kernelshap: Kernel SHAP

Multidimensional refinement of the Kernel SHAP algorithm described in Ian Covert and Su-In Lee (2021) <>. The package allows to calculate Kernel SHAP values in an exact way, by iterative sampling (as in the reference above), or by a hybrid of the two. As soon as sampling is involved, the algorithm iterates until convergence, and standard errors are provided. The package works with any model that provides numeric predictions of dimension one or higher. Examples include linear regression, logistic regression (on logit or probability scale), other generalized linear models, generalized additive models, and neural networks. The package plays well together with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'. Visualizations can be done using the R package 'shapviz'.

Version: 0.3.1
Depends: R (≥ 3.2.0)
Imports: doRNG, foreach, MASS, stats, utils
Suggests: doFuture, testthat (≥ 3.0.0)
Published: 2022-11-18
Author: Michael Mayer [aut, cre], David Watson [ctb]
Maintainer: Michael Mayer <mayermichael79 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: kernelshap results


Reference manual: kernelshap.pdf


Package source: kernelshap_0.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): kernelshap_0.3.1.tgz, r-oldrel (arm64): kernelshap_0.3.1.tgz, r-release (x86_64): kernelshap_0.3.1.tgz, r-oldrel (x86_64): kernelshap_0.3.1.tgz
Old sources: kernelshap archive


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