Nothing
#' lmeresampler: A package for bootstrapping nested linear mixed-effects models
#'
#' The \pkg{lme4} and \pkg{nlme} packages have made fitting nested
#' linear mixed-effects (LME) models quite easy. Using the the
#' functionality of these packages we can easily use maximum
#' likelihood or restricted maximum likelihood to fit a
#' model and conduct inference using our parametric toolkit.
#' In practice, the assumptions of our model are often violated
#' to such a degree that leads to biased estimators and
#' incorrect standard errors. In these situations, resampling
#' methods such as the bootstrap can be used to obtain consistent
#' estimators and standard errors for inference.
#' \code{lmeresampler} provides an easy way to bootstrap nested
#' linear-mixed effects models using either fit using either \pkg{lme4} or
#' \pkg{nlme}.
#'
#'
#' A variety of bootstrap procedures are available:
#' \itemize{
#' \item the parametric bootstrap: \code{\link{parametric_bootstrap}}
#' \item the residual bootstrap: \code{\link{resid_bootstrap}}
#' \item the cases (i.e. non-parametric) bootstrap: \code{\link{case_bootstrap}}
#' \item the random effects block (REB) bootstrap: \code{\link{reb_bootstrap}}
#' \item the Wild bootstrap: \code{\link{wild_bootstrap}}
#' }
#'
#' In addition to the individual bootstrap functions, \code{lmeresampler} provides
#' a unified interface to bootstrapping LME models in its \code{bootstrap} function.
#' @docType package
#' @name lmeresampler
#' @aliases lmeresampler package-lmeresampler
#' @keywords package
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.