What? If you’re in the know, you know there are three major ways to handle missing data:
full-information maximum likelihood, multiple imputation, and one-step full-luxury1 Bayesian imputation. If you’re a frequentist, you only have the first two options.

What When you fit a logistic regression model, there are a lot of ways to display the results. One of the least inspiring ways is to report a summary of the coefficients in prose or within a table.

Context Someone recently posted a thread on the Stan forums asking how one might make item-characteristic curve (ICC) and item-information curve (IIC) plots for an item-response theory (IRT) model fit with brms.

tl;dr You too can make model diagrams with the tidyverse and patchwork packages. Here’s how.
Diagrams can help us understand statistical models. I’ve been working through John Kruschke’s Doing Bayesian data analysis, Second Edition: A tutorial with R, JAGS, and Stan and translating it into brms and tidyverse-style workflow.

A colleague reached out to me earlier this week with a plotting question. They had fit a series of Bayesian models, all containing a common parameter of interest. They knew how to plot their focal parameter one model at a time, but were stumped on how to combine the plots across models into a seamless whole.

[edited Apr 21, 2021]
tl;dr You too can make sideways Gaussian density curves within the tidyverse. Here’s how.
Here’s the deal: I like making pictures. Over the past several months, I’ve been slowly chipping away1 at John Kruschke’s Doing Bayesian data analysis, Second Edition: A tutorial with R, JAGS, and Stan.