whitening: Whitening and High-Dimensional Canonical Correlation Analysis
Implements the whitening methods (ZCA, PCA, Cholesky,
ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018)
"Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>,
as well as the whitening approach to canonical correlation analysis allowing
negative canonical correlations described in Jendoubi and Strimmer (2019)
"A whitening approach to probabilistic canonical correlation analysis for omics
data integration", <doi:10.1186/s12859-018-2572-9>. 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).
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