Package: RegimeChange 0.1.1

José Mauricio Gómez Julián

RegimeChange: Comprehensive Regime Change Detection in Time Series

A unified framework for detecting regime changes (changepoints) in time series data. Implements both frequentist methods including Cumulative Sum (CUSUM, Page (1954) <doi:10.1093/biomet/41.1-2.100>), Pruned Exact Linear Time (PELT, Killick, Fearnhead, and Eckley (2012) <doi:10.1080/01621459.2012.737745>), Binary Segmentation, and Wild Binary Segmentation, as well as Bayesian methods such as Bayesian Online Changepoint Detection (BOCPD, Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742> and Shiryaev-Roberts. Supports offline analysis for retrospective detection and online monitoring for real-time surveillance. Provides rigorous uncertainty quantification through confidence intervals and posterior distributions. Handles univariate and multivariate series with detection of changes in mean, variance, trend, and distributional properties.

Authors:José Mauricio Gómez Julián [aut, cre]

RegimeChange_0.1.1.tar.gz
RegimeChange_0.1.1.zip(r-4.7-any)RegimeChange_0.1.1.zip(r-4.6-any)RegimeChange_0.1.1.zip(r-4.5-any)
RegimeChange_0.1.1.tgz(r-4.6-any)RegimeChange_0.1.1.tgz(r-4.5-any)
RegimeChange_0.1.1.tar.gz(r-4.7-any)RegimeChange_0.1.1.tar.gz(r-4.6-any)
RegimeChange_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RegimeChange/json (API)

# Install 'RegimeChange' in R:
install.packages('RegimeChange', repos = c('https://isadorenabi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/isadorenabi/regimechange/issues

Datasets:

On CRAN:

Conda:

changepoint-detectiontime-series

5.26 score 1 stars 2 packages 5 scripts 359 downloads 47 exports 18 dependencies

Last updated from:8049c521ff. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK225
source / vignettesOK333
linux-releaseOK244
macos-releaseOK210
macos-oldrelOK164
windows-develOK183
windows-releaseOK183
windows-oldrelOK185
wasm-releaseOK111

Exports:adjusted_rand_indexautoencoder_detectbenchmark_backendsbinary_segmentationbocpdcompare_methodsconstant_hazardcovering_metriccpc_detectcrops_detectcusumdetect_peltdetect_regimesedivisive_detectensemble_dl_detectevaluatef1_scorefpop_detectgeometric_hazardhausdorff_distanceinit_juliainverse_gamma_varjulia_availablejulia_statuskernel_cpd_detectmean_absolute_errornegbin_hazardnormal_gammanormal_known_varnormal_wishartnot_detectpeltplot_compareplot_interactiveplot_summarypoisson_gammaprecision_scorerand_indexrecall_scoreregime_detectorresetrmse_changepointsshiryaev_robertssparse_projection_cpdtcn_detecttransformer_detectwild_binary_segmentation

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecyclemagrittrR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Bayesian Changepoint Detection
Introduction | BOCPD: Bayesian Online Changepoint Detection | Prior Specification | Normal-Gamma Prior | Normal Prior with Known Variance | Poisson-Gamma Prior | Hazard Prior | Geometric Hazard | Constant Hazard | Negative Binomial Hazard | Posterior Analysis | Probability of Change at Each Point | Posterior Visualization | Run Length Distribution | Shiryaev-Roberts Procedure | Uncertainty Quantification | Credible Intervals | Existence Probability | Online Mode | Multivariate BOCPD | Comparison: BOCPD vs Frequentist | Advantages and Limitations | Advantages | Limitations | Best Practices | References
Rendered frombayesian-methods.Rmdusingknitr::rmarkdown

Last update: 2026-04-07
Started: 2026-04-07

Introduction to RegimeChange
Overview | Installation | Quick Start | Visualizing Results | Available Methods | Frequentist Methods | Bayesian Methods | Types of Changes | Prior Specification (Bayesian Methods) | Evaluation | Method Comparison | Online Detection | Uncertainty Quantification | Next Steps
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-04-07
Started: 2026-04-07

