RJwrapper.bbl

\begin{thebibliography}{40}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Biecek(2018)]{DALEX-pkg}
P.~Biecek.
\newblock Dalex: Explainers for complex predictive models in r.
\newblock \emph{Journal of Machine Learning Research}, 19\penalty0
  (84):\penalty0 1--5, 2018.
\newblock URL \url{http://jmlr.org/papers/v19/18-416.html}.

\bibitem[Biecek(2019)]{ingredients-pkg}
P.~Biecek.
\newblock \emph{ingredients: Effects and Importances of Model Ingredients},
  2019.
\newblock URL \url{https://CRAN.R-project.org/package=ingredients}.
\newblock R package version 0.3.3.

\bibitem[Bischl et~al.(2016)Bischl, Lang, Kotthoff, Schiffner, Richter,
  Studerus, Casalicchio, and Jones]{mlr-pkg}
B.~Bischl, M.~Lang, L.~Kotthoff, J.~Schiffner, J.~Richter, E.~Studerus,
  G.~Casalicchio, and Z.~M. Jones.
\newblock {mlr}: Machine learning in r.
\newblock \emph{Journal of Machine Learning Research}, 17\penalty0
  (170):\penalty0 1--5, 2016.
\newblock URL \url{http://jmlr.org/papers/v17/15-066.html}.

\bibitem[Breiman(1996)]{bagging-breiman-1996}
L.~Breiman.
\newblock Bagging predictors.
\newblock \emph{Machine Learning}, 8\penalty0 (2):\penalty0 209--218, 1996.
\newblock URL \url{https://doi.org/10.1023/A:1018054314350}.

\bibitem[Breiman(2001)]{random-breiman-2001}
L.~Breiman.
\newblock Random forests.
\newblock \emph{Machine Learning}, 45\penalty0 (1):\penalty0 5--32, 2001.
\newblock URL \url{https://doi.org/10.1023/A:1010933404324}.

\bibitem[Breiman et~al.(1984)Breiman, Friedman, and Charles
  J.~Stone]{classification-breiman-1984}
L.~Breiman, J.~Friedman, and R.~A.~O. Charles J.~Stone.
\newblock \emph{Classification and Regression Trees}.
\newblock The Wadsworth and Brooks-Cole statistics-probability series. Taylor
  \& Francis, 1984.
\newblock ISBN 9780412048418.

\bibitem[Chang(2019)]{R6-pkg}
W.~Chang.
\newblock \emph{R6: Encapsulated Classes with Reference Semantics}, 2019.
\newblock URL \url{https://CRAN.R-project.org/package=R6}.
\newblock R package version 2.4.0.

\bibitem[Chen et~al.(2017)Chen, He, and Benesty]{xgboost-pkg}
T.~Chen, T.~He, and M.~Benesty.
\newblock \emph{xgboost: Extreme Gradient Boosting}, 2017.
\newblock URL \url{https://CRAN.R-project.org/package=xgboost}.
\newblock R package version 0.6-4.

\bibitem[Cleveland(1979)]{robust-cleveland-1979}
W.~S. Cleveland.
\newblock Robust locally weighted regression and smoothing scatterplots.
\newblock \emph{Journal of the American Statistical Association}, 74\penalty0
  (368):\penalty0 829--836, 1979.
\newblock \doi{10.1080/01621459.1979.10481038}.

\bibitem[Fisher and Dominici(2018)]{fisher-model-2018}
R.~C. Fisher, A. and F.~Dominici.
\newblock Model class reliance: Variable importance measures for any machine
  learning model class, from the "rashomon" perspective.
\newblock \emph{arXiv preprint arXiv:1801.01489}, 2018.

\bibitem[Friedman et~al.(2010)Friedman, Hastie, and Tibshirani]{glmnet-pkg}
J.~Friedman, T.~Hastie, and R.~Tibshirani.
\newblock Regularization paths for generalized linear models via coordinate
  descent.
\newblock \emph{Journal of Statistical Software}, 33\penalty0 (1):\penalty0
  1--22, 2010.
\newblock URL \url{http://www.jstatsoft.org/v33/i01/}.

\bibitem[Friedman(1991)]{multivariate-friedman-1991}
J.~H. Friedman.
\newblock Multivariate adaptive regression splines.
\newblock \emph{The Annals of Statistics}, 19\penalty0 (1):\penalty0 1--67,
  1991.
\newblock URL \url{https://doi.org/10.1214/aos/1176347963}.

\bibitem[Garson(1991)]{interpreting-garson-1991}
D.~G. Garson.
\newblock Interpreting neural-network connection weights.
\newblock \emph{Artificial Intelligence Expert}, 6\penalty0 (4):\penalty0
  46--51, 1991.

