VLTimeCausality: Variable-Lag Time Series Causality Inference Framework

A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2019) <arXiv:1912.10829> when referring to this package in publications.

Version: 0.1.1
Depends: R (≥ 3.5.0), dtw, tseries, RTransferEntropy
Imports: ggplot2 (≥ 3.0)
Suggests: knitr, rmarkdown
Published: 2020-05-17
Author: Chainarong Amornbunchornvej ORCID iD [aut, cre]
Maintainer: Chainarong Amornbunchornvej <grandca at gmail.com>
BugReports: https://github.com/DarkEyes/VLTimeSeriesCausality/issues
License: GPL-3
URL: https://github.com/DarkEyes/VLTimeSeriesCausality
NeedsCompilation: no
Language: en-US
Citation: VLTimeCausality citation info
Materials: README NEWS
CRAN checks: VLTimeCausality results


Reference manual: VLTimeCausality.pdf
Vignettes: VLTimeCausality_demo
Package source: VLTimeCausality_0.1.1.tar.gz
Windows binaries: r-devel: VLTimeCausality_0.1.1.zip, r-release: VLTimeCausality_0.1.1.zip, r-oldrel: VLTimeCausality_0.1.1.zip
macOS binaries: r-release: VLTimeCausality_0.1.1.tgz, r-oldrel: VLTimeCausality_0.1.1.tgz
Old sources: VLTimeCausality archive


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