not: 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. 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) <doi:10.1111/rssb.12322>.

Version:	1.6
Depends:	graphics, stats, splines
Published:	2024-09-23
DOI:	10.32614/CRAN.package.not
Author:	Rafal Baranowski [aut], Yining Chen [aut, cre], Piotr Fryzlewicz [aut]
Maintainer:	Yining Chen <y.chen101 at lse.ac.uk>
License:	GPL-2
NeedsCompilation:	yes
CRAN checks:	not results

Documentation:

Reference manual:

not.html , not.pdf

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Package source:	not_1.6.tar.gz
Windows binaries:	r-devel: not_1.6.zip, r-release: not_1.6.zip, r-oldrel: not_1.6.zip
macOS binaries:	r-release (arm64): not_1.6.tgz, r-oldrel (arm64): not_1.6.tgz, r-release (x86_64): not_1.6.tgz, r-oldrel (x86_64): not_1.6.tgz
Old sources:	not archive

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