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#' Instruction
#'
#' A data frame on school instruction results.
#'
#'
#' @name instruction
#' @docType data
#' @format A data frame with 1190 observations on the following 13 variables.
#' \describe{
#' \item{X}{a numeric vector}
#' \item{girl}{a numeric vector}
#' \item{minority}{a numeric vector}
#' \item{mathkind}{a numeric vector}
#' \item{mathgain}{a numeric vector}
#' \item{ses}{a numeric vector}
#' \item{yearstea}{a numeric vector}
#' \item{mathknow}{a numeric vector}
#' \item{housepov}{a numeric vector}
#' \item{mathprep}{a numeric vector}
#' \item{classid}{a numeric vector identifying the class within school}
#' \item{schoolid}{a numeric vector identifying the school}
#' \item{childid}{a numeric vector}
#' }
#' @source West, B., Welch, K. B., & Galecki, A. T. (2006). Linear mixed
#' models: a practical guide using statistical software. Chapman & Hall/CRC.
#' @keywords datasets
#' @examples
#'
#' # The following code takes a few minutes to run.
#' # In the interest of saving CRAN's example testing time,
#' # it has been commented out. If you want to use it,
#' # just uncomment and run.
#'
#' # data(instruction)
#' # attach(instruction)
#'
#' # data = data.frame(
#' # y = mathgain,
#' # mathkind = mathkind,
#' # girl = girl,
#' # minority = minority,
#' # ses = ses,
#' # school = factor(schoolid),
#' # section = factor(classid))
#'
#'
#' # fit.rlme = rlme(y ~ 1 + mathkind + girl + minority + ses + (1 | school) + (1 | school:section),
#' # data = data,
#' # method = "gr")
#'
#' # summary(fit.rlme)
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#' rlme
#'
#' An R package for rank-based robust estimation and prediction in random
#' effects nested models
#'
#' \tabular{ll}{ Package: \tab rlme\cr Type: \tab Package\cr Version: \tab
#' 0.2\cr Date: \tab 2013-07-07\cr License: \tab GPL (>= 2)\cr }
#'
#' @name rlme-package
#' @docType package
#' @author Yusuf Bilgic \email{bilgic@@geneseo.edu}, Herb Susmann
#' \email{hps1@@geneseo.edu} and Joseph McKean \email{joemckean@@yahoo.com}
#'
#' Maintainer: Yusuf Bilgic \email{bilgic@@geneseo.edu} or
#' \email{yusuf.k.bilgic@@gmail.com}
#' @seealso \code{\link{rlme}}
#' @keywords models package
#' @import stats
#' @import graphics
#' @import Rcpp
#' @useDynLib rlme
#' @examples
#'
#'
#' library(rlme)
#' data(schools)
#' formula = y ~ 1 + sex + age + (1 | region) + (1 | region:school)
#' rlme.fit = rlme(formula, schools)
#' summary(rlme.fit)
#'
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#' PISA Literacy Data
#'
#' The data in Program for International Assessment (PISA) on academic
#' proficiency in schools around the world.
#'
#'
#' @name schools
#' @docType data
#' @format A data frame with 334 observations on the following 6 variables.
#' \describe{
#' \item{y}{a numeric vector indicating student literacy}
#' \item{socio}{a numeric vector}
#' \item{sex}{a numeric vector}
#' \item{age}{a numeric vector}
#' \item{region}{a numeric vector indicating four regions}
#' \item{school}{a numeric vector indicating the schools within region}
#' }
#' @references OECD (2010). PISA 2009 Results. http://www.oecd.org/
#' @keywords datasets
#' @examples
#'
#' #
#' # The example takes a few seconds to run, so in order to
#' # save CRAN's testing time it has been commented out.
#' # To run, simply uncomment and execute.
#' #
#'
#' # data(schools)
#' # rlme.fit = rlme(y ~ 1 + sex + age + (1 | region) + (1 | region:school),
#' # schools, method="gr")
#' # summary(rlme.fit)
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