English

varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets

Machine Learning 2018-04-20 v1 Machine Learning

Abstract

This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is particularly suitable for exploring multivariate datasets requiring a holistic analysis. The core functionality is a general implementation of the minimum redundancy maximum relevance (mRMRe) model. This approach is based on information theory metrics. It is compatible with discrete and continuous data which are discretised using a large choice of possible rules. The two main problems that can be addressed by this package are the selection of the most representative variables for modeling a collection of variables of interest, i.e., dimension reduction, and variable ranking with respect to a set of variables of interest.

Keywords

Cite

@article{arxiv.1804.07134,
  title  = {varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets},
  author = {Gilles Kratzer and Reinhard Furrer},
  journal= {arXiv preprint arXiv:1804.07134},
  year   = {2018}
}

Comments

18 pages, 4 figures

R2 v1 2026-06-23T01:28:40.214Z