breakfast

R package breakfast: Multiple Change-Point Detection and Segmentation. The breakfast package performs multiple change-point detection in data sequences, or sequence segmentation, using computationally efficient multiscale methods. This version of the package implements the ”Tail-Greedy Unbalanced Haar”, ”Wild Binary Segmentation” and ”Adaptive Wild Binary Segmentation” change-point detection and segmentation methodologies. To start with, see the function segment.mean.


References in zbMATH (referenced in 11 articles , 1 standard article )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Anastasiou, Andreas; Fryzlewicz, Piotr: Detecting multiple generalized change-points by isolating single ones (2022)
  2. Liu, Bin; Zhang, Xinsheng; Liu, Yufeng: High dimensional change point inference: recent developments and extensions (2022)
  3. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  4. Bouzebda, Salim; Ferfache, Anouar Abdeldjaoued: Asymptotic properties of (M)-estimators based on estimating equations and censored data in semi-parametric models with multiple change points (2021)
  5. Bücher, Axel; Dette, Holger; Heinrichs, Florian: Are deviations in a gradually varying mean relevant? A testing approach based on sup-norm estimators (2021)
  6. Heinrichs, Florian; Dette, Holger: A distribution free test for changes in the trend function of locally stationary processes (2021)
  7. Peiliang Bai, Yue Bai, Abolfazl Safikhani, George Michailidis: Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package (2021) arXiv
  8. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  9. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  10. Charles Truong, Laurent Oudre, Nicolas Vayatis: ruptures: change point detection in Python (2018) arXiv
  11. Fryzlewicz, Piotr: Tail-greedy bottom-up data decompositions and fast multiple change-point detection (2018)