DESCRIPTION

Package: vip
Type: Package
Title: Variable Importance Plots
Version: 0.4.1
Authors@R: c(
  person(c("Brandon", "M."), family = "Greenwell", 
         email = "greenwell.brandon@gmail.com",
         role = c("aut", "cre"), 
         comment = c(ORCID = "0000-0002-8120-0084")),
  person("Brad", "Boehmke",
         email = "bradleyboehmke@gmail.com",
         role = c("aut"),
         comment = c(ORCID = "0000-0002-3611-8516"))
  )
Description: 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) <arXiv:1805.04755>. A 
  variance-based method for quantifying the relative strength of interaction 
  effects is also included (see the previous reference for details).
License: GPL (>= 2)
URL: https://github.com/koalaverse/vip/,
    https://koalaverse.github.io/vip/
BugReports: https://github.com/koalaverse/vip/issues
Encoding: UTF-8
VignetteBuilder: knitr
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
LazyData: true
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)