ShinyStan provides immediate, informative, customizable visual and
numerical summaries of model parameters and convergence diagnostics for
MCMC simulations. The ShinyStan interface is coded primarily in R using
the Shiny web application framework and is
available via the **shinystan** R package.

- mc-stan.org/shinystan (Website with online documentation)
- Ask a question (Stan Forums on Discourse)
- Open an issue (GitHub issues for bug reports, feature requests)

- Install the latest release from CRAN:

```
install.packages("shinystan")
```

- Install the development version from GitHub (requires devtools package):

```
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("stan-dev/shinystan", build_vignettes = TRUE)
```

After installing run

```
library("shinystan")
launch_shinystan_demo()
```

Applied Bayesian data analysis is primarily implemented through the 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 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.

ShinyStan 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 RStudio's Shiny web application framework and works with the output of MCMC programs written in any programming language (and has extended functionality for models fit using RStan and the No-U-Turn sampler).

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

**shinystan** also provides the `deploy_shinystan`

function,
which lets you easily deploy your own ShinyStan apps online using RStudio's
ShinyApps service for any of
your models. Each of your apps (each of your models) will have a unique url
and is compatible with Safari, Firefox, Chrome, and most other browsers.

The **shinystan** R package and ShinyStan interface are open source licensed under
the GNU Public License, version 3 (GPLv3).

Get A Weekly Email With Trending Projects For These Topics

No Spam. Unsubscribe easily at any time.

R (71,383)

R Package (1,571)

Bayesian Inference (612)

Shiny Apps (409)

Bayesian (370)

Mcmc (315)

Stan (279)

Bayesian Statistics (218)

Bayesian Methods (149)

Related Projects