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, see R. Baranowski, Y. Chen and P. Fryzlewicz (2019) <doi:10.1111/rssb.12322>. 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.

Version: 1.2
Depends: graphics, stats, splines
Published: 2019-07-04
Author: Rafal Baranowski, Yining Chen, Piotr Fryzlewicz
Maintainer: Rafal Baranowski <package_maintenance at>
License: GPL-2
NeedsCompilation: yes
CRAN checks: not results


Reference manual: not.pdf
Package source: not_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: not_1.2.tgz, r-oldrel: not_1.2.tgz
Old sources: not archive


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