R/ICABOD_help_page.R

In ICAOD: Optimal Designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm (ICA)

#' @title ICAOD: Finding Optimal Designs for Nonlinear Models Using Imperialist Competitive Algorithm
#'
#' @description Different functions are available to find optimal designs for linear and nonlinear models using the imperialist competitive algorithm (ICA).
#' Because the optimality criteria for linear and nonlinear models depend on the unknown parameters,
#' one should choose on of the following method to deal with the parameter-dependency based on the available information for the unknown parameters:
#'\itemize{
#'  \item{\code{\link{locally}}: }{finds locally optimal designs. A vector of initial estimates or guess is available for the vector of model parameters from a pilot or similar study.}
#'  \item{\code{\link{bayes}}: }{finds Bayesian optimal designs. A continuous prior is available for the vector of unknown model parameters.}
#'  \item{\code{\link{robust}}: }{finds robust or optimum-in-average designs. It is similar to \code{\link{bayes}}, but uses a discrete prior.}
#'  \item{\code{\link{minimax}}: }{finds minimax and standardized maximin optimal designs. Each of the unknown  parameters belongs to a user-specified interval. The purpose is to find a design that protects the user against the worst scenario over the parameter space.
#'  Standardized designs should be used when locally optimal design of the model of interest has an analytical solution.}
#'}
#'
#'  Some functions are also available to find optimal designs for special applications:
#'\itemize{
#'  \item{\code{\link{multiple}}: }{finds locally multiple objective optimal designs for the 4-parameter Hill model with application in dose-response stuides. It uses the same strategy as  \code{locally} to deal with the unknown model parameters.}
#'  \item{\code{\link{bayescomp}}: }{finds a design that  meets the dual goal of the parameter estimation and
#'   increasing the probability of a particular outcome in a binary response  model.  It uses the same strategy as the function \code{bayes} to deal with the unknown mode parameters and applicable in medicine studies.}
#'}
#'@details
#'
#' The functions \code{\link{locally}} and \code{\link{robust}} are very easy to be applied and
#' they are usually fast. The speed of the functions \code{\link{bayes}} and \code{\link{minimax}}
#' considerably depends on the value of the tuning parameters.
#'
#' The following functions may also be used  to verify the optimality of an output design for each of the above criterion:
#' \itemize{
#' \item{\code{\link{senslocally}}}
#' \item{\code{\link{sensrobust}}}
#' \item{\code{\link{sensbayes}}}
#' \item{\code{\link{sensminimax}}}
#'  \item{\code{\link{sensmultiple}}}
#'  \item{\code{\link{sensbayescomp}}}
#' }
#' For more details see Masoudi et al. (2017, 2019).
#' @references
#  Atashpaz-Gargari, E, & Lucas, C (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). IEEE. \cr
#' Masoudi E, Holling H, Wong WK (2017). Application of Imperialist Competitive Algorithm to Find Minimax and Standardized Maximin Optimal Designs. Computational Statistics and Data Analysis, 113, 330-345. <doi:10.1016/j.csda.2016.06.014> \cr
#' Masoudi E, Holling H, Duarte BP, Wong Wk (2019). Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models. Journal of Computational and Graphical Statistics. <doi:10.1080/10618600.2019.1601097>
#'
#' @docType package
#' @name ICAOD
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