• 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.