crywolf - Bayesian False-Positive Occupancy Models with Calibration Checks

Fits single-season occupancy models that correct for false positives from automated detectors, such as acoustic classifiers, using the multiple-methods (site-confirmation) design of Miller et al. (2011) <doi:10.1890/10-1396.1>, in which a subset of detections is verified. Models are written in 'Stan' and fitted through 'cmdstanr'. The false-positive rate can be constant or depend on site covariates, and the true-detection process accepts observation covariates and a recorder random effect. The package also simulates data under homogeneous, unstructured, and structured false-positive regimes and runs simulation-based calibration checks, so users can ask whether the correction keeps nominal interval coverage for their own design and how much verification it needs.