Package: BayesianQDM
Type: Package
Title: Bayesian Quantitative Decision-Making Framework for Binary and
        Continuous Endpoints
Version: 0.1.0
Authors@R: c(
    person(given  = "Gosuke",
           family = "Homma",
           role   = c("aut", "cre"),
           email  = "my.name.is.gosuke@gmail.com"),
    person(given  = "Yusuke",
           family = "Yamaguchi",
           role   = c("aut")))
Description: Provides comprehensive methods to calculate posterior probabilities,
    posterior predictive probabilities, and Go/NoGo/Gray decision probabilities
    for quantitative decision-making under a Bayesian paradigm in clinical trials.
    The package supports both single and two-endpoint analyses for binary and
    continuous outcomes, with controlled, uncontrolled, and external designs.
    For single continuous endpoints, three calculation methods are
    available: numerical integration (NI), Monte Carlo simulation (MC), and
    Moment-Matching approximation (MM). For two continuous endpoints, a bivariate
    Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods.
    For two binary endpoints, a Dirichlet-multinomial model is implemented.
    External designs incorporate historical data through power priors
    using exact conjugate representations (Normal-Inverse-Chi-squared for single
    continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for
    binary endpoints), enabling closed-form posterior computation without Markov
    chain Monte Carlo (MCMC) sampling. This approach significantly reduces
    computational burden while preserving complete Bayesian rigor. The package
    also provides grid-search functions to find optimal Go and NoGo thresholds
    that satisfy user-specified operating characteristic criteria for all
    supported endpoint types and study designs. S3 print() and plot() methods
    are provided for all decision probability classes, enabling formatted display
    and visualisation of Go/NoGo/Gray operating characteristics across treatment
    scenarios.
    See Kang, Yamaguchi, and Han (2026) <doi:10.1080/10543406.2026.2655410>
    for the methodological framework.
License: GPL (>= 2)
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.3
Imports: ggplot2 (>= 3.4.0), gridExtra, mvtnorm, stats
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, purrr,
        spelling
VignetteBuilder: knitr
URL: https://gosukehommaEX.github.io/BayesianQDM/,
        https://github.com/gosukehommaEX/BayesianQDM
BugReports: https://github.com/gosukehommaEX/BayesianQDM/issues
NeedsCompilation: no
Packaged: 2026-04-16 12:04:09 UTC; i_lik
Author: Gosuke Homma [aut, cre],
  Yusuke Yamaguchi [aut]
Maintainer: Gosuke Homma <my.name.is.gosuke@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-21 18:12:38 UTC
Built: R 4.4.3; ; 2026-04-21 21:35:50 UTC; unix
