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. (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. LAPACK and BLAS libraries are used for matrix decompositions. The ‘RFCCA’ package includes the header files ‘lapacke.h’ and ‘cblas.h’ from the LAPACK and BLAS libraries. The LAPACK library is licensed under modified BSD license.

Version: 1.0.11
Depends: R (≥ 3.5.0)
Imports: CCA, PMA
Suggests: knitr, rmarkdown, testthat
Published: 2023-09-19
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), Intel Corporation [cph] (Copyright holder of included LAPACKE codes), Keita Teranishi [ctb] (Author of included cblas_dgemm.c 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_1.0.11.tar.gz
Windows binaries: r-devel: RFCCA_1.0.11.zip, r-release: RFCCA_1.0.11.zip, r-oldrel: RFCCA_1.0.11.zip
macOS binaries: r-release (arm64): RFCCA_1.0.11.tgz, r-oldrel (arm64): RFCCA_1.0.11.tgz, r-release (x86_64): RFCCA_1.0.11.tgz, r-oldrel (x86_64): RFCCA_1.0.11.tgz


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