Despite their advantages, the application of Bayesian regression models is still the exception compared to frequentist regression models. Here, we present our R package shinybrms which provides a graphical user interface for fitting Bayesian regression models, with the frontend consisting of a shiny app and the backend relying on the R package brms which in turn relies on Stan. With shinybrms, we hope that Bayesian regression models (and regression models in general) will become more popular in applied research, data analyses, and teaching. Here, we illustrate our graphical user interface by the help of an example from medical research.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-027.zip
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For attribution, please cite this work as
Weber, et al., "shinybrms: Fitting Bayesian Regression Models Using a Graphical User Interface for the R Package brms", The R Journal, 2022
BibTeX citation
@article{RJ-2022-027, author = {Weber, Frank and Ickstadt, Katja and Glass, Änne}, title = {shinybrms: Fitting Bayesian Regression Models Using a Graphical User Interface for the R Package brms}, journal = {The R Journal}, year = {2022}, note = {https://doi.org/10.32614/RJ-2022-027}, doi = {10.32614/RJ-2022-027}, volume = {14}, issue = {2}, issn = {2073-4859}, pages = {96-120} }