https://github.com/carloscinelli/brease
R package BREASE
https://github.com/carloscinelli/brease
Last synced: 3 months ago
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R package BREASE
- Host: GitHub
- URL: https://github.com/carloscinelli/brease
- Owner: carloscinelli
- License: other
- Created: 2026-02-16T18:39:25.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-02-17T01:37:20.000Z (4 months ago)
- Last Synced: 2026-02-17T02:54:22.114Z (4 months ago)
- Language: R
- Size: 8.16 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# brease: Causally Sound Priors for Binary Experiments
[](https://github.com/carloscinelli/brease/actions/workflows/R-CMD-check.yaml)
The `brease` R package implements the **BREASE** (**B**aseline **R**isk, **E**fficacy, and **A**dverse **S**ide **E**ffects) framework for Bayesian analysis of randomized controlled trials with binary treatment and binary outcome, as described in:
> Irons, N.J. and Cinelli, C. (2025). [Causally Sound Priors for Binary Experiments](https://doi.org/10.1214/25-BA1506). *Bayesian Analysis*.
## Installation
You can install the development version from GitHub:
```r
# install.packages("devtools")
devtools::install_github("carloscinelli/brease")
```
## Quick Example
```r
library(brease)
# Aspirin and Myocardial Infarction (Physicians' Health Study)
result <- brease(y0 = 26, y1 = 10, N0 = 11034, N1 = 11037)
result
# COVID-19 Vaccine Trial (Pfizer)
result <- brease(y0 = 169, y1 = 9, N0 = 20172, N1 = 19965)
result
# Sensitivity analysis
sens <- bf_seq(y0 = 26, y1 = 10, N0 = 11034, N1 = 11037)
contour_bf(sens)
```
## Overview
The BREASE framework parameterizes the likelihood of a binary experiment in terms of three clinically meaningful causal quantities:
- **Baseline risk** (theta_0): probability of the event without treatment
- **Efficacy** (eta_e): fraction of would-be cases prevented by treatment
- **Side-effect risk** (eta_s): fraction of would-be non-cases harmed by treatment
The package provides:
- **Exact posterior sampling** and analytic marginal likelihoods/Bayes factors
- **Optional MCMC backends** via Stan or JAGS
- **Sensitivity analysis** tools (contour plots, side-effect sensitivity)
- **Comparison methods**: Independent Beta and Logit-Transform priors
- **Bounds** on probabilities of causation
## Documentation
For a detailed introduction, see the [package vignette](https://carloscinelli.com/brease/articles/brease.html).
## Citation
If you use this package, please cite:
```
Irons, N.J. and Cinelli, C. (2025). Causally Sound Priors for Binary
Experiments. Bayesian Analysis. doi:10.1214/25-BA1506
```