A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) <doi:10.1214/12-EJS696> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.

Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs).  A number of EGPs are supported, including the families identified in Butts (2023) <doi:10.1080/0022250X.2023.2180001>, as are functions for hazard calculation and timing calibration.

Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.

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.

A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2023) <doi:10.1080/01621459.2023.2242627>.