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 |
Reference manual: | pdp.pdf |
Package source: | pdp_0.6.0.tar.gz |
Windows binaries: | r-devel: pdp_0.6.0.zip, r-release: pdp_0.6.0.zip, r-oldrel: pdp_0.6.0.zip |
OS X El Capitan binaries: | r-release: pdp_0.6.0.tgz |
OS X Mavericks binaries: | r-oldrel: pdp_0.6.0.tgz |
Old sources: | pdp archive |
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