shinystan R package ('ShinyStan' graphical user interface)

Stan Development Team

Applied Bayesian data analysis is primarily implemented through the Markov chain Monte Carlo (MCMC) algorithms offered by various software packages. When analyzing a posterior sample obtained by one of these algorithms the first step is to check for signs that the chains have converged to the target distribution and and also for signs that the algorithm might require tuning or might be ill-suited for the given model. There may also be theoretical problems or practical inefficiencies with the specification of the model. The ShinyStan app provides interactive plots and tables helpful for analyzing a posterior sample, with particular attention to identifying potential problems with the performance of the MCMC algorithm or the specification of the model. ShinyStan is powered by the Shiny web application framework by RStudio (https://shiny.rstudio.com/) and works with the output of MCMC programs written in any programming language (and has extended functionality for models fit using the rstan package and the No-U-Turn sampler).

ShinyStan has extended functionality for Stan models

Stan (https://mc-stan.org/) models can be run in R using the rstan package. Other packages like rstanarm and brms provide higher-level interfaces to Stan that use rstan internally.

Saving and sharing

The shinystan package allows you to store the basic components of an entire project (code, posterior samples, graphs, tables, notes) in a single object, a shinystan object (sso, for short). Users can save many of the plots as ggplot2 objects for further customization and easy integration in reports or post-processing for publication.

The deploy_shinystan function lets you easily deploy your own ShinyStan apps online for any of your models using the shinyapps.io service from 'RStudio'. Each of your apps (each of your models) will have a unique url and will be compatible with most web browsers.

License

The shinystan package is open source licensed under the GNU Public License, version 3 (GPLv3).

Demo

Check out the demo using launch_shinystan_demo or try it with one of your own models using launch_shinystan.

Resources

• Web page with online documentation (https://mc-stan.org/shinystan/)

• Stan Forums on Discourse (https://discourse.mc-stan.org)

• GitHub issue tracker (https://github.com/stan-dev/shinystan/issues)

References

Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology. 14(2), 99--119. https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf

Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. *J. R. Stat. Soc. A*, 182: 389-402. doi:10.1111/rssa.12378 ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [preprint arXiv:1709.01449](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper))

See also

as.shinystan for creating shinystan objects.

launch_shinystan_demo to try a demo.

launch_shinystan to launch the 'ShinyStan' interface using a particular shinystan object.