muriteams/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.

Getting started

Package details

AuthorGeorge Vega Yon [cre, aut] (<https://orcid.org/0000-0002-3171-0844>), Kayla de la Haye [ths] (<https://orcid.org/0000-0002-2536-7701>), Army Research Laboratory and the U.S. Army Research Office [fnd] (Grant Number W911NF-15-1-0577)
MaintainerGeorge Vega Yon <g.vegayon@gmail.com>
LicenseMIT + file LICENSE
Version0.3-0
URL https://muriteams.github.io/ergmito
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("muriteams/ergmito")
muriteams/ergmito documentation built on Aug. 10, 2020, 5:41 p.m.