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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# The Meta-Uncertainty Framework
<!-- badges: start -->
<!-- badges: end -->
<center>
![](inst/meta-uncertainty-figure-1.png){ width=85% }
</center>
This repository contains the code for the paper _Meta-Uncertainty in Bayesian Model Comparison_: [https://arxiv.org/abs/2210.07278](https://arxiv.org/abs/2210.07278)
Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via `library(MetaUncertaintyPaper)`.
## Installation Instructions
### The `{ggsimplex}` plot package
The current paper code uses a highly experimental version of the [ggsimplex](https://github.com/marvinschmitt/ggsimplex) R package. Install it from GitHub via
```
devtools::install_github('marvinschmitt/ggsimplex')
```
### R environment
The R environment is captured with `renv`. Install the `renv` package and load the environment with
```
renv::restore()
```
### Python environment
The package requirements of the Python environment (except BayesFlow, see below) are captured in the `requirements.txt` file. Recreate the environment using
```
pip install -r requirements.txt
```
The amortized model comparison network (BayesFlow) in Experiment 3 uses [BayesFlow](https://github.com/stefanradev93/BayesFlow) at commit `c4208418ad19b6648be216cfe013c8f5317a652c`: https://github.com/stefanradev93/BayesFlow/tree/c4208418ad19b6648be216cfe013c8f5317a652c.
Should you fail to install this BayesFlow version or encounter unexpected errors, you can load the trained neural networks' weights from the folder `python/checkpoints_exp3/` and avoid re-training the network.