{"id":16272867,"url":"https://github.com/robitalec/statistical-rethinking-colearning-2022","last_synced_at":"2025-07-12T01:08:12.207Z","repository":{"id":109020834,"uuid":"447398078","full_name":"robitalec/statistical-rethinking-colearning-2022","owner":"robitalec","description":"Statistical Rethinking colearning 2022","archived":false,"fork":false,"pushed_at":"2023-02-01T15:20:48.000Z","size":36618,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-03T08:11:09.461Z","etag":null,"topics":["bayesian","r","rethinking","rstats","stan","statistics"],"latest_commit_sha":null,"homepage":"","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/robitalec.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":"CODE_OF_CONDUCT.md","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":"2022-01-12T23:11:47.000Z","updated_at":"2022-03-10T16:45:56.000Z","dependencies_parsed_at":"2023-05-01T01:00:27.303Z","dependency_job_id":null,"html_url":"https://github.com/robitalec/statistical-rethinking-colearning-2022","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/robitalec/statistical-rethinking-colearning-2022","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robitalec%2Fstatistical-rethinking-colearning-2022","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robitalec%2Fstatistical-rethinking-colearning-2022/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robitalec%2Fstatistical-rethinking-colearning-2022/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robitalec%2Fstatistical-rethinking-colearning-2022/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/robitalec","download_url":"https://codeload.github.com/robitalec/statistical-rethinking-colearning-2022/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robitalec%2Fstatistical-rethinking-colearning-2022/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264922866,"owners_count":23683701,"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":["bayesian","r","rethinking","rstats","stan","statistics"],"created_at":"2024-10-10T18:20:04.616Z","updated_at":"2025-07-12T01:08:12.185Z","avatar_url":"https://github.com/robitalec.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\ntitle: Statistical Rethinking colearning 2022\noutput: \n  github_document: \n    toc: true\n---\n\n```{r include = FALSE}\nknitr::opts_chunk$set( fig.path = \"graphics/\" )\n```\n\n---\n\nThis repository contains resources and information for a colearning group\nmeeting regularly to discuss lectures and homework assignments from the \n[Statistical Rethinking 2022](https://github.com/rmcelreath/stat_rethinking_2022)\ncourse. \n\n\n\n## Schedule\n\nAdjusting from Richard's schedule for our pace. Note these are meeting dates\nindicating when lectures, readings and homework are **assigned**, to be \ndiscussed on/completed by the next meeting. \n\n| Meeting date | Lectures | Reading | Homework |\n|---|---|---|---|\n| 2022-01-13 | [(1) The Golem of Prague](https://youtu.be/cclUd_HoRlo), [(2) Bayesian Inference](https://www.youtube.com/watch?v=guTdrfycW2Q\u0026list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN\u0026index=2) | Chapters 1, 2 and 3 | [Homework 1](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week01.pdf) |\n| 2022-01-26 | [(3) Basic Regression](https://www.youtube.com/watch?v=zYYBtxHWE0A), [(4) Categories \u0026 Curves](https://youtu.be/QiHKdvAbYII) | Chapter 4 | [Homework 2](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week02.pdf) |\n| 2022-02-11 | [(5) Confounding](https://youtu.be/UpP-_mBvECI), [(6) Even Worse Confounding](https://www.youtube.com/watch?v=NSuTaeW6Orc) | Chapters 5 and 6 | [Homework 3](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week03.pdf)  |\n| 2022-02-24   |  [(7) Overfitting](https://www.youtube.com/watch?v=odGAAJDlgp8\u0026list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN\u0026index=7)| Chapter 7 | | \n| 2022-03-11 | [(8) Markov Chain Monte Carlo](https://www.youtube.com/watch?v=Qqz5AJjyugM\u0026list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN\u0026index=8\u0026pp=sAQB) | Chapter 8, 9  |  [Homework 4](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week04.pdf) | \n| 2022-03-25 | [(9) Logistic and Binomial GLMs](https://www.youtube.com/watch?v=nPi5yGbfxuo\u0026list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN\u0026index=9), [(10) Sensitivity and Poisson GLMs](https://www.youtube.com/watch?v=YrwL6t0kW2I) | Chapters 