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. (2023) <doi:10.1186/s12859-023-05377-y>. 'CovRegRF' uses
'randomForestSRC' package (Ishwaran and Kogalur, 2022)
<https://cran.r-project.org/package=randomForestSRC> 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.
