@article{Baumann2021,
title = {A Linear Threshold Model for Optimal Stopping Behavior},
author = {Chrisitiane Baumann and Henrik Singmann and Samuel Gershman and Bettina von Helversen},
url = {http://singmann.org/download/publications/Baumann_Optimal_Stopping_PNAS_preprint.pdf, preprint},
year = {2020},
date = {2020-06-09},
journal = {Proceedings of the National Academy of Sciences},
volume = {117},
number = {23},
pages = {12750-12755},
keywords = {Decision Making, hierarchical-Bayesian modeling, mathematical modeling, measurement models},
pubstate = {published},
tppubtype = {article}
}

@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{Trippas2018,
title = {Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical-Bayesian Meta-Analysis of ROC Data},
author = {Dries Trippas and David Kellen and Henrik Singmann and Gordon Pennycook and Derek J. Koehler and Jonathan A. Fugelsang and Chad Dubé},
url = {http://singmann.org/download/publications/Trippas-et-al.-2018-Characterizing-belief-bias-in-syllogistic-reasonin.pdf, published version
http://singmann.org/download/publications/trippas_kellen_singmann_et_al_submitted_online.pdf, accepted manuscript
https://osf.io/8dfyv/, data and modeling code},
year = {2018},
date = {2018-12-01},
journal = {Psychonomic Bulletin & Review},
volume = {25},
number = {6},
pages = {2141–2174},
keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, Reasoning, Signal detection, syllogistic reasoning},
pubstate = {published},
tppubtype = {article}
}

@article{Bartsch2018,
title = {The Effects of Refreshing and Elaboration on Working Memory Performance, and their Contributions to Long-Term Memory Formation},
author = {Lea Bartsch and Henrik Singmann and Klaus Oberauer},
url = {http://singmann.org/download/publications/Bartsch-et-al.-2018-The-effects-of-refreshing-and-elaboration-on-worki.pdf, publisher version
https://osf.io/weuc2/, data and analysis code},
year = {2018},
date = {2018-07-01},
journal = {Memory & Cognition},
volume = {46},
number = {5},
pages = {796-808},
keywords = {hierarchical-Bayesian modeling, working memory},
pubstate = {published},
tppubtype = {article}
}

@article{kellen_modeling_2014,
title = {Modeling source-memory overdistribution},
author = {Kellen, David and Singmann, Henrik and Klauer, Karl Christoph},
url = {http://singmann.org/download/publications/Kellen%20et%20al.%20-%202014%20-%20Modeling%20source-memory%20overdistribution.pdf, published article
http://singmann.org/download/publications/data-scripts/2014_kellen_singmann_klauer.zip, individual data},
year = {2014},
date = {2014-10-01},
journal = {Journal of Memory and Language},
volume = {76},
pages = {216--236},
keywords = {Familiarity, hierarchical-Bayesian modeling, mathematical modeling, measurement models, memory, MPT models, overdistribution, source memory},
pubstate = {published},
tppubtype = {article}
}