differentiableLikelihoods

<p>Return a character vector of 
likelihoods which are differentiable and hence
the second derivative method can be used to estimate
variance-covariance.  Any likelihoods not in this list
must use bootstrapping. This vector is polled in 
a few Rdistance routines, notably <code>parseModel</code>
and <code>varcovarEstim</code>.</p>

Distance-sampling (<doi:10.1007/978-3-319-19219-2>)
is a field survey and analytical method that estimates density and
abundance of survey targets (e.g., animals) when
detection probability declines with observation distance.
Distance-sampling is popular in ecology,
especially when survey targets are observed from aerial platforms (e.g.,
airplane or drone), surface vessels (e.g., boat or truck), or along
walking transects. Analysis involves fitting smooth (parametric) curves to
histograms of observation distances and using those functions to
adjust density estimates for missed targets.  Routines included here
fit curves to observation distance histograms, estimate effective
sampling area, density of targets in surveyed areas, and the abundance
of targets in a surrounding study area. Confidence interval estimation
uses built-in bootstrap resampling. Help files are extensive and have been
vetted by multiple authors. Many tutorials are available on the package's
website (URL below).

Trent McDonald

Rdistance

Density and Abundance from Distance-Sampling Surveys

Jason Carlisle

Aidan McDonald

Ryan Nielson

Ben Augustine

James Griswald

Patrick McKann

Lacey Jeroue

Hoffman Abigail

Kleinsausser Michael

Joel Reynolds

Pham Quang

Earl Becker

Aaron Christ

Brook Russelland

Stefan Emmons

Will McDonald

Reid Olson

differentiableLikelihoods function

Differentiable likelihoods in Rdistance — differentiableLikelihoods

Differentiable likelihoods in Rdistance

differentiableLikelihoods: Differentiable likelihoods in Rdistance

Description

Usage

Arguments

Value