MPTinR: Analyze Multinomial Processing Tree Models. MPTinR provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The ’classical’ .EQN file format for model files is also supported. Besides MPTs, MPTinR can fit a wide variety of other cognitive models such as SDT models (see fit.model). MPTinR supports multicore fitting and FIA calculation using the snowfall package. MPTinR can generate data from a model for e.g., simulation or parametric bootstrap and plot predicted versus observed data.

References in zbMATH (referenced in 15 articles )

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  1. Groß, Julia; Pachur, Thorsten: Parameter estimation approaches for multinomial processing tree models: a comparison for models of memory and judgment (2020)
  2. Jobst, Lisa J.; Heck, Daniel W.; Moshagen, Morten: A comparison of correlation and regression approaches for multinomial processing tree models (2020)
  3. Kellen, David; Klauer, Karl Christoph: Selecting amongst multinomial models: an apologia for normalized maximum likelihood (2020)
  4. Schnuerch, Martin; Erdfelder, Edgar; Heck, Daniel W.: Sequential hypothesis tests for multinomial processing tree models (2020)
  5. Schweickert, Richard; Dhir, Pritha; Zheng, Xiaofang; Poirier, Marie: A multinomial processing tree inferred from age-related memory-error probabilities: possibility of inferring more if response times were available (2020)
  6. Gronau, Quentin F.; Wagenmakers, Eric-Jan; Heck, Daniel W.; Matzke, Dora: A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling (2019)
  7. Segert, Simon; Davis-Stober, Clintin P.: A general approach to prior transformation (2019)
  8. Boehm, Udo; Annis, Jeffrey; Frank, Michael J.; Hawkins, Guy E.; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D.; Palmeri, Thomas J.; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey J.; Voss, Andreas; Wiecki, Thomas V.; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations (2018)
  9. Heck, Daniel W.; Erdfelder, Edgar; Kieslich, Pascal J.: Generalized processing tree models: jointly modeling discrete and continuous variables (2018)
  10. Schweickert, Richard; Zheng, Xiaofang: Tree inference: selective influence in multinomial processing trees with supplementary measures such as response time (2018)
  11. Heck, Daniel W.; Wagenmakers, Eric-Jan: Adjusted priors for Bayes factors involving reparameterized order constraints (2016)
  12. Kellen, David; Erdfelder, Edgar; Malmberg, Kenneth J.; Dubé, Chad; Criss, Amy H.: The ignored alternative: an application of Luce’s low-threshold model to recognition memory (2016)
  13. Klauer, Karl Christoph; Kellen, David: The flexibility of models of recognition memory: the case of confidence ratings (2015)
  14. Klauer, Karl Christoph; Singmann, Henrik; Kellen, David: Parametric order constraints in multinomial processing tree models: an extension of Knapp and Batchelder (2004) (2015)
  15. Heck, Daniel W.; Moshagen, Morten; Erdfelder, Edgar: Model selection by minimum description length: lower-bound sample sizes for the Fisher information approximation (2014)