int minRedundancy, boolean verbose)	Creates a new instance of a TscTestBlockDiscoverer, which is an implementation of the BlockDiscoverer interface.
static BlockDiscoverer	BlockDiscoverers.tsc(DataSet data, CovarianceMatrix pooled, double alpha, int ess, double ridge, int rMax, SingletonClusterPolicy policy, int minRedundancy, boolean verbose)	TSC over a pooled covariance matrix; see TscTestBlockDiscoverer.
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.

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

Modifier and Type	Method	Description
static CovarianceMatrix	BlocksUtil.pooledCovariance(List<DataSet> imputations)	Pools several imputations of one data set into a single covariance matrix: the elementwise average of the imputations' covariance matrices, at the imputations' (shared) sample size.
static int	BlocksUtil.pooledEffectiveSampleSize(List<DataSet> imputations)	Estimates an effective sample size for analyses run on the pooled covariance of several imputations, from the disagreement among the imputations themselves.

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, SingletonClusterPolicy 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.
	BpcBlockDiscoverer(DataSet dataSet, CovarianceMatrix pooledCovariance, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	Constructs a BpcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
	FofcBlockDiscoverer(DataSet dataSet, double alpha, int ess, SingletonClusterPolicy 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.
	FofcBlockDiscoverer(DataSet dataSet, CovarianceMatrix pooledCovariance, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	Constructs a FofcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
	FofcBlockDiscoverer(DataSet dataSet, CovarianceMatrix pooledCovariance, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose, String domainColumn)	Constructs a FofcBlockDiscoverer that runs in stratified (domain-pooled) mode when a domain column is named: the rows are split by that column, which is removed from the variable list, and every rank decision pools the within-domain Bartlett statistics across domains (the Phase 1 upgrade of LCD-NOD, Dong et al., ICML 2026).
	FtfcBlockDiscoverer(DataSet dataSet, double alpha, int ess, SingletonClusterPolicy 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, CovarianceMatrix pooledCovariance, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	Constructs a FtfcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
	GffcBlockDiscoverer(DataSet dataSet, double alpha, int ess, int rMax, SingletonClusterPolicy 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, CovarianceMatrix pooledCovariance, double alpha, int ess, int rMax, SingletonClusterPolicy policy, boolean verbose)	Constructs a GffcBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).
	TscTestBlockDiscoverer(DataSet dataSet, double alpha, int ess, double ridge, int rMax, SingletonClusterPolicy 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.
	TscTestBlockDiscoverer(DataSet dataSet, CovarianceMatrix pooledCovariance, double alpha, int ess, double ridge, int rMax, SingletonClusterPolicy policy, int minRedundancy, boolean verbose)	Constructs a TscTestBlockDiscoverer over a pooled covariance matrix--for example, the averaged covariance of several imputations of a data set with missing values (see BlocksUtil.pooledCovariance).

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 the conditional distribution of y given Z changes with the environment variable, by testing the OLS residuals of y on Z against env.
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.
boolean	MsepOracleChangeTest.changes(DataSet data, Node y, Set<Node> Z, Node env, double alpha)	Returns true iff y and env are m-connected given Z in the true graph.
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)	Builds the baseline PAG from the given dataset.
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.harness

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

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 DataSet in edu.cmu.tetrad.search.harness.tsbi

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

Modifier and Type	Method	Description
Graph	TsbiRunner.run(DataSet data, List<Set<Node>> trueClusters, List<Node> trueLatentLeaders)	Runs PC with TSBI given a dataset and a pre-determined clustering.

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

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

Modifier and Type	Method	Description
DataSet	SemParameterizer.parameterizeAndSimulate(Graph graph, int sampleSize)	Parameterises graph and simulates sampleSize rows of linear Gaussian data in a single call.
static DataSet	SemParameterizer.simulate(SemIm im, int sampleSize)	Simulates sampleSize rows of data from an already-parameterised SemIm.

