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:Toby Dylan Hocking [aut, cre]

penaltyLearning_2024.9.3.tar.gz
penaltyLearning_2024.9.3.zip(r-4.7)penaltyLearning_2024.9.3.zip(r-4.6)penaltyLearning_2024.9.3.zip(r-4.5)
penaltyLearning_2024.9.3.tgz(r-4.6-x86_64)penaltyLearning_2024.9.3.tgz(r-4.6-arm64)penaltyLearning_2024.9.3.tgz(r-4.5-x86_64)penaltyLearning_2024.9.3.tgz(r-4.5-arm64)
penaltyLearning_2024.9.3.tar.gz(r-4.7-arm64)penaltyLearning_2024.9.3.tar.gz(r-4.7-x86_64)penaltyLearning_2024.9.3.tar.gz(r-4.6-arm64)penaltyLearning_2024.9.3.tar.gz(r-4.6-x86_64)
penaltyLearning_2024.9.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

6.06 score 16 stars 2 packages 119 scripts 892 downloads 30 exports 18 dependencies

Last updated from:1c0e2ea199. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK162
linux-devel-x86_64OK167
source / vignettesOK208
linux-release-arm64OK160
linux-release-x86_64OK179
macos-release-arm64OK147
macos-release-x86_64OK220
macos-oldrel-arm64OK192
macos-oldrel-x86_64OK406
windows-develOK165
windows-releaseOK148
windows-oldrelOK132
wasm-releaseOK116

Exports:change.colorschange.labelschangeLabelcheck_features_targetscheck_target_predcoef.IntervalRegressionfeatureMatrixfeatureVectorgeom_tallrectGeomTallRectIntervalRegressionCVIntervalRegressionCVmarginIntervalRegressionInternalIntervalRegressionRegularizedIntervalRegressionUnregularizedlabelErrorlargestContinuousMinimumClargestContinuousMinimumRmodelSelectionmodelSelectionCmodelSelectionRplot.IntervalRegressionpredict.IntervalRegressionprint.IntervalRegressionROChangesquared.hingetargetIntervalResidualtargetIntervalROCtargetIntervalstheme_no_space

Dependencies:clicpp11data.tablefarverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Definition of penalty function learning

Rendered fromDefinition.Rnwusingutils::Sweaveon May 31 2026.

Last update: 2017-11-27
Started: 2017-04-17

Readme and manuals

Help Manual

Help pageTopics
change colorschange.colors
change labelschange.labels
changeLabelchangeLabel
check features targetscheck_features_targets
check target predcheck_target_pred
coef IntervalRegressioncoef.IntervalRegression
PeakSegFPOP demo data setdemo8
featureMatrixfeatureMatrix
featureVectorfeatureVector
geom tallrectgeom_tallrect
GeomTallRectGeomTallRect
IntervalRegressionCVIntervalRegressionCV
IntervalRegressionCVmarginIntervalRegressionCVmargin
IntervalRegressionInternalIntervalRegressionInternal
IntervalRegressionRegularizedIntervalRegressionRegularized
IntervalRegressionUnregularizedIntervalRegressionUnregularized
Compute incorrect labelslabelError
largestContinuousMinimumClargestContinuousMinimumC
largestContinuousMinimumRlargestContinuousMinimumR
Compute exact model selection functionmodelSelection
Exact model selection functionmodelSelectionC
Exact model selection functionmodelSelectionR
Processed neuroblastoma data set with features and targetsneuroblastomaProcessed
Interval regression problem that was not convergingnotConverging
oneSkiponeSkip
plot IntervalRegressionplot.IntervalRegression
predict IntervalRegressionpredict.IntervalRegression
print IntervalRegressionprint.IntervalRegression
ROC curve for changepointsROChange
squared hingesquared.hinge
targetIntervalResidualtargetIntervalResidual
targetIntervalROCtargetIntervalROC
Compute target intervalstargetIntervals
theme no spacetheme_no_space