silkeszy/Pomona: Identification of relevant variables in omics data sets using Random Forests
Version 1.0.0

This package provides different methods for identifying relevant variables in omics data sets using Random Forests. It implements the following approaches: empirical and parametric permutation (Altmann), Boruta, Vita, r2VIM (recurrent relative veriable importance), and RFE (recursive feature elimination). All approaches use unscaled permutation variable importance and the R package ranger to generate the forests. The package also includes a function to simulate correlated gene expression data.

Getting started

Package details

Maintainer
LicenseGPL-3
Version1.0.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("silkeszy/Pomona")
silkeszy/Pomona documentation built on May 26, 2017, 9:08 a.m.