A five-day intensive workshop covering probability, distributions, GLMs, mixed models, model selection, maximum likelihood, and Bayesian hierarchical modeling — using R, JAGS, and Nimble.
The Bayesian labs need a working toolchain. Install everything and confirm it runs before Day 1, and learn the R Markdown workflow used for every hand-in.
1 · Install R, JAGS & Nimble (Windows & Mac) 2 · R Markdown tutorial 3 · Take the pre-course exam (Day 1 morning)Each morning and afternoon has a single graded problem set — the best problems distilled into one coherent assignment, worth 20 points and submitted on Canvas.
Problems load real ecological data (salmon eDNA, Titanic survival, fish trophic position, ant richness, lizard occupancy, growth and recruitment data) so you practice the full workflow, not just formulas.
Explore distributions, link functions, shrinkage, and a live MCMC sampler — drag sliders and watch the concepts move, right in the browser.
In-class practice labs have visible, reveal-able answers. The graded problem sets are separate and their keys are released only after grading.
Each day has a non-mathematical companion explaining the WHY before the HOW — read it before lab to set up the concepts.
All problem sets and the pre-course exam are submitted as knitted R Markdown HTML. A tutorial covers install, knit, and embedding photos of written work.
A short pre-course exam on Day 1 morning checks where you're starting from — scored generously, it just sets a baseline so the labs can meet you where you are.
A full screen-reader-optimized version of the site, with accessible lecture notes and navigable math, for students who use assistive technology.