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## Citation
-To cite this work, see the `CITATION.cff` file in this repository or use the "Cite this repository" button on GitHub.
+If you this book supports your work, please **cite our paper**:
-You can also cite the archived version of this work on Zenodo: https://doi.org/10.5281/zenodo.17094155.
+> Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
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+You may choose to also cite the software repository or archived version:
+
+* Repository details are also provided in the `CITATION.cff` file in this repository or via the "Cite this repository" button on GitHub.
+* Archived version of this work on Zenodo: https://doi.org/10.5281/zenodo.17094155.
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## Funding
This project is supported by the Medical Research Council [grant number [MR/Z503915/1](https://gtr.ukri.org/projects?ref=MR%2FZ503915%2F1)] from 1st May 2024 to 31st October 2026.
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+It is also supported by the National Institute for Health and Care Research (NIHR) under the NIHR Applied Research Collaboration South West Peninsula (Grant Reference Number NIHR200167). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
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page-footer:
left:
- text: |
- The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1].
+ The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1]. It is also supported by the National Institute for Health and Care Research (NIHR) under the NIHR Applied Research Collaboration South West Peninsula (Grant Reference Number NIHR200167). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
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Part of the STARS research project.
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Changelog
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- Heather, A., Monks, T., Mustafee, N., Harper, A., Alidoost, F., Challen, R., & Slater, T. (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.
+ Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 (https://doi.org/10.3310/nihropenres.14296.1)
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This open book is a self-paced training resource that teaches you how to design, implement, and share discrete-event simulation (DES) models in Python and R as part of a reproducible analytical pipeline. It combines a **step-by-step guide** with **complete example repositories** that you can adapt for your own projects.
+If you this book supports your work, please **cite our paper**:
+
+> Heather A, Monks T, Harper A et al. Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples [version 1; peer review: awaiting peer review]. NIHR Open Res 2026, 6:68 ()
+
The material is designed for analysts, researchers, and students in health and operations research who want to build transparent, trustworthy simulation models. To get the most from this resource, you should be comfortable with basic programming in either Python or R and have some familiarity with probability and basic statistics. **No prior DES experience is required**: short introductions to DES, reproducible analytical pipelines, and free and open source software are provided in the "Intros" section and linked below.
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* Dr. **Rob Challen** [{fig-alt="ORCID logo"}](https://orcid.org/0000-0002-5504-7768)
* **Tom Slater** [{fig-alt="ORCID logo"}](https://orcid.org/0009-0007-0838-7499)
-The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1] from 1st May 2024 to 31st October 2026. The listed researchers are associated with the **University of Exeter** Medical and Business Schools, and the **University of Bristol** School of Engineering, Mathematics and Technology.
+The STARS project is supported by the Medical Research Council [grant number MR/Z503915/1] from 1st May 2024 to 31st October 2026. It is also supported by the National Institute for Health and Care Research (NIHR) under the NIHR Applied Research Collaboration South West Peninsula (Grant Reference Number NIHR200167). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
-You can find out more about our project on the [**STARS project website**](https://pythonhealthdatascience.github.io/stars/){target="_blank"}. If you use this resource, **please cite us:**
+The listed researchers are associated with the **University of Exeter** Medical and Business Schools, and the **University of Bristol** School of Engineering, Mathematics and Technology. You can find out more about our project on the [**STARS project website**](https://pythonhealthdatascience.github.io/stars/){target="_blank"}.
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-> Heather, A., Monks, T., Mustafee, N., Harper, A., Alidoost, F., Challen, R., & Slater, T. (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.
+Keywords: discrete-event simulation; reproducible analytical pipelines; health services research; Python; R; simulation modelling; research software engineering; open-source tools; SimPy; simmer.
-Keywords: discrete-event simulation; reproducible analytical pipelines; health services research; Python; R; simulation modelling; research software engineering; open-source tools; SimPy; simmer.
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