# corpcor v1.6.9

0

0th

Percentile

## 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.

## Functions in corpcor

 Name Description fast.svd Fast Singular Value Decomposition corpcor-internal Internal corpcor Functions pcor.shrink Shrinkage Estimates of Partial Correlation and Partial Variance powcor.shrink Fast Computation of the Power of the Shrinkage Correlation Matrix invcov.shrink Fast Computation of the Inverse of the Covariance and Correlation Matrix pseudoinverse Pseudoinverse of a Matrix cov.shrink Shrinkage Estimates of Covariance and Correlation cor2pcor Compute Partial Correlation from Correlation Matrix -- and Vice Versa corpcor-package The corpcor Package mpower Compute the Power of a Real Symmetric Matrix wt.scale Weighted Expectations and Variances shrink.intensity Estimation of Shrinkage Intensities rank.condition Positive Definiteness of a Matrix, Rank and Condition Number rebuild.cov Rebuild and Decompose the (Inverse) Covariance Matrix smtools Some Tools for Handling Symmetric Matrices No Results!