| 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.