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. The 'randomForestSRC' package implements
'OpenMP' by default, contingent upon the support provided by the target
architecture and operating system. In this package, 'LAPACK' and 'BLAS'
libraries are used for matrix decompositions.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
CCA, PMA |
| Suggests: |
knitr, rmarkdown, testthat |
| Published: |
2024-02-09 |
| DOI: |
10.32614/CRAN.package.RFCCA |
| 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 |
Documentation:
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