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pmsims  

Simulation-Based Sample Size Tools for Prediction Models
View on CRAN: Click here


Download and install pmsims package within the R console
Install from CRAN:
install.packages("pmsims")

Install from Github:
library("remotes")
install_github("cran/pmsims")

Install by package version:
library("remotes")
install_version("pmsims", "1.0.0")



Attach the package and use:
library("pmsims")
Maintained by
Ewan Carr
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-17
Latest Update: 2026-09-17
Description:
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>.
How to cite:
Ewan Carr (2026). pmsims: Simulation-Based Sample Size Tools for Prediction Models. R package version 1.0.0, https://cran.r-project.org/web/packages/pmsims. Accessed 29 Sep. 2026.
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Full pmsims package functions and examples
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