A general framework for constructing partial dependence (i.e.,
marginal effect) plots from various types machine learning models in R.
| Version: |
0.6.0 |
| Depends: |
R (≥ 3.2.5) |
| Imports: |
ggplot2 (≥ 0.9.0), grDevices, gridExtra, lattice, magrittr, methods, mgcv, plyr, stats, viridis, utils |
| Suggests: |
adabag, C50, caret, Cubist, e1071, earth, gbm, ipred, kernlab, MASS, mda, nnet, party, partykit, randomForest, ranger, rpart, testthat, xgboost (≥ 0.6-0) |
| Published: |
2017-07-20 |
| Author: |
Brandon Greenwell [aut, cre] |
| Maintainer: |
Brandon Greenwell <greenwell.brandon at gmail.com> |
| BugReports: |
https://github.com/bgreenwell/pdp/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/bgreenwell/pdp |
| NeedsCompilation: |
yes |
| Citation: |
pdp citation info |
| Materials: |
README NEWS |
| In views: |
MachineLearning |
| CRAN checks: |
pdp results |