R: Estimation of Parameter-Dependent Network Centrality Measures

# Estimation of Parameter-Dependent Network Centrality Measures

## Documentation for package ‘econet’ version 1.0.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## 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.