Tenenhaus/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.

Getting started

Package details

MaintainerArthur Tenenhaus <arthur.tenenhaus@centralesupelec.fr>
URL https://github.com/rgcca-factory/RGCCA https://rgcca-factory.github.io/RGCCA/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
Tenenhaus/RGCCA documentation built on Feb. 12, 2024, 8:34 a.m.