2020

6.  Baumann, Chrisitiane; Singmann, Henrik; Gershman, Samuel; von Helversen, Bettina A Linear Threshold Model for Optimal Stopping Behavior Journal Article Proceedings of the National Academy of Sciences, 117 (23), pp. 1275012755, 2020. Links  BibTeX  Tags: Decision Making, hierarchicalBayesian modeling, mathematical modeling, measurement models @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 = {20200609},
journal = {Proceedings of the National Academy of Sciences},
volume = {117},
number = {23},
pages = {1275012755},
keywords = {Decision Making, hierarchicalBayesian modeling, mathematical modeling, measurement models},
pubstate = {published},
tppubtype = {article}
}

5.  Gronau, Quentin F; Singmann, Henrik; Wagenmakers, EricJan bridgesampling: An R Package for Estimating Normalizing Constants Journal Article Journal of Statistical Software, 92 (10), 2020. Abstract  Links  BibTeX  Tags: hierarchicalBayesian modeling, R, Software, Statistics  Computation @article{Gronau2020b,
title = {bridgesampling: An R Package for Estimating Normalizing Constants},
author = {Quentin F Gronau and Henrik Singmann and EricJan 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 = {20200227},
urldate = {20180925},
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 highdimensional 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 easytouse fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples.},
keywords = {hierarchicalBayesian modeling, R, Software, Statistics  Computation},
pubstate = {published},
tppubtype = {article}
}
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 highdimensional 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 easytouse fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples. 
2018

4.  Trippas, Dries; Kellen, David; Singmann, Henrik; Pennycook, Gordon; Koehler, Derek J; Fugelsang, Jonathan A; Dubé, Chad Characterizing Belief Bias in Syllogistic Reasoning: A HierarchicalBayesian MetaAnalysis of ROC Data Journal Article Psychonomic Bulletin & Review, 25 (6), pp. 2141–2174, 2018. Links  BibTeX  Tags: hierarchicalBayesian modeling, mathematical modeling, measurement models, Meta Analysis, Reasoning, Signal detection, syllogistic reasoning @article{Trippas2018,
title = {Characterizing Belief Bias in Syllogistic Reasoning: A HierarchicalBayesian MetaAnalysis 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/Trippasetal.2018Characterizingbeliefbiasinsyllogisticreasonin.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 = {20181201},
journal = {Psychonomic Bulletin & Review},
volume = {25},
number = {6},
pages = {2141–2174},
keywords = {hierarchicalBayesian modeling, mathematical modeling, measurement models, Meta Analysis, Reasoning, Signal detection, syllogistic reasoning},
pubstate = {published},
tppubtype = {article}
}

3.  Singmann, Henrik; Kellen, David; Mizrak, Eda; Öztekin, Ilke Using Ensembles of Cognitive Models to Answer Substantive Questions Inproceedings Rogers, Tim; Rau, Marina; Zhu, Jerry; Kalish, Chuck (Ed.): Proceedings of the 40th Annual Conference of the Cognitive Science Society, pp. 1070–1075, Austin TX: Cognitive Science Society, 2018. Links  BibTeX  Tags: hierarchicalBayesian modeling, mathematical modeling, measurement models, model selection @inproceedings{singmann_using_2018,
title = {Using Ensembles of Cognitive Models to Answer Substantive Questions},
author = {Henrik Singmann and David Kellen and Eda Mizrak and Ilke Öztekin},
editor = {Tim Rogers and Marina Rau and Jerry Zhu and Chuck Kalish},
url = {http://singmann.org/download/publications/Singmannetal.2018UsingEnsemblesofCognitiveModelstoAnswerSubs.pdf, published version},
year = {2018},
date = {20180729},
booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society},
pages = {10701075},
publisher = {Austin TX: Cognitive Science Society},
keywords = {hierarchicalBayesian modeling, mathematical modeling, measurement models, model selection},
pubstate = {published},
tppubtype = {inproceedings}
}

2.  Bartsch, Lea; Singmann, Henrik; Oberauer, Klaus The Effects of Refreshing and Elaboration on Working Memory Performance, and their Contributions to LongTerm Memory Formation Journal Article Memory & Cognition, 46 (5), pp. 796808, 2018. Links  BibTeX  Tags: hierarchicalBayesian modeling, working memory @article{Bartsch2018,
title = {The Effects of Refreshing and Elaboration on Working Memory Performance, and their Contributions to LongTerm Memory Formation},
author = {Lea Bartsch and Henrik Singmann and Klaus Oberauer},
url = {http://singmann.org/download/publications/Bartschetal.2018Theeffectsofrefreshingandelaborationonworki.pdf, publisher version
https://osf.io/weuc2/, data and analysis code},
year = {2018},
date = {20180701},
journal = {Memory & Cognition},
volume = {46},
number = {5},
pages = {796808},
keywords = {hierarchicalBayesian modeling, working memory},
pubstate = {published},
tppubtype = {article}
}

2014

1.  Kellen, David ; Singmann, Henrik ; Klauer, Karl Christoph Modeling sourcememory overdistribution Journal Article Journal of Memory and Language, 76 , pp. 216–236, 2014. Links  BibTeX  Tags: Familiarity, hierarchicalBayesian modeling, mathematical modeling, measurement models, memory, MPT models, overdistribution, source memory @article{kellen_modeling_2014,
title = {Modeling sourcememory overdistribution},
author = {Kellen, David and Singmann, Henrik and Klauer, Karl Christoph},
url = {http://singmann.org/download/publications/Kellen%20et%20al.%20%202014%20%20Modeling%20sourcememory%20overdistribution.pdf, published article
http://singmann.org/download/publications/datascripts/2014_kellen_singmann_klauer.zip, individual data},
year = {2014},
date = {20141001},
journal = {Journal of Memory and Language},
volume = {76},
pages = {216236},
keywords = {Familiarity, hierarchicalBayesian modeling, mathematical modeling, measurement models, memory, MPT models, overdistribution, source memory},
pubstate = {published},
tppubtype = {article}
}
