Package: tulpaObs 0.6.1

tulpaObs: Bayesian Occupancy, Abundance and Detection Models via 'tulpa'

Fits Bayesian models for species observed with imperfect detection: single-season and dynamic site occupancy (MacKenzie et al. (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2>), N-mixture abundance from replicated counts (Royle (2004) <doi:10.1111/j.0006-341X.2004.00142.x>), distance sampling, removal and double-observer counts, false-positive and time-to-detection occupancy, integrated models combining several data sources, multi-species and joint species distribution models, and a hurdle model for vegetation cover. Linear predictors take fixed, random, spatial and temporal effects. Each model supplies its observation likelihood to the 'tulpa' engine, which fits it by Laplace approximation, by nested Laplace integration over the hyperparameters, or by Hamiltonian Monte Carlo with the No-U-Turn Sampler (NUTS) using automatic differentiation. Simulation, prediction and diagnostic functions (posterior predictive checks, leave-one-out cross-validation, simulation-based calibration) accompany each model.

Authors:Gilles Colling [aut, cre, cph]