R/Rdistance-package.R

#' @name Rdistance-package
#' 
#' @title Rdistance - Distance Sampling Analyses for Abundance Estimation
#' 
#' 
#' \code{Rdistance} contains functions and associated routines to analyze
#' distance-sampling data collected on point or line transects. 
#' Some of \code{Rdistance}'s features include:
#' \itemize{
#'    \item Accommodation of both point and line transect analyses 
#'     in one routine (\code{\link{dfuncEstim}}).
#'    \item Regression-like formula for inclusion of covariate in 
#'    distance functions (\code{\link{dfuncEstim}}).
#'    \item Automatic bootstrap confidence intervals 
#'    (\code{\link{abundEstim}}).
#'    \item Availability of both study-area and site-level abundance
#'    estimates (\code{\link{abundEstim}}).
#'    \item Classical, parametric distance functions
#'    (\code{\link{halfnorm.like}}, \code{\link{hazrate.like}}), and
#'    expansion functions (\code{\link{cosine.expansion}}, 
#'    \code{\link{hermite.expansion}}, \code{\link{simple.expansion}}).
#'    
#'    \item Non-classic distance functions (\code{\link{Gamma.like}}, 
#'    \code{\link{negexp.like}}, \code{\link{uniform.like}}) 
#'    and a non-parametric smoother 
#'    \code{\link{dfuncSmu}}).
#'    
#'    \item User defined distance functions. 
#'     
#'    \item Automated distance function fits and selection 
#'    \code{\link{autoDistSamp}}.
#'    \item Extended vignettes.
#'    \item \code{print}, \code{plot}, \code{predict}, \code{coef}, 
#'    and \code{summary} 
#'    methods for distance function objects and abundance classes.
#' }
#' 
#' 
#' @section Background:
#' Distance-sampling is a popular method for abundance estimation in
#' ecology. Line transect surveys are conducted by traversing
#' randomly placed transects in a study area with the objective of
#' sighting animals and estimating density or abundance.  Data collected
#' during line transect surveys consists of sighting records for
#' \emph{targets}, usually either individuals or groups of individuals.  Among
#' the collected data, off-transect distances are 
#' recorded or computed from
#' other information (see
#' \code{\link{perpDists}}).  Off-transect distances are the perpendicular
#' distances from the transect to the location of the initial sighting cue.
#' The actual locations of sighted targets are often recorded or computed.
#' When groups are the target, the number of individuals in the group is
#' recorded.
#' 
#' Point transect surveys are similar except that observers stop one 
#' or more times along the transect to observe targets.  This is a 
#' popular method for avian surveys where detections are often auditory 
#' cues, but is also appropriate when automated detectors are placed along
#' a route.  Point transect surveys collect distances from the observer to 
#' the target and are sometimes called \emph{radial} distances.   
#' 
#' 
#' A fundamental characteristic of both line and point-based 
#' distance sampling analyses is that
#' probability of detecting a target declines as
#' off-transect or radial distances increase. Targets far from 
#' the observer are usually 
#' harder to detect than closer targets.  In most
#' classical line transect studies, targets on the transect (off-transect
#' distance = 0) are assume to be sighted with 100\% probability.  This
#' assumption allows estimation of the proportion of targets missed during the
#' survey, and thus it is possible to adjust the actual number of 
#' sighted targets for the proportion of targets missed. 
#' Some studies utilize two observers searching the same areas to
#' estimate the proportion of individuals missed and thereby eliminating the
#' assumption that all individuals on the line have been observed.
#' 
#' @section Relationship to other software: 
#' A detailed comparison of
#' \code{Rdistance} to other options for distance sampling analysis (e.g.,
#' Program DISTANCE, R package \code{Distance}, and R package \code{unmarked})
#' is forthcoming.  While some of the functionality in \code{Rdistance} 
#' is not unique, our aim is to provide an easy-to-use, rigorous,
#' and flexible analysis option for distance-sampling data.  
#' We understand that beginning
#' users often need software that is both easy to use and easy to understand,
#' and that advanced users often require greater flexibility and customization.
#' Our aim is to meet the demands of both user groups.  \code{Rdistance} is
#' under active development, so please contact us with issues, feature
#' requests, etc. through the package's GitHub website
#' (\url{https://github.com/tmcd82070/Rdistance}).
#' 
#' @section Resources:
#' The best place to start learning about \code{Rdistance}
#' is at the package's GitHub Wiki, which hosts several tutorial vignettes and
#' FAQs (\url{https://github.com/tmcd82070/Rdistance/wiki}).
#' Additionally, the examples in the help files for
#' \code{\link{dfuncEstim}}, \code{\link{abundEstim}}, and
#' \code{\link{autoDistSamp}} highlight the package's primary functionality.
#' 
#' A list of routines can be obtained by loading \code{Rdistance} and issuing
#' \code{help(package="Rdistance")}.
#' 
#' 
#' @aliases Rdistance-package distance Rdistance point-transect line-transect
#' 
#' @docType package
#' 
#' @author Main author and maintainer: Trent McDonald
#' <tmcdonald@@west-inc.com>
#' 
#' Coauthors: Ryan Nielson, Jason Carlisle, and Aidan McDonald
#' 
#' Contributors: Ben Augustine, James Griswald, Joel Reynolds, Pham Quang, Earl
#' Becker, Aaron Christ, Brook Russelland, Patrick McKann, Lacey Jeroue, 
#' Abigail Hoffman, and Michael Kleinsasser.
#' 
#' @keywords package
#' 
#' 
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Rdistance documentation built on May 2, 2019, 3:49 a.m.