Description Usage Arguments Author(s)

This function implements the idea of Bayesian Lasso Binary quantile regression using a likelihood function that is based on the asymmetric Laplace distribution (Rahim, 2016). The asymmetric Laplace error distribution is written as a scale mixture of normal distributions.

1 | ```
BLBqr(x,y, tau = 0.5, runs = 11000, burn = 1000, thin=1)
``` |

`x` |
Matrix of predictors. |

`y` |
Vector of dependent variable. |

`tau` |
The quantile of interest. Must be between 0 and 1. |

`runs` |
Length of desired Gibbs sampler output. |

`burn` |
Number of Gibbs sampler iterations before output is saved. |

`thin` |
thinning parameter of MCMC draws. |

Rahim Alhamzawi

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