Data whitening is a widely used preprocessing step to remove correlation structure since statistical models often assume independence. Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance.
| Version: |
0.1.6.4 |
| Depends: |
R (≥ 4.2.0), methods |
| Imports: |
Rfast, irlba, graphics, Rcpp, CholWishart, Matrix, utils, stats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, pander, whitening, CCA, yacca, mvtnorm, ggplot2, cowplot, colorRamps, RUnit, latex2exp, clusterGeneration, rmarkdown |
| Published: |
2025-07-18 |
| DOI: |
10.32614/CRAN.package.decorrelate |
| Author: |
Gabriel Hoffman 
[aut, cre] |
| Maintainer: |
Gabriel Hoffman <gabriel.hoffman at mssm.edu> |
| BugReports: |
https://github.com/GabrielHoffman/decorrelate/issues |
| License: |
Artistic-2.0 |
| URL: |
https://gabrielhoffman.github.io/decorrelate/ |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
decorrelate results |