Occupancy modeling is a common approach to assess species distribution patterns across space and/or time while explicitly accounting for false absences in detection-nondetection data. Numerous extensions of the basic single-species occupancy model exist to model multiple species, spatial autocorrelation, and to integrate multiple data types. This presentation discusses spOccupancy, an R package designed to fit a variety of Bayesian single-species and multi-species occupancy models. We first give a brief introduction of occupancy modeling as a robust form of species distribution model as well as spatial autocorrelation and how it arises in detection-nondetection data. We then introduce the
spOccupancy package and detail how to fit single-species and multi-species spatial and non-spatial occupancy models. In the associated repository, we provide multiple examples of the following forms of occupancy models fit by spOccupancy (1) single-species models; (2) multi-species models, (3) integrated occupancy models, (4) multi-season (spatio-temporal) occupancy models, and (5) multi-species occupancy models with species correlations.