Modeling Phylogenetic Signals using Eigenvector Maps
Guillaume Guenard, with contribution from Pierre Legendre
Computational tools to represent phylogenetic signals using adapted eigenvector maps.

Analyze Multinomial Processing Tree Models
The package provides a user-friendly way for 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 individual and multi-individual data. The main functions perform model fitting and model selection. Model selection can be done using e.g. the minimum description length measure based on the Fischer information approximation (FIA). The FIA can be obtained for any MPT model using the algorithm provided by Wu, Myung, & Batchelder (2010, JMP) ported to R. The model and restrictions can easily be specified in external files. The 'classical' .EQN file format for model files is also supported. Furthermore, MPTinR supports multicore fitting using the snowfall package.

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 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.