Multidimensional version of the iterative Kernel SHAP algorithm described in Ian Covert and Su-In Lee (2021) <http://proceedings.mlr.press/v130/covert21a>. SHAP values are calculated iteratively until convergence, along with approximate standard errors. The package allows to work with any model that provides numeric predictions of dimension one or higher. Examples include linear regression, logistic regression (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: | 0.2.0 |
Depends: | R (≥ 3.2.0) |
Imports: | doRNG, foreach, MASS, stats, utils |
Suggests: | doFuture, testthat (≥ 3.0.0) |
Published: | 2022-09-05 |
Author: | Michael Mayer [aut, cre], David Watson [ctb] |
Maintainer: | Michael Mayer <mayermichael79 at gmail.com> |
BugReports: | https://github.com/mayer79/kernelshap/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/mayer79/kernelshap |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | kernelshap results |
Reference manual: | kernelshap.pdf |
Package source: | kernelshap_0.2.0.tar.gz |
Windows binaries: | r-devel: kernelshap_0.2.0.zip, r-release: kernelshap_0.2.0.zip, r-oldrel: kernelshap_0.2.0.zip |
macOS binaries: | r-release (arm64): kernelshap_0.1.0.tgz, r-oldrel (arm64): kernelshap_0.1.0.tgz, r-release (x86_64): kernelshap_0.2.0.tgz, r-oldrel (x86_64): kernelshap_0.2.0.tgz |
Old sources: | kernelshap archive |
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