R package which implements **Cov**ariance
**Reg**ression with **R**andom
**F**orests (**CovRegRF**).

**CovRegRF** is a random forest method for estimating
the covariance matrix of a multivariate response *Y*, given a set
of covariates *X*. The forest trees are built with a splitting
rule specifically designed to partition the data to maximize the
distance between the sample covariance matrix estimates of the child
nodes.

For theoretical details and example data analysis, you can look at
the vignette from within `R`

by using the following
command:

`vignette("CovRegRF")`

The package **CovRegRF** can be installed from GitHub
using the `devtools`

package. Run the following code in
`R`

to install:

```
if (!require(devtools)) {
install.packages("devtools")
library(devtools)
}::install_github('calakus/CovRegRF', build_vignettes = TRUE) devtools
```

- Alakus, C., Larocque, D., and Labbe, A. (2023). Covariance
regression with random forests.
*BMC Bioinformatics*24, 258.