basket: Basket Trial Analysis

#' @title Basket Trial Analysis
#' @name basket-package
#' @aliases basket-package
#' @docType package
#' @references Vemurafenib in multiple nonmelanoma cancers with braf v600
#' mutations Hyman DM, Puzanov I, Subbiah V, Faris JE, Chau I, Blay JY,
#' Wolf J, Raje NS, Diamond EL, Hollebecque A, et al.
#' New England Journal of Medicine 2015; 373(8):726–736.
#' <https://doi:10.1056/NEJMoa1502309>
#' @references Bayesian basket trial design with exchangeability monitoring
#' BP Hobbs, R Landin Statistics in medicine 37 (25), 3557-357.
#' <https://doi.org/10.1002/sim.7893>
#' @references Statistical challenges posed by uncontrolled master protocols:
#' sensitivity analysis of the vemurafenib study BP Hobbs, MJ Kane, DS Hong,
#' R Landin Annals of Oncology 29 (12), 2296-2301. <doi:10.1093/annonc/mdy457>
#' @references Bayesian hierarchical modeling based on multisource
#' exchangeability AM Kaizer, JS Koopmeiners, BP Hobbs Biostatistics 19 (2),
#' 169-184. <https://doi.org/10.1093/biostatistics/kxx031>
#' @description The R basket package provides for the designs and analysis of
#' basket trials for multi-source exchangeability models (MEM)
#' <https://doi.org/10.1093/biostatistics/kxx031>
#' allowing arms to "share" power with similar arms in a trial. The package
#' is intended to perform the exact or MCMC computation of the operating
#' characteristics of different scenarios. Calculations derived from these
#' analyses include the posterior probabilities, HPD boundaries, effective
#' sample sizes (ESS), mean and median estimations can be calculated with
#' this package using the MEM method. Along with providing "basketwise"
#' analyses, the package includes similar calculations for "clusterwise"
#' analyses where a cluster a set of similar baskets. In addition plotting
#' tools are provided to visualize basket and cluster density as well as their
#' exchangeability. The package includes the following main functions:
#' \itemize{
#'   \item{[basket_name()] }{Get the basket names in an analysis}
#'   \item{[basket_pep()] }{Get the Posterior Exchangeability Probability
#'                          (PEP) matrix for the arms in the trial.}
#'   \item{[basket_map()] }{Get the Maximum A Posteriori (MAP) matrix for the
#'                          arms in a trial.}
#'   \item{[cluster_pep()] }{Get the Posterior Exchangeability Probability
#'                           (PEP) matrix for the arms in the trial.}
#'   \item{[cluster_map()] }{Get the Maximum A Posteriori (MAP) matrix for
#'                           the arms in a trial.}
#'   \item{[sample_posterior()] }{Sample from the posterior distribution of
#'                                the arms in the trial.}
#'   \item{[mem_exact()] }{Create a basket analysis using the exact method.}
#'   \item{[mem_mcmc()] }{Create a basket analysis using the exact method.}
#'   \item{[summary()] }{Summarize the results of an analysis.}
#'   \item{[update_p0()] }{Update the null that a basket response rate is
#'                         above a specified value.}
#'   \item{[plot_density()] }{Plot the estimated densities of arms or clusters.}
#'   \item{[plot_pep()] }{Show the exchangeogram of the PEP matrix.}
#'   \item{[plot_mem()] }{Plot the arm prior, MAP, and PEP of a basket trial.}
#' }
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