Project for MSc. in Statistics and Data Science
The iglm package in R provides a unified statistical framework for modeling individual responses and social network structures simultaneously in connected populations.
In real-world social and economic systems, individual behaviors and relationship ties co-depend on each other, meaning classical regression models that assume independent observations yield biased results.
Modeling the joint probability distribution of node-level attributes and connections, iglm allows researchers to capture peer influence, contagion, and selection effects in one unifying framework.
Empirical survey data frequently suffer from missing network ties, non-responses, and unmeasured individual traits that induce hidden clustering. This project implements a data augmentation sampling algorithm to iteratively fill in missing data.
Solving these challenges provides practitioners with a robust platform that maintains valid statistical conclusions despite noisy or incomplete observational data.
This thesis constitutes both a rigorous statistical modeling endeavor and a core software engineering contribution within an actively developed R package.
Students will join a scientific programming team whose tools are directly used by empirical social scientists, epidemiologists, and economists to analyze human behavior and evaluate policy interventions.