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.
Version: | 1.0.9 |
Depends: | R (≥ 3.5.0) |
Imports: | CCA, PMA |
Suggests: | knitr, rmarkdown, testthat |
Published: | 2022-04-13 |
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 |
Citation: | RFCCA citation info |
Materials: | README NEWS |
CRAN checks: | RFCCA results |
Reference manual: | RFCCA.pdf |
Vignettes: |
Random Forest with Canonical Correlation Analysis |
Package source: | RFCCA_1.0.9.tar.gz |
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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