Resources

Bayesian modeling

• Super quick crash course explanation of frequentism vs. Bayesianism, including this table summarizing the two approaches’ philosophies

• Bayes Rules! An Introduction to Applied Bayesian Modeling: This is the absolute best introductory textbook for Bayesian methods and multilevel models, and it’s free! I’ve created a notebook translating all its code to {brms} and raw Stan here.

• The super canonical everyone-has-this-book book is Statistical Rethinking by Richard McElreath. At that page he also has an entire set of accompanying lectures on YouTube. He doesn’t use {brms}, but Solomon Kurz has translated all his models to tidyverse-based brms code here.

• Visualizing the differences between Bayesian posterior predictions, linear predictions, and the expectation of posterior predictions: A guide to different types of Bayesian posterior distributions and the nuances of posterior_predict(), posterior_epred(), and posterior_linpred()

Fancier regression

• Poisson & Negative Bionomial Regression, chapter 12 in Bayes Rules!

• A guide to modeling proportions with Bayesian beta and zero-inflated beta regression models: A guide to Beta, zero-inflated Beta, one-inflated Beta, and zero-one-inflated Beta regression

• Working with tricky outcomes with lots of zeros: A notebook illustrating zero-inflated and hurdle models in practice

Marginal effects

• {marginaleffects} documentation: Incredible book-style documentation showing how the {marginaleffects} package works

• Marginalia: A guide to figuring out what the heck marginal effects, marginal slopes, average marginal effects, marginal effects at the mean, and all these other marginal things are: Big practical guide to calculating different types of marginal effects