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:	0.1.2
Depends:	R (≥ 3.5.0)
Imports:	methods, magrittr, tidyr, dplyr, foreach, pracma, corpcor, matrixStats, RSpectra
Suggests:	codetools, SMUT, igraph, testthat (≥ 3.0.0), pkgload, rrpack, Matrix, glmnet, CCA, CVXR, PMA, doParallel, crayon
Published:	2026-06-13
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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