G_model_A_test

<p>Column-normalized adjacency matrix representing a subsample of the alumni network of the 111th U.S. Congress.</p>

datasets

Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear 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, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette <doi:10.18637/jss.v102.i08>.

Valerio Leone Sciabolazza

econet

Estimation of Parameter-Dependent Network Centrality Measures

Marco Battaglini

Eleonora Patacchini

Sida Peng

G_model_A_test function

<p>Data set for runtime executions. An object of class <code>Matrix</code> with 42 rows and 42 columns</p>

Format

Column-normalized adjacency matrix representing a subsample of the alumni 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.

G_model_A_test: Column-normalized adjacency matrix representing a subsample of the alumni network of the 111th U.S. Congress.

Description

Usage

Arguments

Format

References