Package: PeakSegDP 2024.1.24

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.

Authors:Toby Dylan Hocking, Guillem Rigaill

PeakSegDP_2024.1.24.tar.gz
PeakSegDP_2024.1.24.zip(r-4.7)PeakSegDP_2024.1.24.zip(r-4.6)PeakSegDP_2024.1.24.zip(r-4.5)
PeakSegDP_2024.1.24.tgz(r-4.6-x86_64)PeakSegDP_2024.1.24.tgz(r-4.6-arm64)PeakSegDP_2024.1.24.tgz(r-4.5-x86_64)PeakSegDP_2024.1.24.tgz(r-4.5-arm64)
PeakSegDP_2024.1.24.tar.gz(r-4.7-arm64)PeakSegDP_2024.1.24.tar.gz(r-4.7-x86_64)PeakSegDP_2024.1.24.tar.gz(r-4.6-arm64)PeakSegDP_2024.1.24.tar.gz(r-4.6-x86_64)
PeakSegDP_2024.1.24.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
PeakSegDP/json (API)
NEWS

# Install 'PeakSegDP' in R:
install.packages('PeakSegDP', repos = c('https://tdhock.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tdhock/peaksegdp/issues

Datasets:

On CRAN:

Conda:

2.40 score 25 scripts 747 downloads 1 mentions 4 exports 0 dependencies

Last updated from:618128bb81. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK103
linux-devel-x86_64OK132
source / vignettesOK160
linux-release-arm64OK108
linux-release-x86_64OK109
macos-release-arm64OK92
macos-release-x86_64OK196
macos-oldrel-arm64OK96
macos-oldrel-x86_64OK132
windows-develOK66
windows-releaseOK74
windows-oldrelOK74
wasm-releaseOK92

Exports:cDPAgetPathPeakSegDPPoissonLoss

Dependencies: