Package: pmsims
Title: Simulation-Based Sample Size Tools for Prediction Models
Version: 1.0.0
Authors@R: 
    c(
      person(given = "Ewan",
             family = "Carr",
             role = c("aut", "cre"),
             email = "ewan.carr@kcl.ac.uk",
             comment = c(ORCID = "0000-0002-1146-4922")),
      person(given = "Gordon",
             family = "Forbes",
             role = c("aut"),
             comment = c(ORCID = "0000-0001-8662-3636")),
      person(given = "Ridwan",
             family = "Olaniran",
             role = c("aut"),
             comment = c(ORCID = "0000-0001-7342-8639")),
      person(given = "Diana",
             family = "Shamsutdinova",
             role = "aut",
             comment = c(ORCID = "0000-0003-2434-3641")),
      person(given = "Daniel",
             family = "Stahl",
             role = "aut",
             comment = c(ORCID = "0000-0001-7987-6619")),
      person(given = "Sarah",
             family = "Markham",
             role = "aut",
             comment = c(ORCID = "0000-0002-8755-5935")),
      person(given = "Felix",
             family = "Zimmer",
             role = "aut",
             comment = c(ORCID = "0000-0002-8127-0007"))
    )
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>.
License: GPL (>= 3)
URL: https://pmsims-package.github.io/pmsims/,
        https://github.com/pmsims-package/pmsims
BugReports: https://github.com/pmsims-package/pmsims/issues
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
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-10-17 10:39:48 UTC; root
Author: Ewan Carr [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-1146-4922>), Gordon Forbes [aut]
        (ORCID: <https://orcid.org/0000-0001-8662-3636>), Ridwan
        Olaniran [aut] (ORCID:
        <https://orcid.org/0000-0001-7342-8639>), Diana Shamsutdinova
        [aut] (ORCID: <https://orcid.org/0000-0003-2434-3641>), Daniel
        Stahl [aut] (ORCID: <https://orcid.org/0000-0001-7987-6619>),
        Sarah Markham [aut] (ORCID:
        <https://orcid.org/0000-0002-8755-5935>), Felix Zimmer [aut]
        (ORCID: <https://orcid.org/0000-0002-8127-0007>)
Maintainer: Ewan Carr <ewan.carr@kcl.ac.uk>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-09-17 09:30:02 UTC
RemoteUrl: https://github.com/cran/pmsims
RemoteRef: HEAD
RemoteSha: 09303f3c02910853ff3a6345edaadd33d90a941c
Built: R 4.6.0; ; 2026-10-17 10:42:35 UTC; unix
