@article{Singmann2025,

title = {Evaluating the Robustness of Parameter Estimates in Cognitive Models: A Meta-Analytic Review of Multinomial Processing Tree Models Across the Multiverse of Estimation Methods},

author = {Henrik Singmann and Daniel W Heck and Marius Barth and Edgar Erdfelder and Nina R Arnold and Frederik Aust and Jimmy Calanchini and Fabian E Gümüsdagli and Sebastian S Horn and David Kellen and Karl C. Klauer and Dora Matzke and Franziska Meissner and Martha Michalkiewicz and Marie Luisa Schaper and Christoph Stahl and Beatrice G. Kuhlmann and Julia Groß},

url = {http://singmann.org/download/publications/MPT-multiverse.pdf, accepted manuscript

https://osf.io/preprints/psyarxiv/sd4xp, preprint on OSF},

doi = {10.31234/osf.io/sd4xp},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {Psychological Bulletin},

publisher = {PsyArXiv},

keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, MPT models, Statistics - Computation},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Nickson2023,

title = {Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records},

author = {David Nickson and Henrik Singmann and Caroline Meyer and Carla Toro and Lukasz Walasek},

url = {https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-023-00160-2, open access publisher website

http://singmann.org/download/publications/Nickson-DAPR-Replication-accepted.docx, accepted manuscript},

doi = {10.1186/s41512-023-00160-2},

year  = {2023},

date = {2023-12-05},

urldate = {2024-10-10},

journal = {Diagnostic and Prognostic Research},

volume = {7},

pages = {25},

keywords = {applied, clinical, prediction, replication, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Statistics in the Service of Science: Don't let the Tail Wag the Dog},

author = {Henrik Singmann and Gregory Edward Cox and David Kellen and Suyog Chandramouli and Clintin Davis-Stober and John C Dunn and Quentin Frederik Gronau and Michael Kalish and Sara D McMullin and Danielle Navarro and Richard M Shiffrin},

url = {https://link.springer.com/content/pdf/10.1007/s42113-022-00129-2.pdf?pdf=button, publisher pdf

https://link.springer.com/article/10.1007/s42113-022-00129-2, publisher website

https://psyarxiv.com/kxhfu/download?format=pdf, accepted manuscript},

year  = {2023},

date = {2023-03-01},

urldate = {2023-04-01},

journal = {Computational Brain & Behavior},

volume = {6},

issue = {1},

pages = {64-83},

keywords = {Bayes Factor, Bayesian modelling, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{vanDoorn2023,

title = {Bayes Factors for Mixed Models: a Discussion},

author = {Johnny van Doorn and Julia M. Haaf and Angelika M. Stefan and Eric-Jan Wagenmakers and Gregory Edward Cox and Clintin P. Davis-Stober and Andrew Heathcote and Daniel W. Heck and Michael Kalish and David Kellen and Dora Matzke and Richard D. Morey and Bruno Nicenboim and Don van Ravenzwaaij and Jeffrey N. Rouder and Daniel J. Schad and Richard M. Shiffrin and Henrik Singmann and Shravan Vasishth and João Veríssimo and Florence Bockting and Suyog Chandramouli and John C. Dunn and Quentin F. Gronau and Maximilian Linde and Sara D. McMullin and Danielle Navarro and Martin Schnuerch and Himanshu Yadav and Frederik Aust },

url = {https://link.springer.com/content/pdf/10.1007/s42113-022-00160-3.pdf?pdf=button, publisher PDF

https://link.springer.com/article/10.1007/s42113-022-00160-3, publisher website},

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-01},

journal = {Computational Brain & Behavior},

volume = {6},

issue = {1},

pages = {140-158},

keywords = {Bayes Factor, mixed models, Statistics - Computation},

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}

}