RFCCA

Random Forest with Canonical Correlation Analysis

CRAN Package

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) . 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. LAPACK and BLAS libraries are used for matrix decompositions. The ‘RFCCA’ package includes the header files ‘lapacke.h’ and ‘cblas.h’ from the LAPACK and BLAS libraries. The LAPACK library is licensed under modified BSD license.

  • Version1.0.12
  • R version≥ 3.5.0
  • LicenseGPL (≥ 3)
  • Needs compilation?Yes
  • Last release12/07/2023

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  • Depends1 package
  • Imports2 packages
  • Suggests3 packages