ergmito: Exponential Random Graph Models for Small Networks

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics. 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: 0.2-1
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: 2020-02-12
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 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
CRAN checks: ergmito results

Downloads:

Reference manual: ergmito.pdf
Vignettes: ERGM equations
Extending ergmito
Package source: ergmito_0.2-1.tar.gz
Windows binaries: r-prerelease: ergmito_0.2-1.zip, r-release: ergmito_0.2-1.zip, r-oldrel: ergmito_0.2-1.zip
macOS binaries: r-prerelease: ergmito_0.2-1.tgz, r-release: ergmito_0.2-1.tgz, r-oldrel: ergmito_0.2-1.tgz
Old sources: ergmito archive

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