R package ecp: Non-Parametric Multiple Change-Point Analysis of Multivariate Data. Implements various procedures for finding multiple change-points. Two methods make use of dynamic programming and probabilistic pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information.

References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Fisher, Thomas J.; Zhang, Jing; Colegate, Stephen P.; Vanni, Michael J.: Detecting and modeling changes in a time series of proportions (2022)
  2. Yu, Mengjia; Chen, Xiaohui: A robust bootstrap change point test for high-dimensional location parameter (2022)
  3. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  4. Azadeh Khaleghi, Lukas Zierahn: PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series (2021) arXiv
  5. Li, Xiuqi; Ghosal, Subhashis: Bayesian change point detection for functional data (2021)
  6. Madrid Padilla, Oscar Hernan; Yu, Yi; Wang, Daren; Rinaldo, Alessandro: Optimal nonparametric change point analysis (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. Shi, Xiaoping; Wu, Yuehua: An empirical-characteristic-function-based change-point test for detection of multiple distributional changes (2021)
  9. Siddiqa, Hajra; Ali, Sajid; Shah, Ismail: Most recent changepoint detection in censored panel data (2021)
  10. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  11. Grundy, Thomas; Killick, Rebecca; Mihaylov, Gueorgui: High-dimensional changepoint detection via a geometrically inspired mapping (2020)
  12. Hlávka, Zdeněk; Hušková, Marie; Meintanis, Simos G.: Change-point methods for multivariate time-series: paired vectorial observations (2020)
  13. Arlot, Sylvain; Celisse, Alain; Harchaoui, Zaid: A kernel multiple change-point algorithm via model selection (2019)
  14. Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
  15. Herlands, William; Neill, Daniel B.; Nickisch, Hannes; Wilson, Andrew Gordon: Change surfaces for expressive multidimensional changepoints and counterfactual prediction (2019)
  16. Plasse, Joshua; Adams, Niall M.: Multiple changepoint detection in categorical data streams (2019)
  17. Avanesov, Valeriy; Buzun, Nazar: Change-point detection in high-dimensional covariance structure (2018)
  18. Celisse, A.; Marot, G.; Pierre-Jean, M.; Rigaill, G. J.: New efficient algorithms for multiple change-point detection with reproducing kernels (2018)
  19. Charles Truong, Laurent Oudre, Nicolas Vayatis: ruptures: change point detection in Python (2018) arXiv
  20. Wang, Tengyao; Samworth, Richard J.: High dimensional change point estimation via sparse projection (2018)

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