@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://psyarxiv.com/kxhfu/download?format=pdf, accepted manuscript},

year  = {2023},

date = {2023-04-01},

journal = {Computational Brain & Behavior},

keywords = {Bayes Factor, Bayesian modelling, Statistics - Computation},

pubstate = {forthcoming},

tppubtype = {article}

}

@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}

}