R/GGMnonreg-package.R

In donaldRwilliams/GGMnonreg: Non-Regularized Gaussian Graphical Models

#' GGMnonreg:  Non-Regularized Gaussian Graphical Models
#'
#' @description The goal of \strong{GGMnonreg} is to estimate non-regularized
#' graphical models. Note that the title is a bit of a misnomer, in that Ising
#' and mixed graphical models are also supported. Graphical modeling is quite
#' common in fields with \emph{wide} data, that is, when there are more variables
#' than observations. Accordingly, many regularization-based approaches have been
#' developed for those kinds of data. There are key drawbacks of regularization
#' when the goal is inference, including, but not limited to, the fact that
#' obtaining a valid measure of parameter uncertainty is very (very) difficult.
#'
#' More recently, graphical modeling has emerged in psychology,
#' where the data are typically long or low-dimensional
#' \insertCite{williams_rethinking,williams2019nonregularized}{GGMnonreg}.
#' The primary purpose of  \strong{GGMnonreg} is to provide methods specifically
#' for low-dimensional data
#'
#'
#' \strong{Supported Models}
#'
#' \itemize{
#'  \item{Gaussian graphical model. The following data types are supported.}
#'  \itemize{
#'  \item{Gaussian}
#'  \item{Ordinal}
#'  \item{Binary}
#'  }
#'  \item{Ising model}
#'  \item{Mixed graphical model}
#' }
#'
#' \strong{Additional Methods}
#'
#' \itemize{
#' \item{Expected network replicability} \insertCite{williams2020learning}{GGMnonreg}
#'
#' \item{Compare Gaussian graphical models}
#'
#' \item{Measure of uncertainty} \insertCite{williams_2021_conf}{GGMnonreg}
#'
#' \item{Edge inclusion "probabilities"}
#'
#' \item{Network visualization}
#'
#' \item{Constrained precision matrix (the network, given an assumed graph)}
#'
#' \item{Predictability (variance explained)}
#' }
#'
#' @references
#' \insertAllCited{}
#'
#' @docType package
#'
#' @name GGMnonreg-package
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