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. (2022)
<arXiv:2209.08173>. '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.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
data.table, data.tree, DiagrammeR |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2022-09-22 |
| 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) |
| Maintainer: |
Cansu Alakus <cansu.alakus at hec.ca> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
CovRegRF results |
Documentation:
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