Package: RGCCA 3.0.3
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:
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')) |
Bug tracker:https://github.com/rgcca-factory/rgcca/issues
Last updated 4 months agofrom:cc7f958f6b. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 12 2024 |
R-4.5-win | OK | Nov 12 2024 |
R-4.5-linux | OK | Nov 12 2024 |
R-4.4-win | OK | Nov 12 2024 |
R-4.4-mac | OK | Nov 12 2024 |
R-4.3-win | OK | Nov 12 2024 |
R-4.3-mac | OK | Nov 12 2024 |
Exports:available_methodsrgccargcca_bootstraprgcca_cvrgcca_permutationrgcca_predictrgcca_stabilityrgcca_transform
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tableDerivdiagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2ggrepelglobalsgluegowergridExtragtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmatrixStatsmgcvModelMetricsmunsellnlmennetnumDerivparallellypbapplypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Available methods for RGCCA | available_methods |
European Customer Satisfaction Index | ECSI |
Plot a fitted object from the RGCCA package | plot.rgcca plot.rgcca_bootstrap plot.rgcca_cv plot.rgcca_permutation plot.rgcca_stability |
Print a fitted object from the RGCCA package | print.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-values | rgcca_bootstrap |
Tune RGCCA parameters by cross-validation | rgcca_cv |
Tune the S/RGCCA hyper-parameters by permutation | rgcca_permutation |
Make predictions using RGCCA | rgcca_predict |
Identify the most stable variables with SGCCA | rgcca_stability |
Reduce dimensionality using RGCCA | rgcca_transform |
Russett data | Russett |
Summary of a fitted object from the RGCCA package | summary.rgcca summary.rgcca_bootstrap summary.rgcca_cv summary.rgcca_permutation summary.rgcca_stability |