Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).
| Version: | 0.7-8 |
| Depends: | R (≥ 2.0.0), randomForest, parallel |
| Published: | 2017-07-10 |
| Author: | Ramon Diaz-Uriarte |
| Maintainer: | Ramon Diaz-Uriarte <rdiaz02 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://ligarto.org/rdiaz/Software/Software.html, http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html, https://github.com/rdiaz02/varSelRF |
| NeedsCompilation: | no |
| Citation: | varSelRF citation info |
| Materials: | README |
| In views: | ChemPhys, HighPerformanceComputing, MachineLearning |
| CRAN checks: | varSelRF results |
| Reference manual: | varSelRF.pdf |
| Package source: | varSelRF_0.7-8.tar.gz |
| Windows binaries: | r-devel: varSelRF_0.7-8.zip, r-release: varSelRF_0.7-8.zip, r-oldrel: varSelRF_0.7-8.zip |
| macOS binaries: | r-release (arm64): varSelRF_0.7-8.tgz, r-oldrel (arm64): varSelRF_0.7-8.tgz, r-release (x86_64): varSelRF_0.7-8.tgz, r-oldrel (x86_64): varSelRF_0.7-8.tgz |
| Old sources: | varSelRF archive |
| Reverse imports: | a4Classif |
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