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 (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE (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: 2.0-2
Depends: R (≥ 3.0.0)
Imports: Rcpp (≥ 0.12.3), R.cache (≥ 0.10.0), digest (≥ 0.6.10), stats, graphics, methods
LinkingTo: Rcpp
Suggests: testthat (≥ 1.0.0), knitr
Published: 2018-04-06
Author: Pein Florian [aut, cre], Thomas Hotz [aut], Hannes Sieling [aut], Timo Aspelmeier [ctb]
Maintainer: Pein Florian <fpein at uni-goettingen.de>
License: GPL-3
NeedsCompilation: yes
Classification/MSC: 62G08, 92C40, 92D20
Citation: stepR citation info
Materials: ChangeLog
CRAN checks: stepR results

Downloads:

Reference manual: stepR.pdf
Vignettes: R package stepR
Package source: stepR_2.0-2.tar.gz
Windows binaries: r-devel: stepR_2.0-2.zip, r-release: stepR_2.0-2.zip, r-oldrel: stepR_2.0-2.zip
OS X binaries: r-release: stepR_2.0-2.tgz, r-oldrel: stepR_2.0-2.tgz
Old sources: stepR archive

Reverse dependencies:

Reverse imports: clampSeg, FDRSeg, linearQ

Linking:

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