Imports: Rcpp, future, future.apply, parallelly

Description: Implementation of the model-agnostic Kernel SHAP algorithm by
<http://proceedings.mlr.press/v130/covert21a>.  Due to its iterative
nature, standard errors of the SHAP values are provided and
convergence is monitored.  The package allows to work with any model
that provides numeric predictions.  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 'caret' or 'mlr3'. Visualizations can be done using the R package

person("Michael", "Mayer", , "mayermichael79@gmail.com", role = c("aut", "cre")),
person("David", "Watson", , "david.s.watson11@gmail.com", role = "ctb")
Description: Multidimensional version of the iterative Kernel SHAP
algorithm described in Ian Covert and Su-In Lee (2021)
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'.
Imports: doRNG, foreach, MASS, stats, utils

Description: Multidimensional refinement of the Kernel SHAP algorithm
described in Ian Covert and Su-In Lee (2021)
<http://proceedings.mlr.press/v130/covert21a>.  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
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'.