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.


Reference manual

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1.0.4 by Cansu Alakus, 2 months ago


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