ergmito: Exponential Random Graph Models for Small Networks

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 Markov-Chain Maximum Likelihood Estimator (MC-MLE) 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 simulation-based algorithms.

Version: 0.3-1
Depends: R (≥ 3.3.0)
Imports: ergm, network, MASS, Rcpp, texreg, stats, parallel, utils, methods, graphics
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, sna, lmtest, fmcmc, coda, knitr, rmarkdown, tinytest
Published: 2023-06-14
DOI: 10.32614/CRAN.package.ergmito
Author: George Vega Yon ORCID iD [cre, aut], Kayla de la Haye ORCID iD [ths], Army Research Laboratory and the U.S. Army Research Office [fnd] (Grant Number W911NF-15-1-0577)
Maintainer: George Vega Yon <g.vegayon at>
License: MIT + file LICENSE
NeedsCompilation: yes
Language: en-US
Citation: ergmito citation info
Materials: NEWS
CRAN checks: ergmito results


Reference manual: ergmito.pdf
Vignettes: ERGM equations
Extending ergmito


Package source: ergmito_0.3-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ergmito_0.3-1.tgz, r-oldrel (arm64): ergmito_0.3-1.tgz, r-release (x86_64): ergmito_0.3-1.tgz, r-oldrel (x86_64): ergmito_0.3-1.tgz
Old sources: ergmito archive


Please use the canonical form to link to this page.