pmsims: Simulation-Based Sample Size Tools for Prediction Models
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)
<doi:10.1186/s12874-026-02935-9> and Shamsutdinova et al. (2026)
<doi:10.48550/arXiv.2602.23507>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
cli, ggplot2, lifecycle, mlpwr, pROC, stats, survival, timeROC, utils |
| Suggests: |
covr, DescTools, doParallel, foreach, glmnet, knitr, mlbench, mlr, randomForestSRC, ranger, rmarkdown, synthpop, testthat (≥
3.0.0), tuneRanger, xgboost |
| Published: |
2026-09-17 |
| DOI: |
10.32614/CRAN.package.pmsims (may not be active yet) |
| Author: |
Ewan Carr  [aut,
cre],
Gordon Forbes
[aut],
Ridwan Olaniran
[aut],
Diana Shamsutdinova
[aut],
Daniel Stahl
[aut],
Sarah Markham
[aut],
Felix Zimmer
[aut] |
| Maintainer: |
Ewan Carr <ewan.carr at kcl.ac.uk> |
| BugReports: |
https://github.com/pmsims-package/pmsims/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://pmsims-package.github.io/pmsims/,
https://github.com/pmsims-package/pmsims |
| NeedsCompilation: |
no |
| Citation: |
pmsims citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
pmsims results |
Documentation:
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