10, 11 | [Homework 5](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week05.pdf)  |\n| 2022-04-06 | [(11) Ordered Categories](https://www.youtube.com/watch?v=-397DMPooR8\u0026list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN\u0026index=11), [(12) Multilevel Models](https://www.youtube.com/watch?v=SocRgsf202M\u0026list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN\u0026index=12) | Chapters 12, 13 | [Homework 6](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week06.pdf)  |\n| 2022-04-22 | [(13) Multi-Multilevel Models](https://youtu.be/n2aJYtuGu54), [(14) Correlated varying effects](https://youtu.be/XDoAglqd7ss) | Chapters 13, 14 | [Homework 7](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week07.pdf)  |\n\n\n\n## Resources\n\n* Lectures: https://github.com/rmcelreath/stat_rethinking_2022#calendar--topical-outline\n* Homework: https://github.com/rmcelreath/stat_rethinking_2022/tree/main/homework\n\nAdditional material using other packages or languages\n\n* Original R: https://github.com/rmcelreath/rethinking/\n* R + Tidyverse + ggplot2 + brms: https://bookdown.org/content/4857/\n* Python and PyMC3: Python/PyMC3\n* Julia and Turing: https://github.com/StatisticalRethinkingJulia and https://github.com/StatisticalRethinkingJulia/TuringModels.jl\n\nSee Richard's comments about these here: https://github.com/rmcelreath/stat_rethinking_2022#original-r-flavor\n\nAlso, Alec's notes and solutions of the 2019 material: https://github.com/robitalec/statistical-rethinking and https://www.statistical-rethinking.robitalec.ca/\n\n\n## Installation\n\nPackage specific install directions. We'll update these as we go!\n\nRethinking\n\n* [`rethinking`](https://github.com/rmcelreath/rethinking#installation)\n\nStan\n\n* [`cmdstanr`](https://mc-stan.org/cmdstanr/articles/cmdstanr.html)\n* [`RStan`](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started)\n* [`brms`](r/brms/#how-do-i-install-brms)\n\n\nTargets\n\n* [`targets`](https://github.com/ropensci/targets/#installation)\n* [`stantargets`](https://github.com/ropensci/stantargets/#installation)\n\nV8, needed for the `dagitty` package\n\n* [`V8`](https://github.com/jeroen/v8#installation)\n\n\n\n\n## Project structure\n\nThis repository is structured with a `homework/` folder for homework solutions, \nand `notes/` folder for notes. For folks joining in the colearning group, \nyou are encouraged to make your own branch in this repository and\nshare your notes and/or homework solutions. \n\nThe `R/` folder can be used to store reusable functions useful across\nhomework solutions and your own model situations. \n\nFor example, the `dag_plot` function makes a DAG plot from a DAG: \n\n\n```{r readme_dag, cache = TRUE}\nlibrary(ggplot2)\nlibrary(ggdag)\nlibrary(dagitty)\n\nsource('R/dag_plot.R')\n\ndag \u003c- dagify(\n    Z ~ A + B,\n    B ~ A,\n    exposure = 'A',\n    outcome = 'Z'\n)\n\ndag_plot(dag)\n```\n\n## Branches\n\nSee the full list of [branches](https://github.com/robitalec/statistical-rethinking-colearning-2022/branches). \n\n* [Matteo](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/matteo)\n* [Jillian](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/jillian)\n* [Alec](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/alec)\n* [Levi](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/levi)\n* [Katrien](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/katrien)\n* [Bella](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/bella)\n* [Hannah](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/hannah)\n* [Frankie](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/frankie)\n\n\n\n## Thanks\n\nMany thanks to Richard McElreath for a continued emphasis on teaching \nBayesian statistics and for providing this incredible resource of lectures\nand homework assignments free for everyone. \n\nAlso thank you to the developers of R, Stan and innumerous R packages that \nallow us to pursue this interest. \n\n\n\n## Code of Conduct\n\nPlease note that this project is released with a [Code of\nConduct](CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobitalec%2Fstatistical-rethinking-colearning-2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frobitalec%2Fstatistical-rethinking-colearning-2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobitalec%2Fstatistical-rethinking-colearning-2022/lists"}