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

Packages that use Matrix

Package	Description
edu.cmu.tetrad.algcomparison.algorithm.continuous.dag
edu.cmu.tetrad.cluster
edu.cmu.tetrad.data
edu.cmu.tetrad.regression
edu.cmu.tetrad.search	Contains classes for searching for (mostly structural) causal models given data.
edu.cmu.tetrad.search.score	Contains classes for various sorts of scores for running score-based algorithms.
edu.cmu.tetrad.search.test	Contains classes for running conditional independence tests for various sorts of data.
edu.cmu.tetrad.search.utils	Contains some utility classes for search algorithms.
edu.cmu.tetrad.search.work_in_progress	Contains some classes that aren't ready for prime time.
edu.cmu.tetrad.sem
edu.cmu.tetrad.util

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

Methods in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag that return Matrix

Modifier and Type	Method	Description
Matrix	IcaLingam.getBHat()	Retrieves the bHat matrix.

Methods in edu.cmu.tetrad.algcomparison.algorithm.continuous.dag that return types with arguments of type Matrix

Modifier and Type	Method	Description
List<Matrix>	IcaLingD.getStableBHats()	Retrieves the list of stable B matrices generated by the algorithm.
List<Matrix>	IcaLingD.getUnstableBHats()	Retrieves the list of unstable B matrices generated by the algorithm.

Uses of Matrix in edu.cmu.tetrad.cluster

Methods in edu.cmu.tetrad.cluster with parameters of type Matrix

Modifier and Type	Method	Description
void	ClusteringAlgorithm.cluster(Matrix data)	Clusters the given data set.
void	KMeans.cluster(Matrix data)	Clusters the given data set.

Uses of Matrix in edu.cmu.tetrad.data

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

Modifier and Type	Method	Description
static Matrix	DataTransforms.centerData(Matrix data)	centerData.
static Matrix	DataTransforms.concatenate(Matrix... dataSets)	concatenate.
static Matrix	DataUtils.cov(Matrix data)	cov.
static Matrix	DataTransforms.getBootstrapSample(Matrix data, int sampleSize)	getBootstrapSample.
Matrix	BoxDataSet.getCorrelationMatrix()	getCorrelationMatrix.
Matrix	DataSet.getCorrelationMatrix()	If this is a continuous data set, returns the correlation matrix.
Matrix	NumberObjectDataSet.getCorrelationMatrix()	getCorrelationMatrix.
Matrix	BoxDataSet.getCovarianceMatrix()	getCovarianceMatrix.
Matrix	DataSet.getCovarianceMatrix()	If this is a continuous data set, returns the covariance matrix.
Matrix	NumberObjectDataSet.getCovarianceMatrix()	getCovarianceMatrix.
Matrix	TimeSeriesData.getData()	getData.
Matrix	BoxDataSet.getDoubleData()	getDoubleData.
Matrix	DataSet.getDoubleData()	getDoubleData.
Matrix	NumberObjectDataSet.getDoubleData()	getDoubleData.
final Matrix	CorrelationMatrixOnTheFly.getMatrix()	getMatrix.
final Matrix	CovarianceMatrix.getMatrix()	getMatrix.
final Matrix	CovarianceMatrixOnTheFly.getMatrix()	Getter for the field matrix.
final Matrix	CovarianceMatrixOnTheFly.getMatrix(int[] rows)	Getter for the field matrix.
Matrix	ICovarianceMatrix.getMatrix()	Retrieves the covariance matrix.
Matrix	CorrelationMatrix.getSelection(int[] rows, int[] cols)	Returns a submatrix based on the specified rows and columns.
Matrix	CorrelationMatrixOnTheFly.getSelection(int[] rows, int[] cols)	Returns a submatrix based on the specified rows and columns.
Matrix	CovarianceMatrix.getSelection(int[] rows, int[] cols)	Returns a submatrix based on the specified rows and columns.
Matrix	CovarianceMatrixOnTheFly.getSelection(int[] rows, int[] cols)	Returns a submatrix based on the specified rows and columns.
Matrix	CovarianceMatrixOnTheFly.getSelection(int[] rows, int[] cols, int[] dataRows)	getSelection.
Matrix	ICovarianceMatrix.getSelection(int[] rows, int[] cols)	Returns a submatrix based on the specified rows and columns.
static Matrix	DataTransforms.standardizeData(Matrix data)	standardizeData.
static Matrix	DataTransforms.standardizeData(Matrix data, List<Node> variables)	Standardizes the columns of the given data matrix by centering and scaling.
static Matrix	DataUtils.subMatrix(ICovarianceMatrix m, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	DataUtils.subMatrix(ICovarianceMatrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	DataUtils.subMatrix(Matrix m, List<Node> variables, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	DataUtils.subMatrix(Matrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)	subMatrix.

