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 + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | ccar3 results |
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