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

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), RFE (recursive feature elimination) and Hybrid, combining Vita and Boruta. 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.

install.packages("remotes")
remotes::install_github("silkeszy/Pomona")