{"id":20405508,"url":"https://github.com/bambinos/formulae","last_synced_at":"2026-02-06T16:12:03.237Z","repository":{"id":40242769,"uuid":"322466441","full_name":"bambinos/formulae","owner":"bambinos","description":"Formulas for mixed-effects models in Python","archived":false,"fork":false,"pushed_at":"2025-01-30T22:13:57.000Z","size":7013,"stargazers_count":58,"open_issues_count":13,"forks_count":14,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-28T10:04:53.957Z","etag":null,"topics":["bambi","mixed-models","python","wilkinson-formulas"],"latest_commit_sha":null,"homepage":"https://bambinos.github.io/formulae/","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/bambinos.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","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":"2020-12-18T02:24:59.000Z","updated_at":"2025-02-13T19:10:14.000Z","dependencies_parsed_at":"2024-06-18T15:49:30.764Z","dependency_job_id":"577eca76-64f9-4e4d-9d10-0eaecaf27d70","html_url":"https://github.com/bambinos/formulae","commit_stats":{"total_commits":212,"total_committers":4,"mean_commits":53.0,"dds":"0.018867924528301883","last_synced_commit":"05facf05a815adc02e527436ec1132d10e0e911d"},"previous_names":[],"tags_count":18,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bambinos%2Fformulae","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bambinos%2Fformulae/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bambinos%2Fformulae/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bambinos%2Fformulae/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bambinos","download_url":"https://codeload.github.com/bambinos/formulae/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247166168,"owners_count":20894654,"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":["bambi","mixed-models","python","wilkinson-formulas"],"created_at":"2024-11-15T05:11:36.906Z","updated_at":"2026-02-06T16:12:03.232Z","avatar_url":"https://github.com/bambinos.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg src=\"docs/logo/formulae_large.png\" width=250\u003e\u003c/img\u003e\n\n[![PyPI version](https://badge.fury.io/py/formulae.svg)](https://badge.fury.io/py/formulae)\n[![codecov](https://codecov.io/gh/bambinos/formulae/branch/master/graph/badge.svg)](https://codecov.io/gh/bambinos/formulae)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n\n# formulae\n\nformulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations like [Patsy](https://github.com/pydata/patsy) or [formulaic](https://github.com/matthewwardrop/formulaic) is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively).\n\nThis package has been written to make it easier to specify models with group effects in [Bambi](https://github.com/bambinos/bambi), a package that makes it easy to work with Bayesian GLMMs in Python, but it could be used independently as a backend for another library. The approach in this library is to extend classical statistical formulas in a similar way than in R package [lme4](https://CRAN.R-project.org/package=lme4).\n\n## Installation\n\nformulae requires a working Python interpreter (3.8+) and the libraries NumPy, SciPy and Pandas with versions specified in the [pyproject.toml](https://github.com/bambinos/formulae/blob/master/pyproject.toml) file.\n\nAssuming a standard Python environment is installed on your machine (including pip), the latest release of formulae can be installed in one line using pip:\n\n`pip install formulae`\n\nAlternatively, if you want the development version of the package you can install from GitHub:\n\n`pip install git+https://github.com/bambinos/formulae.git`\n\n## Documentation\n\nThe official documentation can be found [here](https://bambinos.github.io/formulae)\n\n## Notes\n\n- The `data` argument only accepts objects of class `pandas.DataFrame`.\n- `y ~ .` is not implemented and won't be implemented in a first version. However, it is planned to be included in the future.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbambinos%2Fformulae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbambinos%2Fformulae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbambinos%2Fformulae/lists"}