@article{Gronau2020b,
title = {bridgesampling: An R Package for Estimating Normalizing Constants},
author = {Quentin F Gronau and Henrik Singmann and Eric-Jan Wagenmakers},
url = {https://www.jstatsoft.org/index.php/jss/article/view/v092i10/v92i10.pdf, published version
https://arxiv.org/pdf/1710.08162.pdf, preprint
http://arxiv.org/abs/1710.08162, on ArXiV},
doi = {10.18637/jss.v092.i10},
year = {2020},
date = {2020-02-27},
urldate = {2018-09-25},
journal = {Journal of Statistical Software},
volume = {92},
number = {10},
abstract = {Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples.},
keywords = {hierarchical-Bayesian modeling, R, Software, Statistics - Computation},
pubstate = {published},
tppubtype = {article}
}

@article{Boehm2018,
title = {Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations},
author = {Udo Boehm and Jeff Annis and Michael Frank and Guy Hawkins and Andrew Heathcote and David Kellen and Angelos-Miltiadis Krypotos and Veronika Lerche and Gordon D Logan and Thomas Palmeri and Don van Ravenzwaaij and Mathieu Servant and Henrik Singmann and Jeffrey Starns and Andreas Voss and Thomas Wiecki and Dora Matzke and Eric-Jan Wagenmakers},
url = {https://psyarxiv.com/km28u/, preprint on OSF},
doi = {10.31234/osf.io/km28u},
year = {2018},
date = {2018-12-01},
urldate = {2018-09-25},
journal = {Journal of Mathematical Psychology},
volume = {87},
pages = {46-75},
abstract = {For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.},
keywords = {Diffusion model, mathematical modeling, measurement models, Software},
pubstate = {published},
tppubtype = {article}
}

@article{Gauvrit2016,
title = {Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method},
author = {Nicolas Gauvrit and Henrik Singmann and Fernando Soler-Toscano and Hector Zenil},
url = {http://singmann.org/download/publications/Gauvrit-et-al.-2016-Algorithmic-complexity-for-psychology-a-user-frie.pdf, published article
http://singmann.org/download/publications/submitted/2015_gauvrit_et_al_complexity_final.pdf, final manuscript
http://arxiv.org/abs/1409.4080, on arXiv
https://github.com/singmann/acss, code on GitHub},
year = {2016},
date = {2016-04-04},
journal = {Behavior Research Methods},
volume = {48},
pages = {314-329},
keywords = {complexity, new paradigm psychology of reasoning, R, Software},
pubstate = {published},
tppubtype = {article}
}

@article{singmann_mptinr:_2013,
title = {MPTinR: Analysis of multinomial processing tree models in R},
author = {Singmann, Henrik and Kellen, David},
url = {http://singmann.org/download/publications/Singmann%20&%20Kellen%20(2013)%20MPTinR.pdf, published article
http://CRAN.R-project.org/package=MPTinR, MPTinR on CRAN},
issn = {1554-3528},
year = {2013},
date = {2013-01-01},
journal = {Behavior Research Methods},
volume = {45},
number = {2},
pages = {560--575},
keywords = {Fisher information, mathematical modeling, measurement models, MPT models, R, Software},
pubstate = {published},
tppubtype = {article}
}