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

Modifier and Type	Method	Description
List<Set<Node>>	AlgorithmRunner.run(DataSet dataSet)	Runs the algorithm on dataSet and returns the recovered clusters.
List<Set<Node>>	BpcRunner.run(DataSet dataSet)
List<Set<Node>>	FofcRunner.run(DataSet dataSet)
List<Set<Node>>	FtfcRunner.run(DataSet dataSet)
List<Set<Node>>	TscRunner.run(DataSet dataSet)

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.mimic

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

Modifier and Type	Method	Description
DataSet	MimicTrialResult.getMeasuredData()	Returns the measured data.

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

Modifier and Type	Method	Description
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.
void	BossTrekMimic.setDataSet(DataSet dataSet)	Deprecated. Sets the data set.
void	TrekMimic.setDataSet(DataSet dataSet)	Sets the data set.

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

Modifier	Constructor	Description
	BossTrekMimic(DataSet dataSet, Parameters parameters, Score score)	Deprecated. Constructs a BossTrekMimic search with data, parameters, and score.
	LatentGraphRefinement(List<Node> variables, org.ejml.simple.SimpleMatrix s, DataSet dataSet, int sampleSize, double alpha)	Constructs a LatentGraphRefinement with the correlation context needed to run both operations.
	MimicTrialResult(MimicModel trueModel, DataSet measuredData, Knowledge knowledge, Map<String,Graph> estimatedGraphs, Map<String, MimicEvaluation> evaluations, Map<String, LatentLatentEvaluator.Report> latentLatentReports)	Constructs a new trial result.
	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 DataSet in edu.cmu.tetrad.search.rlcd

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

Modifier and Type	Method	Description
List<DataSet>	StratifiedChi2RankTest.getDomains()	Returns the per-domain data sets.

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

Modifier and Type	Method	Description
static String	TestwiseDeletionRankTest.describe(DataSet dataSet)	The variable names of the data, for messages.
static StratifiedChi2RankTest	StratifiedChi2RankTest.fromDataSet(DataSet dataSet, String domainColumnName)	Constructs the test from a single data set containing a discrete domain column, which is removed from the variable list and used to split the rows; see StratifiedRankTests.split(DataSet, Node).

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

Modifier	Constructor	Description
	Chi2RankTest(DataSet dataSet)	Constructs the test from a data set.
	TestwiseDeletionRankTest(DataSet dataSet)	Constructs the test over a data set that may contain missing values.
	TestwiseDeletionRankTest(DataSet dataSet, double nScaling)	Constructs the test over a data set that may contain missing values.

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

Modifier	Constructor	Description
	PooledRankTest(List<DataSet> imputations)	Constructs the test from the imputed data sets.
	StratifiedChi2RankTest(List<DataSet> domains)	Constructs the test from one continuous data set per domain.

