c(
person("Michael", "Mayer", , "mayermichael79@gmail.com", role = c("aut", "cre")),
person("David", "Watson", , "david.s.watson11@gmail.com", role = "ctb")
)
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'.
Michael Mayer [aut, cre],
David Watson [ctb]

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
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'.

Kernel Estimation of Hazard Function in Survival Analysis
c(
person(given = "Iveta", family = "Selingerova", email = "selingerova@math.muni.cz", role = c("aut", "cre")),
person(given = "Marie", family = "Langrova", role = "ctb"))
Producing kernel estimates of the unconditional and conditional hazard
function for right-censored data including methods of bandwidth selection.
Iveta Selingerova [aut, cre],
Marie Langrova [ctb]