corpcor

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.

Total

860,285

Last month

26,177

Last week

5,794

Average per day

873

Daily downloads

Total downloads

Description file content

Package
corpcor
Version
1.6.9
Date
2017-03-31
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
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) 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
Packaged
2017-03-31 17:06:45 UTC; strimmer
NeedsCompilation
no
Repository
CRAN
Date/Publication
2017-04-01 06:30:37 UTC

install.packages('corpcor')

1.6.9

02 years ago

http://strimmerlab.org/software/corpcor/

Korbinian Strimmer

GPL (>= 3)

Depends on

R (>= 3.0.2)

Imports

stats

Discussions