{"id":15554873,"url":"https://github.com/nimble-dev/nimble","last_synced_at":"2026-04-02T22:15:49.801Z","repository":{"id":17851422,"uuid":"20771527","full_name":"nimble-dev/nimble","owner":"nimble-dev","description":"The base NIMBLE package for R","archived":false,"fork":false,"pushed_at":"2026-03-27T14:41:15.000Z","size":80834,"stargazers_count":192,"open_issues_count":94,"forks_count":26,"subscribers_count":18,"default_branch":"devel","last_synced_at":"2026-03-28T00:33:01.213Z","etag":null,"topics":["bayesian-inference","bayesian-methods","hierarchical-models","mcmc","probabilistic-programming","r"],"latest_commit_sha":null,"homepage":"http://R-nimble.org","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nimble-dev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":".zenodo.json","notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2014-06-12T14:58:42.000Z","updated_at":"2026-03-27T23:00:15.000Z","dependencies_parsed_at":"2025-12-16T23:09:01.451Z","dependency_job_id":null,"html_url":"https://github.com/nimble-dev/nimble","commit_stats":{"total_commits":4384,"total_committers":37,"mean_commits":"118.48648648648648","dds":0.6181569343065694,"last_synced_commit":"30c24f9b9754fb7dfc847f7983a3c4e04b5b83f2"},"previous_names":[],"tags_count":54,"template":false,"template_full_name":null,"purl":"pkg:github/nimble-dev/nimble","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nimble-dev%2Fnimble","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nimble-dev%2Fnimble/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nimble-dev%2Fnimble/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nimble-dev%2Fnimble/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nimble-dev","download_url":"https://codeload.github.com/nimble-dev/nimble/tar.gz/refs/heads/devel","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nimble-dev%2Fnimble/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31266813,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-01T02:49:12.781Z","status":"ssl_error","status_checked_at":"2026-04-01T02:49:05.845Z","response_time":53,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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-inference","bayesian-methods","hierarchical-models","mcmc","probabilistic-programming","r"],"created_at":"2024-10-02T15:03:49.063Z","updated_at":"2026-04-02T22:15:49.795Z","avatar_url":"https://github.com/nimble-dev.png","language":"C++","readme":"# NIMBLE\n[![Build Status](https://github.com/nimble-dev/nimble/actions/workflows/ci.yaml/badge.svg?branch=devel)](https://github.com/nimble-dev/nimble/actions/workflows/ci.yaml)\n[![CRAN](https://www.r-pkg.org/badges/version/nimble)](https://CRAN.R-project.org/package=nimble)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1211190.svg)](https://zenodo.org/record/1211190)\n[![Google Group](https://img.shields.io/badge/google-group-blue.svg)](https://groups.google.com/forum/#!forum/nimble-users)\n\n[Website](https://r-nimble.org/) |\n[Documentation](https://r-nimble.org/manuals/NimbleUserManual.pdf) |\n[Examples](https://r-nimble.org/examples) |\n[Developing](https://nimble-dev.github.io/nimble-docs) |\n[Workshop materials](https://github.com/nimble-training)\n\nNIMBLE is an R package for hierarchical statistical modeling (aka\ngraphical modeling).  It enables writing general models along with\nmethods such as Markov chain Monte Carlo (MCMC), particle filtering\n(aka sequential Monte Carlo), Laplace approximation and other general methods.\n\nFor writing statistical models, NIMBLE adopts and extends the BUGS\nlanguage, making it largely compatible with\n[BUGS](https://www.mrc-bsu.cam.ac.uk/software/bugs) and\n[JAGS](http://mcmc-jags.sourceforge.net/).  NIMBLE makes BUGS\nextensible, allowing users to add new functions and new distributions.\n\nFor writing algorithms (aka analysis methods), NIMBLE provides a\nmodel-generic programming system embedded within R.  This provides\ncontrol over models as generic objects and mathematical manipulation\nof model variables. In this way, NIMBLE's programming paradigm treats\nprobabilistic graphical models as a basic programming construct.