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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "ccar3" in publications use:'
type: software
license: MIT
title: 'ccar3: Canonical Correlation Analysis via Reduced Rank Regression'
version: 0.1.1
doi: 10.32614/CRAN.package.ccar3
abstract: '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) <https://doi.org/10.48550/arXiv.2405.19539>
  and Wu, Tuzhilina and Donnat (2025) <https://doi.org/10.48550/arXiv.2507.11160>.'
authors:
- family-names: Donnat
  given-names: Claire
  email: cdonnat@uchicago.edu
  orcid: https://orcid.org/0000-0001-7079-8060
- family-names: Tuzhilina
  given-names: Elena
  email: elena.tuzhilina@utoronto.ca
  orcid: https://orcid.org/0000-0002-1898-6010
- family-names: Wu
  given-names: Zixuan
  email: zixuanwu@uchicago.edu
  orcid: https://orcid.org/0009-0006-4745-0000
repository: https://cran.r-universe.dev
commit: 3d815436eaf2f757fcaafc5d421b1f8048c435da
date-released: '2026-04-10'
contact:
- family-names: Donnat
  given-names: Claire
  email: cdonnat@uchicago.edu
  orcid: https://orcid.org/0000-0001-7079-8060