hierarchicalDS: Functions for performing hierarchical analysis of distance sampling data

Functions for performing hierarchical analysis of distance sampling data, with ability to use an areal spatial ICAR model on top of user supplied covariates to get at variation in abundance intensity. The detection model can be specified as a function of observer and individual covariates, where a parametric model is supposed for the population level distribution of covariate values. The model uses data augmentation and a reversible jump MCMC algorithm to sample animals that were never observed. Also included is the ability to include point independence (increasing correlation multiple observer's observations as a function of distance, with independence assumed for distance=0 or first distance bin). New in version 2.0 is the ability to model species misclassification rates using a multinomial logit formulation on data from double observers.

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