{"id":21723591,"url":"https://github.com/fabsig/comparison_glmm_packages","last_synced_at":"2026-04-11T20:42:14.275Z","repository":{"id":113673978,"uuid":"377176495","full_name":"fabsig/Comparison_GLMM_Packages","owner":"fabsig","description":"Comparing Software Packages for Generalized Linear Mixed Effects Models (GLMMs)","archived":false,"fork":false,"pushed_at":"2023-02-02T16:13:38.000Z","size":256,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-09-15T06:33:38.229Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://towardsdatascience.com/generalized-linear-mixed-effects-models-in-r-and-python-with-gpboost-89297622820c","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fabsig.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":"2021-06-15T13:41:31.000Z","updated_at":"2025-08-16T13:23:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"bc8c9f9d-c4f2-41e9-8aab-b2ce5ae73da6","html_url":"https://github.com/fabsig/Comparison_GLMM_Packages","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/fabsig/Comparison_GLMM_Packages","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabsig%2FComparison_GLMM_Packages","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabsig%2FComparison_GLMM_Packages/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabsig%2FComparison_GLMM_Packages/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabsig%2FComparison_GLMM_Packages/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fabsig","download_url":"https://codeload.github.com/fabsig/Comparison_GLMM_Packages/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fabsig%2FComparison_GLMM_Packages/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31695165,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-11T20:18:30.949Z","status":"ssl_error","status_checked_at":"2026-04-11T20:18:29.982Z","response_time":54,"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":[],"created_at":"2024-11-26T02:40:31.145Z","updated_at":"2026-04-11T20:42:14.254Z","avatar_url":"https://github.com/fabsig.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Comparing Software Packages for Generalized Linear Mixed Effects Models (GLMMs)\n\nThis repository contains the code to reproduce the full simulation study described in [this blog post](https://towardsdatascience.com/generalized-linear-mixed-effects-models-in-r-and-python-with-gpboost-89297622820c). \n\nThe simulation is done by the file [Compare_GLMM_packages.R](https://github.com/fabsig/Comparison_GLMM_Packages/blob/master/Compare_GLMM_packages.R). The `reticulate` package is used to call the Python code in [GLMM_statsmodels.py](https://github.com/fabsig/Comparison_GLMM_Packages/blob/master/GLMM_statsmodels.py), which runs the `statsmodels` GLMMs, from R.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabsig%2Fcomparison_glmm_packages","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffabsig%2Fcomparison_glmm_packages","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabsig%2Fcomparison_glmm_packages/lists"}