Nothing
#' joineR
#'
#' @description The joineR package implements methods for analyzing data from
#' longitudinal studies in which the response from each subject consists of a
#' time-sequence of repeated measurements and a possibly censored
#' time-to-event outcome. The modelling framework for the repeated
#' measurements is the linear model with random effects and/or correlated
#' error structure (Laird and Ware, 1982). The model for the time-to-event
#' outcome is a: Cox proportional hazards model with log-Gaussian frailty
#' (Cox, 1972). A cause-specific hazards model is used when competing risks
#' are present. Stochastic dependence is captured by allowing the Gaussian
#' random effects of the linear model to be correlated with the frailty term
#' of the Cox proportional hazards model. The methodology used to fit the
#' model is described in Henderson et al. (2002) in the case of a single event
#' time, and by Williamson et al. (2008) in the case of competing risks data.
#' Both models exploit the general methodology proposed by Wulfsohn and
#' Tsiatis (1997).
#'
#' The package offers several types of functions for the analysis of joint data.
#'
#' @section Data manipulation functions:
#'
#' There are several functions, including \code{jointdata},
#' \code{sample.jointdata}, \code{subset.jointdata}, \code{to.balanced},
#' \code{to.unbalanced}, and \code{UniqueVariables}, which offer the ability
#' to construct a joint model dataset and manipulate it, e.g. take a sample
#' according to a baseline covariate or outcome.
#'
#' @section Plot functions:
#'
#' The plot function can be applied to \code{jointdata} and \code{vargm}
#' (variogram) objects. In addition, \code{points} and \code{lines} can also
#' be used with \code{jointplot} objects.
#'
#' @section Model fitting functions:
#'
#' The primary function for fitting a joint model is \code{joint}. Standard
#' errors can be estimated using \code{jointSE}.
#'
#' @note Further details on the package are given in the vignette. To access
#' this, run \code{vignette("joineR")}.
#'
#' @references
#'
#' Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data
#' measured with error. \emph{Biometrics.} 1997; \strong{53(1)}: 330-339.
#'
#' Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal
#' measurements and event time data. \emph{Biostatistics.} 2000; \strong{1(4)}:
#' 465-480.
#'
#' Cox DR. Regression models and life-tables. \emph{J R Stat Soc Ser B Stat
#' Methodol.} 1972; \strong{34(2)}: 187-220.
#'
#' Laird NM, Ware JH. Random-effects models for longitudinal data.
#' \emph{Biometrics.} 1982; \strong{38(4)}: 963-974.
#'
#' Williamson PR, Kolamunnage-Dona R, Philipson P, Marson AG. Joint modelling of
#' longitudinal and competing risks data. \emph{Stat Med.} 2008; \strong{27}:
#' 6426-6438.
#'
#' @docType package
#' @name joineR
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.