{"id":37084984,"url":"https://github.com/florent-leclercq/pyselfi","last_synced_at":"2026-01-14T10:26:41.571Z","repository":{"id":65530683,"uuid":"197575311","full_name":"florent-leclercq/pyselfi","owner":"florent-leclercq","description":"Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation","archived":false,"fork":false,"pushed_at":"2023-01-27T15:11:19.000Z","size":46920,"stargazers_count":10,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-23T18:52:58.444Z","etag":null,"topics":["approximate-bayesian-computation","bayesian-data-analysis","cosmology","galaxy-clustering","large-scale-structure","likelihood-free-inference"],"latest_commit_sha":null,"homepage":"http://pyselfi.florent-leclercq.eu/","language":"Jupyter Notebook","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/florent-leclercq.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}},"created_at":"2019-07-18T11:37:20.000Z","updated_at":"2025-01-15T01:42:35.000Z","dependencies_parsed_at":"2023-02-15T10:30:57.447Z","dependency_job_id":null,"html_url":"https://github.com/florent-leclercq/pyselfi","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/florent-leclercq/pyselfi","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florent-leclercq%2Fpyselfi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florent-leclercq%2Fpyselfi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florent-leclercq%2Fpyselfi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florent-leclercq%2Fpyselfi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/florent-leclercq","download_url":"https://codeload.github.com/florent-leclercq/pyselfi/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florent-leclercq%2Fpyselfi/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28417147,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T10:25:19.714Z","status":"ssl_error","status_checked_at":"2026-01-14T10:22:49.371Z","response_time":107,"last_error":"SSL_read: 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":["approximate-bayesian-computation","bayesian-data-analysis","cosmology","galaxy-clustering","large-scale-structure","likelihood-free-inference"],"created_at":"2026-01-14T10:26:40.883Z","updated_at":"2026-01-14T10:26:41.560Z","avatar_url":"https://github.com/florent-leclercq.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pySELFI #\n\n[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A1902.10149-B31B1B.svg?style=flat)](https://arxiv.org/abs/1902.10149)\n[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A2209.11057-B31B1B.svg?style=flat)](https://arxiv.org/abs/2209.11057)\n[![GitHub version](https://img.shields.io/github/tag/florent-leclercq/pyselfi.svg?label=version)](https://github.com/florent-leclercq/pyselfi)\n[![GitHub commits](https://img.shields.io/github/commits-since/florent-leclercq/pyselfi/v2.0.svg)](https://github.com/florent-leclercq/pyselfi/commits)\n[![DOI](https://zenodo.org/badge/197575311.svg)](https://zenodo.org/badge/latestdoi/197575311)\n[![GPLv3 license](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/florent-leclercq/pyselfi/blob/master/LICENSE)\n[![PyPI version](https://badge.fury.io/py/pyselfi.svg)](https://badge.fury.io/py/pyselfi)\n[![Docs](https://readthedocs.org/projects/pyselfi/badge/)](http://pyselfi.readthedocs.io/en/latest/)\n[![Website florent-leclercq.eu](https://img.shields.io/website-up-down-green-red/http/pyselfi.florent-leclercq.eu.svg)](http://pyselfi.florent-leclercq.eu/)\n\nSimulator Expansion for Likelihood-Free Inference (SELFI): a python implementation.\n\n## Documentation ##\n\nThe code's homepage is [https://pyselfi.florent-leclercq.eu](https://pyselfi.florent-leclercq.eu). The documentation is available on readthedocs at [https://pyselfi.readthedocs.io/](https://pyselfi.readthedocs.io/). Limited user-support may be asked from the main author, Florent Leclercq.\n\n## Contributors ##\n\n* Florent Leclercq, florent.leclercq@iap.fr\n\n## Reference ##\n\nTo acknowledge the use of pySELFI in research papers, please cite its [doi:10.5281/zenodo.3341588](https://doi.org/10.5281/zenodo.3341588) (or for the latest version, see the badge above), as well as the papers [Leclercq \u003ci\u003eet al.