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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.
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Package details |
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| 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@hec.ca> |
| License | GPL (>= 3) |
| Version | 2.0.1 |
| Package repository | View on CRAN |
| Installation |
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