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

arXiv: https://arxiv.org/abs/1804.07134 · HTML: https://mlsanity.com/paper/1804.07134/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [forester: A Tree-Based AutoML Tool in R](https://mlsanity.com/paper/2409.04789/) (2024)
- [Use of High Dimensional Modeling for automatic variables selection: the best path algorithm](https://mlsanity.com/paper/2105.03173/) (2021)
- [The Best Path Algorithm automatic variables selection via High Dimensional Graphical Models](https://mlsanity.com/paper/2211.07267/) (2022)

## Abstract (arXiv)

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.