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Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression, by Hocking, Rigaill, Vert, Bach <http://proceedings.mlr.press/v28/hocking13.html> published in proceedings of ICML2013.
| Version: | 2024.1.25 |
| Depends: | R (≥ 2.10) |
| Imports: | data.table (≥ 1.9.8), ggplot2 |
| Suggests: | neuroblastoma, jointseg, testthat, future, future.apply, directlabels (≥ 2017.03.31) |
| Published: | 2024-02-01 |
| Author: | Toby Dylan Hocking |
| Maintainer: | Toby Dylan Hocking <toby.hocking at r-project.org> |
| BugReports: | https://github.com/tdhock/penaltyLearning/issues |
| License: | GPL-3 |
| URL: | https://github.com/tdhock/penaltyLearning |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | penaltyLearning results |
| Reference manual: | penaltyLearning.pdf |
| Package source: | penaltyLearning_2024.1.25.tar.gz |
| Windows binaries: | r-devel: penaltyLearning_2024.1.25.zip, r-release: penaltyLearning_2024.1.25.zip, r-oldrel: penaltyLearning_2024.1.25.zip |
| macOS binaries: | r-release (arm64): penaltyLearning_2024.1.25.tgz, r-oldrel (arm64): penaltyLearning_2024.1.25.tgz, r-release (x86_64): penaltyLearning_2024.1.25.tgz, r-oldrel (x86_64): penaltyLearning_2024.1.25.tgz |
| Old sources: | penaltyLearning archive |
| Reverse imports: | PeakSegJoint, PeakSegOptimal |
| Reverse suggests: | aum, binsegRcpp, PeakSegDP |
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