PeakSegDP: Dynamic Programming Algorithm for Peak Detection in ChIP-Seq Data

A quadratic time dynamic programming algorithm can be used to compute an approximate solution to the problem of finding the most likely changepoints with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, etc. For more info read <http://proceedings.mlr.press/v37/hocking15.html> "PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data" by TD Hocking et al, proceedings of ICML2015.

Version: 2024.1.24
Depends: R (≥ 2.10)
Suggests: ggplot2 (≥ 2.0), testthat, penaltyLearning
Published: 2024-01-24
Author: Toby Dylan Hocking, Guillem Rigaill
Maintainer: Toby Dylan Hocking <toby.hocking at r-project.org>
BugReports: https://github.com/tdhock/PeakSegDP/issues
License: GPL-3
URL: https://github.com/tdhock/PeakSegDP
NeedsCompilation: yes
Materials: NEWS
CRAN checks: PeakSegDP results

Documentation:

Reference manual: PeakSegDP.pdf

Downloads:

Package source: PeakSegDP_2024.1.24.tar.gz
Windows binaries: r-devel: PeakSegDP_2024.1.24.zip, r-release: PeakSegDP_2024.1.24.zip, r-oldrel: PeakSegDP_2024.1.24.zip
macOS binaries: r-release (arm64): PeakSegDP_2024.1.24.tgz, r-oldrel (arm64): PeakSegDP_2024.1.24.tgz, r-release (x86_64): PeakSegDP_2024.1.24.tgz
Old sources: PeakSegDP archive

Reverse dependencies:

Reverse suggests: PeakSegOptimal

Linking:

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