ergmito
2025
ActivePackage2025CRAN0.3-2downloads28kstars9Cited by47
Citation counts from OpenAlex; downloads from CRAN. Updated 2026-08-15.
Exponential Random Graph Models for Small Networks
About
Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics, as shown in Vega Yon et al. (2020). As a difference from the ‘ergm’ package, ‘ergmito’ circumvents using a Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses a 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 simulation-based algorithms.
Cite
@Manual{ergmito,
title = {ergmito: Exponential Random Graph Models for Small Networks},
author = {{Vega Yon}, {George G.} and {de la Haye}, {Kayla}},
year = {2025},
month = {dec},
doi = {10.32614/CRAN.package.ergmito},
url = {https://cran.r-project.org/package=ergmito},
note = {R package version 0.3-2},
}