Description Arguments Value Author(s)

`bayesQR`

is an MCMC sampler to fit a Bayesian quantile regression model. This does not assume a factor structure.

`formula` |
A formula of the form |

`dataSet` |
An optional data frame, list, or environment containing the variables in the model. |

`pQuant` |
Response quantile to model. Defaults to |

`nSamp` |
Number of MCMC iterations, with a default of |

`burn` |
Iterations of burn-in, with a default of |

`thin` |
Number of iterations to skip between stored values, with a default of |

`C0` |
Prior shape for |

`D0` |
Prior scale for |

`B0` |
Prior precision (i.e., inverse variance) for |

`betaZero` |
Starting value for |

`verbose` |
If |

Returns an item of the class `bayesQR`

composed of the following components:

`param` |
Matrix of sampled parameter values. |

`call` |
The matched call. |

`betLen` |
The number of |

`nObs` |
The number of observations. |

`burn` |
The number of Gibbs iterations before samples were stored. |

`thin` |
The number of Gibbs iterations between stored values. |

`nSamp` |
The total number of Gibbs iterations. |

Lane F. Burgette, Department of Statistical Science, Duke University. lb131@stat.duke.edu

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