Package: corpcor
Version: 1.6.10
Date: 2021-09-16
Title: Efficient Estimation of Covariance and (Partial) Correlation
Author: Juliane Schafer, Rainer Opgen-Rhein, Verena Zuber, Miika Ahdesmaki,
A. Pedro Duarte Silva, and Korbinian Strimmer.
Maintainer: Korbinian Strimmer <strimmerlab@gmail.com>
Depends: R (>= 3.0.2)
Imports: stats
Suggests:
Description: 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 Schafer and Strimmer (2005)
<DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007)
<DOI:10.2202/1544-6115.1252>.  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 and partial variances.
The inverse of the covariance and correlation matrix
can be efficiently computed, 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: https://strimmerlab.github.io/software/corpcor/
NeedsCompilation: no
Packaged: 2021-09-16 14:05:52 UTC; strimmer
Repository: CRAN
Date/Publication: 2021-09-16 15:30:08 UTC

Package: corpcor
Version: 1.6.2
Date: 2012-01-21
Title: Efficient Estimation of Covariance and (Partial) Correlation
Author: Juliane Schaefer, Rainer Opgen-Rhein, Verena Zuber, A. Pedro
Duarte Silva, and Korbinian Strimmer.
Maintainer: Korbinian Strimmer <strimmer@uni-leipzig.de>
Depends: R (>= 2.10.0)
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 and partial variances.  The
inverse of the covariance and correlation matrix can be
efficiently computed, 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: 2012-01-21 22:23:40 UTC; strimmer
Repository: CRAN
Date/Publication: 2012-01-22 08:06:00