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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2024.1.24 |
rolling linux/jammy R-4.5 | PeakSegDP_2024.1.24.tar.gz |
529.5 KiB |
2024.1.24 |
rolling linux/noble R-4.5 | PeakSegDP_2024.1.24.tar.gz |
529.6 KiB |
2024.1.24 |
rolling source/ R- | PeakSegDP_2024.1.24.tar.gz |
498.1 KiB |
2024.1.24 |
latest linux/jammy R-4.5 | PeakSegDP_2024.1.24.tar.gz |
529.5 KiB |
2024.1.24 |
latest linux/noble R-4.5 | PeakSegDP_2024.1.24.tar.gz |
529.6 KiB |
2024.1.24 |
latest source/ R- | PeakSegDP_2024.1.24.tar.gz |
498.1 KiB |
2024.1.24 |
2026-04-26 source/ R- | PeakSegDP_2024.1.24.tar.gz |
498.1 KiB |
2024.1.24 |
2026-04-23 source/ R- | PeakSegDP_2024.1.24.tar.gz |
498.1 KiB |
2024.1.24 |
2026-04-09 windows/windows R-4.5 | PeakSegDP_2024.1.24.zip |
537.4 KiB |
2024.1.24 |
2025-04-20 source/ R- | PeakSegDP_2024.1.24.tar.gz |
498.1 KiB |
Dependencies (latest)
Suggests
- ggplot2 (>= 2.0)
- testthat
- penaltyLearning