#' Bayesian source apportionment model
#'
#' Estimates sources of particulate matter (PM) air pollution using
#' chemical constituent concentrations of PM. The model is developed
#' using Nikolov et al. 2008 and Hackstadt and Peng 2014.
#'
#' To estimate sources, users need data from an ambient monitor, which is
#' a matrix of days by number of chemical constituents. Then, users apply
#' the function \code{\link{mcmcsa}} to the data, specifying the number
#' of sources and necessary conditions for identifiability.
#'
#' @references Margaret C. Nikolov, Brent A. Coull, Paul J. Catalano, et al.
# (2008). J. R. Statist. Soc. C.
#' Statistical methods to evaluate health effects associated with
#' major sources of air pollution: a case-study of breathing
#' patterns during exposure to concentrated Boston air
#' particles, 57(3) 357-378.
#' @references Amber J. Hackstadt and Roger D. Peng (2014). Environmetrics.
#' A Bayesian multivariate receptor model for estimating source contributions
#' to particulate matter pollution using national databases, 25(7) 513-527.
#' @name MCMCsa
#' @docType package
#' @import truncnorm
#' @import corpcor
#' @import mvtnorm
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