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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.
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Package details |
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| 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@hec.ca> |
| License | GPL (>= 3) |
| Version | 1.0.4 |
| URL | https://github.com/calakus/RFCCA |
| Package repository | View on CRAN |
| Installation |
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