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RFCCA  

Random Forest with Canonical Correlation Analysis
View on CRAN: Click here


Download and install RFCCA package within the R console
Install from CRAN:
install.packages("RFCCA")

Install from Github:
library("remotes")
install_github("cran/RFCCA")

Install by package version:
library("remotes")
install_version("RFCCA", "2.0.0")



Attach the package and use:
library("RFCCA")
Maintained by
Cansu Alakus
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-12-04
Latest Update: 2023-09-05
Description:
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. 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.
How to cite:
Cansu Alakus (2020). RFCCA: Random Forest with Canonical Correlation Analysis. R package version 2.0.0, https://cran.r-project.org/web/packages/RFCCA. Accessed 21 Dec. 2024.
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