Would you like all your posteriors in one plot?

In response to a DM question, here we practice a few different ways you can combine the posterior samples from your Bayesian models into a single plot.

Stein’s Paradox and What Partial Pooling Can Do For You

In many instances, partial pooling leads to better estimates than taking simple averages will, a finding sometimes called Stein’s Paradox. In 1977, Efron and Morris published a great paper discussing the phenomenon. In this post, I’ll walk out Efron and Morris’s baseball example and then link it to contemporary Bayesian multilevel models.

Bayesian Correlations: Let’s Talk Options.

There’s more than one way to fit a Bayesian correlation in brms. Here we explore a few.

Bayesian robust correlations with brms (and why you should love Student’s \(t\))

In this post, we’ll show how Student’s t-distribution can produce better correlation estimates when your data have outliers. As is often the case, we’ll do so as Bayesians.

Robust Linear Regression with Student’s \(t\)-Distribution

The purpose of this post is to demonstrate the advantages of the Student’s t-distribution for regression with outliers, particularly within a Bayesian framework.

Make rotated Gaussians, Kruschke style

You too can make sideways Gaussian density curves within the tidyverse. Here’s how.

Bayesian meta-analysis in brms

This is an early draft of my first attempt at explaining the connection between meta-analyses and the Bayesian multilevel model. Enjoy!

Don’t pay to meditate

Stop using fee-based meditation apps! Let me tell you why.

bookdown, My Process

The purpose of this post is to give readers a sense of how I used bookdown to make my first ebooks. I propose there are three fundamental skill sets you need basic fluency in before playing with bookdown: (a) R and R Studio, (b) scripts and R Markdown files, and (c) Git and GitHub.