Offline Changepoint Detection
Introduction | When to Use Offline Detection | PELT: Pruned Exact Linear Time | Penalty Selection | Minimum Segment Length | Binary Segmentation | Wild Binary Segmentation | Detecting Different Types of Changes | Mean Changes | Variance Changes | Mean and Variance Changes | Visualization | Segment Analysis | Uncertainty Quantification | Evaluation Against Ground Truth | Comparing Methods | Best Practices | Tips for Difficult Cases | Closely Spaced Changepoints | Small Change Magnitudes | Many Changepoints
Rendered fromoffline-detection.Rmdusingknitr::rmarkdown

Last update: 2026-04-07
Started: 2026-04-07

Readme and manuals

Help Manual

Help pageTopics
RegimeChange: Comprehensive Regime Change Detection in Time SeriesRegimeChange-package RegimeChange
Adjusted Rand Indexadjusted_rand_index
Autoencoder-based Changepoint Detectionautoencoder_detect
Benchmark Julia vs R Performancebenchmark_backends
Binary Segmentation Changepoint Detectionbinary_segmentation
Bayesian Online Changepoint Detectionbocpd
Compare Multiple Detection Methodscompare_methods
Constant Hazard Prior (Alias for Geometric)constant_hazard
Covering Metriccovering_metric
Contrastive Predictive Coding for Changepoint Detectioncpc_detect
CROPS - Changepoints for a Range of Penaltiescrops_detect
CUSUM Changepoint Detectioncusum
Detect changepoints using enhanced PELT algorithmdetect_pelt
Detect Regime Changes in Time Seriesdetect_regimes
Economic Cycles Dataseteconomic_cycles
E-Divisive Changepoint Detectionedivisive_detect
Ensemble Deep Learning Detectionensemble_dl_detect
Evaluate Changepoint Detection Resultsevaluate
Evaluation Metrics for Changepoint Detectionevaluation
F1 Score with Tolerancef1_score
FPOP - Functional Pruning Optimal Partitioningfpop_detect
Geometric Hazard Priorgeometric_hazard
Hausdorff Distance Between Changepoint Setshausdorff_distance
Industrial Sensor Datasetindustrial_sensor
Initialize Julia Backendinit_julia
Inverse-Gamma Prior for Variance Onlyinverse_gamma_var
Check if Julia Backend is Availablejulia_available
Get Julia Backend Statusjulia_status
Kernel-based Changepoint Detectionkernel_cpd_detect
Mean Absolute Error for Changepointsmean_absolute_error
Negative Binomial Hazard Priornegbin_hazard
Normal-Gamma Prior for Unknown Mean and Variancenormal_gamma
Normal Prior for Unknown Mean with Known Variancenormal_known_var
Normal-Wishart Prior for Multivariate Datanormal_wishart
NOT - Narrowest-Over-Threshold Changepoint Detectionnot_detect
Detect changepoints using PELT algorithmpelt
Plot Multiple Results for Comparisonplot_compare
Create Interactive Plotplot_interactive
Create Multi-Panel Summary Plotplot_summary
Plot Regime Change Detection Resultsplot.regime_result
Gamma-Poisson Prior for Count Datapoisson_gamma
Precision Score with Toleranceprecision_score
Rand Index for Segmentationrand_index
Recall Score with Tolerancerecall_score
Create Online Regime Detectorregime_detector
Reset a detector to its initial statereset reset.regime_detector
RMSE for Changepointsrmse_changepoints
Shiryaev-Roberts Changepoint Detectionshiryaev_roberts
Simulated Changepoints Benchmark Datasetsimulated_changepoints
Sparse Projection Changepoint Detectionsparse_projection_cpd
TCN-based Changepoint Detectiontcn_detect
Transformer-based Changepoint Detectiontransformer_detect
Update Online Detector with New Observationupdate.regime_detector
Well Log Datasetwell_log
Wild Binary Segmentationwild_binary_segmentation