{"id":22215240,"url":"https://github.com/eggplantbren/dnest4","last_synced_at":"2025-04-04T22:03:06.513Z","repository":{"id":47473015,"uuid":"47721004","full_name":"eggplantbren/DNest4","owner":"eggplantbren","description":"Diffusive Nested Sampling","archived":false,"fork":false,"pushed_at":"2024-10-21T20:58:06.000Z","size":7430,"stargazers_count":69,"open_issues_count":11,"forks_count":23,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-03-28T21:03:26.986Z","etag":null,"topics":["bayesian-inference","markov-chain-monte-carlo","nested-sampling","probabilistic-programming","probability"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eggplantbren.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"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}},"created_at":"2015-12-09T21:46:21.000Z","updated_at":"2025-03-22T05:11:36.000Z","dependencies_parsed_at":"2023-01-21T12:30:11.627Z","dependency_job_id":"a6f7bd6f-9f59-4914-b473-4c224d098b50","html_url":"https://github.com/eggplantbren/DNest4","commit_stats":{"total_commits":807,"total_committers":7,"mean_commits":"115.28571428571429","dds":"0.10780669144981414","last_synced_commit":"15254a885f6720006070834e15e845b0df6c6860"},"previous_names":[],"tags_count":17,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eggplantbren%2FDNest4","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eggplantbren%2FDNest4/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eggplantbren%2FDNest4/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eggplantbren%2FDNest4/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eggplantbren","download_url":"https://codeload.github.com/eggplantbren/DNest4/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247256104,"owners_count":20909240,"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-inference","markov-chain-monte-carlo","nested-sampling","probabilistic-programming","probability"],"created_at":"2024-12-02T21:26:42.323Z","updated_at":"2025-04-04T22:03:06.473Z","avatar_url":"https://github.com/eggplantbren.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"DNest4\n======\n\n[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/eggplantbren/DNest4/blob/master/LICENSE)\n\nDNest4 is a C++11 implementation of Diffusive Nested Sampling, a Markov Chain\nMonte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics.\nYou can use it in a few different ways:\n\n* Implement your model in C++, compile it and have it run super fast.\n* Implement trans-dimensional models with the *RJObject* template class.\n* Implement your model by writing just two functions in Python, R, or Julia (this is new and undocumented - email me or take a look in the Templates directory)\n* Implement your model as a Python class.\n* Write up your model in Python, using a BUGS-style approach\n[see here to learn how](https://plausibilitytheory.wordpress.com/2016/08/11/a-jags-like-interface-to-dnest4/)\n(more documentation [here](https://odysee.com/@BrendonBrewer:3/dfs:5)).\n\nPapers\n======\n\nThere is a [paper](https://www.jstatsoft.org/article/view/v086i07)\ndescribing DNest4 installation and usage in the Journal of\nStatistical software. You might also want to read the original\n[paper](http://arxiv.org/abs/0912.2380) describing the\nDiffusive Nested Sampling algorithm itself.\nIf you find this software useful in your\nresearch, please cite one or both of these papers. Here are the citations:\n\nBrewer, B., \u0026 Foreman-Mackey, D. (2018).\nDNest4: Diffusive Nested Sampling in C++ and Python.\u003cbr\u003e\nJournal of Statistical Software, 86(7), 1 - 33. doi:http://dx.doi.org/10.18637/jss.v086.i07\n\nBrewer, B. J., Pártay, L. B., \u0026 Csányi, G. (2011). Diffusive nested sampling.\u003cbr\u003e\nStatistics and Computing, 21(4), 649-656.\n\nDependencies\n============\n\nYou will need a C++ compiler that\nsupports the C++11 standard, along with Python 3 and the\nPython packages NumPy, scipy, matplotlib, and Cython.\n\nCompiling\n=========\n\n## Note for Mac users:\nOn some Macs, `g++` is an alias for `clang`, which is a C compiler. If this\nis the case for you, you'll need to edit the first line of the Makefile so\nthat it uses `clang++`, which is a C++ compiler.\n\nYou can compile the DNest4 library (`libdnest4`) using the Makefile in the\n`code` directory using:\n\n```bash\ncd code\nmake\n```\n\nAlong with building the library this will compile all the examples.\nThen, install the Python package:\n\n```bash\npython setup.py install\n```\n\nin the root directory of this repository.\n\nAlternative build process with SCons\n====================================\n\nHowever, you can also compile *and* install the library using [SCons](http://scons.org/).\nTo do this you just need to run:\n\n```bash\nscons install\n```\n\nBy default it will attempt to install the library in `/usr/local` (with the library files in\n`/usr/local/lib` and the headers in `/usr/local/include/dnest4`), so the above command\nmust be run as a user with root access or using `sudo`. To install to a different location\nyou can instead run:\n\n```bash\nscons install --prefix \u003cinstall_location\u003e\n```\n\nwhere `\u003cinstall_location\u003e` is the base path for the install.\n\nTo install with [GDB](https://www.gnu.org/software/gdb/) enabled during the library's compilation you can add the `--debug-mode`\nflag to the install command.\n\nCurrently, the Scons installation does not compile the examples or the Python library. Any additions\nto this installation process are welcome.\n\n(c) 2015--2018 Brendon J. Brewer and contributors.\nLICENCE: MIT.\nSee the LICENSE file for details.\n\n*This work was supported by a Marsden Fast Start grant from the\nRoyal Society of New Zealand.*\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feggplantbren%2Fdnest4","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feggplantbren%2Fdnest4","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feggplantbren%2Fdnest4/lists"}