Morris Martina, Handcock Mark S, Hunter David R
Departments of Sociology and Statistics University of Washington Seattle, WA 98195, United States of America.
J Stat Softw. 2008;24(4):1548-7660. doi: 10.18637/jss.v024.i04.
Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo (MCMC) algorithm that the package uses for estimation. These controls a ect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package.
指数族随机图模型（ERGMs）通过用户选择的项来表示网络中链接形成所遵循的过程。这些项指定了足以表示该规模网络空间上概率分布的网络统计量。可以使用许多类别的统计量。在本文中，我们描述了ergm包中当前可用的统计量类别。我们还描述了用于控制该包用于估计的马尔可夫链蒙特卡罗（MCMC）算法的方法。这些控制会影响基础的梅特罗波利斯-黑斯廷斯算法在样本空间上的提议分布，或者影响样本空间本身的约束。最后，我们描述了ergm包核心函数的各种其他参数。
