Package: corpcor
Version: 1.5.0
Date: 2008-12-01
Title: Efficient Estimation of Covariance and (Partial) Correlation
Author: Juliane Schaefer, Rainer Opgen-Rhein, and Korbinian Strimmer.
Maintainer: Korbinian Strimmer <strimmer@uni-leipzig.de>
Depends: R (>= 2.7.0)
Suggests:
Description: This package implements an analytic shrinkage approach
for inferring the covariance matrix.  The estimator is statistically
highly accurate and efficient, applicable to "small n, large p" data,
and always returns a positive definite and well-conditioned matrix.
Nevertheless, this method requires only little a priori modeling and is
computationally cheap. In addition to covariance estimation the
package contains similar functions for inferring variances, correlations,
partial correlations, partial variances, and regression coefficients.
Furthermore, it provides  functions for fast SVD computation, for computing
the pseudoinverse, for checking the rank and positive definiteness of a
matrix, and for the computationally fast inversion of the covariance
and correlation matrix.
License: GPL (>= 3)
URL: http://strimmerlab.org/software/corpcor/
Packaged: Mon Dec  1 20:11:33 2008; strimmer

Package: corpcor
Version: 1.5.1
Date: 2008-12-22
Title: Efficient Estimation of Covariance and (Partial) Correlation
Author: Juliane Schaefer, Rainer Opgen-Rhein, and Korbinian Strimmer.
Maintainer: Korbinian Strimmer <strimmer@uni-leipzig.de>
Depends: R (>= 2.7.0)
Suggests:
Description: 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.
License: GPL (>= 3)
URL: http://strimmerlab.org/software/corpcor/
Packaged: Mon Dec 22 19:30:57 2008; strimmer