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) . 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.


Reference manual

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0.3-0 by George Vega Yon, a year ago


Report a bug at https://github.com/muriteams/ergmito/issues

Browse source code at https://github.com/cran/ergmito

Authors: 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)

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports ergm, network, MASS, Rcpp, texreg, stats, parallel, utils, methods, graphics

Suggests covr, sna, lmtest, fmcmc, coda, knitr, rmarkdown, tinytest

Linking to Rcpp, RcppArmadillo

See at CRAN