Reproducible Discrete-Event Simulation in Python and R
๐ก About this resource
This practical guide helps you build discrete-event simulation models (DES) which are reproducible and part of a robust reproducible analytical pipeline (RAP) - a structured, automated, and repeatable approach to analysis. Learn more โ
Packages we use:
| Tool | Purpose | Link |
|---|---|---|
| ๐ SimPy | DES in Python | https://simpy.readthedocs.io/en/latest/ |
| ๐งฎ simmer | DES in R | https://r-simmer.org/ |
๐บ๏ธ Why use this guide?
Build models that last. Set up your projects in a way thatโs easy to run, revisit, and reuse - whether itโs tomorrow or next year.
Informed by best practice. It adheres to recommendations from Heather et al. 2025 and the โLevels of RAPโ framework from the NHS RAP Community of Practice. . Learn more โ
Not just for healthcare. The coding and reproducibility tips are useful to all kinds of models, fields, and analysis techniques.
Developed as part of the STARS project
Author: Amy Heather (GitHub) (ORCID).
Reviewers: Nav Mustafee (GitHub) (ORCID)
Licences: All code is under the MIT Licence. Text is CC-BY-SA 4.0.
Progress tracker: Complete, unless marked โ.