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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) <<a href="https://doi.org/10.48550%2FarXiv.2405.19539" target="_top">doi:10.48550/arXiv.2405.19539</a>> and Wu, Tuzhilina and Donnat (2025) <<a href="https://doi.org/10.48550%2FarXiv.2507.11160" target="_top">doi:10.48550/arXiv.2507.11160</a>>.