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: | 2019.5.29 |
Depends: | R (≥ 2.10) |
Imports: | data.table (≥ 1.9.8), geometry, ggplot2 |
Suggests: | Segmentor3IsBack, neuroblastoma, microbenchmark, testthat, future, future.apply, directlabels (≥ 2017.03.31) |
Published: | 2019-06-09 |
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_2019.5.29.tar.gz |
Windows binaries: | r-devel: penaltyLearning_2019.5.29.zip, r-release: penaltyLearning_2019.5.29.zip, r-oldrel: penaltyLearning_2019.5.29.zip |
OS X binaries: | r-release: penaltyLearning_2019.5.29.tgz, r-oldrel: penaltyLearning_2019.5.29.tgz |
Old sources: | penaltyLearning archive |
Reverse imports: | PeakSegJoint, PeakSegOptimal |
Reverse suggests: | PeakSegDisk, PeakSegDP |
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