hierarchicalDS: Functions For Performing Hierarchical Analysis of Distance Sampling Data
Version 2.9

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. New in version 2.1 is the ability to include zero inflation, but this is only recommended for cases where sample sizes and spatial coverage of the survey are high.

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AuthorP.B. Conn \email{paul.conn@@noaa.gov}
Date of publication2014-11-26 08:09:54
MaintainerPaul B Conn <paul.conn@noaa.gov>
LicenseUnlimited
Version2.9
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("hierarchicalDS")

Man pages

calc_linex_a: estimate optimal 'a' parameter for linex loss function
convert.HDS.to.mcmc: function to convert HierarchicalDS MCMC list vector (used in...
generate_inits: generate initial values for MCMC chain if not already...
generate_inits_misID: generate initial values for misID model if not already...
get_confusion_array: Fill confusion array - one confusion matrix for each...
get_confusion_mat: Fill a list with confusion matrices for each record
get_mod_matrix: function to produce a design matrix given a dataset and...
hierarchical_DS: Primary function for hierarchical, areal analysis of distance...
linear_adj: Produce an adjacency matrix for a vector
log_lambda_gradient: compute the first derivative of log_lambda likelihood...
log_lambda_log_likelihood: compute the likelihood for nu parameters
mcmc_ds: Function for MCMC analysis
plot_N_map: function to plot a map of abundance. this was developed for...
plot_obs_pred: plot 'observed' versus predicted values for abundance of each...
post_loss: function to calculate posterior predictive loss given the...
probit.fct: Mrds probit detection and related functions
rect_adj: Produce an RW1 adjacency matrix for a rectangular grid for...
rect_adj_RW2: Produce an RW2 Adjacency matrix for a rectangular grid for...
rrw: SIMULATE AN ICAR PROCESS
simdata: MCMC output from running example in Hierarchical DS
sim_out: MCMC output from running example in Hierarchical DS
simulate_data: function to simulate double observer spatial distance...
square_adj: Produce an adjacency matrix for a square grid
stack_data: function to stack data (going from three dimensional array to...
stack_data_misID: function to stack data for midID updates (going from four...
summary_N: calculate parameter estimates and confidence intervals for...
switch_pdf: function to calculate the joint pdf for a sample of values...
switch_sample: function to sample from a specified probability density...
switch_sample_prior: function to sample from hyperpriors of a specified...
table.mcmc: function to export posterior summaries from an mcmc object to...

Functions

calc_linex_a Man page Source code
convert.HDS.to.mcmc Man page Source code
generate_inits Man page Source code
generate_inits_misID Man page Source code
get_confusion_array Man page Source code
get_confusion_mat Man page Source code
get_mod_matrix Man page Source code
hierarchical_DS Man page Source code
linear_adj Man page Source code
log_lambda_gradient Man page Source code
log_lambda_log_likelihood Man page Source code
mcmc_ds Man page Source code
plot_N_map Man page Source code
plot_obs_pred Man page Source code
post_loss Man page Source code
probit.fct Man page Source code
rect_adj Man page Source code
rect_adj_RW2 Man page Source code
rrw Man page Source code
sim_out Man page
simdata Man page
simulate_data Man page Source code
square_adj Man page Source code
stack_data Man page Source code
stack_data_misID Man page Source code
summary_N Man page Source code
switch_pdf Man page Source code
switch_sample Man page Source code
switch_sample_prior Man page Source code
table.mcmc Man page Source code

Files

inst
inst/cache
inst/cache/cache-example11_dec9ea02145bf8c1095cf437a7785a4c.rdx
inst/cache/cache-example7_1640587fd6f8e638b98c524b7123ab4b.RData
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inst/cache/cache-install2_ad7f9b4fa93b009394879a017973668b.rdb
inst/HierarchicalDS_vignette.Rnw
inst/HierarchicalDS_vignette.tex
inst/HierarchicalDS_vignette.toc
inst/NEWS.txt
inst/figure
inst/figure/graphics-example22.png
inst/HierarchicalDS_vignette.pdf
inst/HierarchicalDS_vignette.log
inst/simulate_data_overd.R
NAMESPACE
data
data/simdata.rda
data/sim_out.rda
data/datalist
data/simdata.rdata
R
R/simulate_data.R
R/spat_funcs.R
R/mcmc_ds.R
R/hierarchical_DS.R
MD5
DESCRIPTION
man
man/plot_obs_pred.Rd
man/rect_adj.Rd
man/square_adj.Rd
man/rrw.Rd
man/post_loss.Rd
man/hierarchical_DS.Rd
man/switch_sample_prior.Rd
man/get_mod_matrix.Rd
man/linear_adj.Rd
man/sim_out.Rd
man/generate_inits_misID.Rd
man/stack_data_misID.Rd
man/log_lambda_gradient.Rd
man/log_lambda_log_likelihood.Rd
man/convert.HDS.to.mcmc.Rd
man/get_confusion_array.Rd
man/switch_pdf.Rd
man/switch_sample.Rd
man/calc_linex_a.Rd
man/probit.fct.Rd
man/rect_adj_RW2.Rd
man/simdata.Rd
man/stack_data.Rd
man/generate_inits.Rd
man/simulate_data.Rd
man/get_confusion_mat.Rd
man/table.mcmc.Rd
man/summary_N.Rd
man/mcmc_ds.Rd
man/plot_N_map.Rd
hierarchicalDS documentation built on May 19, 2017, 10:28 p.m.