CovRegRF-package {CovRegRF} | R Documentation |

## CovRegRF: A package for estimating covariance matrix of a multivariate
response given a set of covariates with random forests

### 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.

### CovRegRF functions

`covregrf`

`predict.covregrf`

`significance.test`

`vimp.covregrf`

`plot.vimp.covregrf`

`print.covregrf`

### References

Alakus, C., Larocque, D., and Labbe, A. (2023). Covariance
regression with random forests. BMC Bioinformatics 24, 258.

Ishwaran H., Kogalur U. (2022). Fast Unified Random Forests for
Survival, Regression, and Classification (RF-SRC). R package version 3.1.0,
https://cran.r-project.org/package=randomForestSRC.

[Package

*CovRegRF* version 1.0.4

Index]