Title: Parties, Models, Mobsters: A New Implementation of Model-Based Recursive
Partitioning in R

Authors: Achim Zeileis (1), Torsten Hothorn (2)

Affiliation: (1) Universität Innsbruck, (2) Universität Zürich

Abstract: MOB is a generic algorithm for model-based recursive partitioning
(Zeileis, Hothorn, Hornik 2008). Rather than fitting one global model to a
dataset, it estimates local models on subsets of data that are "learned" by
recursively partitioning. It proceeds in the following way: (1) fit a parametric
model to a data set, (2) test for parameter instability over a set of
partitioning variables, (3) if there is some overall parameter instability,
split the model with respect to the variable associated with the highest
instability, (4) repeat the procedure in each of the child nodes. It is
discussed how these steps of the conceptual algorithm are translated into
computational tools in an object-oriented manner, allowing the user to plug in
various types of parametric models. For representing the resulting trees, the R
package partykit is employed and extended with generic infrastructure for
recursive partitions where nodes are associated with statistical models.
Compared to the previously available implementation in the party package, the
new implementation supports more inference options, is easier to extend to new
models, and provides more convenience features.