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. (2021) <doi:10.1093/bioinformatics/btab158>. 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.
|Depends:||R (≥ 3.5.0)|
|Suggests:||knitr, rmarkdown, testthat|
|Author:||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)|
|Maintainer:||Cansu Alakus <cansu.alakus at hec.ca>|
|License:||GPL (≥ 3)|
|Citation:||RFCCA citation info|
|CRAN checks:||RFCCA results|
Random Forest with Canonical Correlation Analysis
|Windows binaries:||r-devel: RFCCA_1.0.9.zip, r-release: RFCCA_1.0.9.zip, r-oldrel: RFCCA_1.0.9.zip|
|macOS binaries:||r-release (arm64): RFCCA_1.0.9.tgz, r-oldrel (arm64): RFCCA_1.0.9.tgz, r-release (x86_64): RFCCA_1.0.9.tgz, r-oldrel (x86_64): RFCCA_1.0.9.tgz|
|Old sources:||RFCCA archive|
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