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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>.

Version: 1.0.0
Depends: R (≥ 3.0.2)
Imports: nleqslv
Suggests: testthat, rmarkdown, pkgdown, ggplot2, knitr, devtools
Published: 2020-01-23
DOI: 10.32614/CRAN.package.LPWC
Author: Thevaa Chandereng ORCID iD [aut, cre, cph], Anthony Gitter ORCID iD [aut, cph]
Maintainer: Thevaa Chandereng <chandereng at wisc.edu>
BugReports: https://github.com/gitter-lab/LPWC/issues
License: MIT + file LICENSE
URL: https://github.com/gitter-lab/LPWC
NeedsCompilation: yes
Citation: LPWC citation info
Materials: README NEWS
CRAN checks: LPWC results

Documentation:

Reference manual: LPWC.pdf
Vignettes: Cluster time series data

Downloads:

Package source: LPWC_1.0.0.tar.gz
Windows binaries: r-devel: LPWC_1.0.0.zip, r-release: LPWC_1.0.0.zip, r-oldrel: LPWC_1.0.0.zip
macOS binaries: r-release (arm64): LPWC_1.0.0.tgz, r-oldrel (arm64): LPWC_1.0.0.tgz, r-release (x86_64): LPWC_1.0.0.tgz, r-oldrel (x86_64): LPWC_1.0.0.tgz
Old sources: LPWC archive

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.