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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("ergmito")

0.3-0 by George Vega Yon, 3 months ago


https://muriteams.github.io/ergmito/


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