Functions useful for exploratory data analysis using random forests which can be used to compute multivariate partial dependence, observation, class, and variable-wise marginal and joint permutation importance as well as observation-specific measures of distance (supervised or unsupervised). All of the aforementioned functions are accompanied by 'ggplot2' plotting functions.
| Version: | 1.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | data.table, ggplot2, mmpf |
| Suggests: | party, randomForest, randomForestSRC, ranger, testthat, rmarkdown, knitr |
| Published: | 2017-03-06 |
| Author: | Zachary M. Jones and Fridolin Linder |
| Maintainer: | Zachary M. Jones <zmj at zmjones.com> |
| BugReports: | https://github.com/zmjones/edarf |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | edarf results |
| Reference manual: | edarf.pdf |
| Vignettes: |
Exploratory Data Analysis Using Random Forests |
| Package source: | edarf_1.1.1.tar.gz |
| Windows binaries: | r-devel: edarf_1.1.1.zip, r-release: edarf_1.1.1.zip, r-oldrel: edarf_1.1.1.zip |
| OS X binaries: | r-release: edarf_1.1.1.tgz, r-oldrel: edarf_1.1.1.tgz |
| Old sources: | edarf archive |
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