https://github.com/jgabry/lander-stan-class-2021

Lander Analytics Bayes/Stan Class 2021
https://github.com/jgabry/lander-stan-class-2021

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Lander Analytics Bayes/Stan Class 2021

• Host: GitHub
• URL: https://github.com/jgabry/lander-stan-class-2021
• Owner: jgabry
• License: mit
• Created: 2021-07-13T17:31:16.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-08-03T17:33:59.000Z (over 3 years ago)
• Last Synced: 2024-10-14T10:50:47.552Z (3 months ago)
• Language: Stan
• Size: 26.7 MB
• Stars: 10
• Watchers: 4
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# lander-stan-class-2021

### Install needed software

```r

# 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")

```

### Interactive MCMC demo

We'll use this on day 2 or 3: 

https://chi-feng.github.io/mcmc-demo/app.html

### Tentative Agenda

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