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 Map<Integer, List<Integer>>	BasisFunctionBicScore.adaptivePrunedEmbedding(DataSet dataSet, int truncationLimit)	Computes the adaptive basis-column decision for a single data set: the embedding that new BasisFunctionBicScore(dataSet, truncationLimit, lambda, true) would score against.
Map<Integer, PenaltyDiscountCalibration.NullFit>	BasisFunctionBicScore.fitNullsByPermutation(DataSet rawData, int samplesPerClass, long seed)	Estimates, by permutation on the raw data, the null distribution of this score's gain for adding one parent, per degrees-of-freedom class.
static Map<Integer, List<Integer>>	BasisFunctionBicScore.fullEmbedding(DataSet dataSet, int truncationLimit)	Computes the unpruned embedding layout for a single data set, for callers that need to combine or validate basis-column decisions across data sets.
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
	BasisFunctionBgeScore(DataSet dataSet, int truncationLimit)	Constructs a BF-BGe score with the Legendre embedding of BasisFunctionBicScore.
	BasisFunctionBgeScore(DataSet dataSet, int truncationLimit, boolean adaptiveBasisSelection)	Constructs a BF-BGe score with the Legendre embedding of BasisFunctionBicScore, optionally applying the same adaptive basis-column screen that BF-BIC uses.
	BasisFunctionBgeScore(DataSet dataSet, int truncationLimit, boolean adaptiveBasisSelection, MissingDataSpec spec)	As BasisFunctionBgeScore(DataSet, int, boolean), with an explicit missing-data specification.
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda)	Constructs a BasisFunctionBicScore object with the specified parameters.
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda, boolean adaptiveBasisSelection)	Constructs a BasisFunctionBicScore object with the specified parameters.
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda, boolean adaptiveBasisSelection, boolean rankTransform)	As BasisFunctionBicScore(DataSet, int, double, boolean), with the option to rank-transform continuous variables to [-1, 1] before the Legendre embedding.
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda, boolean adaptiveBasisSelection, boolean rankTransform, MissingDataSpec spec)	As BasisFunctionBicScore(DataSet, int, double, boolean, boolean), with an explicit missing-data specification.
	BasisFunctionBicScore(DataSet dataSet, int truncationLimit, double lambda, Map<Integer, List<Integer>> embedding, boolean rankTransform)	Constructs a BasisFunctionBicScore that scores against an externally supplied embedding - a map from variable index to the embedded columns to use for that variable.
	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.
	BDeuScore(DataSet dataSet, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	BgeScore(DataSet dataSet)	Constructs a BGe score from a continuous data set.
	BgeScore(DataSet dataSet, MissingDataSpec spec)	Constructs a BGe score from a continuous data set with an explicit missing-data specification.
	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.
	ConditionalGaussianScore(DataSet dataSet, double penaltyDiscount, boolean discretize, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	DegenerateGaussianBgeScore(DataSet dataSet)	Constructs a DG-BGe score.
	DegenerateGaussianBgeScore(DataSet dataSet, MissingDataSpec spec)	Constructs the score with an explicit missing-data specification; LISTWISE and TESTWISE are supported on data with missing values (see BasisFunctionBgeScore(DataSet, int, boolean, MissingDataSpec)).
	DegenerateGaussianScore(DataSet dataSet, boolean precomputeCovariances, double lambda)	Constructs the score using a dataset.
	DegenerateGaussianScore(DataSet dataSet, boolean precomputeCovariances, double lambda, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	DiscreteBicScore(DataSet dataSet)	Constructs the score using a dataset.
	DiscreteBicScore(DataSet dataSet, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	DiscreteBicScoreAdTree(DataSet dataSet)	Constructs the score using a dataset.
	EbicScore(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	FfCordContinuous(DataSet dataSet)	Constructs a KffMl instance for performing kernel-based statistical analysis on the given dataset.
	FfDmlContinuous(DataSet dataSet)	Constructs a KffMl instance for performing kernel-based statistical analysis on the given dataset.
	FfMl(DataSet dataSet)	Constructs an instance of FfMlMixed for the given dataset.
	FfMlContinuous(DataSet dataSet)	Constructs a KffMl instance for performing kernel-based statistical analysis on the given dataset.
	GicScores(DataSet dataSet, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	HeavyTailSemBicScore(DataSet dataSet)	Constructs an instance of the HeavyTailSemBicScore class with the specified data set.
	HuangMarginalLikelihoodScore(DataSet dataSet)	Constructs a HuangMarginalScore object by initializing it with the given dataset and precomputing internal structures required for scoring.
	KcvBicScore(DataSet dataSet)	Constructor for the KcvBicScore class.
	KernelMarginalLikelihoodScore(DataSet dataSet)	Constructs a mixed KML score for the given dataset.
	KernelMarginalLikelihoodScoreContinuous(DataSet dataSet)	Constructs an instance of KernelMarginalLikelihoodScoreContinuous using the provided data set.
	LegendreBicScore(DataSet dataSet)	Constructs an instance of the LegendreBicScore class.
	MagRicfBicScorer(DataSet dataSet)	Constructs the scorer for a continuous data set (its covariance matrix is taken).
	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.
	MvpScore(DataSet dataSet, double structurePrior, int fDegree, boolean discretize, int effectiveSampleSize, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	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, boolean precomputeCovariances, MissingDataSpec spec)	Constructs the score from a dataset with an explicit missing-data specification.
	SemBicScore(DataSet dataSet, double penaltyDiscount, boolean precomputeCovariances)	Constructs the score using a covariance matrix.
	TRffBicScore(DataSet dataSet)	Constructs an instance of MinimaxTRffBicScore using the provided dataset.
	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	FfCi.getData()	Retrieves the current data set associated with this instance.
DataSet	FfCi1.getData()	Retrieves the current data set associated with this instance.
DataSet	FfCiContinuous.getData()	Retrieves the dataset associated with this instance.
DataSet	Gcm.getData()
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	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	IndTestMvpLrt.getData()	Returns the data.
DataSet	IndTestRcotCcaWilkes.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	MinimaxCITest.getData()	Retrieves the dataset associated with this object.
DataSet	MsepTest.getData()	Returns the data set used for the test.
DataSet	Rcit.getData()	Retrieves the dataset associated with this instance.

