Variable-Lag Time Series Causality Inference Framework R package VLTimeCausality version 0.1.5 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 (2021) <doi:10.1145/3441452> when referring to this package in publications. Software Depends: R (>= 3.5.0), dtw, tseries, RTransferEntropy Imports: ggplot2 (>= 3.0) Suggests: knitr, rmarkdown, markdown Chainarong Amornbunchornvej <grandca@gmail.com> Comprehensive R Archive Network (CRAN) Chainarong Amornbunchornvej [aut, cre] (ORCID: <https://orcid.org/0000-0003-3131-0370>) GPL-3 2024-05-28 application/tgz https://CRAN.R-project.org/package=VLTimeCausality doi:10.32614/CRAN.package.VLTimeCausality en-US