2025
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6. | Singmann, Henrik; Heck, Daniel W; Barth, Marius; Erdfelder, Edgar; Arnold, Nina R; Aust, Frederik; Calanchini, Jimmy; Gümüsdagli, Fabian E; Horn, Sebastian S; Kellen, David; Klauer, Karl C.; Matzke, Dora; Meissner, Franziska; Michalkiewicz, Martha; Schaper, Marie Luisa; Stahl, Christoph; Kuhlmann, Beatrice G.; Groß, Julia: Evaluating the Robustness of Parameter Estimates in Cognitive Models: A Meta-Analytic Review of Multinomial Processing Tree Models Across the Multiverse of Estimation Methods. In: Psychological Bulletin, Forthcoming. @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}
}
|
2023
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5. | Nickson, David; Singmann, Henrik; Meyer, Caroline; Toro, Carla; Walasek, Lukasz: Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records. In: Diagnostic and Prognostic Research, vol. 7, pp. 25, 2023. @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}
}
|
4. | Singmann, Henrik; Cox, Gregory Edward; Kellen, David; Chandramouli, Suyog; Davis-Stober, Clintin; Dunn, John C; Gronau, Quentin Frederik; Kalish, Michael; McMullin, Sara D; Navarro, Danielle; Shiffrin, Richard M: Statistics in the Service of Science: Don't let the Tail Wag the Dog. In: Computational Brain & Behavior, vol. 6, iss. 1, pp. 64-83, 2023. @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}
}
|
3. | van Doorn, Johnny; Haaf, Julia M.; Stefan, Angelika M.; Wagenmakers, Eric-Jan; Cox, Gregory Edward; Davis-Stober, Clintin P.; Heathcote, Andrew; Heck, Daniel W.; Kalish, Michael; Kellen, David; Matzke, Dora; Morey, Richard D.; Nicenboim, Bruno; van Ravenzwaaij, Don; Rouder, Jeffrey N.; Schad, Daniel J.; Shiffrin, Richard M.; Singmann, Henrik; Vasishth, Shravan; Veríssimo, João; Bockting, Florence; Chandramouli, Suyog; Dunn, John C.; Gronau, Quentin F.; Linde, Maximilian; McMullin, Sara D.; Navarro, Danielle; Schnuerch, Martin; Yadav, Himanshu; Aust, Frederik: Bayes Factors for Mixed Models: a Discussion. In: Computational Brain & Behavior, vol. 6, iss. 1, pp. 140-158, 2023. @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}
}
|
2020
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2. | Gronau, Quentin F; Singmann, Henrik; Wagenmakers, Eric-Jan: bridgesampling: An R Package for Estimating Normalizing Constants. In: Journal of Statistical Software, vol. 92, no. 10, 2020. @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}
}
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. |
2019
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1. | Singmann, Henrik; Kellen, David: An introduction to linear mixed modeling in experimental psychology. In: New Methods in Cognitive Psychology, pp. 4–31, Psychology Press, 2019. @incollection{Singmann2019,
title = {An introduction to linear mixed modeling in experimental psychology},
author = {Henrik Singmann and David Kellen},
url = {http://singmann.org/download/publications/singmann_kellen-introduction-mixed-models.pdf, preprint},
year = {2019},
date = {2019-11-11},
booktitle = {New Methods in Cognitive Psychology},
pages = {4–31},
publisher = {Psychology Press},
keywords = {mixed models, R, Statistics - Computation},
pubstate = {published},
tppubtype = {incollection}
}
|