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

Modifier and Type	Method	Description
List<DataSet>	CachingIndependenceTest.getDataSets()	Returns the datasets of the base test.
List<DataSet>	EdgePriorTest.getDataSets()	Returns the datasets of the wrapped test.
List<DataSet>	Gcm.getDataSets()	Retrieves a list of data sets associated with the current instance.
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>	IndTestStratifiedRank.getDataSets()	Returns the per-domain data sets.
List<DataSet>	IndTestTrekSep.getDataSets()	Deprecated. Returns the data sets used for the independence test.
List<DataSet>	MinimaxCITest.getDataSets()	Retrieves the list of datasets associated with this object.
List<DataSet>	ScoreIndTest.getDataSets()

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

Modifier and Type	Method	Description
static IndependenceResult	DiscreteIndependenceUtils.conditionalChiSquare(DataSet dataSet, List<Node> variables, List<Integer> rows, Node x, Node y, List<Node> z, IndependenceFact fact, double alpha)	Conditional chi-square test for X _||_ Y | Z, stratifying on Z value tuples.
void	ProcessPythonCiService.initializeIfNeeded(DataSet data, List<Node> vars, Map<String,Object> kciParams)
void	PythonCiService.initializeIfNeeded(DataSet data, List<Node> vars, Map<String,Object> params)	Prepares the service for operation if it has not been initialized yet.

