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. 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. The method is described in Alakus et al. (2020).
Alakus, C., Larocque, D., Jacquemont, S., Barlaam, F., Martin, C.-O., Agbogba, K., Lippe, S., and Labbe, A. (2020). Conditional canonical correlation estimation based on covariates with random forests. arXiv preprint arXiv:2011.11555.
Ishwaran, H., Kogalur, U. (2020). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 2.9.3, https://cran.r-project.org/package=randomForestSRC.
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