RGCCA: Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data (original) (raw)
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 |
| DOI: | 10.32614/CRAN.package.RGCCA |
| 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 |
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