DESCRIPTION

Package: VLTimeCausality
Title: Variable-Lag Time Series Causality Inference Framework
Version: 0.1.5
Authors@R: 
    person(given = "Chainarong",
           family = "Amornbunchornvej",
           role = c("aut", "cre"),
           email = "grandca@gmail.com",
           comment =c(ORCID = "0000-0003-3131-0370"))
Description: 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.  
License: GPL-3
URL: https://github.com/DarkEyes/VLTimeSeriesCausality
BugReports: https://github.com/DarkEyes/VLTimeSeriesCausality/issues
Language: en-US
Encoding: UTF-8
LazyData: false
Depends: 
    R (>= 3.5.0),
    dtw,
    tseries,
    RTransferEntropy 
Imports:
    ggplot2 (>= 3.0)
Suggests: knitr, rmarkdown, markdown
VignetteBuilder: knitr
RoxygenNote: 7.3.1