#' @name Rdistance-package
#'
#' @title Rdistance - Distance Sampling Analyses for Abundance Estimation
#'
#' @description
#' \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 Data sets:
#' \code{Rdistance} contains four example data sets: two collected using
#' line-transect methods (i.e., \code{\link{sparrowDetectionData}} and
#' \code{\link{sparrowSiteData}}) and two collected using point-transect
#' (sometimes called a point count) methods (i.e.,
#' \code{\link{thrasherDetectionData}} and \code{\link{thrasherSiteData}}).
#'
#' These datasets demonstrate the type and format of input data required by
#' \code{Rdistance} to estimate a detection function and abundance from
#' distance sampling data collected by surveying line transects or point
#' transects. They also allow the user to step through the tutorials described
#' in the package vignettes.
#'
#' \code{Rdistance} requires only detection data to fit
#' detection functions, assuming no covariates in the detection function
#' (see \code{\link{dfuncEstim}}). Both detection and
#' site data are required to estimate abundance or to include
#' site-level covariates in the detection function (see
#' \code{\link{abundEstim}}).
#'
#' @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
#'
#'
#' @author Main author and maintainer: Trent McDonald
#' <trent@mcdonalddatasciences.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, Michael Kleinsasser, and Ried Olson
#'
#' @keywords package
#'
#'
"_PACKAGE"
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