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. . 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.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Heck, Daniel W.; Wagenmakers, Eric-Jan: Adjusted priors for Bayes factors involving reparameterized order constraints (2016)
- 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)
- Klauer, Karl Christoph; Kellen, David: The flexibility of models of recognition memory: the case of confidence ratings (2015)
- Klauer, Karl Christoph; Singmann, Henrik; Kellen, David: Parametric order constraints in multinomial processing tree models: an extension of Knapp and Batchelder (2004) (2015)
- Heck, Daniel W.; Moshagen, Morten; Erdfelder, Edgar: Model selection by minimum description length: lower-bound sample sizes for the Fisher information approximation (2014)