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

Modifier and Type	Method	Description
static Matrix	DataTransforms.centerData(Matrix data)	centerData.
static Matrix	DataTransforms.concatenate(Matrix... dataSets)	concatenate.
static boolean	DataUtils.containsMissingValue(Matrix data)	containsMissingValue.
static Matrix	DataUtils.cov(Matrix data)	cov.
static Matrix	DataTransforms.getBootstrapSample(Matrix data, int sampleSize)	getBootstrapSample.
static Vector	DataUtils.mean(Matrix data)	mean.
static Vector	DataUtils.means(Matrix data)	means.
void	CorrelationMatrix.setMatrix(Matrix matrix)	Sets the covariance matrix.
void	CorrelationMatrixOnTheFly.setMatrix(Matrix matrix)	Sets the covariance matrix.
void	CovarianceMatrix.setMatrix(Matrix matrix)	Sets the covariance matrix.
void	CovarianceMatrixOnTheFly.setMatrix(Matrix matrix)	Sets the covariance matrix.
void	ICovarianceMatrix.setMatrix(Matrix matrix)	Sets the covariance matrix.
static Matrix	DataTransforms.standardizeData(Matrix data)	standardizeData.
static Matrix	DataTransforms.standardizeData(Matrix data, List<Node> variables)	Standardizes the columns of the given data matrix by centering and scaling.
static Matrix	DataUtils.subMatrix(Matrix m, List<Node> variables, Node x, Node y, List<Node> z)	subMatrix.
static Matrix	DataUtils.subMatrix(Matrix m, Map<Node,Integer> indexMap, Node x, Node y, List<Node> z)	subMatrix.

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

Modifier	Constructor	Description
	CorrelationMatrix(List<Node> variables, Matrix matrix, int sampleSize)	Constructs a correlation matrix data set using the given information.
	CovarianceMatrix(List<Node> variables, Matrix matrix, int sampleSize)	Protected constructor to construct a new covariance matrix using the supplied continuous variables and the the given symmetric, positive definite matrix and sample size.
	TimeSeriesData(Matrix matrix, List<String> varNames)	Constructs a new time series data contains for the given row-major data array and the given list of variables.

Uses of Matrix in edu.cmu.tetrad.regression

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

Modifier	Constructor	Description
	RegressionDataset(Matrix data, List<Node> variables)	Constructor for RegressionDataset.

Uses of Matrix in edu.cmu.tetrad.search

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

Modifier and Type	Method	Description
static Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA)	Estimates the W matrix using FastICA.
static Matrix	IcaLingD.estimateW(DataSet data, int fastIcaMaxIter, double fastIcaTolerance, double fastIcaA, boolean verbose)	Estimates the W matrix using FastICA.
Matrix	IcaLingam.fit(DataSet D)	Fits an ICA-LiNGAM model to the given dataset using a default method for estimating W.
Matrix	IcaLingam.getAcyclicTrimmedBHat(Matrix W)	Calculates and returns the trimmed BHat matrix in an acyclic form using the given matrix W.
Matrix	FastIca.IcaResult.getK()	Returns the pre-whitening matrix that projects data onto the first n.comp principal components.
Matrix	FactorAnalysis.getResidual()	Returns the matrix of residuals.
Matrix	FastIca.IcaResult.getS()	Returns the estimated source matrix.
static Matrix	IcaLingD.getScaledBHat(PermutationMatrixPair pair)	Returns the BHat matrix, permuted to the variable order of the original data and scaled so that the diagonal consists only of 1's.
Matrix	FastIca.IcaResult.getW()	Returns the estimated un-mixing matrix.
Matrix	FastIca.IcaResult.getX()	Returns the pre-processed data matrix.
static Matrix	IcaLingD.scale(Matrix M)	Scales the given matrix M by diving each entry (i, j) by M(j, j)
Matrix	FactorAnalysis.successiveFactorVarimax(Matrix factorLoadingMatrix)	Returns the matrix result for the varimax algorithm.
Matrix	FactorAnalysis.successiveResidual()	Successive method with residual matrix.
static Matrix	IcaLingD.threshold(Matrix M, double threshold)	Thresholds the given matrix, sending any small entries in absolute value to zero.

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

Modifier and Type	Method	Description
List<Matrix>	IcaLingD.fit(DataSet D)	Fits a LiNG-D model to the given dataset using a default method for estimating W.
List<Matrix>	IcaLingD.getScaledBHats(Matrix W)	Performs the LiNG-D algorithm given a W matrix, which needs to be discovered elsewhere.

