R/bayprior-package.R

In bayprior: Bayesian Prior Elicitation and Diagnostics for Clinical Trials

#' bayprior: Bayesian Prior Elicitation for Clinical Trials
#'
#' A toolkit for constructing, validating, and justifying Bayesian priors
#' in clinical trial settings. Implements SHELF-style expert elicitation
#' (quantile matching, roulette method, moment matching) across six
#' distribution families, linear and logarithmic expert pooling with
#' compatibility validation, prior-data conflict diagnostics (Box p-value,
#' surprise index, KL divergence, Bhattacharyya overlap, Mahalanobis check)
#' for binary, continuous, Poisson/count, and survival data types, sensitivity
#' analyses with tornado and influence plots, sceptical/robust/power priors,
#' and automated HTML/PDF/Word regulatory reports informed by FDA and EMA
#' guidance on Bayesian methods. Includes a fully modular Shiny application with automatic
#' output reset on input change.
#'
#' @section Main workflow:
#' \enumerate{
#'   \item **Elicitation** -- \code{\link{elicit_beta}},
#'     \code{\link{elicit_normal}}, \code{\link{elicit_gamma}},
#'     \code{\link{elicit_lognormal}}, \code{\link{elicit_exponential}},
#'     \code{\link{elicit_weibull}}, \code{\link{elicit_roulette}},
#'     \code{\link{elicit_mixture}}
#'   \item **Expert pooling** -- \code{\link{aggregate_experts}}
#'   \item **Conflict diagnostics** -- \code{\link{prior_conflict}},
#'     \code{\link{conflict_mahalanobis}}
#'   \item **Sensitivity analysis** -- \code{\link{sensitivity_grid}},
#'     \code{\link{sensitivity_cri}}
#'   \item **Robust priors** -- \code{\link{sceptical_prior}},
#'     \code{\link{robust_prior}}, \code{\link{calibrate_power_prior}}
#'   \item **Reporting** -- \code{\link{prior_report}}
#'   \item **Shiny app** -- \code{\link{run_app}}
#' }
#'
#' @section Distribution families:
#' \describe{
#'   \item{\code{beta}}{Response rates and proportions -- support (0, 1)}
#'   \item{\code{normal}}{Mean differences and log odds ratios -- support
#'     (-Inf, Inf)}
#'   \item{\code{gamma}}{Event rates and median survival -- support (0, Inf)}
#'   \item{\code{lognormal}}{Hazard ratios and PK parameters -- support (0, Inf)}
#'   \item{\code{exponential}}{Constant hazard rates and Poisson rate priors
#'     -- support (0, Inf). Conjugate with Poisson and survival data via
#'     Gamma-Poisson/Exponential updating.}
#'   \item{\code{weibull}}{Non-constant hazard survival times (OS, PFS)
#'     -- support (0, Inf). Posterior approximated via Normal matching.}
#' }
#'
#' @section Data types for conflict diagnostics and sensitivity:
#' \describe{
#'   \item{\code{"binary"}}{Events / sample size (x, n). Conjugate:
#'     Beta-Binomial.}
#'   \item{\code{"continuous"}}{Observed mean, SD, sample size (x, sd, n).
#'     Conjugate: Normal-Normal.}
#'   \item{\code{"poisson"}}{Event count / exposure person-time (x, n).
#'     Conjugate: Gamma-Poisson.}
#'   \item{\code{"survival"}}{Events / total follow-up time (x, n).
#'     Conjugate: Gamma-Exponential.}
#' }
#'
#' @section References:
#' \itemize{
#'   \item O'Hagan et al. (2006). \emph{Uncertain Judgements}. Wiley.
#'   \item Box (1980). JRSS-A, 143, 383--430.
#'   \item Schmidli et al. (2014). \emph{Biometrics}, 70, 1023--1032.
#'   \item Ibrahim & Chen (2000). \emph{Statistical Science}, 15, 46--60.
#'   \item Spiegelhalter et al. (1994). JRSS-A, 157, 357--416.
#'   \item FDA (2026). Use of Bayesian Methodology in Clinical Trials of
#'         Drug and Biological Products.
#' }
#'
#' @author **Maintainer**: Ndoh Penn \email{ndohpenn9@gmail.com}
#'
#' @docType package
#' @name bayprior
#' @aliases bayprior
"_PACKAGE"