This repository contains the R package EMGS (currently still developer's version), described in

Zehang R Li and Tyler H McCormick. An Expectation Conditional Maximization approach for Gaussian graphical models, 2018 arXiv

You can install the package with devtools

install_github("richardli/EMGS", subdir = "EMGS")
library(EMGS)

This example demonstrates the bias reduction of EMGS. It creates a plot (illustraion-emgs.pdf) under the figures/ directory.

setwd("codes/")
source("example1.R")

This example demonstrates the informative priors. It creates a plot (structure.pdf) under the figures/ directory.

source("example2.R")

This example demonstrates the missing data imputation for Burke Gilman Trail example. It creates a plot (burke.pdf) under the figures/ directory. For quicker illustration, this example models only two blocks by combining the pedestrians and bikes from both directions instead of modeling four blocks.

source("example3.R")

Running the full version of the simulation as described in the paper takes a long time and is recommended to be implemented on a cluster. Below is a low dimensional example.

source("simulation-sim.R")
source("simulation-example.R")