[aut, cre],
Brad Boehmke [aut]A general framework for constructing variable importance plots from various types machine learning models in R. Aside from some standard model- based variable importance measures, this package also provides a novel approach based on partial dependence plots (PDPs) and individual conditional expectation (ICE) curves as described in Greenwell et al. (2018) <arXiv:1805.04755>.
| Version: | 0.1.0 |
| Imports: | dplyr, ggplot2 (≥ 0.9.0), gridExtra, magrittr, pdp, plyr, stats, tibble, tidyr, utils |
| Suggests: | C50, caret, earth, gbm, h2o, knitr, party, partykit, ranger, rpart, randomForest, rmarkdown, xgboost, glmnet, testthat |
| Published: | 2018-06-15 |
| Author: | Brandon Greenwell [aut, cre],
Brad Boehmke [aut] |
| Maintainer: | Brandon Greenwell <greenwell.brandon at gmail.com> |
| BugReports: | https://github.com/koalaverse/vip/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/koalaverse/vip |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | vip results |
| Reference manual: | vip.pdf |
| Package source: | vip_0.1.0.tar.gz |
| Windows binaries: | r-devel: vip_0.1.0.zip, r-release: vip_0.1.0.zip, r-oldrel: vip_0.1.0.zip |
| OS X binaries: | r-release: vip_0.1.0.tgz, r-oldrel: not available |
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