Teaching and Tutorials
| Classes | |
|---|---|
| Biol 355/356: Intro to Data Science for Biology | My undergraduate data science course. Covers everything from intro to R to building Shiny apps with a supçon of data analysis. |
| Biol 607/617: Biostatistics and Experimental Design | My graduate biostats class. It constantly evolves. See here for past versions - or just put a / and enter the year number after the main class URL. v1.0 is a hoot. |
| Biol 609: Advanced Data Analysis for Biology | My grad Bayesian data analysis class mostly using McElreath’s Statistical Rethinking. |
| Biol 697: Meta-analysis for Ecology | Once upon a time I taught a course in meta-analysis using Handbook of Meta-Analysis in Ecology and Evolution. It went well, led to a paper, but, so much has changed… And I haven’t had a chance to revisit or re-teach. |
| Structural Equation Modeling for Ecology and Evolutionary Biology | The course Jon Lefcheck and I co-teach periodically on SEM. |
| Marine Biology and Ecology | An undergraduate marine bio class. The link is to the syllabus, as the course is on canvas… I should change that. |
| Underwater Research | Most summers find me at the Shoals Marine Lab for two weeks teaching an AAUS sci-dive class that has a heavy emphasis on designing and conducting research projects underwater. |
| 2019 Geospatial Data Carpentry Workshop at UMB | I’m a trained data carpentry instructor, and really excited about their Geospatial curriculum. Here’s a workshop a group of us taught at UMB. |
| 2024 R Geospatial and Shiny Workshop for COBALT | As a part of my NASA MUREP grant, a group of us taught how to do geospatial in R as well as how to build R Shiny Apps to a group at the Osher Maps Library in Portland, ME. |
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| Tutorials | |
|---|---|
| Full Luxury Bayesian Structural Equation Modeling | For ages, I’ was curious about how to reproduce’ve been frustrated with the inability to work latent variables and other things into piecewise SEM using a frequentist approach. I knew Bayesian piecewise SEM was the answer, but was having a difficult time getting LVs to work properly. Then I got inspired, and, in a fit of mad procrastination, created this tutorial with brms. |
| Same Model, Different Bayesian Code | There are several ways to fit Bayesian models in R. While teaching using the rethinking package, I decided to introduce my students to stan, brms, and inla as alternatives. |
| [Using Modelbased for Model Viz in R | I’ve become a big fan of the modelbased package from easystats in R for quick visualization of marginal effects and counterfactual scenarios. But, some of the documentation isn’t super clear. So I wrote this to crytalize a few lessons learned that I keep forgetting and that others might find useful. |
| tidyeval for dynamic functions | I always forget - quo, quosure, {{}}, or what?! So I wrote this for myself. Mostly so that when I google this stuff, I re-find my own blog entry. |
| Simulating Posteriors from Non-Bayesian Fits | I love tidybayes and other R packages to simulate from Bayesian posteriors. While the arm package got us part way there, how can we automate more? |
| Linear Model Power Analysis with dplyr in R | Honestly, I prefer power analysis by simulation to power analysis by equation. Why? Because you are not constrained in model types. So here are the basics for a linear model. |
| Gluten Free Pizza Crust | When my daughter was diagnosed with Celiac, I had to rework a lot of things in the kitchen. Here’s a dough that always works. |
| dagitty and ggdag | A quick tutorial on these useful packages for dag visualization and analysis |
| Cross Validation in R | A quick tutorial on some of the underlying basics. |
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