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).

Current Version: 1.1.1

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

Documentation and Installation:

• Manual (pdf file) and release history.
• Download of whitening version 1.1.1 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 two functions whiteningMatrix and whitening.
• 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.