https://github.com/bgoodri/ethzurich_rstan_course
Material for my RStan course at ETH Zurich
https://github.com/bgoodri/ethzurich_rstan_course
brms r rstan rstanarm stan
Last synced: 7 months ago
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Material for my RStan course at ETH Zurich
- Host: GitHub
- URL: https://github.com/bgoodri/ethzurich_rstan_course
- Owner: bgoodri
- License: gpl-3.0
- Created: 2020-03-07T23:00:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-08T18:38:20.000Z (over 5 years ago)
- Last Synced: 2025-01-11T00:44:50.825Z (9 months ago)
- Topics: brms, r, rstan, rstanarm, stan
- Language: Stan
- Homepage: https://www.zhrcourses.uzh.ch/en/programm2020/bayesian-analysis.html
- Size: 46.9 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Installation
The easy way to install the relevant R packages (plus some that are not relevant to the course) is to execute in R
```{r}
install.packages(c("rstanarm", "brms", "rstantools", "devtools"), dependencies = TRUE)
```
However, you also need to install a C++ toolchain (if you do not have one already) in order to compile Stan programs. Instructions for that vary by operating system but are available from
https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
# Getting Help
If you encounter any issues before (or after) the course, please ask at
http://discourse.mc-stan.org/
and someone will be able to help you.
# Reading
Before the class, please read the first two chapters of the second edition of _Statistical Rethinking_ by Richard McElreath
http://xcelab.net/rmpubs/sr2/statisticalrethinking2_chapters1and2.pdf
The rest of the book is available to pre-order from
https://www.crcpress.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-STAN/McElreath/p/book/9780367139919
In addition, there is a great video on Bayes' Rule by Grant Sanderson
https://t.co/5GBieRzt4O?amp=1