Package: breakfast 2.5

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

A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.

Authors:Andreas Anastasiou [aut], Yining Chen [aut, cre], Haeran Cho [aut], Piotr Fryzlewicz [aut]

breakfast_2.5.tar.gz
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breakfast_2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
breakfast/json (API)

# Install 'breakfast' in R:
install.packages('breakfast', repos = c('https://ychen101.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.70 score 1 stars 50 scripts 335 downloads 1 mentions 17 exports 19 dependencies

Last updated from:9b56d0d75d. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK141
linux-devel-x86_64OK153
source / vignettesOK219
linux-release-arm64OK145
linux-release-x86_64OK145
macos-release-arm64OK108
macos-release-x86_64OK229
macos-oldrel-arm64OK94
macos-oldrel-x86_64OK330
windows-develOK128
windows-releaseOK185
windows-oldrelOK133
wasm-releaseOK127

Exports:breakfastmodel.fixednummodel.gsamodel.icmodel.lpmodel.sdllmodel.threshplot.breakfast.cptsprint.breakfast.cptsprint.cptmodelsol.idetectsol.idetect_seqsol.notsol.tguhsol.wbssol.wbs2sol.wcm

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleplyrR6RColorBrewerRcpprlangS7scalesvctrsviridisLitewithr

Vignette for R Breakfast Package

Rendered frombreakfast-vignette.Rmdusingknitr::rmarkdownon May 15 2026.

Last update: 2024-04-27
Started: 2020-11-23

Readme and manuals

Help Manual

Help pageTopics
Breakfast: Methods for Fast Multiple Change-point Detection and Estimationbreakfast-package
Methods for fast detection of multiple change-pointsbreakfast
Estimate the location of change-points when the number of them is fixedmodel.fixednum
Estimating change-points in the piecewise-constant mean of a noisy data sequence with auto-regressive noise via gappy Schwarz algorithmmodel.gsa
Estimating change-points or change-point-type features in the mean of a noisy data sequence via the strengthened Schwarz information criterionmodel.ic
Estimating change-points in the piecewise-constant mean of a noisy data sequence via the localised pruningmodel.lp
Estimating change-points in the piecewise-constant or piecewise-linear mean of a noisy data sequence via the Steepest Drop to Low Levels methodmodel.sdll
Estimating change-points in the piecewise-constant or piecewise-linear mean of a noisy data sequence via thresholdingmodel.thresh
Change-points estimated by the "breakfast" routineplot.breakfast.cpts
Change-points estimated by the "breakfast" routineprint.breakfast.cpts
Change-points estimated by solution path generation + model selection methodsprint.cptmodel
Solution path generation via the Isolate-Detect methodsol.idetect
Solution path generation using the sequential approach of the Isolate-Detect methodsol.idetect_seq
Solution path generation via the Narrowest-Over-Threshold methodsol.not
Solution path generation via the Tail-Greedy Unbalanced Haar methodsol.tguh
Solution path generation via the Wild Binary Segmentation methodsol.wbs
Solution path generation via the Wild Binary Segmentation 2 methodsol.wbs2
Solution path generation via the Wild Contrast Maximisation methodsol.wcm