TY - JOUR
T1 - hergm
T2 - Hierarchical exponential-family random graph models
AU - Schweinberger, Michael
AU - Luna, Pamela
N1 - Funding Information:
from the National Science Foundation (NSF award
Funding Information:
The first author acknowledges support from the National Science Foundation (NSF award DMS-1513644).
Publisher Copyright:
© 2018, American Statistical Association. All rights reserved.
PY - 2018
Y1 - 2018
N2 - We describe the R package hergm that implements hierarchical exponential-family random graph models with local dependence. Hierarchical exponential-family random graph models with local dependence tend to be superior to conventional exponential-family random graph models with global dependence in terms of goodness-of-fit. The advantage of hierarchical exponential-family random graph models is rooted in the local dependence induced by them. We discuss the notion of local dependence and the construction of models with local dependence along with model estimation, goodness-of-fit, and simulation. Simulation results and three applications are presented.
AB - We describe the R package hergm that implements hierarchical exponential-family random graph models with local dependence. Hierarchical exponential-family random graph models with local dependence tend to be superior to conventional exponential-family random graph models with global dependence in terms of goodness-of-fit. The advantage of hierarchical exponential-family random graph models is rooted in the local dependence induced by them. We discuss the notion of local dependence and the construction of models with local dependence along with model estimation, goodness-of-fit, and simulation. Simulation results and three applications are presented.
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U2 - 10.18637/jss.v085.i01
DO - 10.18637/jss.v085.i01
M3 - Article
AN - SCOPUS:85048560384
SN - 1548-7660
VL - 85
JO - Journal of Statistical Software
JF - Journal of Statistical Software
ER -