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

Modifier and Type	Method	Description
static void	FastIca.center(Matrix x)	Centers each row of the given matrix by subtracting the mean of the row from each element.
Matrix	IcaLingam.getAcyclicTrimmedBHat(Matrix W)	Calculates and returns the trimmed BHat matrix in an acyclic form using the given matrix W.
List<Matrix>	IcaLingD.getScaledBHats(Matrix W)	Performs the LiNG-D algorithm given a W matrix, which needs to be discovered elsewhere.
boolean	IcaLingam.isAcyclic(Matrix scaledBHat)	Determines whether a BHat matrix parses to an acyclic graph.
static boolean	IcaLingD.isStable(Matrix bHat)	Whether the BHat matrix represents a stable model.
static @NotNull Graph	IcaLingD.makeGraph(Matrix B, List<Node> variables)	Returns a graph given a coefficient matrix and a list of variables.
static PermutationMatrixPair	IcaLingD.maximizeDiagonal(Matrix W)	Finds a column permutation of the W matrix that maximizes the sum of 1 / |Wii| for diagonal elements Wii in W.
static Matrix	IcaLingD.scale(Matrix M)	Scales the given matrix M by diving each entry (i, j) by M(j, j)
double	Lofs.scoreRow(int rowIndex, Matrix data, List<List<Integer>> rows, List<List<Double>> parameters)	Calculates the score for a given row in a matrix using the specified parameters.
void	FastIca.setWInit(Matrix wInit)	Sets the initial un-mixing matrix of dimension (n.comp, n.comp).
Matrix	FactorAnalysis.successiveFactorVarimax(Matrix factorLoadingMatrix)	Returns the matrix result for the varimax algorithm.
static Matrix	IcaLingD.threshold(Matrix M, double threshold)	Thresholds the given matrix, sending any small entries in absolute value to zero.

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

Modifier	Constructor	Description
	FastIca(Matrix X, int numComponents)	Constructs an instance of the Fast ICA algorithm, taking as arguments the two arguments that cannot be defaulted: the data matrix itself and the number of components to be extracted.
	IcaResult(Matrix X, Matrix K, Matrix W, Matrix S)	Constructs an instance of the IcaResult class, taking as arguments the four matrices that are the result of the Fast ICA algorithm.

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

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

Modifier and Type	Method	Description
Matrix	SemBicScore.CovAndCoefs.b()	Returns the value of the b record component.
Matrix	SemBicScore.CovAndCoefs.cov()	Returns the value of the cov record component.
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.

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

Modifier and Type	Method	Description
static Matrix	SemBicScore.getCov(List<Integer> rows, int[] cols, int[] all, DataSet dataSet, Matrix cov)	Computes the covariance matrix for the given subset of rows and columns in the provided data set.
static SemBicScore.CovAndCoefs	SemBicScore.getCovAndCoefs(int i, int[] parents, Matrix data, ICovarianceMatrix covariances, boolean calculateRowSubsets, double lambda)	Returns the covariance matrix of the regression of the ith variable on its parents and the regression coefficients.
static @NotNull SemBicScore.CovAndCoefs	SemBicScore.getCovAndCoefs(int i, int[] parents, Matrix data, ICovarianceMatrix covariances, double lambda, List<Integer> rows)	Returns the covariance matrix of the regression of the ith variable on its parents and the regression
static double	SemBicScore.getResidualVariance(int i, int[] parents, Matrix data, ICovarianceMatrix covariances, boolean calculateRowSubsets, double lambda)	Returns the variance of the residual of the regression of the ith variable on its parents.

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

Modifier	Constructor	Description
	CovAndCoefs(Matrix cov, Matrix b)	Creates an instance of a CovAndCoefs record class.

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

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

Modifier and Type	Method	Description
double	IndTestHsic.empiricalHSIC(Matrix Ky, Matrix Kx, int m)	Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y
double	IndTestHsic.empiricalHSICincompleteCholesky(Matrix Gy, Matrix Gx, int m)	Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y using incomplete Cholesky decomposition to approximate Gram matrices
double	IndTestHsic.empiricalHSICincompleteCholesky(Matrix Gy, Matrix Gx, Matrix Gz, int m)	Empirical unconditional Hilbert-Schmidt Dependence Measure for X and Y given Z using incomplete Cholesky decomposition to approximate Gram matrices

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

Modifier	Constructor	Description
	IndTestFisherZ(Matrix data, List<Node> variables, double alpha)	Constructs a new Fisher Z independence test with the listed arguments.
	IndTestHsic(Matrix data, List<Node> variables, double alpha)	Constructs a new HSIC Independence test.

