log_lambda_gradient

current observed valus (all cells)

Vector giving the cell identities for all sampled cells

Sampled

Proportional area of each sampled cell that is covered by one or more transects

Area

variance of the overdispersion process

var.nu

compute the first derivative of log_lambda likelihood component for Langevin-Hastings

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), as well as the ability to model species
misclassification rates using a multinomial logit formulation on data
from double observers.  There is also the the ability to
include zero inflation, but this is only recommended for cases where
sample sizes and spatial coverage of the survey are high.

log_lambda_gradient: compute the first derivative of log_lambda likelihood component for Langevin-Hastings

Description

Usage

Arguments

Value