R package changepoint: Analysis of Changepoint Models. Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean, cpt.var, cpt.meanvar functions should be your first point of call.

References in zbMATH (referenced in 52 articles , 1 standard article )

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  1. Martin Ondrus, Ivor Cribben: fabisearch: A Package for Change Point Detection in and Visualization of the Network Structure of Multivariate High-Dimensional Time Series in R (2022) arXiv
  2. 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
  3. Yu, Mengjia; Chen, Xiaohui: A robust bootstrap change point test for high-dimensional location parameter (2022)
  4. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  5. Azadeh Khaleghi, Lukas Zierahn: PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series (2021) arXiv
  6. Jeong, Yong Dam; Kim, Sangil; Jung, Il Hyo; Cho, Giphil: Optimal harvesting strategy for hairtail, \textitTrichiuruslepturus, in Korea sea using discrete-time age-structured model (2021)
  7. Lattanzi, Chiara; Leonelli, Manuele: A change-point approach for the identification of financial extreme regimes (2021)
  8. Madrid Padilla, Oscar Hernan; Yu, Yi; Wang, Daren; Rinaldo, Alessandro: Optimal nonparametric change point analysis (2021)
  9. Peiliang Bai, Yue Bai, Abolfazl Safikhani, George Michailidis: Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package (2021) arXiv
  10. Plasse, Joshua; Hoeltgebaum, Henrique; Adams, Niall M.: Streaming changepoint detection for transition matrices (2021)
  11. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  12. Grundy, Thomas; Killick, Rebecca; Mihaylov, Gueorgui: High-dimensional changepoint detection via a geometrically inspired mapping (2020)
  13. Lykou, R.; Tsaklidis, G.; Papadimitriou, E.: Change point analysis on the Corinth Gulf (Greece) seismicity (2020)
  14. Majumdar, Anandamayee: Tagore’s song-counts by thematic and non-thematic classification: a statistical case study (2020)
  15. Moura e Silva, Wyara Vanesa; Ferraz do Nascimento, Fernando; Bourguignon, Marcelo: A change-point model for the (r)-largest order statistics with applications to environmental and financial data (2020)
  16. Alsaker, Cody; Breidt, F. Jay; van der Woerd, Mark J.: Minimum mean squared error estimation of the radius of gyration in small-angle X-ray scattering experiments (2019)
  17. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  18. Arlot, Sylvain; Celisse, Alain; Harchaoui, Zaid: A kernel multiple change-point algorithm via model selection (2019)
  19. Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
  20. Duan, Qihong; Liu, Junrong; Zhao, Dengfu: A fast algorithm for short term electric load forecasting by a hidden semi-Markov process (2019)

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