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.