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 |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=ccar3
to link to this page.