We introduce R package iglm, which implements a comprehensive framework for studying relationships among predictors and outcomes under interference. The implemented regression framework facilitates the study of spillover and other phenomena in connected populations and has important advantages over existing packages, among them scalability and provable theoretical guarantees. On the computational side, the regression framework relies on scalable methods that can be applied to small and large data sets, by solving a convex optimization program based on pseudo-likelihoods using Minorization-Maximization and Quasi-Newton algorithms. On the statistical side, the regression framework comes with provable theoretical guarantees. To increase the versatility of iglm, users can add custom-built model terms. We showcase iglm using two data sets, including hate speech on the social media platform X and communications among students.

翻译：我们介绍R包iglm，该包实现了在干扰条件下研究预测变量与结果变量之间关系的综合框架。所实现的回归框架能促进连通群体中溢出效应及其他现象的研究，相较于现有包具有重要优势，包括可扩展性和可证明的理论保证。在计算方面，该回归框架采用可扩展方法，通过基于伪似然的凸优化程序，结合极小化-最大化算法与拟牛顿算法求解，既能处理小规模数据集，也能应用于大规模数据。在统计方面，该回归框架提供可证明的理论保证。为增强iglm的通用性，用户可添加自定义模型项。我们通过两个数据集展示iglm的应用，包括社交媒体平台X上的仇恨言论以及学生间的通讯数据。