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.

*CovRegRF*version 2.0.1 Index]