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corpcor (version 1.4.3)

Efficient Estimation of Covariance and (Partial) Correlation

Description

This package implements an analytic shrinkage approach for inferring the covariance matrix. The estimator is statistically highly accurate and efficient, applicable to "small n, large p" data, and always returns a positive definite and well-conditioned matrix. Nevertheless, this method requires only little a priori modeling and is computationally cheap. In addition to covariance estimation the package contains similar functions for inferring variances, correlations, partial correlations, partial variances, and regression coefficients. Furthermore, it provides functions for fast SVD computation, for computing the pseudoinverse, 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

1.4.3

License

GPL version 2 or newer

Maintainer

Korbinian Strimmer

Last Published

September 16th, 2021

Functions in corpcor (1.4.3)

cor2pcor

Compute Partial Correlation from Correlation Matrix -- and Vice Versa
pcor.shrink

Shrinkage Estimates of Partial Correlation and Partial Variance
fast.svd

Fast Singular Value Decomposition
corpcor-internal

Internal corpcor Functions
invcov.shrink

Fast Computation of the Inverse of the Covariance and Correlation Matrix
rebuild.cov

Rebuild and Decompose the (Inverse) Covariance Matrix
mvr.shrink

Multivariate Shrinkage Regression
cov.shrink

Shrinkage Estimates of Covariance and Correlation
smtools

Some Tools for Handling Symmetric Matrices
rank.condition

Positive Definiteness of a Matrix, Rank and Condition Number
wt.scale

Weighted Expectations and Variances
pseudoinverse

Pseudoinverse of a Matrix