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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "CovRegRF" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'CovRegRF: Covariance Regression with Random Forests'
version: 2.0.1
doi: 10.32614/CRAN.package.CovRegRF
abstract: 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) <https://doi.org/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.
authors:
- family-names: Alakus
  given-names: Cansu
  email: cansu.alakus@hec.ca
- family-names: Larocque
  given-names: Denis
  email: denis.larocque@hec.ca
- family-names: Labbe
  given-names: Aurelie
  email: aurelie.labbe@hec.ca
repository: https://CRAN.R-project.org/package=CovRegRF
date-released: '2024-07-15'
contact:
- family-names: Alakus
  given-names: Cansu
  email: cansu.alakus@hec.ca