breakfast-package |
Breakfast: Methods for Fast Multiple Change-point Detection and Estimation |

breakfast |
Methods for fast detection of multiple change-points |

model.fixednum |
Estimate the location of change-points when the number of them is fixed |

model.gsa |
Estimating change-points in the piecewise-constant mean of a noisy data sequence with auto-regressive noise via gappy Schwarz algorithm |

model.ic |
Estimating change-points or change-point-type features in the mean of a noisy data sequence via the strengthened Schwarz information criterion |

model.lp |
Estimating change-points in the piecewise-constant mean of a noisy data sequence via the localised pruning |

model.sdll |
Estimating change-points in the piecewise-constant or piecewise-linear mean of a noisy data sequence via the Steepest Drop to Low Levels method |

model.thresh |
Estimating change-points in the piecewise-constant or piecewise-linear mean of a noisy data sequence via thresholding |

plot.breakfast.cpts |
Change-points estimated by the "breakfast" routine |

print.breakfast.cpts |
Change-points estimated by the "breakfast" routine |

print.cptmodel |
Change-points estimated by solution path generation + model selection methods |

sol.idetect |
Solution path generation via the Isolate-Detect method |

sol.idetect_seq |
Solution path generation using the sequential approach of the Isolate-Detect method |

sol.not |
Solution path generation via the Narrowest-Over-Threshold method |

sol.tguh |
Solution path generation via the Tail-Greedy Unbalanced Haar method |

sol.wbs |
Solution path generation via the Wild Binary Segmentation method |

sol.wbs2 |
Solution path generation via the Wild Binary Segmentation 2 method |

sol.wcm |
Solution path generation via the Wild Contrast Maximisation method |