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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: |
0.5 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, FNN, grDevices |
| Suggests: |
Boruta, FSelector, caret, e1071, mlbench, psych, varSelRF, gplots, entropy, testthat, knitr, markdown |
| Published: |
2022-10-12 |
| DOI: |
10.32614/CRAN.package.varrank |
| Author: |
Gilles Kratzer 
[aut],
Reinhard Furrer
[ctb],
Annina Cincera [cre] |
| Maintainer: |
Annina Cincera <annina.cincera 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 |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.