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This repository is complementary to:
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> Thomas Monks, Alison Harper, Amy Heather, and Navonil Mustafee. **Using Large Language Models to support researchers reproduce and reuse unpublished health care discrete-event simulation computer models: a feasibility and pilot study in Python**. https://github.com/pythonhealthdatascience/llm_simpy.
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> Thomas Monks, Alison Harper, and Amy Heather. **Using Large Language Models to support researchers reproduce and reuse unpublished health care discrete-event simulation computer models: a feasibility and pilot study in Python**. https://github.com/pythonhealthdatascience/llm_simpy.
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It contains the final formatted code from each of the SimPy discrete-event simulation models that were generated by Perplexity as part of that project.
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The project uses `conda` to manage dependencies. Navigate your terminal to the directory containing the code and run:
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## 📝 Citation
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Please cite this repository as:
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Please cite the archived repository:
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> Thomas Monks, Alison Harper, Amy Heather, and Navonil Mustafee. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. <https://github.com/pythonhealthdatascience/llm_simpy_models>.
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> Thomas Monks, Alison Harper, and Amy Heather. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. <https://doi.org/10.5281/zenodo.15082494>.
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A `CITATION.cff` file is also provided.
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You can also cite this GitHub repository as:
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<!--TODO: Archive repository on Zenodo, and cite that -->
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> Thomas Monks, Alison Harper, and Amy Heather. **Complementary repository: Final discrete-event simulation models and streamlit applications from Large Language Models**. <https://github.com/pythonhealthdatascience/llm_simpy_models>.
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