R/CInLPN-package.R

In bachirtadde/CInLPN: Causal Inference for Latent Processes Network

#' A package for Causal Inference in a Latent Processes Network
#'
#' This package Contains functions to fit a dynamic model for multiple latent processes. 
#' The methodology is based on the combinaison 
#' of a linear mixed model for the latent processes trajectories and a system of 
#' differences equations for assessing their temporal influences. 
#' The estimation is done in the maximum likelihood framework.
#'
#' \tabular{ll}{ Package: \tab CInLPN\cr Type: \tab Package\cr
#' Version: \tab 0.2.1\cr Date: \tab 2020-02-27\cr License: \tab GPL (>= 2.0)\cr}
#' 
#' @exportPattern ^[[:alpha:]]+
#' @importFrom graphics abline axis lines par plot points title
#' @importFrom stats as.formula model.matrix na.action na.omit pchisq pnorm printCoefmat quantile terms var
#' @importFrom Rcpp evalCpp 
#' @importFrom marqLevAlg marqLevAlg
#' @name CInLPN-package
#' @docType package
#' @author Bachirou Tadd\'e, C\'ecile Proust-Lima
#' Tadd\'e O. B., et al. (2018). Dynamic modelling of Multivariate Dimensions and 
#' Their Temporal Relationships using Latent Processes: Application to Alzheimer's 
#' Disease. Biometrics. 24 oct 2019
#' 
#' @keywords "Causality", "Dynamic model"," Latent processes"," multivariate longitudinal data"
#' @useDynLib CInLPN, .registration = TRUE
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