R package strucchange: Testing, Monitoring, and Dating Structural Changes. Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.

References in zbMATH (referenced in 51 articles , 2 standard articles )

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  1. Battaglia, Francesco; Cucina, Domenico; Rizzo, Manuel: Parsimonious periodic autoregressive models for time series with evolving trend and seasonality (2020)
  2. Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
  3. Kai Wenger; Janis Becker: memochange: An R package for estimation procedures and tests for persistent time series (2019) not zbMATH
  4. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  5. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  6. Kirch, Claudia; Weber, Silke: Modified sequential change point procedures based on estimating functions (2018)
  7. Mair, Patrick: Modern psychometrics with R (2018)
  8. Ruggieri, Eric: A pruned recursive solution to the multiple change point problem (2018)
  9. Wang, Ting; Strobl, Carolin; Zeileis, Achim; Merkle, Edgar C.: Score-based tests of differential item functioning via pairwise maximum likelihood estimation (2018)
  10. Fong, Youyi; Di, Chongzhi; Huang, Ying; Gilbert, Peter B.: Model-robust inference for continuous threshold regression models (2017)
  11. Maderitsch, Robert: 24-hour realized volatilities and transatlantic volatility interdependence (2017)
  12. Carvalho, Arthur; Dimitrov, Stanko; Larson, Kate: How many crowdsourced workers should a requester hire? (2016) ioport
  13. Jin, Baisuo; Wu, Yuehua; Shi, Xiaoping: Consistent two-stage multiple change-point detection in linear models (2016)
  14. Kelly, G. E.: Approximations to the (p)-values of tests for a change-point under non-standard conditions (2016)
  15. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016) not zbMATH
  16. Ruggieri, Eric; Antonellis, Marcus: An exact approach to Bayesian sequential change point detection (2016)
  17. van Berkum, Frank; Antonio, Katrien; Vellekoop, Michel: The impact of multiple structural changes on mortality predictions (2016)
  18. Gordon Ross: Parametric and Nonparametric Sequential Change Detection in R: The cpm Package (2015) not zbMATH
  19. Kelly, Morgan; Gráda, Cormac Ó.: Change points and temporal dependence in reconstructions of annual temperature: did Europe experience a little ice age? (2014)
  20. Merkle, Edgar C.; Fan, Jinyan; Zeileis, Achim: Testing for measurement invariance with respect to an ordinal variable (2014)

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