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CovRegRF  

Covariance Regression with Random Forests
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", "2.0.0")



Attach the package and use:
library("CovRegRF")
Maintained by
Cansu Alakus
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-09-22
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 (2022). CovRegRF: Covariance Regression with Random Forests. R package version 2.0.0, https://cran.r-project.org/web/packages/CovRegRF
Previous versions and publish date:
1.0.4 (2023-09-22 17:40), 1.0.5 (2023-12-07 01:20)
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