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Narrowest-Over-Threshold Change-Point Detection
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Maintained by
Yining Chen
[Scholar Profile | Author Map]
First Published: 2019-07-04
Latest Update: 2023-02-01
Description:
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) .
How to cite:
Yining Chen (2019). not: Narrowest-Over-Threshold Change-Point Detection. R package version 1.6, https://cran.r-project.org/web/packages/not. Accessed 01 May. 2025.
Previous versions and publish date:
1.0 (2020-06-27 07:42), 1.2 (2019-07-04 22:20), 1.3 (2022-03-04 13:50), 1.4 (2022-10-06 10:10), 1.5 (2023-02-02 00:40)
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Some associated functions: aic.penalty . features . loglik.not . not-package . not . plot.not . predict.not . random.intervals . residuals.not . sic.penalty . 
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