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 [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

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