High-Performance Open-Source Archive
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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