hierarchicalDS: Functions to Perform 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), 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.

Version: 3.0
Depends: R (≥ 2.10)
Imports: truncnorm, mvtnorm, Matrix, coda, xtable, mc2d, ggplot2, rgeos, MCMCpack
Published: 2019-07-02
Author: P.B. Conn \email{paul.conn@@noaa.gov}
Maintainer: Paul B Conn <paul.conn at noaa.gov>
License: Unlimited
NeedsCompilation: no
CRAN checks: hierarchicalDS results


Reference manual: hierarchicalDS.pdf


Package source: hierarchicalDS_3.0.tar.gz
Windows binaries: r-devel: hierarchicalDS_3.0.zip, r-release: hierarchicalDS_3.0.zip, r-oldrel: hierarchicalDS_3.0.zip
macOS binaries: r-release (arm64): hierarchicalDS_3.0.tgz, r-oldrel (arm64): hierarchicalDS_3.0.tgz, r-release (x86_64): hierarchicalDS_3.0.tgz, r-oldrel (x86_64): hierarchicalDS_3.0.tgz
Old sources: hierarchicalDS archive


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