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ccar3  

Canonical Correlation Analysis via Reduced Rank Regression
View on CRAN: Click here


Download and install ccar3 package within the R console
Install from CRAN:
install.packages("ccar3")

Install from Github:
library("remotes")
install_github("cran/ccar3")

Install by package version:
library("remotes")
install_version("ccar3", "0.1.0")



Attach the package and use:
library("ccar3")
Maintained by
Claire Donnat
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-09-16
Latest Update: 2025-09-16
Description:
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 assumedto 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>.
How to cite:
Claire Donnat (2025). ccar3: Canonical Correlation Analysis via Reduced Rank Regression. R package version 0.1.0, https://cran.r-project.org/web/packages/ccar3. Accessed 16 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:25), 0.1.0 (2025-09-16 10:00), 0.1.1 (2026-04-29 09:00)
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Complete documentation for ccar3
Functions, R codes and Examples using the ccar3 R package
Full ccar3 package functions and examples
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