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HWEBayes (version 1.4)

Bayesian investigation of Hardy-Weinberg Equilibrium via estimation and testing.

Description

Estimation and testing of HWE using Bayesian methods. Three models are currently considered: HWE, a model parameterized in terms of the allele frequencies and a single inbreeding coefficient f, and the saturated model. Testing is based on Bayes factors.

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Version

Install

install.packages('HWEBayes')

Monthly Downloads

50

Version

1.4

License

GPL-2

Maintainer

Jon Wakefield

Last Published

December 18th, 2013

Functions in HWEBayes (1.4)

Importance sampling to calculate the normalizing constant under the single f model

Obtains values for the prior specification for lambda

Samples from the posterior for the single f model

Data on diabetes patients antigen classes

Evaluates the Bayes factor in the k=2 allele case with a "triangular" prior under the null

Simulate samples from a Dirichlet prior or posterior under HWE

Evaluates the maximum likelihood estimates of the parameters of various models in the k allele case

Converts a set of k-1 baseline logits into a set of probabilities

Simulate data under the single f model with k alleles.

Evaluates the normalizing constant under the HWE model, for the "triangular" prior distribution

Samples from the single f prior.

Simulate samples from a Dirichlet prior or posterior under the saturated model

Evaluates the normalizing constant (as used in the denominator of a Bayes factor) for a conjugate prior

Calculates a set of baseline logits from a set of probabilities

Evaluates the Bayes factor in the k=2 allele case under conjugate priors

Evaluates the Multinomial likelihood under the single f model

Evaluates the Multinomial likelihood under the single f model

Evaluates the normalizing constant under the HWE model, with a conjugate prior