Merge pull request #98 from pythonhealthdatascience/dev

Dev

Author: amyheather

CHANGELOG.md

@@ -5,6 +5,33 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). Dates formatted as YYYY-MM-DD as per [ISO standard](https://www.iso.org/iso-8601-date-and-time-format.html).
 
+## v2.0.0 - 2026-03-06
+
+This release introduces several changes and fixes - including a new approach to choosing length of warm-up, fix to `run_scenarios()`, and inclusion of all metrics when choosing replications, among other things.
+
+### Added
+
+* Add coverage badge
+* New approach to choosing length of warm-up: changed from time series inspection and interval audit, to `WarmupAuditor`. Changed parameters for this analysis.
+* Add retrospective QA summary.
+
+### Changed
+
+* Set bin widths for more consistency with R.
+* Import replications functions from sim-tools.
+* Improve `CONTRIBUTING.md` and `README.md`, and completed STRESS.
+* Print parameters when run scenarios.
+* Update author list.
+* Minor corrections to docstrings and documentation.
+* Moved `period` to `Patient` and log end time.
+* Include all metrics in choosing replications notebook.
+
+### Fixed
+
+* Fixed display of input modelling figures on GitHub by making them static.
+* Fix set-up of separate `param` in `run_scenarios` so it uses a fresh instance.
+* Upgraded packages to address dependabot security risks.
+
 ## v1.3.0 - 2025-08-05
 
 This release introduces several new features including input modelling, code-based model validation, new key performance indicators, and more - as well as improvements to documentation and code.

CITATION.cff

@@ -25,5 +25,5 @@ abstract: >-
   Reproducible analytical pipeline (RAP) for python discrete-event simulation
   (DES) implementing a simple M/M/s queueing model.
 license: MIT
-version: '1.3.0'
-date-released: '2025-08-25'
+version: '2.0.0'
+date-released: '2026-03-06'

README.md

@@ -193,7 +193,7 @@ This repository was developed with thanks to several others sources. These are a
 | Amy Heather, Thomas Monks, Alison Harper, Navonil Mustafee, Andrew Mayne (2025) On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models (https://doi.org/10.48550/arXiv.2501.13137). | `docs/heather_2025.md` |
 | NHS Digital (2024) RAP repository template (https://github.com/NHSDigital/rap-package-template) (MIT Licence) | `simulation/logging.py`<br>`docs/nhs_rap.md` |
 | Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence) | `simulation/model.py`<br>`simulation/patient.py`<br>`simulation/runner.py`<br>`notebooks/choosing_cores.ipynb` |
-| Tom Monks (2025) sim-tools: tools to support the Discrete-Event Simulation process in python (https://github.com/TomMonks/sim-tools) (MIT Licence)<br>Who themselves cite Hoad, Robinson, & Davies (2010). Automated selection of the number of replications for a discrete-event simulation (https://www.jstor.org/stable/40926090), and Knuth. D "The Art of Computer Programming" Vol 2. 2nd ed. Page 216. | `simulation/confidence_interval_method.py`<br>`simulation/onlinestatistics.py`<br>`simulation/plotly_confidence_interval_method.py`<br>`simulation/replicationsalgorithm.py`<br>`simulation/replicationtabulizer.py`<br>`notebooks/choosing_replications.ipynb` |
+| Tom Monks (2025) sim-tools: tools to support the Discrete-Event Simulation process in python (https://github.com/sim-tools/sim-tools) (MIT Licence)<br>Who themselves cite Hoad, Robinson, & Davies (2010). Automated selection of the number of replications for a discrete-event simulation (https://www.jstor.org/stable/40926090), and Knuth. D "The Art of Computer Programming" Vol 2. 2nd ed. Page 216. | `simulation/confidence_interval_method.py`<br>`simulation/onlinestatistics.py`<br>`simulation/plotly_confidence_interval_method.py`<br>`simulation/replicationsalgorithm.py`<br>`simulation/replicationtabulizer.py`<br>`notebooks/choosing_replications.ipynb` |
 | Tom Monks, Alison Harper and Amy Heather (2025) An introduction to Discrete-Event Simulation (DES) using Free and Open Source Software (https://github.com/pythonhealthdatascience/intro-open-sim/tree/main). (MIT Licence) - who themselves also cite Law. Simulation Modeling and Analysis 4th Ed. Pages 14 - 17. | `simulation/monitoredresource.py` |
 | Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence). | `simulation/warmupauditor.py`<br>`notebooks/analysis.ipynb`<br>`notebooks/choosing_replications.ipynb`<br>`notebooks/choosing_warmup.ipynb` |
 | Monks T and Harper A. Improving the usability of open health service delivery simulation models using Python and web apps (https://doi.org/10.3310/nihropenres.13467.2) [version 2; peer review: 3 approved]. NIHR Open Res 2023, 3:48.<br>Who themselves cite a [Stack Overflow](https://stackoverflow.com/questions/59406167/plotly-how-to-filter-a-pandas-dataframe-using-a-dropdown-menu) post. | `notebooks/analysis.ipynb` |