The ShinyStan interface provides visual and numerical summaries of model parameters and convergence diagnostics.

# S3 method for stanreg
  ppd = TRUE,
  seed = 1234,
  model_name = NULL,
  note = NULL,
  rstudio = getOption("shinystan.rstudio"),



A fitted model object returned by one of the rstanarm modeling functions. See stanreg-objects.


Should rstanarm draw from the posterior predictive distribution before launching ShinyStan? The default is TRUE, although for very large objects it can be convenient to set it to FALSE as drawing from the posterior predictive distribution can be time consuming. If ppd is TRUE then graphical posterior predictive checks are available when ShinyStan is launched.


Passed to pp_check if ppd is TRUE.

model_name, note

Optional arguments passed to as.shinystan.


Only relevant for 'RStudio' users. The default (FALSE) is to launch the app in the user's default web browser rather than the pop-up Viewer provided by 'RStudio'. Users can change the default to TRUE by setting the global option options(shinystan.rstudio = TRUE).


Optional arguments passed to runApp.


The launch_shinystan function will accept a stanreg object as input. Currently, almost any model fit using one of rstanarm's model-fitting functions can be used with ShinyStan. The only exception is that ShinyStan does not currently support rstanarm models fit using algorithm='optimizing'. See the shinystan package documentation for more information.

Faster launch times

For some rstanarm models ShinyStan may take a very long time to launch. If this is the case with one of your models you may be able to speed up launch_shinystan in one of several ways:

Prevent ShinyStan from preparing graphical posterior predictive checks:

When used with a stanreg object (rstanarm model object) ShinyStan will draw from the posterior predictive distribution and prepare graphical posterior predictive checks before launching. That way when you go to the PPcheck page the plots are immediately available. This can be time consuming for models fit to very large datasets and you can prevent this behavior by creating a shinystan object before calling launch_shinystan. To do this use as.shinystan with optional argument ppd set to FALSE (see the Examples section below). When you then launch ShinyStan and go to the PPcheck page the plots will no longer be automatically generated and you will be presented with the standard interface requiring you to first specify the appropriate \(y\) and \(yrep\), which can be done for many but not all rstanarm models.

Use a shinystan object:

Even if you don't want to prevent ShinyStan from preparing graphical posterior predictive checks, first creating a shinystan object using as.shinystan can reduce future launch times. That is, launch_shinystan(sso) will be faster than launch_shinystan(fit), where sso is a shinystan object and fit is a stanreg object. It still may take some time for as.shinystan to create sso initially, but each time you subsequently call launch_shinystan(sso) it will reuse sso instead of internally creating a shinystan object every time. See the Examples section below.


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, arXiv preprint, code on GitHub)

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.


# \dontrun{ if (!exists("example_model")) example(example_model) # Launch the ShinyStan app without saving the resulting shinystan object if (interactive()) launch_shinystan(example_model) # Launch the ShinyStan app (saving resulting shinystan object as sso) if (interactive()) sso <- launch_shinystan(example_model) # First create shinystan object then call launch_shinystan sso <- shinystan::as.shinystan(example_model)
#> #> Hang on... preparing graphical posterior predictive checks for rstanarm model. #> See help('shinystan', 'rstanarm') for how to disable this feature.
if (interactive()) launch_shinystan(sso) # Prevent ShinyStan from preparing graphical posterior predictive checks that # can be time consuming. example_model is small enough that it won't matter # much here but in general this can help speed up launch_shinystan sso <- shinystan::as.shinystan(example_model, ppd = FALSE) if (interactive()) launch_shinystan(sso) # }