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https://github.com/stan-dev/shinystan

shinystan R package and ShinyStan GUI
https://github.com/stan-dev/shinystan

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics mcmc r r-package shiny-apps stan statistical-graphics

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shinystan R package and ShinyStan GUI

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README 

        
# ShinyStan 



[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/shinystan?color=blue)](http://cran.r-project.org/web/packages/shinystan)

[![RStudio CRAN Mirror Downloads](http://cranlogs.r-pkg.org/badges/grand-total/shinystan?color=blue)](http://cran.rstudio.com/package=shinystan)

[![Codecov](http://codecov.io/gh/stan-dev/shinystan/branch/master/graph/badge.svg)](https://codecov.io/gh/stan-dev/shinystan)



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](http://shiny.rstudio.com) web application framework and is

available via the **shinystan** R package.



### Resources



* [mc-stan.org/shinystan](http://mc-stan.org/shinystan) (Website with online documentation)

* [Ask a question](http://discourse.mc-stan.org) (Stan Forums on Discourse)

* [Open an issue](https://github.com/stan-dev/shinystan/issues) (GitHub issues for bug reports, feature requests)



### Installation



* Install the latest release from CRAN:



```r

install.packages("shinystan")

```



* Install the development version from GitHub (requires [devtools](https://github.com/r-lib/devtools) package):



```r

if (!require("devtools")) {

  install.packages("devtools")

}

devtools::install_github("stan-dev/shinystan", build_vignettes = TRUE)

```



### Demo



After installing run



```r

library("shinystan")

launch_shinystan_demo()

```



### Screenshots






### About ShinyStan



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](http://mc-stan.org/interfaces/rstan.html)

and the No-U-Turn sampler).



#### Saving and deploying (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.

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](https://www.shinyapps.io) 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.



### Licensing



The **shinystan** R package and ShinyStan interface are open source licensed under

the GNU Public License, version 3 (GPLv3).