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#' \code{\linkS4class{Pacs}} class
#'
#' This class contains all the input parameters to run CLERE.
#'
#' \describe{
#' \item{Y}{[numeric]: The vector of observed responses - size \code{n}.}
#' \item{X}{[matrix]: The matrix of predictors - size \code{n} rows and \code{p} columns.}
#' \item{lambda}{[numeric]: A non-negative penalty term that controls simultaneouly clusetering and sparsity.}
#' \item{betaInput}{[numeric]: A vector of initial guess of the model parameters. The authors suggest to use coefficients obtained after fitting a ridge regression with the shrinkage parameter selected using AIC criterion.}
#' \item{epsPACS}{[numeric]: A tolerance threshold that control the convergence of the algroithm. The default value fixed in Bondell's initial script is 1e-5.} \item{nItMax}{[integer]: Maximum number of iterations in the algorithm.}
#' \item{a0}{[numeric]: Fitted intercept.} \item{K}{[integer]: Model dimensionality.}
#' }
#'
#' @name Pacs-class
#' @docType class
#' @section Methods:
#' \describe{
#' \item{object["slotName"]:}{Get the value of the field \code{slotName}.}
#' \item{object["slotName"]<-value:}{Set \code{value} to the field \code{slotName}.}
#' }
#'
#' @seealso Overview : \code{\link{clere-package}} \cr
#' Classes : \code{\linkS4class{Clere}}, \code{\linkS4class{Pacs}} \cr
#' Methods : \code{\link{plot}}, \code{\link{clusters}}, \code{\link{predict}}, \code{\link{summary}} \cr
#' Functions : \code{\link{fitClere}}, \code{\link{fitPacs}}
#' Datasets : \code{\link{numExpRealData}}, \code{\link{numExpSimData}}, \code{\link{algoComp}}
#'
#' @keywords Pacs Clere class methods method
#' @export
#'
methods::setClass(
Class = "Pacs",
representation = methods::representation(
y = "numeric",
x = "matrix",
n = "integer",
p = "integer",
nItMax = "integer",
lambda = "numeric",
epsPACS = "numeric",
betaInput = "numeric",
betaOutput = "numeric",
a0 = "numeric",
K = "integer"
),
prototype = methods::prototype(
y = numeric(),
x = matrix(),
n = integer(),
p = integer(),
nItMax = integer(),
lambda = numeric(),
epsPACS = numeric(),
betaInput = numeric(),
betaOutput = numeric(),
a0 = numeric(),
K = integer()
)
)
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