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: | 0.1 |
Imports: | stats, FNN, grDevices |
Suggests: | knitr, rmarkdown, Boruta, DAAG, FSelector, caret, e1071, mlbench, psych, varSelRF, gplots |
Published: | 2018-04-23 |
Author: | Gilles Kratzer [aut, cre], Reinhard Furrer [ctb] |
Maintainer: | Gilles Kratzer <gilles.kratzer at math.uzh.ch> |
License: | GPL-3 |
NeedsCompilation: | no |
Citation: | varrank citation info |
CRAN checks: | varrank results |
Reference manual: | varrank.pdf |
Vignettes: |
varrank |
Package source: | varrank_0.1.tar.gz |
Windows binaries: | r-devel: varrank_0.1.zip, r-release: varrank_0.1.zip, r-oldrel: varrank_0.1.zip |
OS X binaries: | r-release: varrank_0.1.tgz, r-oldrel: varrank_0.1.tgz |
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