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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.
Package details |
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| 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@centralesupelec.fr> |
| License | GPL-3 |
| Version | 3.0.2 |
| URL | https://github.com/rgcca-factory/RGCCA https://rgcca-factory.github.io/RGCCA/ |
| Package repository | View on CRAN |
| Installation |
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