{"id":14068365,"url":"https://github.com/stan-dev/shinystan","last_synced_at":"2025-04-10T02:27:25.601Z","repository":{"id":27811081,"uuid":"31300539","full_name":"stan-dev/shinystan","owner":"stan-dev","description":"shinystan R package and ShinyStan GUI","archived":false,"fork":false,"pushed_at":"2022-08-04T14:39:03.000Z","size":34449,"stargazers_count":197,"open_issues_count":24,"forks_count":51,"subscribers_count":33,"default_branch":"master","last_synced_at":"2024-10-29T14:21:57.277Z","etag":null,"topics":["bayesian","bayesian-data-analysis","bayesian-inference","bayesian-methods","bayesian-statistics","mcmc","r","r-package","shiny-apps","stan","statistical-graphics"],"latest_commit_sha":null,"homepage":"https://mc-stan.org/shinystan","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/stan-dev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null},"funding":{"github":"stan-dev","custom":"https://mc-stan.org/support/"}},"created_at":"2015-02-25T06:31:51.000Z","updated_at":"2024-09-12T21:05:46.000Z","dependencies_parsed_at":"2022-07-07T11:30:57.431Z","dependency_job_id":null,"html_url":"https://github.com/stan-dev/shinystan","commit_stats":null,"previous_names":[],"tags_count":10,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stan-dev%2Fshinystan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stan-dev%2Fshinystan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stan-dev%2Fshinystan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stan-dev%2Fshinystan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stan-dev","download_url":"https://codeload.github.com/stan-dev/shinystan/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247686097,"owners_count":20979187,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian","bayesian-data-analysis","bayesian-inference","bayesian-methods","bayesian-statistics","mcmc","r","r-package","shiny-apps","stan","statistical-graphics"],"created_at":"2024-08-13T07:06:07.361Z","updated_at":"2025-04-10T02:27:25.569Z","avatar_url":"https://github.com/stan-dev.png","language":"R","readme":"# ShinyStan \u003cimg src=\"man/figures/stanlogo.png\" align=\"right\" width=\"120\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/shinystan?color=blue)](http://cran.r-project.org/web/packages/shinystan)\n[![RStudio CRAN Mirror Downloads](http://cranlogs.r-pkg.org/badges/grand-total/shinystan?color=blue)](http://cran.rstudio.com/package=shinystan)\n[![Codecov](http://codecov.io/gh/stan-dev/shinystan/branch/master/graph/badge.svg)](https://codecov.io/gh/stan-dev/shinystan)\n\u003c!-- badges: end --\u003e\n\nShinyStan provides immediate, informative, customizable visual and\nnumerical summaries of model parameters and convergence diagnostics for\nMCMC simulations. The ShinyStan interface is coded primarily in R using\nthe [Shiny](http://shiny.rstudio.com) web application framework and is\navailable via the **shinystan** R package.\n\n### Resources\n\n* [mc-stan.org/shinystan](http://mc-stan.org/shinystan) (Website with online documentation)\n* [Ask a question](http://discourse.mc-stan.org) (Stan Forums on Discourse)\n* [Open an issue](https://github.com/stan-dev/shinystan/issues) (GitHub issues for bug reports, feature requests)\n\n### Installation\n\n* Install the latest release from CRAN:\n\n```r\ninstall.packages(\"shinystan\")\n```\n\n* Install the development version from GitHub (requires [devtools](https://github.com/r-lib/devtools) package):\n\n```r\nif (!require(\"devtools\")) {\n  install.packages(\"devtools\")\n}\ndevtools::install_github(\"stan-dev/shinystan\", build_vignettes = TRUE)\n```\n\n### Demo\n\nAfter installing run\n\n```r\nlibrary(\"shinystan\")\nlaunch_shinystan_demo()\n```\n\n### Screenshots\n\n\u003cimg src=https://github.com/stan-dev/shinystan/blob/master/images/home.png width=19% /\u003e\u003cimg src=https://github.com/stan-dev/shinystan/blob/master/images/explore.png width=24.5% /\u003e\u003cimg src=https://github.com/stan-dev/shinystan/blob/master/images/diagnose.png width=24.5% /\u003e\n\n### About ShinyStan\n\nApplied Bayesian data analysis is primarily implemented through the MCMC\nalgorithms offered by various software packages. When analyzing a posterior sample\nobtained by one of these algorithms the first step is to check for signs that\nthe chains have converged to the target distribution and also for signs that\nthe algorithm might require tuning or might be ill-suited for the given model.\nThere may also be theoretical problems or practical inefficiencies with the\nspecification of the model.\n\nShinyStan provides interactive plots and tables helpful for analyzing a\nposterior sample, with particular attention to identifying potential problems\nwith the performance of the MCMC algorithm or the specification of the model.\nShinyStan is powered by RStudio's Shiny web application framework and works with\nthe output of MCMC programs written in any programming language (and has extended\nfunctionality for models fit using [RStan](http://mc-stan.org/interfaces/rstan.html)\nand the No-U-Turn sampler).\n\n#### Saving and deploying (sharing)\n\nThe **shinystan** package allows you to store the basic components of an entire\nproject (code, posterior samples, graphs, tables, notes) in a single object.\nUsers can save many of the plots as ggplot2 objects for further customization\nand easy integration in reports or post-processing for publication.\n\n**shinystan** also provides the `deploy_shinystan` function,\nwhich lets you easily deploy your own ShinyStan apps online using RStudio's\n[ShinyApps](https://www.shinyapps.io) service for any of\nyour models. Each of your apps (each of your models) will have a unique url\nand is compatible with Safari, Firefox, Chrome, and most other browsers.\n\n\n### Licensing\n\nThe **shinystan** R package and ShinyStan interface are open source licensed under\nthe GNU Public License, version 3 (GPLv3).\n","funding_links":["https://github.com/sponsors/stan-dev","https://mc-stan.org/support/"],"categories":["R"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstan-dev%2Fshinystan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstan-dev%2Fshinystan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstan-dev%2Fshinystan/lists"}