Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation

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Documentation for package ‘breakfast’ version 2.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

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