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priorsense

Efficient prior and likelihood sensitivity checks

priorsense is an R package that provides tools for prior diagnostics and sensitivity analysis.

It currently includes functions for performing power-scaling sensitivity analysis on Stan 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).

Power-scaling sensitivity analysis and priorsense are described in Kallioinen et al. (2023).

Resources

Installation

Download the stable version from CRAN with:

install.packages("priorsense")

Download the development version from GitHub with:

# install.packages("pak")
pak::pkg_install("n-kall/priorsense@development")

Contributing

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.

References

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