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: |
2.1-10 |
| 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: |
2024-10-18 |
| 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 lancaster.ac.uk> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Classification/MSC: |
62G08, 92C40, 92D20 |
| Citation: |
stepR citation info |
| Materials: |
ChangeLog |
| CRAN checks: |
stepR results |
Documentation:
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Reverse dependencies:
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