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corpcor (version 1.3.1)

Efficient Estimation of Covariance and (Partial) Correlation

Description

This package implements a shrinkage estimator to allow the efficient inference of large-scale covariance matrices from small sample data. The resulting estimates are always positive definite, much more accurate than the empirical estimate, well conditioned, computationally inexpensive, and require only little a priori modeling. The package also contains similar functions for inferring variances, correlations and partial correlations and partial covariances. In addition, it provides functions for fast svd computation, for computing the pseuoinverse, for checking the rank and positive definiteness of a matrix, and for the computationally fast inversion of the covariance and correlation matrix.

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Version

Install

install.packages('corpcor')

Monthly Downloads

42,579

Version

1.3.1

License

GPL version 2 or newer

Maintainer

Korbinian Strimmer

Last Published

September 16th, 2021

Functions in corpcor (1.3.1)

pcor.shrink

Shrinkage Estimates of Partial Correlation and Partial Covariance
rebuild.cov

Rebuild Covariance Matrix from Correlation Matrix
fast.svd

Fast Singular Value Decomposition
cor2pcor

Compute Partial Correlation from Correlation Matrix -- and Vice Versa
weighted.scale

Weighted Expectations and Variance
invcov.shrink

Fast Computation of the Inverse of the Covariance and Correlation Matrix
smtools

Some Tools for Symmetric Matrices
corpcor-internal

Internal corpcor Functions
pseudoinverse

Pseudoinverse of a Matrix
cov.shrink

Shrinkage Estimates of Covariance and Correlation
rank.condition

Positive Definiteness of a Matrix, Rank and Condition Number