CovRegRF: Covariance Regression with Random Forests

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. (2022) <arXiv:2209.08173>. '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.

Version: 1.0.1
Depends: R (≥ 3.6.0)
Imports: data.table, data.tree, DiagrammeR
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-09-22
Author: Cansu Alakus [aut, cre], Denis Larocque [aut], Aurelie Labbe [aut], Hemant Ishwaran [ctb] (Author of included 'randomForestSRC' codes), Udaya B. Kogalur [ctb] (Author of included 'randomForestSRC' codes)
Maintainer: Cansu Alakus <cansu.alakus at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README
CRAN checks: CovRegRF results


Reference manual: CovRegRF.pdf
Vignettes: CovRegRF: Covariance Regression with Random Forests


Package source: CovRegRF_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CovRegRF_1.0.1.tgz, r-oldrel (arm64): CovRegRF_1.0.1.tgz, r-release (x86_64): CovRegRF_1.0.1.tgz, r-oldrel (x86_64): CovRegRF_1.0.1.tgz


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