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.
	FfCi(DataSet dataSet)	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)	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)	Constructs an instance of FfCiMixed with the specified dataset and default parameters.
	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.
	FfCiContinuous(DataSet dataSet)	Constructs FF-CI with parameters.
	FfCiContinuous(DataSet dataSet, MissingDataSpec spec)	Constructs FF-CI with an explicit missing-data specification.
	Gcm(DataSet data, double alpha)	Constructs a new instance of the Gcm class using the provided continuous data set and the specified alpha value.
	IndTestBasisExpandedGcm(DataSet dataSet, int truncationLimit, int basisType, double lambda, boolean adaptiveBasisSelection)	Constructs the test.
	IndTestBasisExpandedGcm(DataSet dataSet, int truncationLimit, int zTruncationLimit, int basisType, double lambda, boolean adaptiveBasisSelection)	Constructs the test with a separate truncation limit for the Z-side (regression) embedding.
	IndTestBasisFunctionBlocks(DataSet dataSet, int truncationLimit, int basisType)	Constructs an instance of IndTestBasisFunctionBlocks.
	IndTestBasisFunctionBlocks(DataSet dataSet, int truncationLimit, int basisType, boolean adaptiveBasisSelection)	Constructs an instance of IndTestBasisFunctionBlocks, optionally with adaptive basis selection.
	IndTestBasisFunctionBlocks(DataSet dataSet, int truncationLimit, int basisType, boolean adaptiveBasisSelection, MissingDataSpec spec)	As the four-argument constructor, with an explicit missing-data specification.
	IndTestBasisFunctionLrt(DataSet dataSet, int truncationLimit, double lambda)	Deprecated. Constructs an instance of IndTestBasisFunctionLrtCovariance.
	IndTestBasisFunctionLrt(DataSet dataSet, int truncationLimit, double lambda, MissingDataSpec spec)	Deprecated. Constructs the test with an explicit missing-data specification.
	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.
	IndTestCord(DataSet dataSet, double alpha)	Constructs a CORD independence test over the given (continuous or numeric) data set.
	IndTestCordEric(DataSet dataSet, double alpha)	Constructs a CORD independence test over the given (continuous or numeric) data set.
	IndTestDegenerateGaussianLrt(DataSet dataSet)	Constructs the test using the given (mixed) data set.
	IndTestDegenerateGaussianLrt(DataSet dataSet, MissingDataSpec spec)	Constructs the test with an explicit missing-data specification.
	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.
	IndTestFisherZ(DataSet dataSet, double alpha, MissingDataSpec spec)	Constructs the test from a dataset with an explicit missing-data specification.
	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.
	IndTestRcotCcaWilkes(DataSet dataSet)	Constructs an instance of IndTestRcot with default parameters.
	IndTestRcotCcaWilkes(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).
	IndTestStratifiedRank(DataSet dataSet, String domainColumnName, double alpha)	Constructs the test from a single data set containing a discrete domain column.
	Kci(DataSet dataSet)	Constructs a Kci instance with the given DataSet.
	Kci(DataSet dataSet, MissingDataSpec spec)	Constructs a Kci instance with an explicit missing-data specification.
	MinimaxCITest(DataSet data, double alpha)	Constructs a MinimaxCITest object using the given dataset and significance level (alpha).
	PythonKciIndependenceTest(DataSet data, PythonCiService service)	Constructs a PythonKciIndependenceTest object for performing conditional independence tests using a Python backend via the provided PythonCiService.
	PythonRcitIndependenceTest(DataSet data, PythonCiService service)	Constructs a PythonRcitIndependenceTest object for performing conditional independence tests using a Python backend via the provided PythonCiService.
	Rcit(DataSet dataSet)	Constructs an instance of the Rcit class using the provided DataSet and default parameters.
	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.

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.
	IndTestStratifiedRank(List<DataSet> dataSets, double alpha)	Constructs the test from one data set per domain.
	TrekSeparationBlocksIndependence(BlockSpec blockSpec, List<DataSet> imputations)	Constructs the test pooled over several imputations of one data set.

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 using the default spec.
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.createLagData(DataSet data, int numLags, Knowledge knowledge)	Creates a new time series dataset from the given one, expanding the provided within-lag Knowledge across all time lags.
static DataSet	TsUtils.createShiftedData(DataSet data, int[] shifts)	createShiftedData.
DataSet	MissingnessIndicatorAdder.Result.data()	Returns the value of the data record component.
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.
static DataSet	Embedding.rankTransformToUnitInterval(DataSet dataSet)	Replaces each continuous column by its mid-ranks mapped to a uniform grid on [-1, 1]: the k-th smallest value (0-based) becomes -1 + 2 (k + 0.5) / N.

