keras (>= 2.2.5.0), Metrics, prediction, R6
gower, h2o, knitr, MASS, mlr, mlr3, party, partykit, patchwork,
randomForest, ranger, rmarkdown, rpart, testthat, yaImpute

person("Christoph", "Molnar", , "christoph.molnar@gmail.com", role = c("aut", "cre")),
person("Patrick", "Schratz", , "patrick.schratz@gmail.com", role = "aut",
Description: Interpretability methods to analyze the behavior and
predictions of any machine learning model.  Implemented methods are:
Feature importance described by Fisher et al. (2018)
Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional
expectation ('ice') plots described by Goldstein et al.  (2013)
described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley
Value described by Strumbelj et. al (2014)
Suggests: ALEPlot, bench, bit64, caret, covr, e1071, future.callr,
glmnet, gower, h2o, keras (>= 2.2.5.0), knitr, MASS, mlr, mlr3,
party, partykit, patchwork, randomForest, ranger, rmarkdown,

Authors@R: c(person(given = "Christoph", family = "Molnar",
role = c("aut", "cre"), email = "christoph.molnar@gmail.com"))
Description: Interpretability methods to analyze the behavior and predictions of
Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>,
partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>,
individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>,
local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>,
the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x> and
Imports: R6, checkmate, dplyr, tidyr, ggplot2, partykit, glmnet,
Suggests: randomForest, gower, testthat, rpart, MASS, caret, e1071,

Imports: R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table

the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>,
feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and

Imports: R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table,
knitr, lime, mlr, covr, rmarkdown, devtools, doParallel