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: 2017.08.15
Depends: R (≥ 2.10)
Suggests: ggplot2 (≥ 2.0), testthat, penaltyLearning
Published: 2017-08-15
Author: Toby Dylan Hocking, Guillem Rigaill
Maintainer: Toby Dylan Hocking <toby.hocking at r-project.org>
License: GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: PeakSegDP results

Downloads:

Reference manual: PeakSegDP.pdf
Package source: PeakSegDP_2017.08.15.tar.gz
Windows binaries: r-devel: PeakSegDP_2017.08.15.zip, r-release: PeakSegDP_2017.08.15.zip, r-oldrel: PeakSegDP_2017.08.15.zip
OS X El Capitan binaries: r-release: PeakSegDP_2017.08.15.tgz
OS X Mavericks binaries: r-oldrel: PeakSegDP_2017.08.15.tgz
Old sources: PeakSegDP archive

Reverse dependencies:

Reverse suggests: PeakSegOptimal

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