\bibitem[Gedeon(1997)]{data-gedeon-1997}
T.~Gedeon.
\newblock Data mining of inputs: Analysing magnitude and functional measures.
\newblock \emph{International Journal of Neural Systems}, 24\penalty0
  (2):\penalty0 123--140, 1997.
\newblock URL \url{https://doi.org/10.1007/s10994-006-6226-1}.

\bibitem[Goh(1995)]{back-goh-1995}
A.~Goh.
\newblock Back-propagation neural networks for modeling complex systems.
\newblock \emph{Artificial Intelligence in Engineering}, 9\penalty0
  (3):\penalty0 143--151, 1995.
\newblock \doi{http://dx.doi.org/10.1016/0954-1810(94)00011-S}.

\bibitem[Goldstein et~al.(2015)Goldstein, Kapelner, Bleich, and
  Pitkin]{goldstein-peeking-2015}
A.~Goldstein, A.~Kapelner, J.~Bleich, and E.~Pitkin.
\newblock Peeking inside the black box: Visualizing statistical learning with
  plots of individual conditional expectation.
\newblock \emph{Journal of Computational and Graphical Statistics}, 24\penalty0
  (1):\penalty0 44--65, 2015.
\newblock URL \url{https://doi.org/10.1080/10618600.2014.907095}.

\bibitem[Greenwell and Boehmke(2018)]{vip-pkg}
B.~Greenwell and B.~Boehmke.
\newblock \emph{vip: Variable Importance Plots}, 2018.
\newblock URL \url{https://CRAN.R-project.org/package=vip}.
\newblock R package version 0.1.0.

\bibitem[Greenwell(2017)]{greenwell-pdp-2017}
B.~M. Greenwell.
\newblock pdp: An r package for constructing partial dependence plots.
\newblock \emph{The R Journal}, 9\penalty0 (1):\penalty0 421--436, 2017.
\newblock URL
  \url{https://journal.r-project.org/archive/2017/RJ-2017-016/index.html}.

\bibitem[Greenwell et~al.(2018)Greenwell, Boehmke, and
  McCarthy]{greenwell-simple-2018}
B.~M. Greenwell, B.~C. Boehmke, and A.~J. McCarthy.
\newblock A simple and effective model-based variable importance measure.
\newblock \emph{arXiv preprint arXiv:1805.04755}, 2018.

\bibitem[Hastie et~al.(2009)Hastie, Tibshirani, and
  Friedman]{hastie-elements-2009}
T.~Hastie, R.~Tibshirani, and J.~Friedman.
\newblock \emph{The Elements of Statistical Learning: Data Mining, Inference,
  and Prediction, Second Edition}.
\newblock Springer Series in Statistics. Springer-Verlag, 2009.

\bibitem[Karatzoglou et~al.(2004)Karatzoglou, Smola, Hornik, and
  Zeileis]{kernlab-pkg}
A.~Karatzoglou, A.~Smola, K.~Hornik, and A.~Zeileis.
\newblock Kernlab -- an {S4} package for kernel methods in {R}.
\newblock \emph{Journal of Statistical Software}, 11\penalty0 (9):\penalty0
  1--20, 2004.
\newblock URL \url{https://doi.org/10.18637/jss.v011.i09}.

\bibitem[Kuhn(2017{\natexlab{a}})]{AmesHousing-pkg}
M.~Kuhn.
\newblock \emph{AmesHousing: The Ames Iowa Housing Data}, 2017{\natexlab{a}}.
\newblock URL \url{https://CRAN.R-project.org/package=AmesHousing}.
\newblock R package version 0.0.3.

\bibitem[Kuhn(2017{\natexlab{b}})]{caret-pkg}
M.~Kuhn.
\newblock \emph{caret: Classification and Regression Training},
  2017{\natexlab{b}}.
\newblock URL \url{https://CRAN.R-project.org/package=caret}.
\newblock R package version 6.0-76.

\bibitem[Kuhn and Johnson(2013)]{applied-kuhn-2013}
M.~Kuhn and K.~Johnson.
\newblock \emph{Applied Predictive Modeling}.
\newblock SpringerLink : B{\"u}cher. Springer New York, 2013.
\newblock ISBN 9781461468493.

\bibitem[Leisch and Dimitriadou(2012)]{mlbench-pkg}
F.~Leisch and E.~Dimitriadou.
\newblock \emph{mlbench: Machine Learning Benchmark Problems}, 2012.
\newblock URL \url{https://CRAN.R-project.org/package=mlbench}.
\newblock R package version 2.1-1.

\bibitem[Mersmann(2018)]{microbenchmark-pkg}
O.~Mersmann.
\newblock \emph{microbenchmark: Accurate Timing Functions}, 2018.
\newblock URL \url{https://CRAN.R-project.org/package=microbenchmark}.
\newblock R package version 1.4-6.

\bibitem[Milborrow(2017)]{earth-pkg}
S.~Milborrow.
\newblock \emph{earth: Multivariate Adaptive Regression Splines}, 2017.
\newblock URL \url{https://CRAN.R-project.org/package=earth}.
\newblock R package version 4.5.0.

