kernelshap: Kernel SHAP

Efficient implementation of Kernel SHAP, see Lundberg and Lee (2017) <>, and Covert and Lee (2021) <>. For models with up to eight features, the results are exact regarding the selected background data. Otherwise, an almost exact hybrid algorithm involving iterative sampling is used. The package plays well together with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'. Visualizations can be done using the R package 'shapviz'.

Version: 0.3.7
Depends: R (≥ 3.2.0)
Imports: foreach, stats, utils
Suggests: doFuture, testthat (≥ 3.0.0)
Published: 2023-05-17
Author: Michael Mayer [aut, cre], David Watson [aut], Przemyslaw Biecek ORCID iD [ctb]
Maintainer: Michael Mayer <mayermichael79 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
In views: MachineLearning
CRAN checks: kernelshap results


Reference manual: kernelshap.pdf


Package source: kernelshap_0.3.7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): kernelshap_0.3.7.tgz, r-oldrel (arm64): kernelshap_0.3.7.tgz, r-release (x86_64): kernelshap_0.3.7.tgz, r-oldrel (x86_64): kernelshap_0.3.7.tgz
Old sources: kernelshap archive

Reverse dependencies:

Reverse imports: survex


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