R package which implements **R**andom
**F**orest with **C**anonical
**C**orrelation **A**nalysis
(**RFCCA**).

**RFCCA** is a random forest method for estimating the
canonical correlations between two sets of variables, *X* and
*Y*, depending on the subject-related covariates, *Z*. The
trees are built with a splitting rule specifically designed to partition
the data to maximize the canonical correlation heterogeneity between
child nodes.

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

by using the following
command:

`vignette("RFCCA")`

This package is available on CRAN. Alternatively,
you can install **RFCCA** 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/RFCCA', build_vignettes = TRUE) devtools
```

- Alakus, C., Larocque, D., Jacquemont, S., Barlaam, F., Martin, C.-O., Agbogba, K., Lippe, S., and Labbe, A. (2021). Conditional canonical correlation estimation based on covariates with random forests. Bioinformatics, 37(17), 2714-2721. doi:10.1093/bioinformatics/btab158.