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)
Kellen, D., Winiger, S., Dunn, J. C., & Singmann, H. (under review). Testing the Foundations of Signal Detection Theory in Recognition Memory.
Winiger, S., Singmann, H., & Kellen, D. (under review). Bias in Confidence: A critical test for discretestate models of visual working memory.
Stewart, A. J., Singmann, H. Haigh, M., Wood, J. S., Douven, I. (submitted). Tracking the Eye of the Beholder: Is Explanation Subjective?
Singmann, H. (in revision). afex: A UserFriendly Package for the Analysis of Factorial Experiments.
Kellen, D., Singmann, H., Klauer K. C., & Flade, F. (in revision). The Impact of Criterion Noise in Signal Detection Theory: An Evaluation across Recognition Memory Tasks.
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, Forthcoming. (Type: Journal Article  Abstract  Links  BibTeX  Tags: hierarchicalBayesian modeling, R, Software, Statistics  Computation)@article{Gronau2020,
title = {bridgesampling: An R Package for Estimating Normalizing Constants},
author = {Quentin F Gronau and Henrik Singmann and EricJan Wagenmakers},
url = {https://arxiv.org/pdf/1710.08162.pdf, preprint
http://arxiv.org/abs/1710.08162, on ArXiV},
year = {2020},
date = {20200924},
urldate = {20180925},
journal = {Journal of Statistical Software},
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 = {forthcoming},
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. 
1.  Singmann, Henrik; Kellen, David: An introduction to linear mixed modeling in experimental psychology. New Methods in Cognitive Psychology, Psychology Press, Forthcoming. (Type: Incollection  Links  BibTeX  Tags: mixed models, R, Statistics  Computation)@incollection{Singmann2020,
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 = {2020},
date = {20200901},
booktitle = {New Methods in Cognitive Psychology},
publisher = {Psychology Press},
keywords = {mixed models, R, Statistics  Computation},
pubstate = {forthcoming},
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).