RFCCA: 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) <arXiv:2011.11555>. 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.

Version: 1.0.4
Depends: R (≥ 3.5.0)
Imports: CCA, PMA
Suggests: knitr, rmarkdown, testthat
Published: 2021-02-03
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>
BugReports: https://github.com/calakus/RFCCA/issues
License: GPL (≥ 3)
URL: https://github.com/calakus/RFCCA
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: RFCCA results


Reference manual: RFCCA.pdf
Vignettes: Random Forest with Canonical Correlation Analysis
Package source: RFCCA_1.0.4.tar.gz
Windows binaries: r-devel: RFCCA_1.0.4.zip, r-devel-UCRT: RFCCA_1.0.4.zip, r-release: RFCCA_1.0.4.zip, r-oldrel: RFCCA_1.0.4.zip
macOS binaries: r-release (arm64): RFCCA_1.0.4.tgz, r-release (x86_64): RFCCA_1.0.4.tgz, r-oldrel: RFCCA_1.0.4.tgz
Old sources: RFCCA archive


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