#' @name gibbonsecr-covariates
#' @title Model covariates
#' @description
#' Covariates can be used when constructing formulas for the various model
#' parameters using the \code{model} argument in the
#' \link[gibbonsecr]{gfit} function. However, some parameters can only
#' be modelled with certain types of covariates.
#'
#' Covariates can be divided into the following types, according to the level at
#' which they vary:
#' \describe{
#' \item{\strong{array-level}}{These are consistent within arrays. All
#' listening posts and sampling occasions for a given array will have the same
#' value for an array-level covariate, but values vary between arrays. For
#' example, \emph{season} is likely to be an array-level covariate, since it is
#' highly unlikely that consecutive sampling occasions at a given array will
#' overlap with more than one season.}
#' \item{\strong{trap-level}}{These are consistent within listening posts for a
#' given array. All sampling occasions for a given listening post will have the
#' same value for a trap-level covariate. For example, \emph{habitat} and
#' \emph{elevation} are likely to be trap-level covariates since they may vary
#' between listening posts but will be consistent across occasions.)}
#' \item{\strong{occasion-level}}{These are consistent within sampling
#' occasions for a given array. All listening posts in an array will have the
#' same value for an occasion-level covariate. For example, \emph{weather} is
#' likely to be an occasion-level covariate since it will probably be the same
#' for all listening posts for a given occasion at a given array but may
#' vary between occasions.}
#' \item{\strong{trap-occasion-level}}{These can vary between listening posts
#' and between sampling occasions. For example, \emph{observer} might be
#' a trap-occasion-level covariate since observers will (always) vary between
#' listening posts for a given occasion at a given array, and they may also
#' vary between occasions for a given listening post at a given array.}
#' \item{\strong{mask-level}}{These are spatial covariates and can vary across
#' mask points. Mask-level covariates from GIS polygon and point files can be
#' imported and attached to mask objects using the
#' \link[gibbonsecr]{import_shp} and \link[gibbonsecr]{add_covariates}
#' functions.}
#' }
#' The table below shows which covariates can be used to model which parameters.
#' \tabular{ll}{
#' \strong{Parameter} \tab \strong{Covariate level} \cr
#' \code{D} \tab array, mask \cr
#' \code{g0} \tab array, trap, occasion, trap-occasion \cr
#' \code{sigma} \tab array, trap, occasion, trap-occasion \cr
#' \code{z} \tab array, trap, occasion, trap-occasion \cr
#' \code{bearings} \tab array, trap, occasion, trap-occasion \cr
#' \code{distances} \tab array, trap, occasion, trap-occasion \cr
#' \code{pcall} \tab array, occasion \cr
#' }
#' @seealso \link[gibbonsecr]{gfit}, \link[gibbonsecr]{import_data},
#' \link[gibbonsecr]{import_shp}
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