Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data R package RGCCA version 3.0.3 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. Software 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 Arthur Tenenhaus <arthur.tenenhaus@centralesupelec.fr> Comprehensive R Archive Network (CRAN) Fabien Girka [aut], Etienne Camenen [aut], Caroline Peltier [aut], Arnaud Gloaguen [aut], Vincent Guillemot [aut], Laurent Le Brusquet [ths], Arthur Tenenhaus [aut, ths, cre] GPL-3 2023-12-11 application/tgz https://CRAN.R-project.org/package=RGCCA doi:10.32614/CRAN.package.RGCCA