PeakSegOptimal: Optimal Segmentation Subject to Up-Down Constraints

Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.

Version: 2024.10.1
Depends: R (≥ 2.10)
Imports: penaltyLearning
Suggests: PeakSegDP (≥ 2016.08.06), ggplot2, testthat, data.table (≥ 1.9.8)
Published: 2024-10-02
DOI: 10.32614/CRAN.package.PeakSegOptimal
Author: Toby Dylan Hocking [aut, cre]
Maintainer: Toby Dylan Hocking <toby.hocking at r-project.org>
BugReports: https://github.com/tdhock/PeakSegOptimal/issues
License: GPL-3
URL: https://github.com/tdhock/PeakSegOptimal
NeedsCompilation: yes
Materials: NEWS
In views: Omics
CRAN checks: PeakSegOptimal results

Documentation:

Reference manual: PeakSegOptimal.pdf

Downloads:

Package source: PeakSegOptimal_2024.10.1.tar.gz
Windows binaries: r-devel: PeakSegOptimal_2024.10.1.zip, r-release: PeakSegOptimal_2024.10.1.zip, r-oldrel: PeakSegOptimal_2024.10.1.zip
macOS binaries: r-release (arm64): PeakSegOptimal_2024.10.1.tgz, r-oldrel (arm64): PeakSegOptimal_2024.10.1.tgz, r-release (x86_64): PeakSegOptimal_2024.10.1.tgz, r-oldrel (x86_64): PeakSegOptimal_2024.10.1.tgz
Old sources: PeakSegOptimal archive

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