Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.

The purpose of this package is to provide a comprehensive
    R interface to the Decision Support System for Agrotechnology
    Transfer Cropping Systems Model (DSSAT-CSM; see <https://dssat.net> for more information).
    The package provides cross-platform functions to read and
    write input files, run DSSAT-CSM, and read output files.

Creates survey designs for distance sampling surveys. These
    designs can be assessed for various effort and coverage statistics.
    Once the user is satisfied with the design characteristics they can 
    generate a set of transects to use in their distance sampling survey.
    Many of the designs implemented in this R package were first made 
    available in our 'Distance' for Windows software and are detailed in 
    Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, 
    ISBN-13: 978-0199225873). Find out more about estimating animal/plant 
    abundance with distance sampling at <http://distancesampling.org/>.

Draw samples from the direct sampling spatial prior model as
    described in G. White, D. Sun, P. Speckman (2019) <arXiv:1906.05575>. The basic model assumes a Gaussian
    likelihood and derives a spatial prior based on thin-plate splines.

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Probability generating function,  formulae for the probabilities (discrete density) and random generation for discrete stable random variables.