Observation sensitivity analysis in beta-binomial model

Using Zellner's G priors, computes the log marginal density for all possible regression models

1 | ```
bayes.model.selection(y, X, c, constant=TRUE)
``` |

`y` |
vector of response values |

`X` |
matrix of covariates |

`c` |
parameter of the G prior |

`constant` |
logical variable indicating if a constant term is in the matrix X |

`mod.prob` |
data frame specifying the model, the value of the log marginal density and the value of the posterior model probability |

`converge` |
logical vector indicating if the laplace algorithm converged for each model |

Jim Albert

1 2 3 4 | ```
data(birdextinct)
logtime=log(birdextinct$time)
X=cbind(1,birdextinct$nesting,birdextinct$size,birdextinct$status)
bayes.model.selection(logtime,X,100)
``` |

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