Package: LPWC 1.0.0

Thevaa Chandereng

LPWC: Lag Penalized Weighted Correlation for Time Series Clustering

Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.

Authors:Thevaa Chandereng [aut, cre, cph], Anthony Gitter [aut, cph]

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LPWC.pdf |LPWC.html✨
LPWC/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/gitter-lab/lpwc/issues

Datasets:

On CRAN:

bioinformaticsclusteringtime-series

9 exports 20 stars 2.19 score 1 dependencies 1 mentions 17 scripts 198 downloads

Last updated 4 years agofrom:56d076d31a. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-winOKSep 13 2024
R-4.5-linuxOKSep 13 2024
R-4.4-winOKSep 13 2024
R-4.4-macOKSep 13 2024
R-4.3-winOKSep 13 2024
R-4.3-macOKSep 13 2024

Exports:best.lagcomp.corrcorr.bestlagfindCprep.datascoreweightweight.lagwt.corr

Dependencies:nleqslv

LPWC: Lag Penalized Weighted Correlation for Clustering Short Time Series

Thevaa Chandereng & Anthony Gitter

Rendered fromLPWC.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2020-01-23
Started: 2018-04-06