{"id":24913185,"url":"https://github.com/pakillo/lm-glm-glmm-intro","last_synced_at":"2025-04-05T17:08:42.918Z","repository":{"id":26099992,"uuid":"29544112","full_name":"Pakillo/LM-GLM-GLMM-intro","owner":"Pakillo","description":"A unified framework for data analysis with GLM/GLMM in R","archived":false,"fork":false,"pushed_at":"2025-01-13T23:26:08.000Z","size":114406,"stargazers_count":116,"open_issues_count":28,"forks_count":29,"subscribers_count":6,"default_branch":"trees","last_synced_at":"2025-03-29T11:42:29.814Z","etag":null,"topics":["glm","glmm","lm","lme4","multilevel-models","r","slide","statistics"],"latest_commit_sha":null,"homepage":"http://pakillo.github.io/LM-GLM-GLMM-intro/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Pakillo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2015-01-20T18:13:24.000Z","updated_at":"2025-02-01T18:54:08.000Z","dependencies_parsed_at":"2024-02-19T12:09:24.642Z","dependency_job_id":"376cd355-e968-4370-9f73-afd09af4c456","html_url":"https://github.com/Pakillo/LM-GLM-GLMM-intro","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pakillo%2FLM-GLM-GLMM-intro","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pakillo%2FLM-GLM-GLMM-intro/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pakillo%2FLM-GLM-GLMM-intro/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pakillo%2FLM-GLM-GLMM-intro/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Pakillo","download_url":"https://codeload.github.com/Pakillo/LM-GLM-GLMM-intro/tar.gz/refs/heads/trees","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247369952,"owners_count":20927928,"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":["glm","glmm","lm","lme4","multilevel-models","r","slide","statistics"],"created_at":"2025-02-02T05:29:57.481Z","updated_at":"2025-04-05T17:08:42.860Z","avatar_url":"https://github.com/Pakillo.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Linear, Generalized, and Mixed/Multilevel models with R\n\n### Course philosophy\n\nIntroductory statistics are typically taught as a sequence of disconnected tests and protocols (e.g. t-test, ANOVA, ANCOVA, regression) while, in reality, all these analyses can be seen as special cases of a more general linear model. In this course, we will introduce Generalised Linear Models as a unified, coherent, and easily extendable framework for the analysis of many different types of data, including Normal (Gaussian), binary, and discrete (count) responses, and both categorical (factors) and continuous predictors.\n\n\u003cbr\u003e\n\n![](images/flowchart.png)\n\n\u003cbr\u003e\n\n### Slides (PDF)\n\n- [Framework](framework.pdf)\n- [Introduction to linear models](lm_intro.pdf)\n- [Linear models](lm.pdf)\n- [Variables and model selection](model_selection.pdf)\n- [Model comparison](model_comparison_trees.pdf)\n- [Generalised Linear Models for binary data](glm_binomial.pdf)\n- [Generalised Linear Models for count data](glm_count.pdf)\n- [Modelling zero-inflated count data](glm_count_zeroinfl.pdf)\n- [Mixed effects / Multilevel models](mixed_models.pdf)\n- [Generalised Additive Models (GAMs)](GAMs.pdf)\n- [An introduction to Bayesian modelling](Bayes_intro.pdf)\n- [Causal inference](causal-inference.pdf)\n- [Regression to the mean](regression-to-the-mean.pdf)\n\n### Interactive tutorials, R scripts, etc\n\nhttps://pakillo.github.io/LM-GLM-GLMM-intro/\n\n\n\n##### LICENSE\n\nThese materials are released with a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/). You can use/adapt them for **non-commercial purposes** as long as you mention the source (this repository) and share the materials with a similar license.\n\n![](images/CClogo.png)\n\n\nFrancisco Rodriguez-Sanchez  \nhttps://frodriguezsanchez.net\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpakillo%2Flm-glm-glmm-intro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpakillo%2Flm-glm-glmm-intro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpakillo%2Flm-glm-glmm-intro/lists"}