Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
| Version: | 0.3 |
| Imports: | partykit, doParallel, data.table, foreach, iterators, parallel, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-05-21 |
| Author: | Sneha Tody |
| Maintainer: | Sneha Tody <sn.tody1 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | logiBin results |
| Reference manual: | logiBin.pdf |
| Vignettes: |
Binning variables before running logistic regression |
| Package source: | logiBin_0.3.tar.gz |
| Windows binaries: | r-devel: logiBin_0.3.zip, r-release: logiBin_0.3.zip, r-oldrel: logiBin_0.3.zip |
| macOS binaries: | r-release: logiBin_0.3.tgz, r-oldrel: logiBin_0.3.tgz |
| Old sources: | logiBin archive |
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