corpcor

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.

Last 30 days

This package has been downloaded 40,432 times in the last 30 days. That's enough downloads to make it mildly famous in niche technical communities. A badge of honor! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1,634 times.

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Last 365 days

This package has been downloaded 421,121 times in the last 365 days. That's a whole lot of downloads. Somewhere, a librarian is trying to figure out why more bandwidth is needed. The day with the most downloads was Oct 30, 2024 with 1,990 downloads.

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