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: | 2018.09.04 |
| Depends: | R (≥ 2.10), data.table (≥ 1.9.8) |
| Imports: | geometry, ggplot2 |
| Suggests: | Segmentor3IsBack, neuroblastoma, microbenchmark, testthat, future, future.apply, directlabels (≥ 2017.03.31) |
| Published: | 2018-09-10 |
| Author: | Toby Dylan Hocking |
| Maintainer: | Toby Dylan Hocking <toby.hocking at r-project.org> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | penaltyLearning results |
| Reference manual: | penaltyLearning.pdf |
| Package source: | penaltyLearning_2018.09.04.tar.gz |
| Windows binaries: | r-devel: penaltyLearning_2018.09.04.zip, r-release: penaltyLearning_2018.09.04.zip, r-oldrel: penaltyLearning_2018.09.04.zip |
| OS X binaries: | r-release: penaltyLearning_2018.09.04.tgz, r-oldrel: penaltyLearning_2018.09.04.tgz |
| Old sources: | penaltyLearning archive |
| Reverse imports: | PeakSegJoint, PeakSegOptimal |
| Reverse suggests: | PeakSegDisk, PeakSegDP |
Please use the canonical form https://CRAN.R-project.org/package=penaltyLearning to link to this page.