Welcome to the RobustiPy homepage!

RobustiPy is an efficient multiversal library with bootstrapping, model selection, averaging, resampling, in-and-out-of-sample analysis (/explainable AI), and joint inference tests. It analyses various output spaces, in addition to the control variable space (e.g. multiple dependent variables, estimands of interest, etc). Developed for Python, it is designed to be both accessible and computationally efficient.

Note: This site (and project!) are a work in progress!

Installation: Installation is as simple (in Python) as:

git clone https://github.com/RobustiPy/robustipy.git
cd robustipy
pip install .

and – as we get towards more stable releases – as:

pip install robustipy

Examples: A series of simulated examples which might be useful to get you up and running can be found here. A series of empirical examples which might be useful can be found here.

Hackathon: We held a hackathon regarding RobustiPy in Oxford during June 2024. Thanks so very much for everyone who came along, and those who helped out in general!

Inspiration: We found great inspiration in the following three papers, which you might also enjoy reading:

1. Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208-1214.
2. Young, C., & Holsteen, K. (2017). Model uncertainty and robustness: A computational framework for multimodel analysis. Sociological Methods & Research, 46(1), 3-40.
3. Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University, 348(1-17), 3.

Contributing: We welcome contributions (or matters arising, suggestions for features, or bug alerts!) from anybody and everybody interested in our work! Please raise all issues on GitHub. If you are interested in participating, kindly see our Code of Conduct.

License: This work is made available under a GNU General Public License v3.0.

Who: RobustiPy is developed and maintained by Daniel Valdenegro Ibarra, Charles Rahal, and Jiani Yan at the Leverhulme Centre for Demographic Science and the Centre for Care. Comms and administrative support gratefully recieved from Bradley Hall-Smith, Hannah Calkin and Dan Williamson.

Thanks!: We’re grateful to comments from participants at an LCDS Internal Meeting, the Centre for Care, IC2S2 (2023), PAA (2024), and the PSI Software Engineering Group. Funding gratefully received funding from the ESRC (Centre for Care), and the Leverhulme Trust (Grant RC-2018-003), the Leverhulme Centre for Demographic Science (LCDS), and Nuffield College.