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https://github.com/mvuorre/workshop
Workshop in applied stats topics
https://github.com/mvuorre/workshop
Last synced: 2 days ago
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Workshop in applied stats topics
- Host: GitHub
- URL: https://github.com/mvuorre/workshop
- Owner: mvuorre
- License: cc-by-4.0
- Created: 2024-11-14T07:54:41.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-11-15T07:58:46.000Z (2 months ago)
- Last Synced: 2025-01-20T22:57:50.297Z (8 days ago)
- Language: R
- Homepage: https://mvuorre.github.io/workshop
- Size: 13.9 MB
- Stars: 1
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# brms workshop
Matti Vuorre
## Plan
We plan to tackle some or all of the topics below:
1. [Collaboration and workflow](modules/workflow/index.qmd)
2. [Multilevel models](modules/models/index.qmd): Priors, outcome
distributions, and model comparison
3. [Ordinal models](modules/ordinal/index.qmd)
4. [Signal Detection Theoretic models](modules/sdt/index.qmd)
5. [Censored models](modules/censored/index.qmd)
Our plan is to walk through (some of) them, and edit the code to address
your questions.
## Materials
The materials of this workshop are organized in
[Quarto](https://quarto.org/) files. The source code is on
[GitHub](https://github.com/mvuorre/workshop).
## Prerequisites
We assume some familiarity with basic statistical modeling and the R
language. If you’d like a refresher, see [Introduction to Modern
Statistics](https://openintro-ims.netlify.app/) & [R for Data
Science](https://r4ds.hadley.nz/). To follow along, you need to have
- R ()
- An R IDE like [RStudio](https://posit.co/download/rstudio-desktop/)
(recommended for beginners) or [Positron](https://positron.posit.co/)
(advanced)
- [Quarto](https://quarto.org/) (for rendering the materials into a
website)
- [Git](https://happygitwithr.com/install-git)
- A [GitHub account](https://happygitwithr.com/github-acct) (if you want
to participate in live troubleshooting / contributing)
### Build / reproduce
Note that building the project will run all the analysis documents,
which involve bayesian models: This will take a long time. Because of
this, you should first create an `.Renviron` file to describe your
system settings (see `.Renviron.example`). For example,
``` bash
echo "
MAX_CORES = 8
BRMS_BACKEND = "cmdstanr"
BRMS_THREADS = 2
BRMS_ITER = 1000
" >> .Renviron
```
Then, to recreate the materials locally, run the following in your
terminal, *not* R. This will download the workshop materials (all source
code), and then run the required code.
``` bash
git clone https://github.com/mvuorre/workshop.git
cd brms-workshop.git
make # (requires GNU Make)
```
If you don’t have make installed (i.e. you are on Windows), you need to
first restore the R environment, prepare the data, and then render the
project:
``` bash
R -e 'renv::restore(prompt = FALSE)'
Rscript prepare-data.R
quarto render
```
## Contribute
Issues & pull requests at are
welcome.