varrank: Heuristics Tools Based on Mutual Information for Variable Ranking

A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.

Version: 0.2
Imports: stats, FNN, grDevices
Suggests: knitr, rmarkdown, Boruta, DAAG, FSelector, caret, e1071, mlbench, psych, varSelRF, gplots, entropy, testthat
Published: 2018-12-20
Author: Gilles Kratzer ORCID iD [aut, cre], Reinhard Furrer ORCID iD [ctb]
Maintainer: Gilles Kratzer <gilles.kratzer at math.uzh.ch>
License: GPL-3
URL: https://www.math.uzh.ch/pages/varrank/
NeedsCompilation: no
Citation: varrank citation info
Materials: NEWS
CRAN checks: varrank results

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Reference manual: varrank.pdf
Vignettes: varrank
Package source: varrank_0.2.tar.gz
Windows binaries: r-devel: varrank_0.2.zip, r-release: varrank_0.2.zip, r-oldrel: varrank_0.2.zip
OS X binaries: r-release: varrank_0.2.tgz, r-oldrel: varrank_0.2.tgz
Old sources: varrank archive

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