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 User-Friendly 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.

@article{Gronau2020,
title = {bridgesampling: An R Package for Estimating Normalizing Constants},
author = {Quentin F Gronau and Henrik Singmann and Eric-Jan Wagenmakers},
url = {https://arxiv.org/pdf/1710.08162.pdf, preprint
http://arxiv.org/abs/1710.08162, on ArXiV},
year = {2020},
date = {2020-09-24},
urldate = {2018-09-25},
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 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 = {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 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.