Bayesian mixture models for marker dosage in autopolyploids
Fits Bayesian mixture models to estimate marker dosage for
dominant markers on autopolyploids using JAGS (1.0 or greater).
May be used in conjunction with polySegratio for simulation
studies and comparison with standard methods

Fits Bayesian mixture models to estimate marker dosage for
dominant markers on autopolyploids using JAGS (1.0 or greater)
as outlined in Baker et al (2010). May be used in conjunction
with polySegratio for simulation studies and comparison with
standard methods.

Fits Bayesian mixture models to estimate marker dosage for dominant markers on autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al (2010). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.

Bayesian Mixture Models for Marker Dosage in Autopolyploids
c(person("Peter", "Baker", role = c("aut", "cre"),
email = "p.baker1@uq.edu.au"))
Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.

Implementation of the Polya-Aeppli distribution
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.

Implementation of the Polya-Aeppli Distribution
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.  More information on the implementation can be found at Conrad J. Burden (2014) <arXiv:1406.2780>.

Glen Meeden <glen@stat.umn.edu> and Radu Lazar
<lazar@stat.umn.edu>
Generate dependent samples from a non-full dimensional
polytope via a Markov Chain sampler