\u003c/i\u003e (2019)](https://arxiv.org/abs/1902.10149) and [Leclercq (2022)](https://arxiv.org/abs/2209.11057):\n\n* *Primordial power spectrum and cosmology from black-box galaxy surveys*\u003cbr/\u003e\nF. Leclercq, W. Enzi, J. Jasche, A. Heavens\u003cbr/\u003e\n\u003ca href=\"http://dx.doi.org/10.1093/mnras/stz2718\" target=\"blank\"\u003eMNRAS \u003cb\u003e490\u003c/b\u003e, 4237 (2019)\u003c/a\u003e, \u003ca href=\"http://arxiv.org/abs/1902.10149\" target=\"blank\"\u003earXiv:1902.10149\u003c/a\u003e [\u003ca href=\"http://arxiv.org/abs/1902.10149\" target=\"blank\"\u003eastro-ph.CO\u003c/a\u003e] [\u003ca href=\"https://ui.adsabs.harvard.edu/?#abs/2019MNRAS.490.4237L\" target=\"blank\"\u003eADS\u003c/a\u003e] [\u003ca href=\"http://arxiv.org/pdf/1902.10149\" class=\"document\" target=\"blank\"\u003epdf\u003c/a\u003e]\n* *Simulation-based inference of Bayesian hierarchical models while checking for model misspecification*\u003cbr/\u003e\nF. Leclercq\u003cbr/\u003e\nProceedings of the \u003ca href=\"https://maxent22.see.asso.fr/\" target=\"blank\"\u003e41st International Conference on Bayesian and Maximum Entropy methods in Science and Engineering (MaxEnt2022)\u003c/a\u003e, 18-22 July 2022, Paris, France\u003cbr /\u003e\n\u003ca href=\"https://doi.org/10.3390/psf2022005004\" target=\"blank\"\u003e\tPhysical Sciences Forum \u003cb\u003e5\u003c/b\u003e, 4 (2022)\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/2209.11057\" target=\"blank\"\u003earXiv:2209.11057\u003c/a\u003e [\u003ca href=\"https://arxiv.org/abs/2209.11057\" target=\"blank\"\u003eastro-ph.CO\u003c/a\u003e] [\u003ca href=\"https://ui.adsabs.harvard.edu/?#abs/2022arXiv220911057L\" target=\"blank\"\u003eADS\u003c/a\u003e] [\u003ca href=\"https://arxiv.org/pdf/2209.11057\" class=\"document\" target=\"blank\"\u003epdf\u003c/a\u003e]\n\n\n        @ARTICLE{pySELFI1,\n            author = {{Leclercq}, Florent and {Enzi}, Wolfgang and {Jasche}, Jens and {Heavens}, Alan},\n            title = \"{Primordial power spectrum and cosmology from black-box galaxy surveys}\",\n            journal = {\\mnras},\n            keywords = {methods: statistical, cosmological parameters, large-scale structure of Universe, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},\n            year = \"2019\",\n            month = \"Dec\",\n            volume = {490},\n            number = {3},\n            pages = {4237-4253},\n            doi = {10.1093/mnras/stz2718},\n            archivePrefix = {arXiv},\n            eprint = {1902.10149},\n            primaryClass = {astro-ph.CO},\n            adsurl = {https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.4237L},\n            }\n\n        @ARTICLE{pySELFI2,\n            author = {{Leclercq}, Florent},\n            title = \"{Simulation-based inference of Bayesian hierarchical models while checking for model misspecification}\",\n            journal = {Physical Sciences Forum},\n            keywords = {Statistics - Methodology, Astrophysics - Instrumentation and Methods for Astrophysics, Mathematics - Statistics Theory, Quantitative Biology - Populations and Evolution, Statistics - Machine Learning},\n            year = \"2022\",\n            month = \"Sep\",\n            volume = {5},\n            pages = {4},\n            doi = {10.3390/psf2022005004},\n            archivePrefix = {arXiv},\n            eprint = {2209.11057},\n            primaryClass = {stat.ME},\n            adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220911057L},\n            }\n\n## License ##\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. By downloading and using pySELFI, you agree to the [LICENSE](https://github.com/florent-leclercq/pyselfi/blob/master/LICENSE), distributed with the source code in a text file of the same name.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflorent-leclercq%2Fpyselfi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fflorent-leclercq%2Fpyselfi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflorent-leclercq%2Fpyselfi/lists"}