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 29 articles , 1 standard article )

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  1. Kirch, Claudia; Weber, Silke: Modified sequential change point procedures based on estimating functions (2018)
  2. Ruggieri, Eric: A pruned recursive solution to the multiple change point problem (2018)
  3. Wang, Ting; Strobl, Carolin; Zeileis, Achim; Merkle, Edgar C.: Score-based tests of differential item functioning via pairwise maximum likelihood estimation (2018)
  4. Fong, Youyi; Di, Chongzhi; Huang, Ying; Gilbert, Peter B.: Model-robust inference for continuous threshold regression models (2017)
  5. Carvalho, Arthur; Dimitrov, Stanko; Larson, Kate: How many crowdsourced workers should a requester hire? (2016) ioport
  6. Jin, Baisuo; Wu, Yuehua; Shi, Xiaoping: Consistent two-stage multiple change-point detection in linear models (2016)
  7. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016)
  8. Gordon Ross: Parametric and Nonparametric Sequential Change Detection in R: The cpm Package (2015)
  9. Kelly, Morgan; Gráda, Cormac Ó.: Change points and temporal dependence in reconstructions of annual temperature: did Europe experience a little ice age? (2014)
  10. Merkle, Edgar C.; Fan, Jinyan; Zeileis, Achim: Testing for measurement invariance with respect to an ordinal variable (2014)
  11. Rebecca Killick; Idris Eckley: changepoint: An R Package for Changepoint Analysis (2014)
  12. Young, Derek S.: Mixtures of regressions with changepoints (2014)
  13. Kleiber, Christian; Zeileis, Achim: Reproducible econometric simulations (2013)
  14. Lee, Yi-Hsuan; von Davier, Alina A.: Monitoring scale scores over time via quality control charts, model-based approaches, and time series techniques (2013)
  15. Merkle, Edgar C.; Zeileis, Achim: Tests of measurement invariance without subgroups: a generalization of classical methods (2013)
  16. Nicholas A. James, David S. Matteson: ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data (2013) arXiv
  17. Zeileis, Achim; Hothorn, Torsten: A toolbox of permutation tests for structural change (2013)
  18. Bettina Grün; Ioannis Kosmidis; Achim Zeileis: Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned (2012)
  19. Gombay, Edit: Change detection in linear regression with time series errors (2010)
  20. Maindonald, John; Braun, W. John: Data analysis and graphics using R -- an example-based approach (2010)

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