process.bootstrap.results

list of arrays containing results from the repeated
analyses.

bootstrap.results

named character vector which acts as a look up table for
duplicate detection function models.

model.index

boolean whether observations are clusters of individuals

clusters

array storing parameter estimates from ddf models

bootstrap.ddf.statistics

numeric value describing which quantile algorithm to use

quantile.type

list describing the type of analysis carried out

analysis.options

Creates summary statistics for each species. These consist of dataframes 
relating to summaries, abundance (N) and density (D) for both individuals 
and clusters. In addition, summary statistics for expected cluster size 
(Expected.S) are also calculated.

Performs distance sampling analyses on a number of species
at once and can account for unidentified sightings, model uncertainty
and covariate uncertainty. Unidentified sightings refer to sightings
which cannot be allocated to a single species but may instead be
allocated to a group of species. The abundance of each unidentified
group is estimated and then prorated to the species estimates. Model
uncertainty should be incorporated when multiple models give equally
good fit to the data but lead to large differences in estimated
density / abundance. Covariate uncertainty should be incorporated
when covariates cannot be measured accurately, for example this is
often the case for group size in marine mammal surveys. Variance
estimation for these methods is via a non parametric bootstrap.
The methods implemented are described in Gerodette T. and Forcada
J. (2005) <doi:10.3354/meps291001> Non-recovery of two spotted and
spinner dolphin populations in the eastern tropical Pacific Ocean.

process.bootstrap.results: Summarises the bootstrap results.

Description

Usage

Arguments

Value