{"id":43188456,"url":"https://github.com/kkovlakas/gaussfree","last_synced_at":"2026-02-01T04:35:27.728Z","repository":{"id":255715634,"uuid":"849764783","full_name":"kkovlakas/gaussfree","owner":"kkovlakas","description":"Tools for non-Gaussian statistics","archived":false,"fork":false,"pushed_at":"2025-04-05T16:26:28.000Z","size":5404,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-05T17:28:33.992Z","etag":null,"topics":["beta-distribution","binomial-distribution","docs","non-gaussian","parameter-estimation","python3","python310","python311","python312","python36","python37","python38","python39","scientific-computing","unimodal-beta","unit-testing"],"latest_commit_sha":null,"homepage":"","language":"Python","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/kkovlakas.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":"2024-08-30T07:47:39.000Z","updated_at":"2025-04-05T16:27:09.000Z","dependencies_parsed_at":"2024-09-12T19:26:55.059Z","dependency_job_id":null,"html_url":"https://github.com/kkovlakas/gaussfree","commit_stats":null,"previous_names":["kkovlakas/gaussfree"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kkovlakas/gaussfree","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kkovlakas%2Fgaussfree","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kkovlakas%2Fgaussfree/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kkovlakas%2Fgaussfree/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kkovlakas%2Fgaussfree/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kkovlakas","download_url":"https://codeload.github.com/kkovlakas/gaussfree/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kkovlakas%2Fgaussfree/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28968016,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-01T03:46:10.227Z","status":"ssl_error","status_checked_at":"2026-02-01T03:46:01.693Z","response_time":56,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 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":["beta-distribution","binomial-distribution","docs","non-gaussian","parameter-estimation","python3","python310","python311","python312","python36","python37","python38","python39","scientific-computing","unimodal-beta","unit-testing"],"created_at":"2026-02-01T04:35:27.106Z","updated_at":"2026-02-01T04:35:27.715Z","avatar_url":"https://github.com/kkovlakas.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# gaussfree\n\n## Tools for non-Gaussian statistics\n\n`gaussfree` is a library aiming at offering tools for\n\n- Statistical operations in situations\n  where the Gaussian ditribution is not correct;\n- Estimation of distirbution parameters\n  avoiding the normal approximation;\n- Bayesian inference; and\n- Additional helper and visualization functions.\n\nWhile it is far from complete, or generic, in its current implementation, this is the ultimate goal of the project.\n\nThe API is explained in detail in the official documentation:\n\n\u003e \u003chttps://kkovlakas.github.io/gaussfree/readme.html\u003e\n\n## Features\n\nFor now, the features are rather dedicated to a specific project, but there are plans to extend and generalize them.\n\n- **Feature 1**: Estimation of the binomial parameter $p$ (success probability), with arbitrary prior distribution.\n- **Feature 2**: Construction of unimodal Beta distributions with a specific mode and variance.\n\n## Installation\n\nSimply download or clone the source, switch to your favourite environment, and run\n\n`pip install .`\n\nfrom the top level of the repository.\n\n*For the Sphinx autogenerated documentation,\ninstall via `pip` the following packages:\n`sphinx_mdinclude`, `sphinx-math-dollar` and `sphinx-rtd-theme`.*\n\n## Examples\n\n### Construct a unimodal Beta distribution\n\n```python\nfrom gaussfree.distributions.beta import get_unimodal_beta\n\nmy_prior = get_unimodal_beta(mode=0.75, variance=0.03, verbose=True)\n```\n\n### Estimate the binomial $p$\n\n```python\nfrom gaussfree.estimation.binomial import EstimateBinomialP\n\nest = EstimateBinomialP(3, 2)  # trials n=3, successes k=2\nest.report()                  # print result and show posterior\n\n# plot the 68% highest-posterior density interval\nplt.errorbar(0.0, est.mode, yerr=[[est.err_lo], [est.err_hi]])\n\nprint(f\"Posterior mean+/-std: {est.mean:.3f} +/- {est.std:.3f}\")\n\n\nwith_prior = EstimateBinomialP(3, 2, prior_dist=my_prior)\n\n```\n\n## License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for details.\n\n## Authors\n\nKonstantinos Kovlakas\n\n## Acknowledgements\n\nI would like to thank Grigoris Maravelias for suggesting publishing my contribution to his paper [https://ui.adsabs.harvard.edu/abs/2025arXiv250401232M/abstract], and his idea on being just the starting point for a more general package.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkkovlakas%2Fgaussfree","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkkovlakas%2Fgaussfree","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkkovlakas%2Fgaussfree/lists"}