Methods for Fast Multiple Change-Point Detection and Estimation

Documentation for package ‘breakfast’ version 2.3

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
plot.breakfast.cpts	Change-points estimated by breakfast
print.breakfast.cpts	Change-points estimated by breakfast
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