stepR: Multiscale Change-Point Inference

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

Version: 2.1-9
Depends: R (≥ 3.3.0)
Imports: Rcpp (≥ 0.12.3), lowpassFilter (≥ 1.0.0), R.cache (≥ 0.10.0), digest (≥ 0.6.10), stats, graphics, methods
LinkingTo: Rcpp
Suggests: testthat (≥ 1.0.0), knitr
Published: 2023-11-13
DOI: 10.32614/CRAN.package.stepR
Author: Pein Florian [aut, cre], Thomas Hotz [aut], Hannes Sieling [aut], Timo Aspelmeier [ctb]
Maintainer: Pein Florian <f.pein at>
License: GPL-3
NeedsCompilation: yes
Classification/MSC: 62G08, 92C40, 92D20
Citation: stepR citation info
Materials: ChangeLog
CRAN checks: stepR results


Reference manual: stepR.pdf
Vignettes: R package stepR


Package source: stepR_2.1-9.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): stepR_2.1-9.tgz, r-oldrel (arm64): stepR_2.1-9.tgz, r-release (x86_64): stepR_2.1-9.tgz, r-oldrel (x86_64): stepR_2.1-9.tgz
Old sources: stepR archive

Reverse dependencies:

Reverse suggests: fastcpd


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