DESCRIPTION

Package: RGCCA
Type: Package
Title: Regularized (or Sparse) Generalized Canonical Correlation Analysis (R/SGCCA) for multi-block data analysis
Version: 3.0.0
Date: 2020-10-26
Authors@R:
    c(person("Arthur", "Tenenhaus", email = "arthur.tenenhaus@supelec.fr", role = c("aut", "cre")),
      person("Vincent", "Guillemot", role = "aut"),
      person("Fabien", "Girka", role = "aut"),
      person("Anatole", "Bourrelier", role = "aut"),
      person("Arnaud", "Gloaguen", role = "aut"),
      person("Caroline", "Peltier", role = "aut"),
      person("Etienne", "Camenen", role = "aut"))
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 (\link{rgcca}), (ii) help the user to find out the optimal parameters for R/SGCCA such as sparsity or number of components(\link{rgcca_permutation}), (iii) evaluate the stability of the RGCCA results and their significance (\link{bootstrap}) Print and plots of all these functionalities are available.
License: GPL-3
LazyData: true
Depends: 
    MASS,
    R (>= 3.2)
Packaged: 2013-07-24 16:36:37 UTC; arthur
Suggests: 
    testthat,
    devtools,
    bsplus,
    knitr,
    rmarkdown,
    markdown,
    pander,
    shiny,
    shinyjs,
    DT,
    ggrepel,
    optparse,
    igraph,
    visNetwork,
    magrittr,
    nnet,
    openxlsx
RoxygenNote: 7.1.1
Imports:
    utils,
    parallel,
    pbapply,	
    grDevices,
    Deriv,
    graphics,
    stats,
    ggplot2,
    methods,
    scales,
    gridExtra,
    plotly
VignetteBuilder: knitr
biocViews: Visualization, PrincipalComponent, DimensionReduction, StructuralEquationModels