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

Modifier and Type	Method	Description
static MissingnessIndicatorAdder.Result	MissingnessIndicatorAdder.add(DataSet dataSet, MissingnessIndicatorAdder.Spec spec)	Adds missingness indicators under the given spec.
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 using the default spec.
static DataSet	TsUtils.ar(DataSet timeSeries, int numLags)	ar.
static DataSet	TsUtils.ar2(DataSet timeSeries, int numLags)	ar2.
static int[]	PerFactEss.blockIds(DataSet data, List<String> blockColumns)	Computes block ids (0..K-1, one per row) from the distinct joint values of the given columns.
static int[][]	MinimaxBinning.computeGroups(DataSet data, int[] zIdx, List<Integer> useRows, int binsPerDim, int minBinSize)	Computes within-bin groups for conditioning set Z using quantile binning.
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.createLagData(DataSet data, int numLags, Knowledge knowledge)	Creates a new time series dataset from the given one, expanding the provided within-lag Knowledge across all time lags.
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 int	PerFactEss.effectiveSampleSize(DataSet data, int[] blockIds, IndependenceFact fact)	Computes the per-fact effective sample size for X _||_ Y | Z under the given block structure.
static int	PerFactEss.effectiveSampleSize(DataSet data, int[] blockIds, Node x, Node y, Set<Node> z)	Computes the per-fact effective sample size for x _||_ y | z under the given block structure.
static @NotNull Embedding.EmbeddedData	Embedding.getEmbeddedData(DataSet dataSet, int truncationLimit, int basisType, double basisScale)	Generates an embedding of the given dataset based on the specified parameters.
int[][]	MinimaxGroupCache.getGroups(DataSet data, int[] zIdx, List<Integer> useRows, int binsPerDim, int minBinSize)	Computes and returns a group of bins based on the input data and parameters.
int[][]	MinimaxGroupCache.getGroups(DataSet data, int[] zIdx, List<Integer> useRows, MinimaxBinningConfig cfg)	Computes and returns groups of data indices based on the input dataset, feature indices, rows to include, and binning configuration.
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
static List<Node>	FlopInitialOrder.initialOrder(DataSet dataSet)	Computes the FLOP initial order for the variables in the given data set.
static Map<Integer, List<Integer>>	Embedding.pruneUninformativeBasisColumns(DataSet dataSet, Map<Integer, List<Integer>> embedding, ICovarianceMatrix embeddedCorr)	Prunes uninformative basis columns from an embedding using a BIC-crossing screen, so that the effective truncation of each continuous variable is chosen by the data rather than by the truncationLimit parameter.
static DataSet	Embedding.rankTransformToUnitInterval(DataSet dataSet)	Replaces each continuous column by its mid-ranks mapped to a uniform grid on [-1, 1]: the k-th smallest value (0-based) becomes -1 + 2 (k + 0.5) / N.
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 AdTreeCount for the given dataset using all rows.
	AdTree(DataSet dataSet, List<Integer> rows)	Constructs an AdTreeCount for the given dataset and row subset.
	AdTreeCount(DataSet dataSet)	Constructs an AdTreeCount for the given dataset using all rows.
	AdTreeCount(DataSet dataSet, List<Integer> rows)	Constructs an AdTreeCount for the given dataset and row subset.
	AdTreeStoreRows(DataSet dataSet)	Constructs an AD Leaf Tree for the given dataset, without subsampling.
	AdTreeStoreRows(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
	Result(DataSet data, List<String> indicatorNames, Map<String,Double> rates, List<String> skipped)	Creates an instance of a Result record class.
	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	NNEstimator.fitAndSimulate(int sampleSize)	Convenience method: fits the model and immediately simulates sampleSize rows.
DataSet	GeneralAdditiveModel.generate()	Generates a simulated dataset according to the structure of the graph and the specified parameters of the General Additive Model.
DataSet	AdditiveNoiseSimulation.generateData()	Generates a synthetic dataset by simulating data propagation through a graph with additive noise.
DataSet	GeneralNoiseSimulation.generateData()	Generates a dataset based on the current graph structure, incorporating random noise and a multi-layer perceptron (MLP) for each node in the 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	NNEstimator.getObservedData()	Returns the observed (input) dataset.
DataSet	NNEstimator.getSimulatedData()	Returns the number of nodes in the DAG.
static DataSet	NNEstimator.restrictToGraph(DataSet data, Graph graph)	Restricts a dataset to the columns named by the nodes of a graph, in the dataset's column order, so that a graph over a subset of the dataset's variables can be estimated.
DataSet	NNEstimator.simulate(int sampleSize)	Simulates a dataset of the requested size from the fitted model and computes an AdequacyReport comparing it to the observed data.
DataSet	PlotMatrixMimicSimulator.simulate(int m, long seed)	Simulates a synthetic dataset by sampling from a multivariate normal distribution, applying optional nonlinear distortions, and mapping the latent variables to real values through inverse empirical cumulative distribution functions.
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.
DataSet	TrainedDagSimulatorGNM.SimResult.toDataSet()	Converts the current simulation result into a DataSet object, combining both continuous and discrete data variables.
DataSet	TrainedDagSimulatorGNM1.SimResult.toDataSet()	Converts the current simulation result into a DataSet object, combining both continuous and discrete data variables.
DataSet	TrainedDagSimulatorGNM2.SimResult.toDataSet()	Converts the current simulation result into a DataSet object, combining both continuous and discrete data variables.

