The "whitening" package implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) as well as the whitening approach to Canonical Correlation Analysis (CCA) allowing negative canonical correlations described in Jendoubi and Strimmer (2019). The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).

Current Version: 1.4.0

Authors: Korbinian Strimmer, Takoua Jendoubi, Agnan Kessy, and Alex Lewin.

Documentation and Installation:

• Manual (pdf file) and release history.
• Download of whitening version 1.4.0 source package.
• Archive of previous versions of whitening.
• Licensed under the GNU GPL version 3 (or any later version).

Publications:

• A. Kessy, A. Lewin, and K. Strimmer. 2018. Optimal whitening and decorrelation. The American Statistician 72: 309-314. (arXiv:1512.00809)
• T. Jendoubi and K. Strimmer. 2019. A whitening approach to probabilistic canonical correlation analysis for omics data integration. BMC Bioinformatics 20: 15. (arXiv:1802.03490)

R Code for Kessy, Lewin, and Strimmer (2018):

• The five discussed natural whitening procedures are implemented in the functions whiteningMatrix, whitening and whiteningLoadings.
• You can also download R code for reproducing the results in Table 2 presented in the paper.

R Code for Jendoubi and Strimmer (2019):

• The whitening approach to canonical correlation analysis is implemented in the functions cca (empirical estimator) and scca (shrinkage estimator).
• R code to reproduce the nutrimouse data analysis.
• R code to reproduce the TCGA LUSC data analysis.
• R code to reproduce the simulations described in the paper.