| btergm-package {btergm} | R Documentation |
Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
Description
Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood.
Details
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals, Markov Chain Monte Carlo maximum likelihood, or Bayesian estimation. 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), MCMC MLE
(mtergm function), or Bayesian estimation (tbergm
function). Goodness of fit assessment for ERGMs, TERGMs, SAOMs, and dyadic
independence models is possible with the generic gof function
and its various methods defined here in the btergm package. 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 (T)ERGM
datasets. To display citation information, type citation("btergm").
Author(s)
Philip Leifeld, Skyler J. Cranmer, Bruce A. Desmarais
References
Cranmer, Skyler J., Tobias Heinrich and Bruce A. Desmarais (2014): Reciprocity and the Structural Determinants of the International Sanctions Network. Social Networks 36(1): 5-22. doi:10.1016/j.socnet.2013.01.001.
Desmarais, Bruce A. and Skyler J. Cranmer (2012): Statistical Mechanics of Networks: Estimation and Uncertainty. Physica A 391: 1865–1876. doi:10.1016/j.physa.2011.10.018.
Desmarais, Bruce A. and Skyler J. Cranmer (2010): Consistent Confidence Intervals for Maximum Pseudolikelihood Estimators. Neural Information Processing Systems 2010 Workshop on Computational Social Science and the Wisdom of Crowds.
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. doi:10.18637/jss.v083.i06.
See Also
Useful links: