Random Forest with Canonical Correlation Analysis (RFCCA) is a
random forest method for estimating the canonical correlations between two
sets of variables depending on the subject-related covariates. The trees are
built with a splitting rule specifically designed to partition the data to
maximize the canonical correlation heterogeneity between child nodes. The
method is described in Alakus et al. (2020)