Provides a set of functions that extend the
        'cancor' function with new numerical and graphical outputs. It
        also include a regularized extension of the canonical
        correlation analysis to deal with datasets with more variables
        than observations.

Loads and creates spatial data, including layers and tools that are relevant
    to the activities of the Commission for the Conservation of Antarctic Marine Living 
    Resources. Provides two categories of functions: load functions and create functions.
    Load functions are used to import existing spatial layers from the online CCAMLR GIS
    such as the ASD boundaries. Create functions are used to create layers from user data
    such as polygons and grids.

Canonical correlation analysis and maximum correlation via
    projection pursuit, as well as fast implementations of correlation
    estimators, with a focus on robust and nonparametric methods.

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>.

Class Cover Catch Digraphs, neighborhood graphs, and
        relatives.

Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.