Deep Learning–Based Changepoint Detection with Local Neural Models R package scanCP version 0.1.0 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. Software Imports: plotly, RSNNS, foreach, doSNOW, parallel, pracma, stats, magrittr, tidyr Arman Azizyan <arman.azizyan@gmail.com> Comprehensive R Archive Network (CRAN) Arman Azizyan [aut, cre], Abolfazl Safikhani [aut] GPL-2 2026-05-30 application/tgz https://CRAN.R-project.org/package=scanCP doi:10.32614/CRAN.package.scanCP