Package: cyclicwave 0.1.0

Şule Şevval Karakaya

cyclicwave: Cyclic Wave Analysis for Time-Series Clustering

A modular toolkit for feature extraction and density-based clustering of time-series data. It provides classical statistical, discrete wavelet, Hilbert-based phase, and circular statistical features. The Hilbert-based phase representation can support the analysis of periodic patterns, phase relationships, and circular behavior in time-series data. The package supports DBSCAN and OPTICS clustering, cluster evaluation, visualization, data preparation, and comparison of multiple feature extraction and clustering combinations. Methods are described in Karakaya and Purutcuoglu (2026) <doi:10.15672/hujms.1821412> and Karakaya et al. (2026) <doi:10.1007/978-3-032-17020-0_27>.

Authors:Şule Şevval Karakaya [aut, cre], Ahmet Bursalı [aut], Vilda Purutçuoğlu [aut]

cyclicwave_0.1.0.tar.gz
cyclicwave_0.1.0.zip(r-4.7-any)cyclicwave_0.1.0.zip(r-4.6-any)
cyclicwave_0.1.0.tar.gz(r-4.7-any)cyclicwave_0.1.0.tar.gz(r-4.6-any)
cyclicwave_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
cyclicwave/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 345 downloads 36 exports 28 dependencies

Last updated from:b53ff7f579. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK134
source / vignettesOK204
linux-releaseOK138
windows-develOK75
windows-releaseOK73
wasm-releaseOK119

Exports:a_star_statistica_statisticanalytic_signalbest_mapchord_lengthcirc_meancirc_rcirc_stdcirc_varcircular_distance_measurescluster_accuracycompare_methodscompute_phasedavies_bouldinextract_circular_featuresfind_elbowfirst_differenceflatten_with_zoneslabel_by_quantileM_statisticmardia_kurtosisnormalize_featuresplot_clusters_pcaplot_k_distanceplot_reachabilityprepare_featuresrolling_statsrun_dbscanrun_opticssegment_signalselect_numeric_columnsthin_datawavelet_approxwavelet_transformwindow_momentswrap_to_pi

Dependencies:classclicpp11dbscane1071farvergenericsggplot2gluegsignalgtableisobandlabelinglifecycleMASSmultitaperpracmaproxyR6RColorBrewerRcpprlangS7scalesvctrsviridisLitewaveslimwithr

Comparing Feature Engineering Approaches
All combinations | Preparing the data | Ground-truth labels | Defining the feature methods | Defining the clustering methods | One call to rule them all
Rendered fromcomparing-methods.Rmdusingknitr::rmarkdown

Last update: 2026-07-03
Started: 2026-07-03

Getting Started with cyclicwave
Overview | The data | Step 1: reshape into long format | Step 2: extract rolling features | Step 3: normalize | Step 4: choose epsilon (visual heuristic) | Step 5: run DBSCAN | Step 6: evaluate
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-07-03
Started: 2026-07-03