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 |