Uses of Class
edu.cmu.tetrad.data.CovarianceMatrix

Packages that use CovarianceMatrix

Package	Description
edu.cmu.tetrad.data
edu.cmu.tetrad.search	This package contains classes for causal graph search algorithms.
edu.cmu.tetrad.search.blocks
edu.cmu.tetrad.search.rlcd
edu.cmu.tetrad.search.work_in_progress	A package for algorithms that are not ready for prime time.
edu.cmu.tetrad.sem

Uses of CovarianceMatrix in edu.cmu.tetrad.data

Subclasses of CovarianceMatrix in edu.cmu.tetrad.data

Modifier and Type	Class	Description
final class	CorrelationMatrix	Stores a correlation matrix together with variable names and sample size; intended as a representation of a data set.

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

Modifier and Type	Method	Description
final CovarianceMatrix	CovarianceMatrix.getSubmatrix(String[] submatrixVarNames)	getSubmatrix.

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

Modifier and Type	Method	Description
static DataSet	DataUtils.choleskySimulation(CovarianceMatrix cov)	choleskySimulation.

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

Modifier	Constructor	Description
	CovarianceMatrix(CovarianceMatrix covMatrix)	Copy constructor.

Uses of CovarianceMatrix in edu.cmu.tetrad.search

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

Modifier and Type	Method	Description
static Set<Set<Integer>>	TscHarnessTest.runOnce(List<Node> vars, CovarianceMatrix cov, double alpha, int ess, int minRedundancy)	Executes the time series clustering (TSC) algorithm once with the given parameters and returns canonicalized clusters as a set of sets of integers.

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

Modifier	Constructor	Description
	Bpc(CovarianceMatrix cov, double alpha, int ess)	Constructor for the Bpc class.
	Tsc(List<Node> variables, CovarianceMatrix cov)	Constructs an instance of the TscScored class using the provided variables and covariance matrix.

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

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

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.

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

Modifier and Type	Method	Description
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, 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, CovarianceMatrix pooled, double alpha, int ess, SingletonClusterPolicy policy, boolean verbose)	FTFC over a pooled covariance matrix; see FtfcBlockDiscoverer.
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 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.

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

Modifier	Constructor	Description
	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, 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, 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, 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, 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 CovarianceMatrix in edu.cmu.tetrad.search.rlcd

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

Modifier and Type	Method	Description
CovarianceMatrix	StratifiedChi2RankTest.withinDomainPooledCovariance()	The within-domain pooled covariance matrix: the per-domain covariance matrices averaged with weights (n_t - 1), which equals the covariance of the data after each domain has been centered at its own means, at sample size N - (number of domains) to account for the per-domain centering.

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

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

Modifier	Constructor	Description
	HbsmsBeam(Graph graph, CovarianceMatrix cov, Knowledge knowledge)	Constructor for HbsmsBeam.

Uses of CovarianceMatrix in edu.cmu.tetrad.sem

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

Modifier and Type	Method	Description
static CovarianceMatrix	ResidualCovariance.residual(SemIm semIm)	The residual covariance of the measured variables under the given estimated model.