The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.
| Version: | 0.0.1 |
| Imports: | randomForest, ggplot2, data.table, stats, rlist, correlbinom, parallel, Rmpfr, methods |
| Suggests: | foreach, knitr, rmarkdown |
| Published: | 2020-01-31 |
| Author: | Samir Rachid Zaim [aut, cre] |
| Maintainer: | Samir Rachid Zaim <samirrachidzaim at math.arizona.edu> |
| License: | GPL-2 |
| URL: | https://www.biorxiv.org/content/10.1101/681973v1.abstract |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | binomialRF results |
| Reference manual: | binomialRF.pdf |
| Vignettes: |
"binomialRF Feature Selection Vignette" |
| Package source: | binomialRF_0.0.1.tar.gz |
| Windows binaries: | r-devel: binomialRF_0.0.1.zip, r-devel-gcc8: not available, r-release: binomialRF_0.0.1.zip, r-oldrel: binomialRF_0.0.1.zip |
| OS X binaries: | r-release: binomialRF_0.0.1.tgz, r-oldrel: binomialRF_0.0.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=binomialRF to link to this page.