Function to sample from the single *f* prior, that is the Dirichlet and normal on *λ*, where *λ=\log((f-f_{\min})/(1-f))*.

1 | ```
SinglefPrior(nsim, alpha, lambdamu, lambdasd)
``` |

`nsim` |
number of simulations from prior |

`alpha` |
vector of |

`lambdamu` |
mean of the normal prior on |

`lambdasd` |
standard deviation of the normal prior on |

`p` |
sample for vector of |

`f` |
sample of |

`lgts` |
samples for logits of baseline logits |

`lambda` |
samples for |

Jon Wakefield (jonno@u.washington.edu)

Wakefield, J. (2010). Bayesian methods for examining Hardy-Weinberg equilibrium. Biometrics; Vol 66:257-65

1 2 | ```
SinglefSamp <- SinglefPrior(nsim=1000,alpha=c(1,1,1,1),
lambdamu=-2.95,lambdasd=1.07)
``` |

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