A software that implements a method for partitioning genetic trends to
    quantify the sources of genetic gain in breeding programmes.
    The partitioning method is described in Garcia-Cortes et al.
    (2008) <doi:10.1017/S175173110800205X>. The package includes the
    main function AlphaPart for partitioning breeding values and auxiliary
    functions for manipulating data and summarizing, visualizing, and saving
    results.

Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.

The successor to the 'AlphaSim' software for breeding program 
  simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. 
  Used for stochastic simulations of breeding programs to the level of DNA 
  sequence for every individual. Contained is a wide range of functions for 
  modeling common tasks in a breeding program, such as selection and crossing. 
  These functions allow for constructing simulations of highly complex plant and 
  animal breeding programs via scripting in the R software environment. Such 
  simulations can be used to evaluate overall breeding program performance and 
  conduct research into breeding program design, such as implementation of 
  genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') 
  for fast simulation of biallelic sequences according to a population 
  demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].

Developed to perform the tasks given by the following. 1-computing the probability density function and distribution function of a univariate stable distribution; 2- generating from univariate stable, truncated stable, multivariate elliptically contoured stable, and bivariate strictly stable distributions; 3- estimating the parameters of univariate symmetric stable, skew stable, Cauchy, multivariate elliptically contoured stable, and multivariate strictly stable distributions; 4- estimating
             the parameters of the mixture of symmetric stable and mixture of Cauchy distributions.