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.
The btergm package implements TERGMs with MPLE and bootstrapped confidence intervals (btergm function) or MCMC MLE (mtergm function). Goodness of fit assessment for ERGMs, TERGMs, SAOMs, and dyadic independence models is possible with the generic gof function and its various methods. New networks can be simulated from TERGMs using the simulate.btergm function. The package also implements micro-level interpretation for ERGMs and TERGMs using the interpret function. Furthermore, the package contains the chemnet and knecht datasets for estimating (T)ERGMs. To display citation information, type citation("btergm").
Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2017): 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.
btergm mtergm simulate.btergm gof interpret btergm-class checkdegeneracy