Functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models. Mallik, Almquist (2017, under review).
This package provides functions for fitting lagged exponential family models on dynamic network data, simulation from the models and model diagnostics.
This package was developed with help from ARO YIP award #W911NF-14-1-0577.
Abhirup Mallik and Zack W. Almquist (2017). "Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models." Working paper. University of Minnesota.
Abhirup Mallik and Zack W. Almquist (2017). "An R Package for Dynamic Network Regression." Working paper. University of Minnesota.
Zack W. Almquist and Carter T. Butts (forthcoming). "Dynamic Network Analysis with Missing Data: Theory and Methods." Statistica Sinica. doi:10.5705/ss.202016.0108.
Zack W. Almquist and Carter T. Butts. (2013). "Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-group Blog Citation Dynamics in the 2004 US Presidential Election." Political Analysis, 21(4), 430-448.
Zack W. Almquist and Carter T. Butts. (2014). "Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics." In Bayesian Inference in the Social Sciences. Ed. by I. Jeliazkov and X.-S. Yang. Hoboken, New Jersey: John Wiley & Sons.
Feb 23, 2018. CRAN release of dnr. July 25, 2018. dnr version 0.3.4 release.