R/maSAE-package.R

In maSAE: Mandallaz' Model-Assisted Small Area Estimators

#' Mandallaz' Model-Assisted Small Area Estimators
#'
#' An S4 implementation of the unbiased extension of the
#' model-assisted' synthetic-regression estimator proposed by
#' Mandallaz (2013), Mandallaz et al. (2013) and Mandallaz (2014).\cr
#' It yields smaller variances than the standard bias correction,
#' the generalised regression estimator.
#'
#' This package provides Mandallaz' extended synthetic-regression estimator for two- and
#' three-phase sampling designs with or without clustering.\cr
#' See vignette("maSAE", package = "maSAE") and demo("maSAE", package = "maSAE") for
#' introductions, \code{"\link[=saeObj-class]{class?maSAE::saeObj}"} and
#' \code{"\link[=predict]{?maSAE::predict}"} for help on the main feature.
#'
#' @note Model-assisted estimators use models to improve the efficiency (i.e. reduce
#' prediction error compared to design-based estimators) but need not assume them to be
#' correct as in the model-based approach, which is advantageous in official
#' statistics.
#' @name maSAE-package
#' @aliases maSAE-package
#' @docType package
#' @seealso There are a couple packages for model-\strong{based} small area estimation, see
#' \code{\link[sae:sae-package]{sae}},
#' \code{\link[rsae:rsae-package]{rsae}},
#' hbsae and
#' \code{\link[JoSAE:JoSAE-package]{JoSAE}}.
#' In 2016, Andreas Hill published
#' \code{\link[forestinventory:forestinventory]{forestinventory}}, another
#' implementation of Mandallaz' model-assisted small area estimators (see
#' \code{vignette("forestinventory_and_maASE", package = "maSAE")} for a comparison).
#' @references
#' \cite{
#' Mandallaz, D. 2013
#' Design-based properties of some small-area estimators in forest
#' inventory with two-phase sampling.
#' Canadian Journal of Forest Research \bold{43}(5), pp. 441--449.
#' \doi{10.1139/cjfr-2012-0381}.
#' }
#'
#' \cite{
#' Mandallaz, and Breschan, J.  and  Hill, A. 2013
#' New regression estimators in forest inventories with two-phase sampling and partially
#' exhaustive information: a design-based Monte Carlo approach with applications to
#' small-area estimation.
#' Canadian Journal of Forest Research \bold{43}(11), pp. 1023--1031.
#' \doi{10.1139/cjfr-2013-0181}.
#' }
#'
#' \cite{
#' Mandallaz, D. 2014
#' A three-phase sampling extension of the generalized regression
#' estimator with partially exhaustive information.
#' Canadian Journal of Forest Research \bold{44}(4), pp. 383--388.
#' \doi{10.1139/cjfr-2013-0449}.
#' }
#'
#' @keywords package
#' @examples
#' \dontrun{
#' vignette("maSAE", package = "maSAE")
#' }
#' \dontrun{
#' demo("design", package = "maSAE")
#' }
#' \dontrun{
#' demo("maSAE", package = "maSAE")
#' }
#'
NULL