Methods for Fast Multiple Change-Point Detection and Estimation


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Documentation for package ‘breakfast’ version 2.1

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breakfast-package Breakfast: Methods for Fast Multiple Change-point Detection and Estimation
breakfast Methods for fast multiple change-point detection and estimation
model.ic Estimating change-points in the piecewise-constant 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 mean of a noisy data sequence via the Steepest Drop to Low Levels method
model.thresh Estimating change-points in the piecewise-constant mean of a noisy data sequence via thresholding
print.breakfast.cpts Change-points estimated by breakfast
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