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)
- Version1.0.12
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release12/07/2023
Documentation
Team
Cansu Alakus
Denis Larocque
Show author detailsRolesAuthorAurelie Labbe
Show author detailsRolesAuthorHemant Ishwaran
Show author detailsRolesContributorUdaya B. Kogalur
Show author detailsRolesContributorIntel Corporation
Show author detailsRolesCopyright holderKeita Teranishi
Show author detailsRolesContributor
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- Depends1 package
- Imports2 packages
- Suggests3 packages