New Features

  • We welcome Heidi Seibold as co-author!
  • Internal re-organisation for ctree by means of new extensible tree infrastructure (available in extree_data and extree_fit). Certain parts of the new infrastructure are still experimental. ctree is fully backward compatible.
  • Use libcoin for computing linear test statistics and p-values for ctree.
  • Use inum for binning (the new nmax argument).
  • Quadratic test statistics for splitpoint selection are now available for ctree via ctree_control(splitstat = "quadratic").
  • Maximally selected test statistics for variable selection are now available for ctree via ctree_control(splittest = TRUE).
  • Missing values can be treated as a separate category, also for splits in numeric variables in ctree via ctree_control(MIA = TRUE).
  • Permutation variable importance, including conditional variable importance, was added to partykit.
  • New offset argument in ctree.
  • New get_paths for computing paths to nodes.
  • node_barplot gained a text argument that can be used to draw text labels for the percentages displayed.
  • The margins used in plot.party can now also be set by the user.

Bugfixes

  • Bug fix in mob() if weights are used and caseweights = TRUE (the default). The statistics for the parameter instability tests were computed incorrectly and consequently the selection of splitting variables and also the stopping criterion were affected/incorrect.
  • Avoid log(p) values of -Inf inside mob() by replacing weighted averaging with naive averaging in the response surface regression output in case the p values are below machine precision.
  • The as.party method for rpart objects without any splits only returned a naked partynode rather than a full party. This has been corrected now.
  • nodeapply did not produce the same results for permutations of ids. Spotted by Heidi Seibold.
  • Out-of-bag predictions in predict.cforest were incorrect.
  • perm in predict was only considered when newdata was given. Spotted by Heidi Seibold.
  • Don't try to search for binary splits in unordered factors with more than 31 levels. This potentially caused an integer overrun in previous versions. party::ctree() uses an approximation for binary split searches in unordered factors; thus, using party might be an alternative.