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) .

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Authors: Thevaa Chandereng and Anthony Gitter


Lag Penalized Weighted Correlation (LPWC) is a method for clustering short time series data. It is designed to identify groups of biological entities (for example, genes or phosphosites) that exhibit the same pattern of activity changes over time. LPWC allows lags to incorporate delayed responses in the biological data. For example, two genes may have similar expression changes over time, but one initiates those changes 5 minutes after the other. LPWC also supports irregular time intervals between time points collected in biological data. The LPWC website is available here.


Prior to analyzing your data, the R package needs to be installed.

The easiest way to install LPWC is through CRAN:


There are other additional ways to download LPWC. The first option is most useful if want to download a specific version of LPWC (which can be found at

devtools::install_github("gitter-lab/[email protected]")
# OR 
devtools::install_version("LPWC", version = "x.x.x", repos = "")

The second option is to download through GitHub.


After successful installation, the package must be loaded into the working space:



See the vignette for usage instructions.


If you use LPWC, please cite

Lag Penalized Weighted Correlation for Time Series Clustering. Thevaa Chandereng, Anthony Gitter. bioRxiv 2018. doi:10.1101/292615


LPWC is available under the open source MIT license.


Reference manual

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1.0.0 by Thevaa Chandereng, 2 years ago

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Browse source code at

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

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports nleqslv

Suggests testthat, rmarkdown, pkgdown, ggplot2, knitr, devtools

See at CRAN