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RFCCA
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]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-12-04
Latest Update:
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 06 Aug. 2026.
Previous versions and publish date:
(2026-07-09 08:18), 2.0.0 (2024-02-09 01:10)
Other packages that cited RFCCA R package
View RFCCA citation profile
Other R packages that RFCCA depends,
imports, suggests or enhances
Complete documentation for RFCCA
Functions, R codes and Examples using
the RFCCA R package
Some associated functions: RFCCA-package . data . global.significance . plot.vimp.rfcca . predict.rfcca . print.rfcca . rfcca . vimp.rfcca .
Some associated R codes: data.R . data.utilities.R . distance.R . factor.utilities.R . find.interaction.rfsrc.R . generic.impute.rfsrc.R . generic.predict.rfcca.R . generic.predict.rfsrc.R . generic.vimp.rfcca.R . global.significance.R . holdout.vimp.rfsrc.R . imbalanced.rfsrc.R . impute.rfsrc.R . max.subtree.rfsrc.R . partial.rfsrc.R . plot.competing.risk.rfsrc.R . plot.quantreg.rfsrc.R . plot.rfsrc.R . plot.subsample.rfsrc.R . plot.survival.rfsrc.R . plot.variable.rfsrc.R . plot.vimp.rfcca.R . predict.rfcca.R . predict.rfsrc.R . print.rfcca.R . print.rfsrc.R . quantreg.rfsrc.R . rfcca-package.R . rfcca.R . rfcca.utilities.R . rfsrc.R . rfsrc.cart.R . rfsrc.fast.R . rfsrc.news.R . stat.split.rfsrc.R . subsample.rfsrc.R . synthetic.rfsrc.R . tune.nodesize.rfsrc.R . tune.rfsrc.R . utilities.R . var.select.rfsrc.R . vimp.rfcca.R . vimp.rfsrc.R . zzz.R . Full RFCCA package functions and examples
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