strucchange

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 57 articles , 2 standard articles )

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  1. Alexander Meier, Claudia Kirch, Haeran Cho: mosum: A Package for Moving Sums in Change-Point Analysis (2021) not zbMATH
  2. Madrid Padilla, Oscar Hernan; Yu, Yi; Wang, Daren; Rinaldo, Alessandro: Optimal nonparametric change point analysis (2021)
  3. Peiliang Bai, Yue Bai, Abolfazl Safikhani, George Michailidis: Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package (2021) arXiv
  4. Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis (2020) not zbMATH
  5. Barik, Aditya Kumar; Sen, Rituparna; Ganguli, Bhaswati: Impact of the COVID-19 epidemic on the aviation industry (2020)
  6. Battaglia, Francesco; Cucina, Domenico; Rizzo, Manuel: Parsimonious periodic autoregressive models for time series with evolving trend and seasonality (2020)
  7. Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Narrowest-over-threshold detection of multiple change points and change-point-like features (2019)
  8. Kai Wenger; Janis Becker: memochange: An R package for estimation procedures and tests for persistent time series (2019) not zbMATH
  9. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  10. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  11. Kirch, Claudia; Weber, Silke: Modified sequential change point procedures based on estimating functions (2018)
  12. Mair, Patrick: Modern psychometrics with R (2018)
  13. Ruggieri, Eric: A pruned recursive solution to the multiple change point problem (2018)
  14. Wang, Ting; Strobl, Carolin; Zeileis, Achim; Merkle, Edgar C.: Score-based tests of differential item functioning via pairwise maximum likelihood estimation (2018)
  15. Yuan Tang: Autoplotly - Automatic Generation of Interactive Visualizations for Popular Statistical Results (2018) arXiv
  16. Fong, Youyi; Di, Chongzhi; Huang, Ying; Gilbert, Peter B.: Model-robust inference for continuous threshold regression models (2017)
  17. Maderitsch, Robert: 24-hour realized volatilities and transatlantic volatility interdependence (2017)
  18. Carvalho, Arthur; Dimitrov, Stanko; Larson, Kate: How many crowdsourced workers should a requester hire? (2016) ioport
  19. Jin, Baisuo; Wu, Yuehua; Shi, Xiaoping: Consistent two-stage multiple change-point detection in linear models (2016)
  20. Kelly, G. E.: Approximations to the (p)-values of tests for a change-point under non-standard conditions (2016)

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