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

Modifier and Type	Method	Description
static double	DagScores.bicDiscrete(DataSet data, Graph dag)	Computes the Bayesian Information Criterion (BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of discrete variables.
static double	DagScores.bicMixedConditionalGaussian(DataSet data, Graph dag)	Computes the Bayesian Information Criterion (BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of mixed or conditional Gaussian relationships.
DagMetricResult	DagMetric.compute(DataSet data, Graph dag)	Computes and returns the result of a metric evaluation for a given dataset and directed acyclic graph (DAG).
double	DagMetric.MetricComputer.compute(DataSet data, Graph dag)	Computes a metric value based on the given dataset and directed acyclic graph (DAG).
static @NotNull List<DagMetric>	DagMetricRegistry.defaultMetricsFor(@NotNull DataSet data)	Returns the default metric list for a dataset type, in display order.
GeneralizedSemIm	GeneralizedSemEstimator.estimate(GeneralizedSemPm pm, DataSet data)	Maximizes likelihood equation by equation.
void	TrainedDagSimulatorGNM.fillRowFromData(DataSet source, int row, double[] contRow, int[] discRow)	Copies one observed row into the parallel continuous / discrete row arrays used by the mechanisms, so a child can be generated conditional on an observed parent configuration.
static boolean	TrainedDagSimulatorGNM.isMissingCell(DataSet data, int row, int col)	True if the cell holds a missing value: NaN or an infinity for a continuous variable, or a negative category code for a discrete variable.
static AdequacyReport	TrainedDagAdequacy.mmd2(DataSet real, DataSet simulated, TrainedDagSimulatorGNM simulator, AdequacyParams params)	Evaluates the adequacy of a trained DAG simulator by comparing simulated data with real data.
static double	DagScores.mmd2ExactRbf(DataSet real, DataSet simulated, double sigma, int maxRows)	Computes the Maximum Mean Discrepancy (MMD) score using the Radial Basis Function (RBF) kernel between two datasets: real and simulated.
static double	DagScores.mmd2ExactRbf(DataSet data, Graph dag)	Computes the Maximum Mean Discrepancy (MMD) score using the Radial Basis Function (RBF) kernel between the real dataset and a simulated dataset.
double	TrainedDagSimulatorGNM.predictNode(int nodeIndex, DataSet data, int row)	Returns the predicted value for the given node at the given row of the supplied dataset, using the already-fitted mechanism for that node.
double[]	TrainedDagSimulatorGNM.predictNodeProbs(int nodeIndex, DataSet data, int row)	Returns the predicted probability distribution for the given node at the given row of the supplied dataset.
static List<String>	ReidentifyVariables.reidentifyVariables2(List<List<Node>> clusters, Graph trueGraph, DataSet data)	reidentifyVariables2.
static DataSet	NNEstimator.restrictToGraph(DataSet data, Graph graph)	Restricts a dataset to the columns named by the nodes of a graph, in the dataset's column order, so that a graph over a subset of the dataset's variables can be estimated.
static int	TrainedDagSimulatorANM.safeGetInt(DataSet data, int row, int col)	Safely retrieves an integer value from a DataSet, handling missing or non-integer values gracefully.
static int	TrainedDagSimulatorGNM.safeGetInt(DataSet data, int row, int col)	Safely retrieves an integer value from the specified row and column of a DataSet.
static int	TrainedDagSimulatorGNM1.safeGetInt(DataSet data, int row, int col)	Safely retrieves an integer value from the specified DataSet at the given row and column.
static int	TrainedDagSimulatorGNM2.safeGetInt(DataSet data, int row, int col)	Safely retrieves an integer value from the specified DataSet at the given row and column.
static double	DagScores.semBicLinearGaussian(DataSet data, Graph dag)	Computes the structural equation model Bayesian Information Criterion (SEM-BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of linear Gaussian relationships.
static double	DagScores.semBicLinearGaussian(DataSet data, Graph dag, double penaltyDiscount)	Computes the structural equation model Bayesian Information Criterion (SEM-BIC) score for a given dataset and directed acyclic graph (DAG) under the assumption of linear Gaussian relationships, with a specified penalty discount factor.
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.
static double[][]	DagScores.toNumericMatrix(DataSet ds)	Converts the given dataset into a numeric matrix representation, where each value in the dataset is cast to a double.