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

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

Modifier and Type	Method	Description
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 Matrix	KernelUtils.constructH(int m)	Constructs the projection matrix on 1/m
Matrix	PermutationMatrixPair.getPermutedMatrix()	Returns W, permuted rowwise by the permutation passed in through the constructor.
static Matrix	KernelUtils.incompleteCholeskyGramMatrix(List<Kernel> kernels, DataSet dataset, List<Node> nodes, double precision)	Approximates Gram matrix using incomplete Cholesky factorization

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

Modifier and Type	Method	Description
static boolean	TsUtils.allEigenvaluesAreSmallerThanOneInModulus(Matrix mat)	allEigenvaluesAreSmallerThanOneInModulus.

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

Modifier	Constructor	Description
	PartialCorrelation(List<Node> nodes, Matrix cov, int sampleSize)	Constructor.
	PermutationMatrixPair(Matrix M, int[] rowPerm, int[] colPerm)	Constructs with a given matrix M and a row and column permutation (which may be null).

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

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

Modifier and Type	Method	Description
Matrix	Glasso.getSs()	Getter for the field ss.
Matrix[]	MixtureModel.getVariances()	getVariances.
Matrix	Glasso.Result.getWwi()	Getter for the field wwi.

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

Modifier	Constructor	Description
	Glasso(Matrix cov)	Constructor for Glasso.
	IndTestFisherZRecursive(Matrix data, List<Node> variables, double alpha)	Constructs a new Fisher Z independence test with the listed arguments.
	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.
	Result(Matrix wwi)	Constructor for Result.

Uses of Matrix in edu.cmu.tetrad.sem

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

Modifier and Type	Method	Description
Matrix	SemEstimatorGibbs.getDataSet()	Getter for the field dataSet.
Matrix	DagScorer.getEdgeCoef()	Getter for the field edgeCoef.
Matrix	Scorer.getEdgeCoef()	getEdgeCoef.
Matrix	SemIm.getEdgeCoef()	Getter for the field edgeCoef.
Matrix	SemIm.getErrCovar()	Getter for the field errCovar.
Matrix	DagScorer.getErrorCovar()	Getter for the field errorCovar.
Matrix	Scorer.getErrorCovar()	getErrorCovar.
Matrix	ISemIm.getImplCovar(boolean recalculate)	getImplCovar.
Matrix	SemIm.getImplCovar(boolean recalculate)	getImplCovar.
Matrix	SemIm.getImplCovar(List<Node> nodes)	Getter for the field implCovar.
Matrix	StandardizedSemIm.getImplCovar()	Getter for the field implCovar.
Matrix	ISemIm.getImplCovarMeas()	getImplCovarMeas.
Matrix	SemIm.getImplCovarMeas()	getImplCovarMeas.
Matrix	StandardizedSemIm.getImplCovarMeas()	Getter for the field implCovarMeas.
Matrix	DagScorer.getSampleCovar()	Getter for the field sampleCovar.
Matrix	Scorer.getSampleCovar()	getSampleCovar.
Matrix	SemIm.getSampleCovar()	getSampleCovar.

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

Modifier and Type	Method	Description
double	ISemIm.getStdDev(Node node, Matrix implCovar)	getStdDev.
double	SemIm.getStdDev(Node node, Matrix implCovar)	getStdDev.
double	ISemIm.getVariance(Node nodeA, Matrix implCovar)	getVariance.
double	SemIm.getVariance(Node node, Matrix implCovar)	Returns the variance for a given node.
SemIm	SemIm.updatedIm(Matrix covariances, Vector means)	updatedIm.

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

Modifier	Constructor	Description
	Mapping(ISemIm semIm, Parameter parameter, Matrix matrix, int i, int j)	Constructs matrix new mapping using the given freeParameters.
	SemIm(SemPm semPm, List<Node> variableNodes, List<Node> measuredNodes, Matrix edgeCoef, double[] variableMeansStdDev)	Constructor for SemIm.

