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-4.5)PeakSegDP_2024.1.24.zip(r-4.4)PeakSegDP_2024.1.24.zip(r-4.3)
PeakSegDP_2024.1.24.tgz(r-4.4-x86_64)PeakSegDP_2024.1.24.tgz(r-4.4-arm64)PeakSegDP_2024.1.24.tgz(r-4.3-x86_64)PeakSegDP_2024.1.24.tgz(r-4.3-arm64)
PeakSegDP_2024.1.24.tar.gz(r-4.5-noble)PeakSegDP_2024.1.24.tar.gz(r-4.4-noble)
PeakSegDP_2024.1.24.tgz(r-4.4-emscripten)PeakSegDP_2024.1.24.tgz(r-4.3-emscripten)
PeakSegDP.pdf |PeakSegDP.html
PeakSegDP/json (API)
NEWS

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

Peer review:

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

Datasets:

On CRAN:

2.40 score 25 scripts 623 downloads 1 mentions 4 exports 0 dependencies

Last updated 10 months agofrom:618128bb81. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64OKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64OKNov 20 2024
R-4.4-mac-x86_64OKNov 20 2024
R-4.4-mac-aarch64OKNov 20 2024
R-4.3-win-x86_64OKNov 20 2024
R-4.3-mac-x86_64OKNov 20 2024
R-4.3-mac-aarch64OKNov 20 2024

Exports:cDPAgetPathPeakSegDPPoissonLoss

Dependencies: