c(
person("Tullia", "Padellini", email = "t.padellini@imperial.ac.uk", role = c("aut", "cre")),
person("Francesco", "Palini", role = "aut"),
person("Pierpaolo", "Brutti", role = "ctb"),
person("Chih-Chung", "Chang", role = c("ctb","cph"), comment = "LIBSVM C++ code"),
person("Chih-Chen", "Lin", role = c("ctb","cph"), comment = "LIBSVM C++ code"),
person("Michael","Kerber", role = c("ctb", "cph"), comment = "HERA C++ code"),
person("Dmitriy","Morozov", role = c("ctb", "cph"), comment = "HERA C++ code"),
person("Arnur","Nigmetov", role = c("ctb", "cph"), comment = "HERA C++ code"))
Provides tools for exploiting topological information into standard statistical learning algorithms.
To this aim, this package contains the most popular kernels defined on the space of persistence diagrams, and persistence images.
Moreover, it provides a solver for kernel Support Vector Machines problems, whose kernels are not necessarily positive semidefinite,
based on the C++ library 'LIBSVM' <https://www.csie.ntu.edu.tw/~cjlin/libsvm/>.
Additionally, it allows to compute Wasserstein distance between persistence diagrams with an arbitrary ground metric,
building an R interface for the C++ library 'HERA' <https://bitbucket.org/grey_narn/hera/src/master/>.
Tullia Padellini [aut, cre],
Francesco Palini [aut],
Pierpaolo Brutti [ctb],
Chih-Chung Chang [ctb, cph] (LIBSVM C++ code),
Chih-Chen Lin [ctb, cph] (LIBSVM C++ code),
Michael Kerber [ctb, cph] (HERA C++ code),
Dmitriy Morozov [ctb, cph] (HERA C++ code),
Arnur Nigmetov [ctb, cph] (HERA C++ code)

Smoothed Bootstrap and Random Generation from Kernel Densities
Smoothed bootstrap and functions for random generation from
univariate and multivariate kernel densities. It does not
estimate kernel densities.

Implementation of the model-agnostic Kernel SHAP algorithm by
Ian Covert and Su-In Lee (2021)
<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
'shapviz'.