A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks. See Krivitsky and Butts (2017) <doi:10.1177/0081175017692623> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM).
    Includes functions to conduct mediation and moderation analyses and to diagnose
    multicollinearity.
    URL: <https://github.com/sduxbury/ergMargins>.
    BugReports: <https://github.com/sduxbury/ergMargins/issues>.
    Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>.
    Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>.
    Mize, Trenton D. (2019) <doi:10.15195/v6.a4>.
    Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>.
    Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>.
    Duxbury, Scott W, Jenna Wertsching (2023) <doi:10.1016/j.socnet.2023.02.003>.
    Huang, Peng, Carter Butts (2023) <doi:10.1016/j.socnet.2023.07.001>.

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.