ccar3: Canonical Correlation Analysis via Reduced Rank Regression
Canonical correlation analysis (CCA) via reduced-rank regression with support for regularization and cross-validation. Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed  to be smooth on the graph. For more details see Donnat and Tuzhilina (2024)  <doi:10.48550/arXiv.2405.19539> and Wu, Tuzhilina and Donnat (2025) <doi:10.48550/arXiv.2507.11160>.
| Version: | 0.1.0 | 
| Depends: | R (≥ 3.5.0) | 
| Imports: | purrr, magrittr, tidyr, dplyr, foreach, pracma, corpcor, matrixStats, RSpectra, caret | 
| Suggests: | SMUT, igraph, testthat (≥ 3.0.0), rrpack, CVXR, Matrix, glmnet, CCA, PMA, doParallel, crayon | 
| Published: | 2025-09-16 | 
| DOI: | 10.32614/CRAN.package.ccar3 | 
| Author: | Claire Donnat  [aut, cre],
  Elena Tuzhilina  [aut],
  Zixuan Wu  [aut] | 
| Maintainer: | Claire Donnat  <cdonnat at uchicago.edu> | 
| License: | MIT + file LICENSE | 
| NeedsCompilation: | no | 
| Materials: | README | 
| CRAN checks: | ccar3 results | 
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