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) when referring to this package in publications.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.1 by Chainarong Amornbunchornvej, 2 months ago

Report a bug at

Browse source code at

Authors: Chainarong Amornbunchornvej [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2

Depends on dtw, tseries, RTransferEntropy

Suggests knitr, rmarkdown

See at CRAN