breakfast               Methods for fast multiple change-point
                        detection and estimation
breakfast-package       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
