Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.
Documentation of the package is available as a JStatSoft article:
Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2018): Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 1-36. http://dx.doi.org/10.18637/jss.v083.i06.
Please contribute to the software development in the following ways:
Fix issues or add functions through pull requests. Cite our article in the Journal of Statistical Software and tell other people about the software. Contribute financially to support the software development by the package maintainer:
