BartMixVs-package |
Varibale Selection Using Bayesian Additive Regression Trees |

abc.vs |
Variable selection with ABC Bayesian forest |

BartMixVs |
Varibale Selection Using Bayesian Additive Regression Trees |

bartModelMatrix |
Create a matrix out of a vector or data frame |

checkerboard |
Generate data for an example of Zhu, Zeng and Kosorok (2015) |

friedman |
Generate data for an example of Friedman (1991) |

mc.abc.vs |
Variable selection with ABC Bayesian forest (using parallel computation) |

mc.backward.vs |
Backward selection with two filters (using parallel computation) |

mc.cores.openmp |
Detecting OpenMP |

mc.pbart |
Probit BART for binary responses with parallel computation |

mc.permute.vs |
Permutation-based variable selection approach with parallel computation |

mc.pwbart |
Predicting new observations based on a previously fitted BART model with parallel computation |

mc.wbart |
BART for continuous responses with parallel computation |

medianInclusion.vs |
Variable selection with DART |

mixone |
Generate data with independent and mixed-type predictors |

mixtwo |
Generate data with correlated and mixed-type predictors |

pbart |
Probit BART for binary responses with Normal latents |

permute.vs |
Permutation-based variable selection approach |

predict.pbart |
Predict new observations with a fitted BART model |

predict.wbart |
Predict new observations with a fitted BART model |

pwbart |
Predicting new observations with a previously fitted BART model |

wbart |
BART for continuous responses |