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.
- Version2.0.0
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- RFCCA citation info
- Last release02/09/2024
Documentation
Team
Cansu Alakus
Udaya B. Kogalur
Show author detailsRolesContributorDenis Larocque
Show author detailsRolesAuthorHemant Ishwaran
Show author detailsRolesContributorAurelie Labbe
Show author detailsRolesAuthorIntel Corporation
Show author detailsRolesCopyright holderKeita Teranishi
Show author detailsRolesContributor
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Last 30 days
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Last 365 days
This package has been downloaded 1,652 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 24 downloads.
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Dependencies
- Imports2 packages
- Suggests3 packages