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 |