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.
c(
person(given = "George", family = "Vega Yon", role = c("cre","aut"),
email = "g.vegayon@gmail.com", comment = c(ORCID = "0000-0002-3171-0844")),
person(given = "Kayla", family = "de la Haye", role = c("ths"),
comment = c(ORCID = "0000-0002-2536-7701")),
person("Army Research Laboratory and the U.S. Army Research Office",
role = "fnd", comment = "Grant Number W911NF-15-1-0577")
)
ergm, network, MASS, Rcpp, texreg, stats, parallel, utils,
methods, graphics
covr, sna, lmtest, fmcmc, coda, knitr, rmarkdown, tinytest
'RcppExports.R' 'boot.R' 'count_stats.R' 'data.R'
'ergm_blockdiagonal.R' 'ergmito-checkers.R' 'ergmito-package.R'
'ergmito.R' 'ergmito_surface.R' 'formulae.R' 'gof.R'
'induced_submat.R' 'network.R' 'powerset.R' 'predict.R'
'random.R' 'same_dist.R' 'sim.R' 'simulate.R' 'texreg.R'
'utils.R'
George 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)

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.