Methodology: Remove one observation. Training the rest of data that are sampled without replacement and given this observation's input, predict the response back. Replicate this N times and for each response, take a sample from these replicates with replacement. Average each responses of sample and again replicate this step N time for each observation. Approximate these N new responses and generate another N responses y'. Training these y' and predict to have N responses of each testing observation. The average of N is the final prediction. Each observation will do the same.

Version: | 0.1 |

Depends: | R (≥ 3.2.5), rpart , parallel , epandist , triangle, caret |

Suggests: | MASS |

Published: | 2018-05-09 |

Author: | Moshu Xie |

Maintainer: | Moshu Xie <mxie622 at gmail.com> |

License: | GPL-2 |

NeedsCompilation: | no |

CRAN checks: | SQB results |

Reference manual: | SQB.pdf |

Package source: | SQB_0.1.tar.gz |

Windows binaries: | r-devel: SQB_0.1.zip, r-release: SQB_0.1.zip, r-oldrel: SQB_0.1.zip |

OS X binaries: | r-release: SQB_0.1.tgz, r-oldrel: SQB_0.1.tgz |

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