Notebooks and data for StanCon2023 tutorial on BRMS
This tutorial works through the example dataset from the paper Visualization in Bayesian workflow:
Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378
Notebooks "eda_air" are visualizations of the dataset in subdirectory data
.
This dataset is GIS data, which requires either
sf
- https://r-spatial.github.io/sf/index.html
GeoPandas
- https://geopandas.org/en/v0.4.0/index.html
Notebooks "fit_air_brms" use BRMS to specify and fit the 3 models from the paper.
To run these notebooks you must install the R packages BRMS
and cmdstanr
.
The input data is in JSON format, and therefore doesn't require the sf
library.