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:

Reference manual:

ccar3.html , ccar3.pdf

Downloads:

Package source:	ccar3_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): ccar3_0.1.0.tgz, r-oldrel (arm64): ccar3_0.1.0.tgz, r-release (x86_64): ccar3_0.1.0.tgz, r-oldrel (x86_64): ccar3_0.1.0.tgz

Linking:

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