Provides functions to fit kernel density functions to
    data on temporal activity patterns of animals; estimate coefficients
    of overlapping of densities for two species; and calculate bootstrap
    estimates of confidence intervals. As in Ridout and Linkie (2009) <doi:10.1198/jabes.2009.08038>.

Functions for estimating the overlapping area of two or more kernel density estimations from empirical data.

Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.

Provides rectangular elements that can be dragged and resized over
 plots in 'shiny' apps. This may be useful in applications where users need
 to mark regions on the plot for further input or processing.

Overture Maps offers free and open geospatial map data sourced from various providers and standardized to a common schema. This tool allows you to download Overture Maps data for a specific region of interest and convert it to several different file formats. For more information, visit <https://overturemaps.org/download/>.

An integrated R interface to the 'Overture' API 
  (<https://docs.overturemaps.org/>). Allows R users to return 'Overture' data as 
  'dbplyr' data frames or materialized 'sf' spatial data frames.

Makes it easy to display descriptive information on a data
    set.  Getting an easy overview of a data set by displaying and
    visualizing sample information in different tables (e.g., time and
    scope conditions).  The package also provides publishable 'LaTeX' code
    to present the sample information.

Provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386> as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) <doi:10.1007/s10182-025-00545-2>.