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ccar3

0.1.2Package record

Canonical Correlation Analysis via Reduced Rank Regression

0packages depend
3.7Kdownloads / year
62.5%test coverage
13/13flavors OK

Overview

About

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.

Install

Health

CRAN checks
13OK
Slowest check: 3.6 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK7 OK2026-06-14
  • ERROR5 OK, 9 ERROR2026-03-10
Advisories

none known

Documentation

In release 0.1.2
READMEYes · 2 wordsVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
21 (1 internal)includes a package overview page
Examples that run
100%3 run in a plain check
Exported functions with examples
15%
Documented parameters
94%
Return-value docs
100%
Help pages citing references
0%

Downloads

3.7K
CRAN downloads in the past year
Rank #17,260 · ~10/day · ~305/mo
Daily download trend is not available in this view yet.
24630 days
74790 days
3.7K1 year
Compare downloads with other packages →
Also on102 r2u10 autocran

Dependencies

Declared dependencies
19 external dependencies (excludes base and recommended)

Installing ccar3 adds 28 packages (16 with compiled code, 22 MB of source tarballs). Heaviest: stringi 11 MB, Rcpp 1.8 MB, RcppEigen 1.7 MB.

Depends (1)
R >= 3.5.0
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (3)
Author, Maintainer since 0.1.0 (2025)
Author
Releases (3)

Pick two releases to compare their code metrics.

  • unarchivedReturned to CRAN
    2026-06-13
  • 0.1.2Latest
    record
    2026-06-13 · current release · diff ↗
  • archivedRemoved from CRAN
    2026-05-15
    issues were not corrected in time
  • unarchivedReturned to CRAN
    2026-04-29
  • 0.1.1
    record
    2026-04-29 · diff ↗
  • R
    R 4.6.0 released · 2026-04-24
  • archivedRemoved from CRAN
    2026-04-11
    issues were not corrected despite reminders
  • 0.1.0
    record
    2025-09-16
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2025-09-16
Total releases
3 / 1 yrs
License
MIT + file LICENSE OSI
Minimum R
≥ 3.5.0
Download size
58 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("ccar3")
Donnat, C., Tuzhilina, E., & Wu, Z. (2026). ccar3: Canonical Correlation Analysis via Reduced Rank Regression (Version 0.1.2) [Computer software]. https://doi.org/10.32614/CRAN.package.ccar3

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for ccar3 version 0.1.2 [Data set]. HJJB, LLC. Data release v2026-10-26. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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