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.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
data.table, data.tree, DiagrammeR |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2024-02-13 |
| Author: |
Cansu Alakus [aut, cre],
Denis Larocque [aut],
Aurelie Labbe [aut],
Hemant Ishwaran [ctb] (Author of included 'randomForestSRC' codes),
Udaya B. Kogalur [ctb] (Author of included 'randomForestSRC' codes),
Intel Corporation [cph] (Copyright holder of included LAPACKE codes),
Keita Teranishi [ctb] (Author of included cblas_dgemm.c codes) |
| Maintainer: |
Cansu Alakus <cansu.alakus at hec.ca> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
CovRegRF results |
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