arXiv:2604.22791 · v3

R Package iglm: Regression under Interference in Connected Populations

首次提交 2026-04-13 18:32

版本更新 2026-11-12 17:57

雷达观测 2026-11-13 14:15

摘要

We introduce R package iglm, which implements a unifying regression framework for studying relationships among binary, count-, and real-valued predictors and outcomes in connected populations with interference due to spillover, contagion, and other complex phenomena. On the computational side, the regression framework provides scalable methods that can be applied to small and large data sets, by solving a convex optimization program based on pseudo-likelihoods via accelerated Minorization-Maximization algorithms. On the statistical side, the regression framework comes with provable theoretical guarantees for statistical estimators, including consistency results and rates of convergence for maximum pseudo-likelihood estimators. To increase the versatility of iglm, users can add custom-built model terms. We showcase iglm using data on hate speech on the social media platform X and communications among students.