# install R packages
install.packages(c("dplyr", "lubridate", "ggplot2", "bayesplot", "posterior", "remotes"))
remotes::install_github("stan-dev/cmdstanr")
# install cmdstan
# please post on discourse.mc-stan.org if you run into errors
cmdstanr::install_cmdstan(cores = 2)
# check if cmdstan installation works properly
# please post on discourse.mc-stan.org if you run into errors
cmdstanr::cmdstanr_example()
# optionally install rstan
# we won't _need_ this but it has some extra features we can use if you have it installed
# if it fails to install don't worry about it
install.packages("rstan")We'll use this on day 2:
https://chi-feng.github.io/mcmc-demo/app.html
(This may change substantially based on how we end up tailoring the content to the specific group we have.)
Day 1 Morning
- Intro Bayesian workflow and Stan
- Intro to the running example we'll use throughout the class
Day 1 Afternoon
- Write first Stan program
Day 2 Morning
- Expand our Stan program and check for improved model fit
- Start discussing hierarchical models if there's time
Day 2 Afternoon
- Hierarchical models with varying intercepts
- Reparameterization based on sampler diagnostics
- How does Stan's MCMC algorithm work?
Topics we won't have time to cover but are included in the workshop materials:
- Varying slopes model
- Time varying parameters
- Forecasting and decision making