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 [cre, aut], Kayla de la Haye [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 gmail.com> BugReports: https://github.com/muriteams/ergmito/issues License: MIT + file LICENSE URL: https://muriteams.github.io/ergmito/ NeedsCompilation: yes Language: en-US Citation: ergmito citation info Materials: NEWS In views: NetworkAnalysis CRAN checks: ergmito results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=ergmito to link to this page.
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