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#' @name Rdistance-package
#'
#' @title Rdistance - Distance Sampling Analyses for Abundance Estimation
#'
#' @description
#' `Rdistance` contains functions and associated routines to analyze
#' distance-sampling data collected on point or line transects.
#' Some of `Rdistance`'s features include:
#'
#' * Accommodation of both point and line transect analyses in one routine ([dfuncEstim()]).
#' * Regression-like formula for inclusion of distance function covariates ([dfuncEstim()]).
#' * Automatic bootstrap confidence intervals ([abundEstim()]).
#' * Parallel processing of bootstrap iterations (`parallel` argument of [abundEstim()]).
#' * Availability of both study-area and site-level abundance estimates (`help("predict.dfunc")`).
#' * Rigorous physical measurement requirements and automated conversion when necessary (\code{\link{%#%}}, [setUnits()]).
#' * Classic parametric distance functions ([halfnorm.like()], [hazrate.like()], [negexp.like()]), and
#' expansion functions ([cosine.expansion()], [hermite.expansion()], [simple.expansion()]).
#' * Mixture distance functions for non-standard shapes and thresholds ([oneStep.like()], [triangle.like()], and [huber.like()]).
#' * Automated distance function fitting and selection [autoDistSamp()].
#' * `print`, `plot`, `predict`, `coef`, and `summary` methods for distance function objects and
#' abundance classes.
#'
#' # 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 *target* sightings,
#' either of individuals or groups, and off-transect distances to the
#' original location of the *target*.
#' When *targets* are sighted in groups, data include the number
#' of individuals in the group.
#'
#' Point transect surveys are similar except that observers stop one
#' or more times along the transect to observe *targets*. Point transects are
#' popular avian survey methods where detections are often auditory
#' cues. Point transects are also for studies using automated auditory detectors
#' or trail cameras. Point transect data consists of radial distances from
#' the observer to the *target*.
#'
#' The defining feature of distance sampling is the tendency for
#' probability of detection to decline as
#' off-transect or radial distances increase. Targets far from
#' the observer are generally
#' harder to detect than those closer. In most
#' line transect studies, *targets* on the transect (off-transect
#' distance = 0) are assumed to be sighted with 100% probability. This
#' assumption allows researchers to estimate the proportion of missed
#' targets and in turn adjust the number of
#' sighted *targets* for missed detections.
#' Some studies utilize two observers searching the same areas and are able to
#' estimate the proportion of individuals missed on the transect line and
#' thereby eliminate the
#' assumption that all individuals on the line have been observed.
#'
#' # Purpose:
#' The author's aims are
#' to provide an easy-to-use, rigorous, and flexible analysis option in R for
#' distance-sampling data. The authors believe that beginning
#' users need easy-to-use and easy-to-understand software,
#' while advanced users require greater flexibility and customization, and
#' their aim is to meet the demands of both groups.
#'
#' # Data sets:
#' `Rdistance` contains the following example data sets:
#'
#' * Line-transect sampling of Brewers sparrows in central Wyoming
#' ([sparrowDf()]).
#' * Point-transect sampling of Sage Thrashers in central Wyoming
#' ([thrasherDf()]).
#'
#'
#' # References
#'
#' Buckland, S.T., Anderson, D.R., Burnham, K.P. and Laake, J.L.
#' 1993. *Distance Sampling: Estimating Abundance of Biological
#' Populations*. Chapman and Hall, London.
#'
#' @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
#'
#' @import units
#'
"_PACKAGE"
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