Estimation of Parameter-Dependent Network Centrality Measures

---

Documentation for package ‘econet’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

a_db_alumni	Dataset from Battaglini, Patacchini (2018)
a_G_alumni_111	Column-normalized adjacency matrix representing the alumni network of the 111th U.S. Congress.
boot	boot: Bootstrap residuals with cross-sectional dependence
boot.econet	boot: Bootstrap residuals with cross-sectional dependence
db_alumni_test	Dataset from Battaglini, Patacchini (2018)
db_cosponsor	Dataset from Battaglini, Leone Sciabolazza, Patacchini (2018)
G_alumni_111	Column-normalized adjacency matrix representing the alumni network of the 111th U.S. Congress.
G_cosponsor_111	Column-normalized adjacency matrix representing the cosponsorship network of the 111th U.S. Congress.
G_model_A_test	Column-normalized adjacency matrix representing a subsample of the alumni network of the 111th U.S. Congress.
horse_race	Compare the explanatory power of parameter.dependent network centrality measures with those of standard measures of network centrality.
net_dep	Implement a number of modifications to the linear-in-means model to obtain different weighted versions of Katz-Bonacich centrality.
quantify	quantify: quantification of marginal effects in linear-in-means models.
quantify.econet	quantify: quantification of marginal effects in linear-in-means models.