Multitree: a computer program for the analysis of multinomial processing tree models. Multinomial processing tree (MPT) models are a family of stochastic models for psychology and related sciences that can be used to model observed categorical frequencies as a function of a sequence of latent states. For the analysis of such models, the present article presents a platform-independent computer program called multiTree, which simplifies the creation and the analysis of MPT models. This makes them more convenient to implement and analyze. Also, multiTree offers advanced modeling features. It provides estimates of the parameters and their variability, goodness-of-fit statistics, hypothesis testing, checks for identifiability, parametric and nonparametric bootstrapping, and power analyses. In this article, the algorithms underlying multiTree are given, and a user guide is provided. The multiTree program can be downloaded from

References in zbMATH (referenced in 12 articles )

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  1. GroƟ, Julia; Pachur, Thorsten: Parameter estimation approaches for multinomial processing tree models: a comparison for models of memory and judgment (2020)
  2. Heck, Daniel W.; Noventa, Stefano: Representing probabilistic models of knowledge space theory by multinomial processing tree models (2020)
  3. Jobst, Lisa J.; Heck, Daniel W.; Moshagen, Morten: A comparison of correlation and regression approaches for multinomial processing tree models (2020)
  4. Kellen, David; Klauer, Karl Christoph: Selecting amongst multinomial models: an apologia for normalized maximum likelihood (2020)
  5. Schnuerch, Martin; Erdfelder, Edgar; Heck, Daniel W.: Sequential hypothesis tests for multinomial processing tree models (2020)
  6. Segert, Simon; Davis-Stober, Clintin P.: A general approach to prior transformation (2019)
  7. Heck, Daniel W.; Erdfelder, Edgar; Kieslich, Pascal J.: Generalized processing tree models: jointly modeling discrete and continuous variables (2018)
  8. Heck, Daniel W.; Wagenmakers, Eric-Jan: Adjusted priors for Bayes factors involving reparameterized order constraints (2016)
  9. Klauer, Karl Christoph; Singmann, Henrik; Kellen, David: Parametric order constraints in multinomial processing tree models: an extension of Knapp and Batchelder (2004) (2015)
  10. Matzke, Dora; Dolan, Conor V.; Batchelder, William H.; Wagenmakers, Eric-Jan: Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items (2015)
  11. Heck, Daniel W.; Moshagen, Morten; Erdfelder, Edgar: Model selection by minimum description length: lower-bound sample sizes for the Fisher information approximation (2014)
  12. Liu, Yin; Tian, Guo-Liang: A variant of the parallel model for sample surveys with sensitive characteristics (2013)