priorsense is an R package that provides tools for prior diagnostics and sensitivity analysis of Bayesian models (Kallioinen et al., 2026).
It currently includes functions for performing power-scaling sensitivity analysis on fitted Bayesian models. This is a way to check how sensitive a posterior is to perturbations of the prior and likelihood and diagnose the cause of sensitivity. For efficient computation, power-scaling sensitivity analysis relies on Pareto smoothed importance sampling (Vehtari et al., 2024) and importance weighted moment matching (Paananen et al., 2021).
Priorsense supports fitted model objects from brms, Stan (cmdstanr and
rstan), JAGS (jagsUI and R2jags) and NIMBLE, along with posterior draws
from other software. Power-scaling sensitivity analysis checks are described in
Kallioinen et al. (2023).
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Check the getting started vignette for a simple example
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For a more detailed modelling example see here
- mc-stan.org/priorsense (online documentation, vignettes)
- Ask a question (Stan Forums on Discourse)
- Open an issue (GitHub issues for bug reports, feature requests)
Download the stable version from CRAN with:
install.packages("priorsense")Download the development version from GitHub with:
# install.packages("pak")
pak::pkg_install("stan-dev/priorsense@development")Contributions are welcome! If you find a bug or have an idea for a feature, open
an issue. If you are able to fix an issue, fork the repository and make a pull
request to the development branch. Read CONTRIBUTING.md for more details.
Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari (2026). priorsense: Efficient prior and likelihood sensitivity checks for Bayesian models in R. Journal of Open Source Software. 11, 123. https://doi.org/10.21105/joss.11036
Noa Kallioinen, Topi Paananen, Paul-Christian Bürkner, Aki Vehtari (2023). Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Statistics and Computing. 34, 57. https://doi.org/10.1007/s11222-023-10366-5
Topi Paananen, Juho Piironen, Paul-Christian Bürkner, Aki Vehtari (2021). Implicitly adaptive importance sampling. Statistics and Computing 31, 16. https://doi.org/10.1007/s11222-020-09982-2
Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research. 25, 72. https://jmlr.org/papers/v25/19-556.html