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.

Multiverse Analysis of Multinomial Processing Tree Models
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.
c(
person("Henrik", "Singmann", role = c("aut", "cre"),
email="singmann@gmail.com", comment=c(ORCID="0000-0002-4842-3657"))
, person(c("Daniel", "W."), "Heck", email = "heck@uni-mannheim.de", role = c("aut"))
, person("Marius", "Barth", email = "marius.barth@uni-koeln.de", role = c("aut"))
, person("Frederik", "Aust", email = "frederik.aust@uni-koeln.de", role = c("ctb"),
comment = c(ORCID = "0000-0003-4900-788X"))
)
parallel, magrittr, tidyr, dplyr, tibble, rlang, reshape2,
ggplot2, MPTinR, TreeBUGS, runjags, coda, purrr, broom, readr
Henrik Singmann [aut, cre] (<https://orcid.org/0000-0002-4842-3657>),
Daniel W. Heck [aut],
Marius Barth [aut],
Frederik Aust [ctb] (<https://orcid.org/0000-0003-4900-788X>)