The R Package geeVerse for Ultra-High-Dimensional Heterogeneous Data Analysis with Generalized Estimating Equations
Pub. online: 18 November 2025
Type: Computing In Data Science
Open Access
Open Access
Received
11 September 2025
11 September 2025
Accepted
2 November 2025
2 November 2025
Published
18 November 2025
18 November 2025
Abstract
High or ultra-high-dimensional data are becoming increasingly common in various fields. They often display diverse characteristics, including heterogeneity, longitudinal responses, and imbalanced measurements. These complexities make it challenging to integrate different modeling options and their combinations in order to fully leverage this rich data source. This paper provides an easy-to-use, and stand-alone, R package, geeVerse, that can implement any combination of 1) simultaneous variable selection and estimation, 2) quantile regression or mean regression for heterogeneous data, 3) longitudinal or cross-sectional data analysis, 4) balanced or imbalanced data, and 5) moderate, high, or even ultra-high-dimensional data. To accomplish this, we propose computationally efficient implementations of penalized generalized estimating equations (GEE) for quantile and mean regression. We present multiple applications with ultra-high-dimensional data including analysis of a resampled genetic dataset, quantile and mean regressions, analysis of cross-sectional and longitudinal data, differing correlation structures, and differing number of repeated measurements per subject. We also demonstrate our approach on two real data applications.
Supplementary material
Supplementary MaterialWe provide supplementary material with a detailed model and an explanation of the computational algorithms used in geeVerse . We also provide the replication script for this paper at a public repository: Github.
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