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 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.
Many real-world networks connect two distinct classes of entities, such as individuals and organizations, or contain multiple types of relationships existing simultaneously.
This project expands the package to handle such so-called bipartite networks and implements specialized network statistics to capture cross-mode and cross-layer interactions.
These features allow social scientists to study complex corporate boards, political affiliations, and multi-relational networks.
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.