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CovRegRF
View on CRAN: Click
here
Download and install CovRegRF package within the R console
Install from CRAN:
install.packages("CovRegRF")
Install from Github:
library("remotes")
install_github("cran/CovRegRF")
Install by package version:
library("remotes")
install_version("CovRegRF", "2.0.1")
Attach the package and use:
library("CovRegRF")
Maintained by
Cansu Alakus
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-22
Latest Update: 2024-02-13
Description:
Covariance Regression with Random Forests (CovRegRF) is a
random forest method for estimating the covariance matrix of a
multivariate response given a set of covariates. Random forest trees
are built with a new splitting rule which is designed to maximize the
distance between the sample covariance matrix estimates of the child
nodes. The method is described in Alakus et al. (2023)
. 'CovRegRF' uses 'randomForestSRC' package
(Ishwaran and Kogalur, 2022)
by freezing at the
version 3.1.0. 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 (2023). CovRegRF: Covariance Regression with Random Forests. R package version 2.0.1, https://cran.r-project.org/web/packages/CovRegRF. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Other R packages that CovRegRF depends,
imports, suggests or enhances
Complete documentation for CovRegRF
Functions, R codes and Examples using
the CovRegRF R package
Some associated functions: CovRegRF-package . covregrf . data . plot.vimp.covregrf . predict.covregrf . print.covregrf . significance.test . vimp.covregrf .
Some associated R codes: build.bop.R . covregrf-package.R . covregrf.R . data.R . distance.R . find.interaction.rfsrc.R . generic.impute.rfsrc.R . generic.predict.covregrf.R . generic.predict.rfsrc.R . get.tree.rfsrc.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.covregrf.R . predict.covregrf.R . predict.rfsrc.R . print.covregrf.R . print.rfsrc.R . quantreg.rfsrc.R . rfsrc.R . rfsrc.anonymous.R . rfsrc.cart.R . rfsrc.fast.R . rfsrc.news.R . sidClustering.rfsrc.R . significance.test.R . stat.split.rfsrc.R . subsample.rfsrc.R . synthetic.rfsrc.R . tune.nodesize.rfsrc.R . tune.rfsrc.R . utilities.R . utilities.data.R . utilities.factor.R . utilities.imbalanced.R . utilities.multivariate.R . utilities.performance.R . utilities.predict.R . utilities.sgreedy.R . utilities.survival.R . utilities.tdc.R . utilities.unsupervised.R . var.select.rfsrc.R . vimp.covregrf.R . vimp.rfsrc.R . Full CovRegRF package functions and examples
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