stepR

R package stepR: Multiscale Change-Point Inference. Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE and HSMUCE. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.


References in zbMATH (referenced in 10 articles )

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  1. Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque: Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data (2022) not zbMATH
  2. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  3. Madrid Padilla, Oscar Hernan; Yu, Yi; Wang, Daren; Rinaldo, Alessandro: Optimal nonparametric change point analysis (2021)
  4. Peiliang Bai, Yue Bai, Abolfazl Safikhani, George Michailidis: Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package (2021) arXiv
  5. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  6. Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
  7. Chengcheng Huang, Housen Li, Lizhi Cheng, Wei Peng: A linear time algorithm for multiscale quantile simulation (2018) arXiv
  8. Pein, Florian: Heterogeneous multiscale change-point inference and its application to ion channel recordings (2017)
  9. Pein, Florian; Sieling, Hannes; Munk, Axel: Heterogeneous change point inference (2017)
  10. Tecuapetla-Gómez, Inder; Munk, Axel: Autocovariance estimation in regression with a discontinuous signal and (m)-dependent errors: a difference-based approach (2017)