Package: RGCCA 3.0.3

Arthur 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.

Authors:Fabien Girka [aut], Etienne Camenen [aut], Caroline Peltier [aut], Arnaud Gloaguen [aut], Vincent Guillemot [aut], Laurent Le Brusquet [ths], Arthur Tenenhaus [aut, ths, cre]

RGCCA_3.0.3.tar.gz
RGCCA_3.0.3.zip(r-4.5)RGCCA_3.0.3.zip(r-4.4)RGCCA_3.0.3.zip(r-4.3)
RGCCA_3.0.3.tgz(r-4.4-any)RGCCA_3.0.3.tgz(r-4.3-any)
RGCCA_3.0.3.tar.gz(r-4.5-noble)RGCCA_3.0.3.tar.gz(r-4.4-noble)
RGCCA_3.0.3.tgz(r-4.4-emscripten)RGCCA_3.0.3.tgz(r-4.3-emscripten)
RGCCA.pdf |RGCCA.html
RGCCA/json (API)
NEWS

# Install 'RGCCA' in R:
install.packages('RGCCA', repos = c('https://rgcca-factory.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rgcca-factory/rgcca/issues

Datasets:
  • ECSI - European Customer Satisfaction Index
  • Russett - Russett data

On CRAN:

7.79 score 10 stars 68 scripts 530 downloads 13 mentions 8 exports 80 dependencies

Last updated 4 months agofrom:cc7f958f6b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winOKNov 12 2024
R-4.5-linuxOKNov 12 2024
R-4.4-winOKNov 12 2024
R-4.4-macOKNov 12 2024
R-4.3-winOKNov 12 2024
R-4.3-macOKNov 12 2024

Exports:available_methodsrgccargcca_bootstraprgcca_cvrgcca_permutationrgcca_predictrgcca_stabilityrgcca_transform

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tableDerivdiagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2ggrepelglobalsgluegowergridExtragtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmatrixStatsmgcvModelMetricsmunsellnlmennetnumDerivparallellypbapplypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Multiblock data analysis with the RGCCA package

Rendered fromRGCCA.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2023-10-05
Started: 2023-04-12

Readme and manuals

Help Manual

Help pageTopics
Available methods for RGCCAavailable_methods
European Customer Satisfaction IndexECSI
Plot a fitted object from the RGCCA packageplot.rgcca plot.rgcca_bootstrap plot.rgcca_cv plot.rgcca_permutation plot.rgcca_stability
Print a fitted object from the RGCCA packageprint.rgcca print.rgcca_bootstrap print.rgcca_cv print.rgcca_permutation print.rgcca_stability
Regularized Generalized Canonical Correlation Analysis (RGCCA)rgcca
Bootstrap confidence intervals and p-valuesrgcca_bootstrap
Tune RGCCA parameters by cross-validationrgcca_cv
Tune the S/RGCCA hyper-parameters by permutationrgcca_permutation
Make predictions using RGCCArgcca_predict
Identify the most stable variables with SGCCArgcca_stability
Reduce dimensionality using RGCCArgcca_transform
Russett dataRussett
Summary of a fitted object from the RGCCA packagesummary.rgcca summary.rgcca_bootstrap summary.rgcca_cv summary.rgcca_permutation summary.rgcca_stability