The raw data and analysis scripts are available for many papers, at least for those papers where I was first author. Just click on “Links”.
Citation information can be found at my Google Scholar profile.
Submitted manuscripts (i.e., under review or in revision)
Singmann, H., Cox, G. E., Kellen, D., Chandramouli, S., Davis-Stober, C., Dunn, J. C., Gronau, Q. F., Kalish, M., McMullin, S. D., Navarro, D., & Shiffrin, R. M. (under review). Statistics in the Service of Science: Don’t let the Tail Wag the Dog.
Foster, K. & Singmann, H. (under review). Another Approximation of the First-Passage Time Densities for the Ratcliff Diffusion Decision Model.
Newall, P. W. S., Weiss-Cohen, L., Singmann, H., Walasek, L., & Ludvig, E. A. (under review). No credible evidence that UK safer gambling messages reduce gambling.
Winiger, S., Singmann, H., & Kellen, D. (under review). Violations of Conditional Independence in Mixed-State Models of Visual Working Memory.
Rey-Mermet, A., Singmann, H., Oberauer, K. (under review). Neither measurement error nor speed-accuracy trade-offs explain the difficulty of establishing attentional control as a psychometric construct: Evidence from a latent-variable analysis using diffusion modeling.
Publications
Note: Publications “in press” are displayed as published in the upcoming year.
2020
|
| 2. | Gronau, Quentin F; Singmann, Henrik; Wagenmakers, Eric-Jan: bridgesampling: An R Package for Estimating Normalizing Constants. Journal of Statistical Software, 92 (10), 2020. (Type: Journal Article | Abstract | Links | BibTeX | Tags: hierarchical-Bayesian modeling, R, Software, Statistics - Computation)@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
|
| 1. | Singmann, Henrik; Kellen, David: An introduction to linear mixed modeling in experimental psychology. New Methods in Cognitive Psychology, pp. 4–31, Psychology Press, 2019. (Type: Incollection | Links | BibTeX | Tags: mixed models, R, Statistics - Computation)@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}
}
|
Other up-to-date lists of my publications can be found at my Google Scholar profile (which contains citation information), my ReseachGate profile, or my ORCID profile (can be a little outdated).