Description: 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 specified in external files or within an R script in an intuitive syntax. 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.
Suggests: snowfall (>= 1.84), RcppEigen, Rcpp

Description: 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 contextree 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.