Tenenhaus/RGCCA: Regularized (or Sparse) Generalized Canonical Correlation Analysis (R/SGCCA) for multi-block data analysis

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 (\link{rgcca}), (ii) help the user to find out the optimal parameters for R/SGCCA such as regularization parameters (tau or sparsity) (\link{rgcca_permutation}, \link{rgcca_cv}), (iii) evaluate the stability of the RGCCA results and their significance (\link{rgcca_bootstrap} and \link{rgcca_stability}), (iv) build predictive models from the R/SGCCA (\link{rgcca_predict}), (v) Generic print() and plot() functions apply to all these functionalities.

Getting started

Package details

Bioconductor views DimensionReduction PrincipalComponent StructuralEquationModels Visualization
MaintainerArthur Tenenhaus <arthur.tenenhaus@centralesupelec.fr>
LicenseGPL-3
Version3.0
URL https://github.com/rgcca-factory/RGCCA
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("Tenenhaus/RGCCA")
Tenenhaus/RGCCA documentation built on Jan. 13, 2023, 1:30 p.m.