Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics. As a difference from the 'ergm' package, 'ergmito' circumvents using MarkovChain Maximum Likelihood Estimator (MCMLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulationbased algorithms.
Package details 


Author  George Vega Yon [cre, aut] (<https://orcid.org/0000000231710844>), Kayla de la Haye [ths] (<https://orcid.org/0000000225367701>), Army Research Laboratory and the U.S. Army Research Office [fnd] (Grant Number W911NF1510577) 
Maintainer  George Vega Yon <g.vegayon@gmail.com> 
License  MIT + file LICENSE 
Version  0.30 
URL  https://muriteams.github.io/ergmito 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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