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>.

Simplifies MCMC setup by automatically looping through sampling 
    functions and saving the results.  Reduces the memory footprint of running 
    MCMC and saves samples to disk as the chain runs.  Allows samples from the 
    chain to be analyzed while the MCMC is still running.  Provides functions 
    for commonly performed operations such as calculating Metropolis acceptance 
    ratios and creating adaptive Metropolis samplers.  References: Roberts and 
    Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>.

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>.