R/CONSURE.R

In SaskiaSchirmer/CONSURE: Estimating Spatially Continuous Survival, Migratory Connectivity and Recovery Probability

#' CONSURE: Spatially continuous
#' survival, use of space and recovery probability estimates
#'
#' The CONSURE package provides functions to perform the continuous and the
#' combined approach from the dissertation of Saskia Schirmer. It can work with
#' both, simulated and real-world data.
#'
#' Nearly all functions are based on a \code{\link{mark_recapture_object}}.
#'
#' A spatial point pattern can be simulated by given survival, migratory
#' connectivity and recovery probability using \code{\link{sim_contin}}.
#'
#' @section Visualizing raw data:
#'
#' Spatial point patterns of raw recoveries can be visualized by
#' \code{\link{plot_raw_recoveries}}. The age distribution can be visualized by
#' \code{\link{plot_age_distribution}}.
#'
#' @section Continuous functions:
#'
#' The continuous estimation approach first estimates the density of the point
#' pattern by \code{\link{est_kde}}. Then, survival can be estimated by
#' \code{\link{est_s}} and finally migratory connectivity by \code{\link{est_m}}
#' and a constant recovery probability by \code{\link{est_r}}. The function
#' \code{\link{est_parameters}} is a wrapper-function performing kernel density
#' estimation and parameter estimation at once.
#'
#' All estimates can be plotted by the appropriate function:
#' \code{\link{plot_kde}}, \code{\link{plot_s}}, \code{\link{plot_m}}. The R^2
#' values of the linear model used to fit survival, migratory connectivity and
#' recovery probability can be plotted by \code{\link{plot_gof_of_lm}}.
#'
#' The number of recovered individuals per area of origin can be summarized by
#' \code{\link{rec_inds_func}}.
#'
#' \code{\link{par_grid}} creates a grid containing the values of a specific
#' function on the grid.
#'
#' @section Uncertainty estimation:
#'
#' The uncertainty of the parameter estimates can be assessed by bootstrapping
#' with the function \code{\link{est_uncertainty}}. Optionally, data can be
#' bootstrapped before starting the estimation process using the function
#' \code{\link{init_bootstrapped_datasets}}. \code{\link{bootstrap_quantiles}}
#' calculates the 0.025- and 0.975-bootstrap quantiles. These quantiles can be
#' visualized in 3D with \code{\link{plotly_param}} or alternatively, as a
#' 2D surface with a bootstrap quantile along a profile line with
#' \code{\link{plot_profile}}.
#'
#' The following functions are used by the functions above:
#' \code{\link{bootstrap_marking_data}} performs the actual bootstrapping of
#' the marking data. \code{\link{get_bootstrap_parameters}} extracs the
#' parameters of the bootstrapped data sets from the mark_recapture_object as a
#' data frame.
#'
#' The profile line is created using \code{\link{profile_of_parameter}},
#' \code{\link{raster_param}}, \code{\link{wrap_profile_of_param}},
#' \code{\link{profile_line}} and \code{\link{profile_points}}.
#'
#' \code{\link{CONSURE}} needs projected data for some functions. Therefore,
#' the data will be projected from longitude/latitude (EPSG:4326) to Mollweide
#' projection (ESRI:54009), unless other projections are specified.
#' \code{\link{project_mark_recapture}}, \code{\link{project_df}} ,
#' \code{\link{project_window}} perform the projection.
#'
#'
#'
#'
#'
#' @docType package
#' @name CONSURE
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