\bibitem[Molnar(2019)]{molnar-2019-iml}
C.~Molnar.
\newblock \emph{Interpretable Machine Learning}.
\newblock 2019.
\newblock \url{https://christophm.github.io/interpretable-ml-book/}.

\bibitem[Molnar et~al.(2018)Molnar, Bischl, and Casalicchio]{iml-pkg}
C.~Molnar, B.~Bischl, and G.~Casalicchio.
\newblock iml: An r package for interpretable machine learning.
\newblock \emph{JOSS}, 3\penalty0 (26):\penalty0 786, 2018.
\newblock \doi{10.21105/joss.00786}.
\newblock URL \url{http://joss.theoj.org/papers/10.21105/joss.00786}.

\bibitem[Müller and Wickham(2019)]{tibble-pkg}
K.~Müller and H.~Wickham.
\newblock \emph{tibble: Simple Data Frames}, 2019.
\newblock URL \url{https://CRAN.R-project.org/package=tibble}.
\newblock R package version 2.1.1.

\bibitem[Olden et~al.(2004)Olden, Joy, and Death]{accurate-olden-2004}
J.~D. Olden, M.~K. Joy, and R.~G. Death.
\newblock An accurate comparison of methods for quantifying variable importance
  in artificial neural networks using simulated data.
\newblock \emph{Ecological Modelling}, 178\penalty0 (3):\penalty0 389--397,
  2004.
\newblock \doi{http://dx.doi.org/10.1016/j.ecolmodel.2004.03.013}.

\bibitem[Polley et~al.(2018)Polley, LeDell, Kennedy, and {van der
  Laan}]{SuperLearner-pkg}
E.~Polley, E.~LeDell, C.~Kennedy, and M.~{van der Laan}.
\newblock \emph{SuperLearner: Super Learner Prediction}, 2018.
\newblock URL \url{https://CRAN.R-project.org/package=SuperLearner}.
\newblock R package version 2.0-24.

\bibitem[{Revolution Analytics} and Weston(2015{\natexlab{a}})]{doParallel-pkg}
{Revolution Analytics} and S.~Weston.
\newblock \emph{doParallel: Foreach Parallel Adaptor for the 'parallel'
  Package}, 2015{\natexlab{a}}.
\newblock URL \url{https://CRAN.R-project.org/package=doParallel}.
\newblock R package version 1.0.10.

\bibitem[{Revolution Analytics} and Weston(2015{\natexlab{b}})]{foreach-pkg}
{Revolution Analytics} and S.~Weston.
\newblock \emph{foreach: Provides Foreach Looping Construct for R},
  2015{\natexlab{b}}.
\newblock URL \url{https://CRAN.R-project.org/package=foreach}.
\newblock R package version 1.4.3.

\bibitem[Ridgeway(2017)]{gbm-pkg}
G.~Ridgeway.
\newblock \emph{gbm: Generalized Boosted Regression Models}, 2017.
\newblock URL \url{https://CRAN.R-project.org/package=gbm}.
\newblock R package version 2.1.3.

\bibitem[Venables and Ripley(2002)]{nnet-pkg}
W.~N. Venables and B.~D. Ripley.
\newblock \emph{Modern Applied Statistics with S}.
\newblock Springer, New York, fourth edition, 2002.
\newblock URL \url{http://www.stats.ox.ac.uk/pub/MASS4}.
\newblock ISBN 0-387-95457-0.

\bibitem[Wickham(2011)]{plyr-pkg}
H.~Wickham.
\newblock The split-apply-combine strategy for data analysis.
\newblock \emph{Journal of Statistical Software}, 40\penalty0 (1):\penalty0
  1--29, 2011.
\newblock URL \url{https://doi.org/10.18637/jss.v040.i01}.

\bibitem[Wickham(2016)]{ggplot2-pkg}
H.~Wickham.
\newblock \emph{ggplot2: Elegant Graphics for Data Analysis}.
\newblock Springer-Verlag New York, 2016.
\newblock ISBN 978-3-319-24277-4.
\newblock URL \url{https://ggplot2.tidyverse.org}.

\bibitem[Wright(2017)]{ranger-pkg}
M.~N. Wright.
\newblock \emph{ranger: A Fast Implementation of Random Forests}, 2017.
\newblock URL \url{https://CRAN.R-project.org/package=ranger}.
\newblock R package version 0.7.0.

\bibitem[Xie et~al.(2018)Xie, Cheng, and Tan]{DT-pkg}
Y.~Xie, J.~Cheng, and X.~Tan.
\newblock \emph{DT: A Wrapper of the JavaScript Library 'DataTables'}, 2018.
\newblock URL \url{https://CRAN.R-project.org/package=DT}.
\newblock R package version 0.5.

\end{thebibliography}