Nothing
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.
Browse package contents
Package details |
|
|---|---|
| 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@hec.ca> |
| License | GPL (>= 3) |
| Version | 2.0.0 |
| URL | https://github.com/calakus/RFCCA |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the RFCCA package in your browser
Any scripts or data that you put into this service are public.
