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ergmito

Exponential Random Graph Models for Small Networks

v0.3-2 · Dec 19, 2025 · MIT + file LICENSE

Description

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.

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NOTE r-oldrel-macos-arm64

installed package size

installed size is  5.8Mb
  sub-directories of 1Mb or more:
    libs   4.3Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is  5.8Mb
  sub-directories of 1Mb or more:
    libs   4.3Mb

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-oldrel-macos-arm64

installed package size

installed size is  5.8Mb
  sub-directories of 1Mb or more:
    libs   4.3Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is  5.8Mb
  sub-directories of 1Mb or more:
    libs   4.3Mb

Dependency Network

Dependencies Reverse dependencies ergm network MASS Rcpp texreg ergmito

Version History

new 0.3-2 Mar 10, 2026
updated 0.3-2 ← 0.3-1 diff Dec 19, 2025
updated 0.3-1 ← 0.3-0 diff Jun 13, 2023
updated 0.3-0 ← 0.2-1 diff Aug 9, 2020
updated 0.2-1 ← 0.2-0 diff Feb 11, 2020
new 0.2-0 Jan 31, 2020