hergm: Hierarchical Exponential-Family Random Graph Models

被引:20
|
作者
Schweinberger, Michael [1 ]
Luna, Pamela [1 ]
机构
[1] Rice Univ, Dept Stat, 6100 Main St,MS-138, Houston, TX 77005 USA
来源
JOURNAL OF STATISTICAL SOFTWARE | 2018年 / 85卷 / 01期
基金
美国国家科学基金会;
关键词
social networks; random graphs; Markov random graph models; exponential-family random graph models; stochastic block models; model-based clustering; NETWORK ANALYSIS;
D O I
10.18637/jss.v085.i01
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:1 / 39
页数:39
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