R/tergmLite-package.r

In tergmLite: Fast Simulation of Simple Temporal Exponential Random Graph Models

#' Fast Simulation of Simple Temporal Exponential Random Graph Models
#'
#' \tabular{ll}{
#'    Package: \tab tergmLite\cr
#'    Type: \tab Package\cr
#'    Version: \tab 2.6.1\cr
#'    Date: \tab 2022-07-20\cr
#'    License: \tab GPL-3\cr
#'    LazyLoad: \tab yes\cr
#' }
#'
#' @details
#' The statistical framework of temporal exponential random graph models (TERGMs)
#' provides a rigorous, flexible approach to estimating generative models for
#' dynamic networks and simulating from them for the purposes of modeling infectious
#' disease transmission dynamics. TERGMs are used within the \code{EpiModel} software
#' package to do just that. While estimation of these models is relatively fast,
#' the resimulation of them using the tools of the \code{tergm} package
#' is computationally burdensome, requiring hours to days to iteratively resimulate
#' networks with co-evolving demographic and epidemiological dynamics. The
#' primary reason for the computational burden is the use of the \code{network}
#' class of object (designed within the package of the same name); these objects
#' have tremendous flexibility in the types of networks they represent but at the
#' expense of object size. Continually reading and writing larger-than-necessary
#' data objects has the effect of slowing the iterative dynamic simulations.
#'
#' The \code{tergmLite} package reduces that computational burden by representing
#' networks less flexibly, but much more efficiently. For epidemic models, the only
#' types of networks that we typically estimate and simulate from are undirected,
#' binary edge networks with no missing data (as it is simulated). Furthermore,
#' the network history (edges or node attributes) does not need to be stored for
#' research-level applications in which summary epidemiological statistics (e.g.,
#' disease prevalence, incidence, and variations on those) at the population-level
#' are the standard output metrics for epidemic models. Therefore, the network
#' may be stored as a cross-sectional edgelist, which is a two-column matrix
#' of current edges between one node (in column one) and another node (in column two).
#' Attributes of the edges that are called within ERGMs may be stored separately in
#' vector format, as they are in \code{EpiModel}. With this approach, the simulation
#' time is sped up by a factor of 25-50 fold, depending on the specific research
#' application.
#'
#' @name tergmLite-package
#' @aliases tergmLite
#'
#' @useDynLib tergmLite
#'
#' @import ergm tergm network
#' @importFrom Rcpp sourceCpp
#' @importFrom stats formula
#' @importFrom statnet.common term.list.formula NVL set.control.class trim_env check.control.class
#'                            append_rhs.formula nonsimp_update.formula
#' @importFrom methods is
#' @importFrom networkDynamic network.collapse
#'
#' @docType package
#' @keywords package
#'
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