@article{ff856f0e196e4b77850d89ed41257d26,
title = "Temporal exponential random graph models with btergm: Estimation and bootstrap confidence intervals",
abstract = "The xergm package is an implementation of extensions to the exponential random graph model (ERGM). It acts as a meta-package for multiple constituent packages. One of these packages is btergm, which implements bootstrap methods for the temporal ERGM estimated by maximum pseudolikelihood. Here, we illustrate the temporal exponential random graph model and its implementation in the package btergm using data on international alliances and a longitudinally observed friendship network in a Dutch school.",
author = "Philip Leifeld and Cranmer, {Skyler J.} and Desmarais, {Bruce A.}",
note = "Funding Information: PL acknowledges that parts of this work were conducted at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), the University of Bern, and the University of Kon-stanz. The Zukunftskolleg at the University of Konstanz provided research funding for the first author. SJC gratefully acknowledges the support of the National Science Foundation (SES-1357622, SES-1461493, and SES-1514750) and the Alexander von Humboldt Foundation. BD acknowledges that this work was supported in part by National Science Foundation grants SES-1558661, SES-1619644, SES-1637089, and CISE-1320219. Any opinions, findings, and conclusions or recommendations are those of the authors and do not necessarily reflect those of the sponsors. Publisher Copyright: {\textcopyright} 2018, American Statistical Association. All rights reserved.",
year = "2018",
doi = "10.18637/jss.v083.i06",
language = "English (US)",
volume = "83",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
}