Package: penaltyLearning 2024.9.3
penaltyLearning: Penalty Learning
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.
Authors:
penaltyLearning_2024.9.3.tar.gz
penaltyLearning_2024.9.3.zip(r-4.5)penaltyLearning_2024.9.3.zip(r-4.4)penaltyLearning_2024.9.3.zip(r-4.3)
penaltyLearning_2024.9.3.tgz(r-4.4-x86_64)penaltyLearning_2024.9.3.tgz(r-4.4-arm64)penaltyLearning_2024.9.3.tgz(r-4.3-x86_64)penaltyLearning_2024.9.3.tgz(r-4.3-arm64)
penaltyLearning_2024.9.3.tar.gz(r-4.5-noble)penaltyLearning_2024.9.3.tar.gz(r-4.4-noble)
penaltyLearning_2024.9.3.tgz(r-4.4-emscripten)penaltyLearning_2024.9.3.tgz(r-4.3-emscripten)
penaltyLearning.pdf |penaltyLearning.html✨
penaltyLearning/json (API)
NEWS
# Install 'penaltyLearning' in R: |
install.packages('penaltyLearning', repos = c('https://tdhock.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tdhock/penaltylearning/issues
- demo8 - PeakSegFPOP demo data set
- neuroblastomaProcessed - Processed neuroblastoma data set with features and targets
- notConverging - Interval regression problem that was not converging
- oneSkip - OneSkip
Last updated 2 months agofrom:1c0e2ea199. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 01 2024 |
R-4.5-win-x86_64 | OK | Nov 01 2024 |
R-4.5-linux-x86_64 | OK | Nov 01 2024 |
R-4.4-win-x86_64 | OK | Nov 01 2024 |
R-4.4-mac-x86_64 | OK | Nov 01 2024 |
R-4.4-mac-aarch64 | OK | Nov 01 2024 |
R-4.3-win-x86_64 | OK | Nov 01 2024 |
R-4.3-mac-x86_64 | OK | Nov 01 2024 |
R-4.3-mac-aarch64 | OK | Nov 01 2024 |
Exports:change.colorschange.labelschangeLabelcheck_features_targetscheck_target_predcoef.IntervalRegressionfeatureMatrixfeatureVectorgeom_tallrectGeomTallRectIntervalRegressionCVIntervalRegressionCVmarginIntervalRegressionInternalIntervalRegressionRegularizedIntervalRegressionUnregularizedlabelErrorlargestContinuousMinimumClargestContinuousMinimumRmodelSelectionmodelSelectionCmodelSelectionRplot.IntervalRegressionpredict.IntervalRegressionprint.IntervalRegressionROChangesquared.hingetargetIntervalResidualtargetIntervalROCtargetIntervalstheme_no_space
Dependencies:clicolorspacedata.tablefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr