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PeakSegOptimal

Optimal Segmentation Subject to Up-Down Constraints

Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.

Versions across snapshots

VersionRepositoryFileSize
2024.10.1 rolling linux/jammy R-4.5 PeakSegOptimal_2024.10.1.tar.gz 169.1 KiB
2024.10.1 rolling linux/noble R-4.5 PeakSegOptimal_2024.10.1.tar.gz 168.7 KiB
2024.10.1 rolling source/ R- PeakSegOptimal_2024.10.1.tar.gz 129.4 KiB
2024.10.1 latest linux/jammy R-4.5 PeakSegOptimal_2024.10.1.tar.gz 169.1 KiB
2024.10.1 latest linux/noble R-4.5 PeakSegOptimal_2024.10.1.tar.gz 168.7 KiB
2024.10.1 latest source/ R- PeakSegOptimal_2024.10.1.tar.gz 129.4 KiB
2024.10.1 2026-04-26 source/ R- PeakSegOptimal_2024.10.1.tar.gz 129.4 KiB
2024.10.1 2026-04-23 source/ R- PeakSegOptimal_2024.10.1.tar.gz 129.4 KiB
2024.10.1 2026-04-09 windows/windows R-4.5 PeakSegOptimal_2024.10.1.zip 244.2 KiB
2024.10.1 2025-04-20 source/ R- PeakSegOptimal_2024.10.1.tar.gz 129.4 KiB

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