Implementation of deep learning–based changepoint detection algorithm designed for time series with smooth local fluctuations. The method fits localized feed‑forward neural networks to approximate the underlying smooth component and constructs a residual‑based detector that isolates abrupt structural changes. A fully data‑adaptive empirical cumulative distribution function (ECDF) based thresholding rule and refinement procedures yield accurate changepoint localization without parametric assumptions on noise or trend structure.
| Version: | 0.1.0 |
| Imports: | plotly, RSNNS, foreach, doSNOW, parallel, pracma, stats, magrittr, tidyr |
| Published: | 2026-05-30 |
| DOI: | 10.32614/CRAN.package.scanCP |
| Author: | Arman Azizyan [aut, cre], Abolfazl Safikhani [aut] |
| Maintainer: | Arman Azizyan <arman.azizyan at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | scanCP results |
| Reference manual: | scanCP.html , scanCP.pdf |
| Package source: | scanCP_0.1.0.tar.gz |
| Windows binaries: | r-devel: scanCP_0.1.0.zip, r-release: scanCP_0.1.0.zip, r-oldrel: scanCP_0.1.0.zip |
| macOS binaries: | r-release (arm64): scanCP_0.1.0.tgz, r-oldrel (arm64): scanCP_0.1.0.tgz, r-release (x86_64): scanCP_0.1.0.tgz, r-oldrel (x86_64): scanCP_0.1.0.tgz |
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