https://github.com/jgabry/lander-stan-class-2023

Materials for Bayes/Stan workshop using CmdStanR
https://github.com/jgabry/lander-stan-class-2023

bayesian mcmc stan

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Materials for Bayes/Stan workshop using CmdStanR

• Host: GitHub
• URL: https://github.com/jgabry/lander-stan-class-2023
• Owner: jgabry
• License: mit
• Created: 2023-08-29T22:15:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-07T17:03:39.000Z (over 1 year ago)
• Last Synced: 2024-10-14T10:50:47.605Z (3 months ago)
• Topics: bayesian, mcmc, stan
• Language: Stan
• Homepage: https://jgabry.github.io
• Size: 20.2 MB
• Stars: 7
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Lander Analytics Bayes/Stan Class 2023

### Install needed software

```r

# install R packages

install.packages(c("dplyr", "lubridate", "ggplot2", "bayesplot", "posterior", "remotes"))

remotes::install_github("stan-dev/cmdstanr")

# install cmdstan

# please reach out if you run into errors

cmdstanr::install_cmdstan(cores = 2)

# check if cmdstan installation works properly

# please reach out 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: 

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