{"id":17723731,"url":"https://github.com/junpenglao/glmm-in-python","last_synced_at":"2026-02-20T16:32:04.185Z","repository":{"id":46596643,"uuid":"62220815","full_name":"junpenglao/GLMM-in-Python","owner":"junpenglao","description":"Generalized linear mixed-effect model in Python","archived":false,"fork":false,"pushed_at":"2018-08-17T05:47:47.000Z","size":12159,"stargazers_count":180,"open_issues_count":2,"forks_count":47,"subscribers_count":15,"default_branch":"master","last_synced_at":"2025-07-06T13:07:05.272Z","etag":null,"topics":["bayesian-inference","glmm","linear-mixed-models","pymc3","statistics"],"latest_commit_sha":null,"homepage":"","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/junpenglao.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-06-29T11:32:01.000Z","updated_at":"2025-06-30T17:36:36.000Z","dependencies_parsed_at":"2022-09-12T15:32:46.978Z","dependency_job_id":null,"html_url":"https://github.com/junpenglao/GLMM-in-Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/junpenglao/GLMM-in-Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/junpenglao%2FGLMM-in-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/junpenglao%2FGLMM-in-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/junpenglao%2FGLMM-in-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/junpenglao%2FGLMM-in-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/junpenglao","download_url":"https://codeload.github.com/junpenglao/GLMM-in-Python/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/junpenglao%2FGLMM-in-Python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29656957,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-20T09:27:29.698Z","status":"ssl_error","status_checked_at":"2026-02-20T09:26:12.373Z","response_time":59,"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","glmm","linear-mixed-models","pymc3","statistics"],"created_at":"2024-10-25T15:43:49.183Z","updated_at":"2026-02-20T16:32:04.169Z","avatar_url":"https://github.com/junpenglao.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Generalized Linear Mixed‐effects Model in Python\n\nor the many ways to perform GLMM in python playground\n  \nA comparison among:  \n[StatsModels](https://github.com/statsmodels/statsmodels)  \n[Theano](https://github.com/Theano/Theano)  \n[PyMC3](https://github.com/pymc-devs/pymc3)(Base on Theano)  \n[TensorFlow](https://github.com/tensorflow/tensorflow)  \n[Stan](https://github.com/stan-dev/stan) and [pyStan](https://github.com/stan-dev/pystan)  \n[Keras](https://github.com/fchollet/keras)  \n[edward](https://github.com/blei-lab/edward)  \n\nWhenever I try on some new machine learning or statistical package, I will fit a mixed effect model. It is better than linear regression (or MNIST for that matter, as it is just a large logistic regression) since linear regressions are almost too easy to fit. Hence this collection of codes that all doing (more or less) the same thing.\n\n## TODO\nEstimate uncertainty related to model parameter using [dropout](http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html) in Theano and TensorFlow  \n[DROPOUT AS A BAYESIAN APPROXIMATION](http://mlg.eng.cam.ac.uk/yarin/publications.html#Gal2015Bayesian)  \nK-Fold Cross Validation and Leave-One-Out (LOO)  \n[WAIC and cross-validation in Stan](http://www.stat.columbia.edu/~gelman/research/unpublished/waic_stan.pdf)  \n[tyarkoni/PPS2016](https://github.com/tyarkoni/PPS2016)\n\n## More information (codes) could be found below (to name a few):  \n[paul-buerkner/brms](https://github.com/paul-buerkner/brms)  \n[vasishth/BayesLMMTutorial](https://github.com/vasishth/BayesLMMTutorial)  \n[jonsedar/pymc3_vs_pystan](https://github.com/jonsedar/pymc3_vs_pystan)  \n[Example from PyMC3](http://pymc-devs.github.io/pymc3/notebooks/GLM-hierarchical.html)  \n[tyarkoni/nipymc](https://github.com/tyarkoni/nipymc)  \n[bambinos/bambi](https://github.com/bambinos/bambi)  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjunpenglao%2Fglmm-in-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjunpenglao%2Fglmm-in-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjunpenglao%2Fglmm-in-python/lists"}