ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM). ”ergm” is a part of the ”statnet” suite of packages for network analysis.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Zhou, Jing; Huang, DanYang; Wang, HanSheng: A dynamic logistic regression for network link prediction (2017)
- Byshkin, Maksym; Stivala, Alex; Mira, Antonietta; Krause, Rolf; Robins, Garry; Lomi, Alessandro: Auxiliary parameter MCMC for exponential random graph models (2016)
- Caimo, Alberto; Mira, Antonietta: Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks (2015)
- Geyer, Charles J.; Ridley, Caroline E.; Latta, Robert G.; Etterson, Julie R.; Shaw, Ruth G.: Local adaptation and genetic effects on fitness: Calculations for exponential family models with random effects (2013)
- Lerner, Jürgen; Indlekofer, Natalie; Nick, Bobo; Brandes, Ulrik: Conditional independence in dynamic networks (2013)
- Dinwoodie, Ian H.: Sequential importance sampling of binary sequences (2012)
- Groendyke, Chris; Welch, David; Hunter, David R.: A network-based analysis of the 1861 Hagelloch measles data (2012)
- Krivitsky, Pavel N.: Exponential-family random graph models for valued networks (2012)
- Okabayashi, Saisuke; Geyer, Charles J.: Long range search for maximum likelihood in exponential families (2012)
- Groendyke, Chris; Welch, David; Hunter, David R.: Bayesian inference for contact networks given epidemic data (2011)
- Schweinberger, Michael: Instability, sensitivity, and degeneracy of discrete exponential families (2011)