R/diffee-package.R

In diffee: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

#' estimating DIFFerential networks via an Elementary Estimator under a
#' high-dimensional situation
#'
#' This is an R implementation of Fast and Scalable Learning of Sparse Changes
#' in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE
#' algorithm can be used to fast estimate the differetial network between two
#' related datasets. For instance, it can identify differential gene network
#' from datasets of case and control. By performing data-driven network
#' inference from two high-dimensional data sets, this tool can help users
#' effectively translate two aggregated data blocks into knowledge of the
#' changes among entities between two Gaussian Graphical Model. Please run
#' demo(diffeeDemo) to learn the basic functions provided by this package. For
#' further details, please read the original paper: Beilun Wang, Arshdeep
#' Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
#'
#' \tabular{ll}{ Package: \tab diffee\cr Type: \tab Package\cr Version: \tab
#' 1.0.0\cr Date: \tab 2018-03-05\cr License: \tab GPL (>= 2)\cr } We focus on
#' the problem of estimating the change in the dependency structures of two
#' p-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse
#' change estimation in GGMs involve expensive and difficult non-smooth
#' optimization. We propose a novel method, DIFFEE for estimating DIFFerential
#' networks via an Elementary Estimator under a high-dimensional situation.
#' DIFFEE is solved through a faster and closed form solution that enables it
#' to work in large-scale settings. We conduct a rigorous statistical analysis
#' showing that surprisingly DIFFEE achieves the same asymptotic convergence
#' rates as the state-of-the-art estimators that are much more difficult to
#' compute. Our experimental results on multiple synthetic datasets and one
#' real-world data about brain connectivity show strong performance
#' improvements over baselines, as well as significant computational benefits.
#'
#' @name diffee-package
#' @docType package
#' @author Beilun Wang, Zhaoyang Wang
#'
#' Maintainer: Beilun Wang - bw4mw at virginia dot edu
#' @references Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018). Fast and
#' Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical
#' Model Structure. <arXiv:1710.11223>
#' @keywords package
#' @examples
#' \dontrun{
#' data(exampleData)
#' result = diffee(exampleData[[1]], exampleData[[2]], 0.45)
#' plot.diffee(result)
#' }
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