Introduction | Installation | Example data | The multiple-methods design | Simulated data | Your own data | The multiple-methods false-positive occupancy model | Basic model description | Parameter names | Fitting the constant-rate model with fp_occu() | Convergence and model adequacy | Convergence | Posterior predictive check | Modelling the false-positive rate | Why the occupancy slope is biased | A false-positive rate that tracks the occupancy covariate | A false-positive rate that tracks the confusing gradient | Sensitivity to the priors | Variation that the covariates do not explain | Model comparison with LOO | Prediction | Detection covariates and recorder effects | The observation-confirmation design | Next steps | References

What a simulation returns | Three false-positive regimes | Homogeneous | Unstructured | Structured | The observation process | From one dataset to a calibration check | References

Data | Binomial rows | Parameters | Likelihood | Parallel likelihood | Priors and model block | Generated quantities | Modifying a copy | References

The workflow | How much to verify | What to expect | Calibrating a fitted model | Plotting

The data | Fitting | Convergence | How often does each classifier err? | Occupancy with and without false positives | Does the false-positive rate track the environment? | Model comparison | Posterior predictive checks | Predicted occupancy | References

The data layout | The same model fitted both ways | Formulas | What crywolf adds | Predictions | References