Econet: An R Package for Parameter-dependent Network Centrality Measures , with M. Battaglini, V. Leone Sciabolazza and S. Peng, R&R Journal of Statistical Software
ABSTRACT 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), which examine the determinants of US campaign contributions when legislators care about the behavior of other legislators to whom they are socially connected.
PeerEffectsGitHub: code for Multidimensional Diffusion Processes in Dynamic Online Networks, Plos One, forthcoming, with D. Easley and C. Rojas
ABSTRACT We estimate how much starring behaviors of individuals are affected by starring behaviors of the people they follow on GitHub. We estimate peer effects by matching agent-repos. We match each agent who follows people who recently starred a particular repo with another agent who is similar (in terms of the matching covariates) but does not follow anyone who recently starred the repo. To determine the matches, we do nearest-neighbor matching. For our preferred specification, we include in the set of matching covariates the preference vectors estimated by the collaborative filtering algorithm Weigthed Regularized Matrix Factorization (WRMF).