Reproducible Discrete-Event Simulation in Python and R

Building transparent reliable simulation models within a reproducible analytical pipeline.

Website last updated

January 6, 2026

Step-by-step guide
Click to follow along with our guide to build your own reproducible DES model.
Examples
The guide is accompanied by four complete example repositories - click to find out more!


Reproducibility frameworks

Our resources are designed to help you meet two important reproducibility standards. Learn more:

NHS Levels of RAP STARS reproducibility recommendations

New to these concepts?

We have dedicated pages explaining the foundations, helpful if you’re new to any of these areas.

Discrete-Event Simulation Reproducible Analytical Pipelines Free & Open Source Software


This resource is an output of STARS, a research project led by Associate Prof. Tom Monks ORCID. The book is written by Amy Heather ORCID and reviewed by Prof. Nav Mustafee ORCID, Dr. Alison Harper ORCID, Associate Prof. Tom Monks ORCID, and Fatemeh Alidoost ORCID. The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1]. The listed researchers are associated with the University of Exeter Medical and Business Schools.

You can find out more about our project on the STARS project website. If you use this resource, please cite us:

Heather, A., Monks, T., Mustafee, N., & Harper, A. (2025). DES RAP Book: Reproducible Discrete-Event Simulation in Python and R. https://github.com/pythonhealthdatascience/des_rap_book. https://doi.org/10.5281/zenodo.17094155.