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Summary Gene-environment (G-E) interactions have important implications for many complex diseases. With higher dimensionality and weaker signals, G-E interaction analysis is more challenged than the analysis of main G (and E) effects. The accumulation of published literature makes it possible to borrow strength from prior information and improve analysis. In a recent study, a “quasi-likelihood + penalization” approach was developed to effectively incorporate prior information. Here, we first extend it to linear, logistic, and Poisson regressions. Such models are much more popular in practice. More importantly, we develop the R package GEInfo, which realizes this approach in a user-friendly manner. To facilitate direct comparison and routine data analysis, the package also includes functions for alternative methods and visualization. Availability The package is available at https://CRAN.R-project.org/package=GEInfo. Supplementary information Supplementary materials are available at Bioinformatics online.