Package: BayesianMCPMod
Title: Simulate, Evaluate, and Analyze Dose Finding Trials with
        Bayesian MCPMod
Version: 1.3.2
Authors@R: c(
    person("Boehringer Ingelheim Pharma GmbH & Co. KG", role = c("cph", "fnd")),
    person("Stephan", "Wojciekowski", , "stephan.wojciekowski@boehringer-ingelheim.com", role = c("aut", "cre")),
    person("Lars", "Andersen", , "lars.andersen@boehringer-ingelheim.com", role = "aut"),
    person("Jonas", "Schick", , "jonas.schick@boehringer-ingelheim.com", role = "ctb"),
    person("Sebastian", "Bossert", , "sebastian.bossert@boehringer-ingelheim.com", role = "aut")
  )
Description: Bayesian MCPMod (Fleischer et al. (2022)
        <doi:10.1002/pst.2193>) is an innovative method that improves
        the traditional MCPMod by systematically incorporating
        historical data, such as previous placebo group data. This
        package offers functions for simulating, analyzing, and
        evaluating Bayesian MCPMod trials with normally and binary
        distributed endpoints. It enables the assessment of trial
        designs incorporating historical data across various true
        dose-response relationships and sample sizes. Robust mixture
        prior distributions, such as those derived with the
        Meta-Analytic-Predictive approach (Schmidli et al. (2014)
        <doi:10.1111/biom.12242>), can be specified for each dose
        group.  Resulting mixture posterior distributions are used in
        the Bayesian Multiple Comparison Procedure and modeling steps.
        The modeling step also includes a weighted model averaging
        approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).
        Estimated dose-response relationships can be bootstrapped and
        visualized.
License: Apache License (>= 2)
URL: https://boehringer-ingelheim.github.io/BayesianMCPMod/,
        https://github.com/Boehringer-Ingelheim/BayesianMCPMod
BugReports: https://github.com/Boehringer-Ingelheim/BayesianMCPMod/issues
Depends: R (>= 4.2)
Imports: checkmate, DoseFinding (>= 1.1-1), dplyr, ggplot2, logistf,
        methods, nloptr, RBesT, stats, tidyr
Suggests: clinDR, doFuture, future.apply, kableExtra, knitr,
        MCPModPack, reactable, rmarkdown, spelling, testthat (>=
        3.0.0), tibble
VignetteBuilder: knitr
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Config/pak/sysreqs: cmake make libicu-dev libx11-dev zlib1g-dev
Repository: https://boehringer-ingelheim.r-universe.dev
Date/Publication: 2026-05-14 19:19:36 UTC
RemoteUrl: https://github.com/boehringer-ingelheim/bayesianmcpmod
RemoteRef: HEAD
RemoteSha: d234ace44eba6162602fb4657b19498b7a459947
NeedsCompilation: no
Packaged: 2026-06-13 07:18:00 UTC; root
Author: Boehringer Ingelheim Pharma GmbH & Co. KG [cph, fnd],
  Stephan Wojciekowski [aut, cre],
  Lars Andersen [aut],
  Jonas Schick [ctb],
  Sebastian Bossert [aut]
Maintainer: Stephan Wojciekowski <stephan.wojciekowski@boehringer-ingelheim.com>