\n\nBoth models and algorithms are compiled via generating customized C++\nand providing seamless interfaces to compiled C++ from R.\n\nNIMBLE's most developed methods are for MCMC.  Users can easily\ncustomize sampler configurations from R and write new samplers in\nNIMBLE's algorithm programming system.\n\nDevelopers of new computational statistical methods can build them in\nNIMBLE to gain the benefits of its graphical modeling language,\ncompilation, and distribution via [CRAN](https://cran.r-project.org/).\n\n## Installation\n\n### Install prerequisites\n\nNIMBLE needs a C++ compiler and the GNU `make` utility.\nTypically, Mac users can obtain these by installing Xcode, including\ncommand line utilities, while Windows users can obtain them by\ninstalling [Rtools](https://cran.r-project.org/bin/windows/Rtools/).\nSee the [User Manual](https://r-nimble.org/manuals/NimbleUserManual.pdf#page=26) for more details.\n\n### Install NIMBLE\n\nThe easiest way to install NIMBLE is via CRAN:\n```r\ninstall.packages(\"nimble\")\n```\n\nTo install from the NIMBLE website:\n```r\nlibrary(devtools)\ninstall.packages(\"nimble\", type = \"source\", repos = \"https://r-nimble.org\")\n```\n\nNote that NIMBLE's Laplace approximation-related methods are (as of version 1.4.0) in the `nimbleQuad` package.\n\nNote that NIMBLE's sequential Monte Carlo (SMC; aka particle filtering) methods are (as of version 0.10.0) in the `nimbleSMC` package.\n\nNote that `MCMCsuite` and `compareMCMCs` have been migrated to the `compareMCMCs` package, now available on CRAN.\n\n## Citation\n\nIn published work that uses or mentions NIMBLE, please cite:\n\nde Valpine, P., D. Turek, C.J. Paciorek, C. Anderson-Bergman,\nD. Temple Lang, and R. Bodik. 2017. Programming with models: writing\nstatistical algorithms for general model structures with\nNIMBLE. Journal of Computational and Graphical Statistics 26:403-413. [https://doi.org/10.1080/10618600.2016.1172487.](https://doi.org/10.1080/10618600.2016.1172487)\n\nIn published work that uses NIMBLE, please also cite the package version:\n\nde Valpine, P., C. Paciorek, D. Turek, N. Michaud, C. Anderson-Bergman, F. Obermeyer, C. Wehrhahn Cortes, A. Rodriguez, D. Temple Lang, W. Zhang, S. Paganin, and P. van Dam-Bates. 2024. NIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling.  doi: 10.5281/zenodo.1211190. R package version 1.4.1, https://cran.r-project.org/package=nimble.\n\nTo help us track usage to justify funding support for NIMBLE, please include the DOI in the citation.\n\n## Licenses\n\nNimble is released under a mixture of licenses,\nand depends on additional third-party libraries with compatible licenses.\n\n- Nimble's non-C++ code (R, bash, Make, etc.) is released under\n  [Revised BSD](LICENSE).\n- Nimble's C++ code is released under\n  [GPL 2](https://www.gnu.org/licenses/gpl-2.0.html).\n- Nimble's [User Manual](UserManual) is released under the\n  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n- The [Eigen C++ library](http://eigen.tuxfamily.org) included with Nimble is\n  licensed under [MPL 2](https://www.mozilla.org/en-US/MPL/2.0/).\n\n## Acknowledgements\n\nThe development of NIMBLE has been funded by:\n\n* an NSF Advances in Biological Informatics grant (DBI-1147230) to P. de Valpine, C. Paciorek, and D. Temple Lang;\n* an NSF SI2-SSI grant  (ACI-1550488) to P. de Valpine, C. Paciorek, and D. Temple Lang;\n* an NSF Collaborative Research grant (DMS-1622444) to P. de Valpine, A. Rodriguez, and C. Paciorek; and\n* an NSF Collaborative Research grant (DMS-2152860) to P. de Valpine, C. Paciorek, and D. Turek.\n\nwith additional support provided by postdoctoral funding for D. Turek from the Berkeley Institute for Data Science and Google Summer of Code fellowships for N. Michaud (2015) and C. Lewis-Beck (2017).\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnimble-dev%2Fnimble","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnimble-dev%2Fnimble","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnimble-dev%2Fnimble/lists"}