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., DavisStober, 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 FirstPassage Time Densities for the Ratcliff Diffusion Decision Model.
Newall, P. W. S., WeissCohen, 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 MixedState Models of Visual Working Memory.
ReyMermet, A., Singmann, H., Oberauer, K. (under review). Neither measurement error nor speedaccuracy tradeoffs explain the difficulty of establishing attentional control as a psychometric construct: Evidence from a latentvariable 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, EricJan: bridgesampling: An R Package for Estimating Normalizing Constants. Journal of Statistical Software, 92 (10), 2020. (Type: Journal Article  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. 
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_kellenintroductionmixedmodels.pdf, preprint},
year = {2019},
date = {20191111},
booktitle = {New Methods in Cognitive Psychology},
pages = {4–31},
publisher = {Psychology Press},
keywords = {mixed models, R, Statistics  Computation},
pubstate = {published},
tppubtype = {incollection}
}

Other uptodate 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).