Uses of Matrix in edu.cmu.tetrad.util

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

Modifier and Type	Method	Description
static Matrix	MatrixUtils.cholesky(Matrix covar)	cholesky.
Matrix	Matrix.chooseInverse(double lambda)	Calculates and returns the appropriate inverse of the matrix based on the provided lambda value: If lambda is 0.0, the inverse of the matrix is computed using the standard inversion method. If lambda is greater than 0.0, the matrix is regularized by adding lambda to the diagonal, ensuring numerical stability for near-singular matrices, and then the inverse is computed. If lambda is less than 0.0, the Moore-Penrose pseudoinverse is computed, which is useful for non-square or singular matrices. This method is designed to handle a variety of inverse computations depending on the requirements, such as regularization or handling singular matrices.
static Matrix	MatrixUtils.convertCovToCorr(Matrix m)	Converts a covariance matrix to a correlation matrix in place; the same matrix is returned for convenience, but m is modified in the process.
Matrix	Matrix.copy()	copy.
Matrix	Vector.diag()	diag.
static Matrix	Vector.diag(Vector diag)	Generates a diagonal matrix using the elements of the given vector.
Matrix	Matrix.getPart(int i, int j, int k, int l)	Extracts a submatrix from the current matrix based on the specified row and column ranges.
static Matrix	Matrix.identity(int rows)	identity.
static Matrix	MatrixUtils.impliedCovar(Matrix edgeCoef, Matrix errCovar)	Calculates the implied covariance matrix from the given edge coefficient matrix and error covariance matrix.
Matrix	Matrix.inverse()	Returns the inverse of the matrix.
Matrix	Matrix.like()	like.
Matrix	MView.mat()	Creates and returns a new Matrix instance representing a submatrix based on the rows and columns specified in the MatrixView.
Matrix	Matrix.minus(Matrix mb)	minus.
Matrix	Matrix.plus(Matrix mb)	plus.
Matrix	Matrix.pseudoinverse()	Returns the Moore-Penrose pseudoinverse of the matrix.
Matrix	Matrix.regularize(double lambda)	Regularizes the diagonal of the matrix by adding a scaled identity matrix to it.
Matrix	Matrix.scalarPlus(double scalar)	Adds the specified scalar value to each element of the matrix and returns the resulting matrix.
Matrix	Matrix.scale(double scalar)	scalarMult.
static Matrix	Matrix.serializableInstance()	Generates a simple exemplar of this class to test serialization.
Matrix	Matrix.sqrt()	sqrt.
Matrix	Matrix.times(Matrix m)	times.
Matrix	Matrix.transpose()	transpose.

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

Modifier and Type	Method	Description
void	Matrix.assign(Matrix matrix)	assign.
void	Matrix.assignPart(int[] range1, int[] range2, Matrix from)	Assigns a part of the given matrix to a specified submatrix while adding the values to the existing data.
static Matrix	MatrixUtils.cholesky(Matrix covar)	cholesky.
static Matrix	MatrixUtils.convertCovToCorr(Matrix m)	Converts a covariance matrix to a correlation matrix in place; the same matrix is returned for convenience, but m is modified in the process.
boolean	Matrix.equals(Matrix m, double tolerance)	equals.
static Matrix	MatrixUtils.impliedCovar(Matrix edgeCoef, Matrix errCovar)	Calculates the implied covariance matrix from the given edge coefficient matrix and error covariance matrix.
static boolean	MatrixUtils.isPositiveDefinite(Matrix matrix)	Return true if the given matrix is symmetric positive definite--that is, if it would make a valid covariance matrix.
Matrix	Matrix.minus(Matrix mb)	minus.
static double	StatUtils.partialCorrelation(Matrix submatrix, double lambda)	Assumes that the given covariance matrix was extracted in such a way that the order of the variables (in either direction) is X, Y, Z1, ..., Zn, where the partial correlation one wants is correlation(X, Y | Z1,...,Zn).
static double	StatUtils.partialCorrelation(Matrix covariance, double lambda, int x, int y, int... z)	partialCorrelation.
static double	StatUtils.partialCorrelationPrecisionMatrix(Matrix submatrix, double lambda)	partialCorrelationPrecisionMatrix.
static double	StatUtils.partialCovarianceWhittaker(Matrix submatrix)	Assumes that the given covariance matrix was extracted in such a way that the order of the variables (in either direction) is X, Y, Z1, ..., Zn, where the partial covariance one wants is covariance(X, Y | Z1,...,Zn).
static double	StatUtils.partialCovarianceWhittaker(Matrix covariance, int x, int y, int... z)	partialCovarianceWhittaker.
static double	StatUtils.partialStandardDeviation(Matrix covariance, int x, int... z)	partialStandardDeviation.
static double	StatUtils.partialVariance(Matrix covariance, int x, int... z)	partialVariance.
Matrix	Matrix.plus(Matrix mb)	plus.
static Vector	MatrixUtils.product(Vector ma, Matrix mb)	product.
void	MView.set(Matrix m)	Sets the values of the current matrix view to match the provided matrix.
Matrix	Matrix.times(Matrix m)	times.

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

Modifier	Constructor	Description
	Matrix(Matrix m)	Constructor for Matrix.