Canonical Correlation Analysis via Reduced Rank Regression R package ccar3 version 0.1.2 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>. Software 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 Claire Donnat <cdonnat@uchicago.edu> Comprehensive R Archive Network (CRAN) Claire Donnat [aut, cre] (ORCID: <https://orcid.org/0000-0001-7079-8060>), Elena Tuzhilina [aut] (ORCID: <https://orcid.org/0000-0002-1898-6010>), Zixuan Wu [aut] (ORCID: <https://orcid.org/0009-0006-4745-0000>) MIT + file LICENSE (https://CRAN.R-project.org/package=ccar3/LICENSE) 2026-06-13 application/tgz https://CRAN.R-project.org/package=ccar3 doi:10.32614/CRAN.package.ccar3