# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "pmsims" in publications use:' type: software license: GPL-3.0-or-later title: 'pmsims: Simulation-Based Sample Size Tools for Prediction Models' version: 1.0.0 identifiers: - type: doi value: 10.32614/CRAN.package.pmsims abstract: Provides a flexible, simulation-based toolkit for exploring how much data are needed to develop reliable prediction models. It works by repeatedly generating data, fitting models, and evaluating performance to show how sample size affects predictive accuracy, calibration, and overfitting. The package supports continuous, binary, and time-to-event outcomes and can be used with both regression-based modelling approaches and machine-learning methods. It is designed to help researchers plan studies, assess feasibility, and build more robust and generalisable models. The methods are described in Olaniran et al. (2026) and Shamsutdinova et al. (2026) . authors: - family-names: Carr given-names: Ewan email: ewan.carr@kcl.ac.uk orcid: https://orcid.org/0000-0002-1146-4922 - family-names: Forbes given-names: Gordon orcid: https://orcid.org/0000-0001-8662-3636 - family-names: Olaniran given-names: Ridwan orcid: https://orcid.org/0000-0001-7342-8639 - family-names: Shamsutdinova given-names: Diana orcid: https://orcid.org/0000-0003-2434-3641 - family-names: Stahl given-names: Daniel orcid: https://orcid.org/0000-0001-7987-6619 - family-names: Markham given-names: Sarah orcid: https://orcid.org/0000-0002-8755-5935 - family-names: Zimmer given-names: Felix orcid: https://orcid.org/0000-0002-8127-0007 preferred-citation: type: manual title: 'pmsims: Simulation-based Sample Size Tools for Prediction Models' authors: - family-names: Carr given-names: Ewan email: ewan.carr@kcl.ac.uk orcid: https://orcid.org/0000-0002-1146-4922 - family-names: Forbes given-names: Gordon orcid: https://orcid.org/0000-0001-8662-3636 - family-names: Olaniran given-names: Ridwan orcid: https://orcid.org/0000-0001-7342-8639 - family-names: Shamsutdinova given-names: Diana orcid: https://orcid.org/0000-0003-2434-3641 - family-names: Stahl given-names: Daniel orcid: https://orcid.org/0000-0001-7987-6619 - family-names: Markham given-names: Sarah orcid: https://orcid.org/0000-0002-8755-5935 - family-names: Zimmer given-names: Felix orcid: https://orcid.org/0000-0002-8127-0007 year: '2026' notes: R package version 1.0.0 url: https://pmsims-package.github.io/pmsims/ repository: https://cran.r-universe.dev repository-code: https://github.com/pmsims-package/pmsims commit: 09303f3c02910853ff3a6345edaadd33d90a941c url: https://pmsims-package.github.io/pmsims/ date-released: '2026-09-17' contact: - family-names: Carr given-names: Ewan email: ewan.carr@kcl.ac.uk orcid: https://orcid.org/0000-0002-1146-4922 references: - type: article title: 'Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package' authors: - family-names: Shamsutdinova given-names: Diana - family-names: Zimmer given-names: Felix - family-names: Olaniran given-names: Oyebayo Ridwan - family-names: Markham given-names: Sarah - family-names: Stahl given-names: Daniel - family-names: Forbes given-names: Gordon - family-names: Carr given-names: Ewan year: '2026' journal: arXiv volume: arXiv:2602.23507 doi: 10.48550/arXiv.2602.23507 url: https://arxiv.org/abs/2602.23507 notes: Preprint - type: article title: 'Adaptive Gaussian process search for simulation-based sample size estimation in clinical prediction models: validation of the pmsims R package' authors: - family-names: Olaniran given-names: Oyebayo Ridwan - family-names: Shamsutdinova given-names: Diana - family-names: Markham given-names: Sarah - family-names: Zimmer given-names: Felix - family-names: Stahl given-names: Daniel - family-names: Forbes given-names: Gordon - family-names: Carr given-names: Ewan year: '2026' journal: BMC Medical Research Methodology doi: 10.1186/s12874-026-02935-9