444 – Toward a Unified Approach for Designing and Developing Software for Mixed-Effects Models: Challenges and Opportunities
Mixed Models Through the Lens of HGLM: Applications and Grand Challenges
Xia Shen
Karolinska Institute and University of Edinburgh
Moudud Alam
Dalarna University
Lars Rönnegård
Dalarna University
The "hglm" package is a hierarchical-likelihood-based solution for mixed models. Apart from generalized linear mixed models (GLMM), hierarchical generalized linear models (HGLM) can also solve models with non-Gaussian random effects, structured dispersion parameters, and correlated random effects. "hglm" provides a unified approach to various statistical modeling problems. We describe examples in our interdisciplinary research based on the "hglm" package, dealing with large-scale biological data, chemometrics data and geographical data. Thereafter, we discuss some big challenges that empirical scientists desire to solve using mixed models, including modeling high-dimensional interaction effects, having random effects in the mixed model dispersion parameters, joint modeling of spatial and genetic correlations, and multivariate analyses with random effects.