cpm

R package cpm: Sequential and Batch Change Detection Using Parametric and Nonparametric Methods. Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points.


References in zbMATH (referenced in 21 articles , 1 standard article )

Showing results 1 to 20 of 21.
Sorted by year (citations)

1 2 next

  1. Anastasiou, Andreas; Fryzlewicz, Piotr: Detecting multiple generalized change-points by isolating single ones (2022)
  2. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  3. Kojadinovic, Ivan; Verdier, Ghislain: Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions (2021)
  4. Rozgonyi-Boissinot, Nikoletta; Buocz, Ildikó; Hatvani, István Gábor; Török, Ákos: Shear strength testing of consolidated claystones: breakpoint detection of shear stress versus shear displacement curves, a statistical approach (2021)
  5. Abbas, Sermad; Fried, Roland: Robust control charts for the mean of a locally linear time series (2020)
  6. Chatla, Suneel Babu; Shmueli, Galit: A tree-based semi-varying coefficient model for the COM-Poisson distribution (2020)
  7. Fryzlewicz, Piotr: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection (2020)
  8. James, Nick; Menzies, Max; Azizi, Lamiae; Chan, Jennifer: Novel semi-metrics for multivariate change point analysis and anomaly detection (2020)
  9. Li, Fuxiao; Chen, Zhanshou; Xiao, Yanting: Sequential change-point detection in a multinomial logistic regression model (2020)
  10. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  11. Herlands, William; Neill, Daniel B.; Nickisch, Hannes; Wilson, Andrew Gordon: Change surfaces for expressive multidimensional changepoints and counterfactual prediction (2019)
  12. Plasse, Joshua; Adams, Niall M.: Multiple changepoint detection in categorical data streams (2019)
  13. Charles Truong, Laurent Oudre, Nicolas Vayatis: ruptures: change point detection in Python (2018) arXiv
  14. Corbet, Shaen; Lucey, Brian; Peat, Maurice; Vigne, Samuel: Bitcoin futures -- what use are they? (2018)
  15. Ruggieri, Eric: A pruned recursive solution to the multiple change point problem (2018)
  16. Mukherjee, Partha Sarathi: On phase II monitoring of the probability distributions of univariate continuous processes (2016)
  17. Ruggieri, Eric; Antonellis, Marcus: An exact approach to Bayesian sequential change point detection (2016)
  18. Gordon Ross: Parametric and Nonparametric Sequential Change Detection in R: The cpm Package (2015) not zbMATH
  19. Rebecca Killick; Idris Eckley: changepoint: An R Package for Changepoint Analysis (2014) not zbMATH
  20. Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel: Ensembles of change-point methods to estimate the change point in residual sequences (2013) ioport

1 2 next