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 using the snowfall package. MPTinR can
generate data generating from a model for e.g., parametric
bootstrap and plot predicted versus observed data.

Henrik Singmann, David Kellen, and Christian Mueller
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 generating from a model for
e.g., parametric bootstrap and plot predicted versus observed
data.
R (>= 2.11.0), numDeriv, RcppEigen, Rcpp