decorrelate: Decorrelation Projection Scalable to High Dimensional Data

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. Our 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:	0.1.6.3
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:	2025-07-17
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

Documentation:

Reference manual:	decorrelate.html , decorrelate.pdf
Vignettes:	Decorrelate (source, R code)

Downloads:

Package source:	decorrelate_0.1.6.3.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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