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
Roxygen: list(markdown = TRUE)
Config/testthat/edition: 3
Config/roxygen2/version: 8.0.0
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libxml2-dev
Repository: https://pmsims-package.r-universe.dev
Date/Publication: 2026-09-17 17:34:36 UTC
RemoteUrl: https://github.com/pmsims-package/pmsims
RemoteRef: HEAD
RemoteSha: af124dd71a6f9df4e79d7966d1200ebde353600e
NeedsCompilation: no
Packaged: 2026-09-18 09:02:28 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>