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VLTimeCausality  

Variable-Lag Time Series Causality Inference Framework
View on CRAN: Click here


Download and install VLTimeCausality package within the R console
Install from CRAN:
install.packages("VLTimeCausality")

Install from Github:
library("remotes")
install_github("cran/VLTimeCausality")

Install by package version:
library("remotes")
install_version("VLTimeCausality", "0.1.5")



Attach the package and use:
library("VLTimeCausality")
Maintained by
Chainarong Amornbunchornvej
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-20
Latest Update: 2022-01-24
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.
How to cite:
Chainarong Amornbunchornvej (2019). VLTimeCausality: Variable-Lag Time Series Causality Inference Framework. R package version 0.1.5, https://cran.r-project.org/web/packages/VLTimeCausality. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2019-12-20 14:50), 0.1.1 (2020-05-17 12:10), 0.1.2 (2021-03-29 08:50), 0.1.3 (2021-05-08 17:00), 0.1.4 (2022-01-24 11:52)
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