Introduction {.unnumbered}

Learning bayesian data analysis with the Statistical Rethinking textbook and online lectures. The original version followed the 2019 lectures, and I am in the progress of working through the 2022 lectures and associated homework with the statistical rethinking colearning 2022 group.

Approach {.unnumbered}

For Chapters 1-4, I use quap from the rethinking package, following along with the book and solutions. For Chapters 5-10, I use cmdstanr and stantargets to benefit from the incredible targets package. All wrapped up with bookdown. Thanks to Richard McElreath for the book and providing lectures available online. And thanks to all the package and Stan developers.

See the full references for all packages.

Links {.unnumbered}

• Course: https://github.com/rmcelreath/statrethinking_winter2019
• targets: https://books.ropensci.org/targets/
• cmdstanr: https://mc-stan.org/cmdstanr/
• stantargets: https://docs.ropensci.org/stantargets/
• bookdown: https://bookdown.org/home/

Adaptations {.unnumbered}

Stan+R: https://vincentarelbundock.github.io/rethinking2/

tidy+rethinking: https://david-salazar.github.io/2020/04/19/statistical-rethinking-week-1/

brms+tidy: https://bookdown.org/content/4857/

Julia+Turing: https://github.com/StatisticalRethinkingJulia/TuringModels.jl

Etc {.unnumbered}

https://chi-feng.github.io/mcmc-demo