Type: Package
Package: RGCCA
Title: Regularized and Sparse Generalized Canonical Correlation
        Analysis for Multiblock Data
Version: 3.0.0
Authors@R: c(
  person("Fabien", "Girka", role = "aut"),
  person("Etienne", "Camenen", role = "aut"),
  person("Caroline", "Peltier", role = "aut"),
  person("Arnaud", "Gloaguen", role = "aut"),
  person("Vincent", "Guillemot", role = "aut"),
  person("Laurent", "Le Brusquet", role = c("ths")),
  person("Arthur", "Tenenhaus", email = "arthur.tenenhaus@centralesupelec.fr",
         role = c("aut", "ths", "cre")))
Maintainer: Arthur Tenenhaus <arthur.tenenhaus@centralesupelec.fr>
Description: 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.
License: GPL-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
VignetteBuilder: knitr
biocViews: Visualization, PrincipalComponent, DimensionReduction,
        StructuralEquationModels
LazyData: true
RoxygenNote: 7.2.3
Encoding: UTF-8
URL: https://github.com/rgcca-factory/RGCCA,
        https://rgcca-factory.github.io/RGCCA/
BugReports: https://github.com/rgcca-factory/RGCCA/issues
NeedsCompilation: no
Packaged: 2023-04-25 19:29:48 UTC; fabiengirka
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]
Repository: CRAN
Date/Publication: 2023-04-27 09:32:34 UTC
