Title: Recursive partitioning for multinomial processing tree models: A 
new implementation based on the partykit package

Authors: Florian Wickelmaier and Achim Zeileis

Affiliation: University of Tuebingen

Abstract: Multinomial processing tree (MPT) models are a class of
statistical models for categorical data.  The parameters of such models
represent the probabilities of cognitive processing steps executed to
arrive at observable response categories (Riefer & Batchelder, 1988).  We
present an implementation of model-based recursive partitioning (Zeileis,
Hothorn, & Hornik, 2008) for multinomial processing tree models that is
based on the new partykit infrastructure (Hothorn & Zeileis, 2014).
Recursive partitioning can be used to investigate the effects of subject
covariates and, in doing so, uncover individual differences in cognitive
processes.  The procedure is illustrated with examples from memory
research.

Hothorn, T., & Zeileis, A. (2014).  partykit: A modular toolkit for
recursive partytioning in R. Working paper 2014-10.  Working Papers in
Economics and Statistics, Research Platform Empirical and Experimental
Economics, Universitaet Innsbruck.

http://EconPapers.RePEc.org/RePEc:inn:wpaper:2014-10

Riefer, D., & Batchelder, W. (1988).  Multinomial modeling and the
measurement of cognitive processes.  Psychological Review, 95, 318-339.

Zeileis, A., Hothorn, T., & Hornik, K. (2008).  Model-based recursive
partitioning.  Journal of Computational and Graphical Statistics, 17,
492-514.