Zero-inflated count data are omnipresent in many fields including health care research and actuarial science. Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) regression are commonly used to model such outcomes. These mixture models typically include a logistic component to model the presence of excess zeros above and beyond those generated by the count component and a Poisson/Negative Binomial component to model the counts. Several methods have been proposed for variable selection in ZIP and ZINB regression models. However, when the features to be associated possess an inherent grouping structure, these individual variable selection approaches are suboptimal. Gooogle is an R package containing algorithms for performing group and bi-level variable selection in ZIP/ZINB regression models.