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 11 articles )

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  1. Charles Truong, Laurent Oudre, Nicolas Vayatis: ruptures: change point detection in Python (2018) arXiv
  2. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  3. Bodenham, Dean A.; Adams, Niall M.: Continuous monitoring for changepoints in data streams using adaptive estimation (2017)
  4. Chatterjee, Subhashis; Shukla, Ankur: An ideal software release policy for an improved software reliability growth model incorporating imperfect debugging with fault removal efficiency and change point (2017)
  5. Shi, Xiaoping; Wang, Xiang-Sheng; Wei, Dongwei; Wu, Yuehua: A sequential multiple change-point detection procedure via VIF regression (2016)
  6. Gordon Ross: Parametric and Nonparametric Sequential Change Detection in R: The cpm Package (2015)
  7. Cleynen, Alice; Dudoit, Sandrine; Robin, Stéphane: Comparing segmentation methods for genome annotation based on RNA-seq data (2014)
  8. Quentin Grimonprez, Alain Celisse, Meyling Cheok, Martin Figeac, Guillemette Marot: MPAgenomics : An R package for multi-patients analysis of genomic markers (2014) arXiv
  9. Killick, R.; Eckley, I.A.; Jonathan, P.: A wavelet-based approach for detecting changes in second order structure within nonstationary time series (2013)
  10. Nicholas A. James, David S. Matteson: ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data (2013) arXiv
  11. Killick, R.; Fearnhead, P.; Eckley, I.A.: Optimal detection of changepoints with a linear computational cost (2012)