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 33 articles , 1 standard article )

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  1. Majumdar, Anandamayee: Tagore’s song-counts by thematic and non-thematic classification: a statistical case study (2020)
  2. 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)
  3. 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)
  4. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  5. Arlot, Sylvain; Celisse, Alain; Harchaoui, Zaid: A kernel multiple change-point algorithm via model selection (2019)
  6. Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
  7. Duan, Qihong; Liu, Junrong; Zhao, Dengfu: A fast algorithm for short term electric load forecasting by a hidden semi-Markov process (2019)
  8. Ezzat, Ahmed Aziz; Jun, Mikyoung; Ding, Yu: Spatio-temporal short-term wind forecast: a calibrated regime-switching method (2019)
  9. Herrera Cortés, Silvia; Juárez Hernández, Bulmaro; Vázquez Guevara, Victor Hugo; Cruz Suárez, Hugo Adán: Parametric methodologies for detecting changes in maximum temperature of Tlaxco, Tlaxcala, México (2019)
  10. Lynch, Christopher; Mestel, Benjamin: Change-point analysis of asset price bubbles with power-law hazard function (2019)
  11. Mohammadi, Hossein; Challenor, Peter; Goodfellow, Marc: Emulating dynamic non-linear simulators using Gaussian processes (2019)
  12. Montoril, Michel H.; Pinheiro, Aluísio; Vidakovic, Brani: Wavelet-based estimators for mixture regression (2019)
  13. Plasse, Joshua; Adams, Niall M.: Multiple changepoint detection in categorical data streams (2019)
  14. Raveendran, Nishanthi; Sofronov, Georgy: Identifying clusters in spatial data via sequential importance sampling (2019)
  15. Aston, John A. D.; Kirch, Claudia: High dimensional efficiency with applications to change point tests (2018)
  16. Charles Truong, Laurent Oudre, Nicolas Vayatis: ruptures: change point detection in Python (2018) arXiv
  17. Fasola, Salvatore; Muggeo, Vito M. R.; Küchenhoff, Helmut: A heuristic, iterative algorithm for change-point detection in abrupt change models (2018)
  18. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  19. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  20. Hyun, Sangwon; G’sell, Max; Tibshirani, Ryan J.: Exact post-selection inference for the generalized lasso path (2018)

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