# install R packages
install.packages(c("dplyr", "lubridate", "ggplot2", "bayesplot", "posterior", "fs", "stringr", "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 or 3:
https://chi-feng.github.io/mcmc-demo/app.html
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
- non-centered parameterization
- How does Stan's MCMC algorithm work?
Day 3 Morning
- Andrew Gelman guest appearance
- Finish Day 2 content
- Varying slopes model
Day 3 Afternoon
- Time varying parameters
- Forecasting
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