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>.
