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whitening (version 1.4.0)

Whitening and High-Dimensional Canonical Correlation Analysis

Description

Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", , 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", . 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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Version

Install

install.packages('whitening')

Monthly Downloads

383

Version

1.4.0

License

GPL (>= 3)

Maintainer

Korbinian Strimmer

Last Published

June 7th, 2022

Functions in whitening (1.4.0)

whitening-package

The whitening Package
scca

Perform Canonical Correlation Analysis
corplot

Plots of Correlations and Loadings
simOrtho

Simulate Random Orthogonal Matrix
explainedVariation

Compute Explained Variation from Loadings
whiten

Whiten Data Matrix
whitening-internal

Internal whitening Functions
lusc

TCGA LUSC Data
forina1986

Forina 1986 Wine Data - Extended UCI Wine Data
whiteningMatrix

Compute Whitening Matrix
nutrimouse

Nutrimouse Data
pitprops14

Pitprops Correlation Data for 14 Variables
whiteningLoadings

Compute Whitening Loadings