Narrowest-Over-Threshold Change-Point Detection

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, see R. Baranowski, Y. Chen and P. Fryzlewicz (2019) . 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.


Reference manual

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1.2 by Rafal Baranowski, 2 years ago

Browse source code at

Authors: Rafal Baranowski , Yining Chen , Piotr Fryzlewicz

Documentation:   PDF Manual  

GPL-2 license

Depends on graphics, stats, splines

See at CRAN