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: 2024.9.3 Depends: R (≥ 2.10) Imports: data.table (≥ 1.9.8), ggplot2 Suggests: neuroblastoma, jointseg, testthat, future, future.apply, directlabels (≥ 2017.03.31) Published: 2024-10-02 DOI: 10.32614/CRAN.package.penaltyLearning Author: Toby Dylan Hocking [aut, cre] 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 Documentation: Reference manual: penaltyLearning.pdf Downloads: Package source: penaltyLearning_2024.9.3.tar.gz Windows binaries: r-devel: penaltyLearning_2024.9.3.zip, r-release: penaltyLearning_2024.9.3.zip, r-oldrel: penaltyLearning_2024.9.3.zip macOS binaries: r-release (arm64): penaltyLearning_2024.9.3.tgz, r-oldrel (arm64): penaltyLearning_2024.9.3.tgz, r-release (x86_64): penaltyLearning_2024.9.3.tgz, r-oldrel (x86_64): penaltyLearning_2024.9.3.tgz Old sources: penaltyLearning archive Reverse dependencies: Reverse imports: PeakSegJoint, PeakSegOptimal Reverse suggests: aum, binsegRcpp, PeakSegDP Linking:Please use the canonical form https://CRAN.R-project.org/package=penaltyLearning to link to this page.
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