Detection of overdispersion in count data for multiple regression analysis.
    Log-linear count data regression is one of the most popular techniques for predictive 
    modeling where there is a non-negative discrete quantitative dependent variable. In 
    order to ensure the inferences from the use of count data models are appropriate, 
    researchers may choose between the estimation of a Poisson model and a negative binomial
    model, and the correct decision for prediction from a count data estimation is directly
    linked to the existence of overdispersion of the dependent variable, conditional to the 
    explanatory variables. Based on the studies of Cameron and Trivedi (1990)
    <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), 
    the overdisp() command is a contribution to researchers, providing a fast and secure 
    solution for the detection of overdispersion in count data. Another advantage is that 
    the installation of other packages is unnecessary, since the command runs in the basic 
    R language.

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>.