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 are co-dependent, meaning that classical regression models assuming independent observations yield biased results.
By modeling the joint probability distribution of node-level attributes and connections, iglm allows researchers to capture peer influence, contagion, and selection effects within a single unifying framework.
Most social survey responses are recorded as ordered rating scales or categorical choices, violating the assumptions of binary or normal distributions.
Therefore, this project expands the range of response families to include ordinal, categorical, and skewed 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.