RGCCA: Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data

Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: to study the relationships between blocks and to identify subsets of variables of each block which are active in their relationships with the other blocks. This package allows to (i) run R/SGCCA and related methods, (ii) help the user to find out the optimal parameters for R/SGCCA such as regularization parameters (tau or sparsity), (iii) evaluate the stability of the RGCCA results and their significance, (iv) build predictive models from the R/SGCCA. (v) Generic print() and plot() functions apply to all these functionalities.

Version: 3.0.3
Depends: R (≥ 3.5)
Imports: caret, Deriv, ggplot2 (≥ 3.4.0), ggrepel, graphics, gridExtra, MASS, matrixStats, methods, parallel, pbapply, rlang, stats
Suggests: devtools, FactoMineR, knitr, pander, rmarkdown, rticles, testthat, vdiffr
Published: 2023-12-11
Author: Fabien Girka [aut], Etienne Camenen [aut], Caroline Peltier [aut], Arnaud Gloaguen [aut], Vincent Guillemot [aut], Laurent Le Brusquet [ths], Arthur Tenenhaus [aut, ths, cre]
Maintainer: Arthur Tenenhaus <arthur.tenenhaus at centralesupelec.fr>
BugReports: https://github.com/rgcca-factory/RGCCA/issues
License: GPL-3
URL: https://github.com/rgcca-factory/RGCCA, https://rgcca-factory.github.io/RGCCA/
NeedsCompilation: no
Citation: RGCCA citation info
Materials: README NEWS
CRAN checks: RGCCA results


Reference manual: RGCCA.pdf
Vignettes: RGCCA


Package source: RGCCA_3.0.3.tar.gz
Windows binaries: r-devel: RGCCA_3.0.3.zip, r-release: RGCCA_3.0.3.zip, r-oldrel: RGCCA_3.0.3.zip
macOS binaries: r-release (arm64): RGCCA_3.0.3.tgz, r-oldrel (arm64): RGCCA_3.0.3.tgz, r-release (x86_64): RGCCA_3.0.3.tgz
Old sources: RGCCA archive

Reverse dependencies:

Reverse suggests: multiblock, RVAideMemoire


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