DESCRIPTION

Package: Pomona
Title: Identification of relevant variables in omics data sets using Random
    Forests
Version: 1.0.2
Authors@R: 
    person("Silke", "Szymczak", email = "silke.szymczak@uni-luebeck.de",
  role = c("aut", "cre"))
  person("Cesaire", "Fouodo", email = "cesaire.kuetefouodo@uni-luebeck.de",
  role = c("cre"))
Description: 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.
Depends:
    R (>= 3.3.1)
Imports:
    ranger (>= 0.5.0),
    Boruta (>= 5.1.0),
    methods
License: GPL-3
LazyData: true
RoxygenNote: 7.1.1
Encoding: UTF-8