[aut, cre],
Reinhard Furrer
[ctb]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: | 0.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, FNN, grDevices |
| Suggests: | Boruta, FSelector, caret, e1071, mlbench, psych, varSelRF, gplots, entropy, testthat, knitr |
| Published: | 2020-01-29 |
| Author: | Gilles Kratzer
[aut, cre],
Reinhard Furrer
[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 |
| Reference manual: | varrank.pdf |
| Vignettes: |
varrank |
| Package source: | varrank_0.3.tar.gz |
| Windows binaries: | r-devel: varrank_0.3.zip, r-release: varrank_0.3.zip, r-oldrel: varrank_0.3.zip |
| macOS binaries: | r-release: varrank_0.3.tgz, r-oldrel: varrank_0.3.tgz |
| Old sources: | varrank archive |
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