# MPTinR v1.11.0

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## Analyze Multinomial Processing Tree Models

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, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.

## Functions in MPTinR

 Name Description get.mpt.fia Convenient function to get FIA for MPT gen.data Generate or bootstrap data and get predictions from a model specified in a model file (or connection). check.mpt Check construction of MPT models. MPTinR-package MPTinR d.broeder Broeder & Schuetz (2009) Experiment 3 bmpt.fia Compute FIA for MPTs fit.mptinr Fit cognitive models for categorical data using an objective function fit.model Fit cognitive models for categorical data using model files fit.mpt.old Function to fit MPT models (old) make.eqn Creates an EQN model file oir MDT data file prepare.mpt.fia Provides MATLAB command to get FIA rb.fig1.data Data to be used for the examples of MPTinR. ROCs Recognition memory ROCs used by Klauer & Kellen (2015) fit.mpt Function to fit MPT models make.mpt.cf Functions to transform MPT models. select.mpt Model Selection with MPTinR prediction.plot Plot observed versus predicted values for categorical data. No Results!