Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. 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 CRAN 
Installation 
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