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.

Statistical or cognitive modeling usually requires a number of more or less 
    arbitrary choices creating one specific path through a 'garden of forking paths'. 
    The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, 
    <doi:10.1177/1745691616658637>) offers a principled alternative in which results 
    for all possible combinations of reasonable modeling choices are reported. 
    MPTmultiverse performs a multiverse analysis for multinomial processing tree 
    (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining 
    maximum-likelihood/frequentist and Bayesian estimation approaches with 
    different levels of pooling (i.e., data aggregation). For the 
    frequentist approaches, no pooling (with and without parametric or nonparametric 
    bootstrap) and complete pooling are implemented using 
    MPTinR <https://cran.r-project.org/package=MPTinR>. 
    For the Bayesian approaches, no pooling, complete pooling, and three different 
    variants of partial pooling are implemented using 
    TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is 
    fit_mpt() who performs the multiverse analysis in one call.