Behavior Research Methods

, Volume 39, Issue 2, pp 267–273 | Cite as

HMMTree: A computer program for latent-class hierarchical multinomial processing tree models

  • Christoph StahlEmail author
  • Karl Christoph Klauer


Latent-class hierarchical multinomial models are an important extension of the widely used family of multinomial processing tree models, in that they allow for testing the parameter homogeneity assumption and provide a framework for modeling parameter heterogeneity. In this article, the computer program HMMTree is introduced as a means of implementing latent-class hierarchical multinomial processing tree models. HMMTree computes parameter estimates, confidence intervals, and goodness-of-fit statistics for such models, as well as the Fisher information, expected category means and variances, and posterior probabilities for class membership. A brief guide to using the program is provided.


Latent Classis Fisher Information Fisher Information Matrix Multinomial Processing Tree Multinomial Processing Tree Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Psychonomic Society, Inc. 2007

Authors and Affiliations

  1. 1.Institut für PsychologieAlbert-Ludwigs-Universität FreiburgFreiburgGermany

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