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Doing Bayesian data analysis in brms and the tidyverse

DOI

Kruschke began the second edition of his text like this: "This book explains how to actually do Bayesian data analysis, by real people (like you), for realistic data (like yours)" (2015, p. 1). In the same way, this project is designed to help those real people do Bayesian data analysis. My contribution is converting Kruschke's JAGS code for use in Bürkner's brms package for fitting Bayesian regression models in R using Hamiltonian Monte Carlo. I also prefer plotting and data wrangling with the packages from the tidyverse. So we'll be using those methods, too.

This project is not meant to stand alone. It's a supplement to the second edition of Kruschke's Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. I follow the structure of his text, chapter by chapter, translating his analyses into brms and tidyverse code. However, the content herein departs at times from the source material. Bayesian data analysis with Hamiltonian Monte Carlo is an active area in terms of both statistical methods and software implementation. There are also times when my thoughts and preferences on Bayesian data analysis diverge a bit from Kruschke's. In those places of divergence, I often provide references and explanations.

The long-term goal is a fully coherent bookdown document. The current 0.4.0 version is a [nearly] complete draft. With any luck, future versions will be marked by greater polish and usefulness. I also hope to fill in the remaining content gaps. If you find typos, have kind suggestions for improvement, or know how to solve any of those remaining content gaps, please share your insights in this repository's issues section.