Welcome to our course site for "Introduction to Bayesian GLMs". Below are instructions for getting setup or jump straight to the notes and data.
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No data required for this course, we simulate it as part of the course.
Introduction to Bayesian theory, GLMs and mixed models. How to fit Bayesian models and interpret them. Gain a deeper understanding of how to develop Bayesian models.
R version 4 or greater R Packages: rstan, brms and ggplot2
So that the course runs efficiently, and to save plenty of time for trying fun things in R, we'd ask that you come to the course prepared.
First, please take this quick quiz so I can tailor the course to the experience level of the class.
This is an advanced level course, so we'll assume you know how to install R and R packages, and that you know how to use GLMs in R already (e.g. with glm()).
It goes without saying that you should have a recent version of R (and preferably Rstudio) installed on your computer.
We are using R version >4.0.2 currently for writing this course, so there may be some minor differences if you have a different version. We definitely recommend making sure you have version 4 or greater.
Once you have R you'll need to install the STAN program, an algorithm for Bayesian inference. This can be tricky, so save lots of time to get this working.
One tip, if working on windows, is to make sure you've installed RTools (>V4) and have it properly connected to R.
If anybody has Rtools connection issues, then try these pages: https://discourse.mc-stan.org/t/error-when-configuring-rstan-c-toolchain/17915/12 https://github.com/stan-dev/rstan/wiki/Configuring-C---Toolchain-for-Windows#r40.
The main issue with stan on Mac occurs if you're using the new M1 chip. Good luck if you are, I haven't tried it with that.
Once rstan is installed, just install brms like a normal R package.