Source: r-cran-corpcor
Standards-Version: 4.7.4
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders:
 Charles Plessy <plessy@debian.org>,
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Build-Depends:
 debhelper-compat (= 13),
 dh-r,
 r-base-dev,
 architecture-is-64-bit,
 architecture-is-little-endian,
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-corpcor
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-corpcor.git
Homepage: https://cran.r-project.org/package=corpcor

Package: r-cran-corpcor
Architecture: all
Depends:
 ${R:Depends},
 ${misc:Depends},
Recommends:
 ${R:Recommends},
Suggests:
 ${R:Suggests},
Description: Efficient Estimation of Covariance and (Partial) Correlation
 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.
