# rmcelreath/stat_rethinking_2022

Statistical Rethinking course winter 2022

Repository: https://github.com/rmcelreath/stat_rethinking_2022
Canonical: https://ross.abutalabs.com/products/stat_rethinking_2022
Language: R
License Family: other
Last push: 2022-03-15T15:07:26+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1790, "days_push": 1632, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4106, forks 432 (observed 2026-08-28T04:08:35.668933+00:00)

## What it is
Course materials for Richard McElreath's Statistical Rethinking 2022 edition, a Bayesian data analysis course. It includes lecture videos, slides, reading schedules, and problem sets teaching scientific modeling and Bayesian inference.

## Use cases
- learn bayesian data analysis from scratch
- study statistical rethinking course lectures
- find problem sets for bayesian statistics practice
- learn causal inference and scientific modeling
- self-study mcelreath statistical rethinking book
- watch lectures on bayesian regression and multilevel models

## When to choose
- you want a structured, free course on Bayesian statistics with video lectures and exercises
- you are a biologist or social scientist learning data analysis with causal models
- you are following the Statistical Rethinking book and want companion materials

## When to avoid
- you need maintained software or a statistical library rather than course content
- you want frequentist statistics or non-Bayesian methods
- you need a license-clear codebase for redistribution, since the repo has no license

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning
- domain: data-science, education, tutorials
- platform: python, cross-platform
- tags: bayesian-statistics, course-materials, r, statistical-rethinking, causal-inference, lectures

## Member repositories
- rmcelreath/stat_rethinking_2022 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:35.668933+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:13.949884+00:00, confidence not recorded.
  - readme: https://github.com/rmcelreath/stat_rethinking_2022 (fetched 2026-08-28T04:08:35.668933+00:00, sha c6a43f78f8e2)
- Data as of 2026-08-30T08:39:29.467469+00:00.