Random Forest with Canonical Correlation Analysis

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) . RFCCA uses 'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the version 2.9.3. The custom splitting rule feature is utilised to apply the proposed splitting rule.


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install.packages("RFCCA")

1.0.3 by Cansu Alakus, 18 days ago


https://github.com/calakus/RFCCA


Report a bug at https://github.com/calakus/RFCCA/issues


Browse source code at https://github.com/cran/RFCCA


Authors: Cansu Alakus [aut, cre] , Denis Larocque [aut] , Aurelie Labbe [aut] , Hemant Ishwaran [ctb] (Author of included randomForestSRC codes) , Udaya B. Kogalur [ctb] (Author of included randomForestSRC codes)


Documentation:   PDF Manual  


GPL (>= 3) license


Imports CCA, PMA

Suggests knitr, rmarkdown, testthat


See at CRAN