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  • 标题:econet: An R Package for Parameter-Dependent Network Centrality Measures
  • 本地全文:下载
  • 作者:Marco Battaglini ; Valerio Leone Sciabolazza ; Eleonora Patacchini
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2022
  • 卷号:102
  • 页码:1-30
  • DOI:10.18637/jss.v102.i08
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要:The R package econet provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both nonlinear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the econet package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Leone Sciabolazza, and Patacchini (2020).
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