This function enumerates and calculates summaries for all models in the model space. Not recommended for problems where p>20.

1 2 |

`data` |
a |

`forced` |
an optional |

`inform` |
if inform=TRUE corresponds to the iBMU algorithm of Quintana and Conti (Submitted) that incorporates user specified external predictor-level covariates into the variant selection algorithm. |

`cov` |
an optional |

`a1` |
a |

`rare` |
if rare=TRUE corresponds to the Bayesian Risk index (BRI) algorithm of Quintana and Conti (2011) that constructs a risk index based on the multiple rare variants within each model. The marginal likelihood of each model is then calculated based on the corresponding risk index. |

`mult.regions` |
when rare=TRUE if mult.regions=TRUE then we include multiple region specific risk indices in each model. If mult.regions=FALSE a single risk index is computed for all variants in the model. |

`regions` |
if mult.regions=TRUE regions is a |

`hap` |
if hap=TRUE we estimate a set of haplotypes from the multiple variants within each model and the marginal likelihood of each model is calculated based on the set of estimated haplotypes. |

This function outputs a list of the following values:

`fitness` |
A vector of the fitness values (log(Model likelihood) - log(Model Prior)) of each enumerated model. |

`logPrM` |
A vector of the log Model Priors of each enumerated model. |

`which` |
A vector identifying the character representation of each model indicator vector. |

`coef` |
If rare=FALSE we report a matrix where each row corresponds to the estimated coefficients for all variables within each enumerated model. If rare=TRUE we report a vector where each entry corresponds to the estimated coefficient of the risk index (or multiple risk indices if mult.regions = TRUE) corresponding to each enumerated model. |

`alpha` |
If inform=FALSE that is simply a vector of 0's. If inform=TRUE we report a matrix where each row corresponds to the specified effects (alpha's) of each predictor-level covariate for each enumerated model. |

Melanie Quintana <maw27.wilson@gmail.com>

Quintana M, Conti D (2011). *Incorporating Model Uncertainty in Detecting Rare Variants:
The Bayesian Risk Index*. Genetic Epidemiology 35:638-649.

Quintana M, Conti D (Submitted). *Integrative Variable Selection via Bayesian Model
Uncertainty*.

1 2 3 4 5 6 | ```
## Load the data for Rare variant example
data(RareData)
## Enumerate model space for a subset of 5 variants and save output to BVS.out
## for rare variant example.
RareBVS.out <- enumerateBVS(data=RareData[,1:6],rare=TRUE)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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