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.
	NNEstimator(DataSet observedData, Graph dag)	Creates an estimator with default parameters.
	NNEstimator(DataSet observedData, Graph dag, NNEstimatorParams params)	Creates an estimator with explicit parameters.
	PlotMatrixMimicSimulator(DataSet data)	Constructs a new instance of PlotMatrixMimicSimulator, designed for processing continuous-only datasets and generating matrices used in copula normal-score simulations.
	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.
	SemSandwichEstimator(ISemIm estSem, DataSet dataSet)	Computes robust standard errors and the scaled chi-square for the given estimated SEM, using the raw data for fourth moments.
	TrainedDagSimulatorANM(DataSet data, Graph dag, TrainedDagSimulatorANM.Params params)	Constructs a TrainedDagSimulatorANM
	TrainedDagSimulatorGNM(DataSet data, Graph dag, TrainedDagSimulatorGNM.Params params)	Constructs a new instance of the TrainedDagSimulatorGNM class.
	TrainedDagSimulatorGNM1(DataSet data, Graph dag, TrainedDagSimulatorGNM1.Params params)	Constructs a new instance of the TrainedDagSimulatorGNM class.
	TrainedDagSimulatorGNM2(DataSet data, Graph dag, TrainedDagSimulatorGNM2.Params params)	Constructs a new instance of the TrainedDagSimulatorGNM class.

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.study.missingness

Methods in edu.cmu.tetrad.study.missingness that return DataSet

Modifier and Type	Method	Description
DataSet	PooledDgLrtTest.getData()

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

Modifier	Constructor	Description
	PooledDgLrtTest(List<DataSet> imputed, double alpha, PooledDgLrtTest.Pooling pooling)	Constructs the pooled test over completed datasets.

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.

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

Modifier and Type	Method	Description
static List<DataSet>	StratifiedRankTests.split(DataSet dataSet, Node domain)	Splits a data set into one data set per value of a discrete domain column, dropping that column.

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

Modifier and Type	Method	Description
static String	StratifiedRankTests.resolveDomainColumn(DataSet dataSet, String requested)	Resolves the name of a domain column for a data set: the named column if it exists; otherwise, if the data set has exactly one discrete column, that column, with a note logged; otherwise an exception listing the discrete columns found.
static List<DataSet>	StratifiedRankTests.split(DataSet dataSet, Node domain)	Splits a data set into one data set per value of a discrete domain column, dropping that column.

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

Modifier and Type	Method	Description
static StratifiedRankTests	StratifiedRankTests.fromDataSets(List<DataSet> dataSets)	Constructs the test from one data set per stratum.

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

Modifier	Constructor	Description
	StratifiedPermutationRankTest(List<DataSet> dataSets)	Constructs the test from one continuous data set per domain, all with the same variables in the same order.

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.