vip: Variable Importance Plots

A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <doi:10.48550/arXiv.1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

Version: 0.4.1
Depends: R (≥ 4.1.0)
Imports: foreach, ggplot2 (≥ 0.9.0), stats, tibble, utils, yardstick
Suggests: bookdown, DT, covr, doParallel, dplyr, fastshap (≥ 0.1.0), knitr, lattice, mlbench, modeldata, NeuralNetTools, pdp, rmarkdown, tinytest (≥ 1.4.1), varImp
Enhances: C50, caret, Cubist, earth, gbm, glmnet, h2o, lightgbm, mixOmics, mlr, mlr3, neuralnet, nnet, parsnip (≥ 0.1.7), party, partykit, pls, randomForest, ranger, rpart, RSNNS, sparklyr (≥ 0.8.0), tidymodels, workflows (≥ 0.2.3), xgboost
Published: 2023-08-21
DOI: 10.32614/
Author: Brandon M. Greenwell ORCID iD [aut, cre], Brad Boehmke ORCID iD [aut]
Maintainer: Brandon M. Greenwell <greenwell.brandon at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: vip citation info
Materials: README NEWS
CRAN checks: vip results


Reference manual: vip.pdf
Vignettes: vip


Package source: vip_0.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): vip_0.4.1.tgz, r-oldrel (arm64): vip_0.4.1.tgz, r-release (x86_64): vip_0.4.1.tgz, r-oldrel (x86_64): vip_0.4.1.tgz
Old sources: vip archive

Reverse dependencies:

Reverse imports: flowml, moreparty, prettyglm, radiant.model
Reverse suggests: easyalluvial, ENMTools, finnts, gbm, pdp, tabnet, vivid, waywiser


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