shapr 0.2.0

Minor CRAN release
Added the new dependence modeling approach “ctree” which handles categorical features in addition to numerical ones. For more information see our paper https://doi.org/10.1007/978-3-030-57321-8_7
Added support to explain models which take as input categorical features for model classes like xgboost which originally takes only numeric input. On the user side, an additional call to the new make_dummies function is required. See the vignette for details.
Slight change in the user procedure for explaining predictions from custom models. This now requires only a single function predict_model.
Introduced a thorough system for extracting and checking the feature information in the model and the data passed to shapr and explain. The features in the data are checked for consistency with what can be extracted from the model object. If the model object is missing some of the necessary information, the info from the data is used instead. The system checks feature labels, classes, and any factor levels.
Due to the previous point, the feature_labels option previously used for custom models is removed.
Added a manual testing script for custom model (currently cannot be handled by testthat due to environment issues).
A few under-the-hood changes for checking in the shapr function.

shapr 0.1.4

Patch to fulfill CRAN policy of using packages under Suggests conditionally (in tests and examples)

CRAN release
Removed unused clustering code
Removed several package dependencies
Moved automatic check and pkgdown site build from Circle CI to GitHub actions
Some minor efficiency fixes
Changed stopping threshold from 12 to 13 features for none-sampling version of KernelSHAP for consistency with our recommendation
Changed package title (shortened)
Minor fixes to fulfill CRAN policy
Improved documentation
Revised internal/external and exported/non-exported functions, leading to far fewer external functions and a cleaner manual.