## 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.