shrink.intensity
(Package: corpcor) :
Estimation of Shrinkage Intensities
The functions estimate.lambda and estimate.lambda.var shrinkage intensities used for correlations and variances used in cor.shrink and var.shrink, respectively.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: estimate.lambda, estimate.lambda.var
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cor2pcor
(Package: corpcor) :
Compute Partial Correlation from Correlation Matrix -- and Vice Versa
cor2pcor computes the pairwise partial correlation coefficients from either a correlation or a covariance matrix.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: cor2pcor, pcor2cor
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cov.shrink
(Package: corpcor) :
Shrinkage Estimates of Covariance and Correlation
The functions var.shrink, cor.shrink, and cov.shrink compute shrinkage estimates of variance, correlation, and covariance, respectively.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: cor.shrink, cov.shrink, var.shrink
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wt.scale
(Package: corpcor) :
Weighted Expectations and Variances
wt.var estimate the unbiased variance taking into account data weights.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: wt.moments, wt.scale, wt.var
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rank.condition
(Package: corpcor) :
Positive Definiteness of a Matrix, Rank and Condition Number
is.positive.definite tests whether all eigenvalues of a symmetric matrix are positive.
● Data Source:
CranContrib
● Keywords: algebra
● Alias: is.positive.definite, make.positive.definite, rank.condition
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This package implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Sch"afer and Strimmer (2005) and Opgen-Rhein and Strimmer (2007). The approach is both computationally as well as statistically very efficient, it is applicable to “small n, large p” data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations, partial variances, and regression coefficients. The inverse of the covariance and correlation matrix can be efficiently computed, and as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: corpcor-package
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The standard definition for the inverse of a matrix fails if the matrix is not square or singular. However, one can generalize the inverse using singular value decomposition. Any rectangular real matrix M can be decomposed as
● Data Source:
CranContrib
● Keywords: algebra
● Alias: pseudoinverse
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powcor.shrink
(Package: corpcor) :
Fast Computation of the Power of the Shrinkage Correlation Matrix
The function powcor.shrink efficiently computes the alpha-th power of the shrinkage correlation matrix produced by cor.shrink.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: crossprod.powcor.shrink, powcor.shrink
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fast.svd
(Package: corpcor) :
Fast Singular Value Decomposition
fast.svd returns the singular value decomposition of a rectangular real matrix
● Data Source:
CranContrib
● Keywords: algebra
● Alias: fast.svd
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invcov.shrink
(Package: corpcor) :
Fast Computation of the Inverse of the Covariance and Correlation Matrix
The functions invcov.shrink and invcor.shrink implement an algorithm to efficiently compute the inverses of shrinkage estimates of covariance (cov.shrink) and correlation (cor.shrink).
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: invcor.shrink, invcov.shrink
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