#' baker: \strong{B}ayesian \strong{A}nalytic \strong{K}it for
#' \strong{E}tiology \strong{R}esearch
#'
#' \code{baker} is designed for disease etiology studies from case-control data
#' with multiple sources of measurements with potential errors. If you are
#' interested in estimating the population etiology pie (a vector of fractions
#' that sum to one), and the probability of each cause for a particular
#' individual case, try \code{baker}.
#'
#' \code{baker} implements hierarchical Bayesian models to infer disease etiology
#' for multivariate binary data. We created \code{baker} to catalyze effective
#' communications between analysts and practicing clinicians that are vital to
#' the success of etiology studies. The \code{baker} package offers
#' modules to
#' \itemize{
#' \item Import and tidy the
#' PERCH data (the study that motivates the creation of this package),
#' \item Transform, explore the data,
#' \item Specify, automatically generate the model files, and fit the models (npLCM),
#' \item Store and visualize posterior summaries for communicating scientific
#' findings, and
#' \item Check and compare the fitted models.
#' }
#'
#' \code{baker} has implemented models for dependent
#' measurements given disease status, regression analyses of etiology,
#' multiple imperfect measurements, different priors for true positive rates
#' among cases with differential measurement characteristics, and
#' multiple-pathogen etiology. Scientists in Pneumonia Etiology Research for
#' Child Health (PERCH) study usually refer to the etiology distribution
#' as "population etiology pie" and "individual etiology pie" for their
#' compositional nature, hence the name of the package (baking the pie).
#'
#' @seealso
#' \itemize{
#' \item \url{https://github.com/zhenkewu/baker} for the source code
#' and system/software requirements to use \code{baker} for your data.
#' }
#'
#' @import rjags
#'
#' @section baker functions:
#' \code{\link{nplcm}}
#'
#' @docType package
#' @name baker
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