CovRegRF

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.

Last 30 days

This package has been downloaded 40 times in the last 30 days. A handful of downloads. Enough to fill a small classroom with curiosity! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 12 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,827 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 26, 2024 with 36 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.