Package: hierarchicalDS
Maintainer: Paul B Conn <paul.conn@noaa.gov>
License: GPL (>= 2)
Title: Functions for performing hierarchical analysis of distance
sampling data
Type: Package
LazyLoad: yes
Author: P.B. Conn \email{paul.conn@noaa.gov}
Description: 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
parameteric 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).
Note that the infrastructure for including species
misidentification is present, but has not been fully
implemented.
Version: 1.0
Depends: truncnorm, MASS, mvtnorm, Matrix, coda, spsurvey, MCMCpack,
ggplot2, xtable, mc2d
Date: 2012-7-11
Collate: 'hierarchical_DS.R' 'mcmc_ds.R' 'simulate_data.R'
'spat_funcs.R'
Packaged: 2012-07-12 16:13:19 UTC; paul.conn
Repository: CRAN
Date/Publication: 2012-07-12 17:46:33

Package: hierarchicalDS
Maintainer: Paul B Conn <paul.conn@noaa.gov>
License: Unlimited
Title: Functions for performing hierarchical analysis of distance
sampling data
Type: Package
LazyLoad: yes
Author: P.B. Conn \email{paul.conn@@noaa.gov}
Description: 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.
Version: 2.01
Depends: truncnorm, MASS, mvtnorm, Matrix, coda, spsurvey, MCMCpack,
xtable, mc2d
Date: 2012-9-28
Collate: 'hierarchical_DS.R' 'mcmc_ds.R' 'simulate_data.R'
'spat_funcs.R'
Packaged: 2012-09-28 15:22:09 UTC; paul.conn
Repository: CRAN
Date/Publication: 2012-09-28 16:48:44