Yaqian Guo <yaqiang@stat.stanford.edu> Trevor Hastie
<hastie@stat.stanford.edu> Robert Tibshirani
<tibs@stat.stanford.edu>
Shrunken Centroids Regularized Discriminant Analysis for
the classification purpose in high dimensional data.

Yaqian Guo <yaqiang@stat.stanford.edu> Trevor Hastie
<hastie@stat.stanford.edu> Robert Tibshirani
<tibs@stanford.edu>
Orphaned and corrected on 2018-07-21 as check problems
were not corrected despite reminders.

BioC:MLInterfaces depends on this

Hierarchical Partitioning for Canonical Analysis
This function calculates the independent contribution of each explanatory variable to explained variation (R-squared) on RDA,CCA and db-RDA, applying the hierarchy algorithm of Chevan, A. and Sutherland, M. 1991 Hierarchical Partitioning.The American Statistician, 90-96 <DOI:10.1080/00031305.1991.10475776>.

Hierarchical and Variation Partitioning for Canonical Analysis
Without Limits for the Number of Predictors (Matrices)
This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and independent contributions of each predictor (or matrix) to explained variation (R-squared and adjusted R-squared) on canonical analysis (RDA,CCA and db-RDA), applying the hierarchy algorithm of Chevan, A. and Sutherland, M. 1991 Hierarchical Partitioning.The American Statistician, 90-96 <DOI:10.1080/00031305.1991.10475776>.

Hierarchical and Variation Partitioning for Canonical Analysis

This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Chevan, A. and Sutherland, M. 1991 Hierarchical Partitioning.The American Statistician, 90-96 <doi:10.1080/00031305.1991.10475776>.

This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,<DOI:10.1111/2041-210X.13800>.