Functions, Classes & Methods for estimation, prediction, and
simulation (bootstrap) of VLMC -- Variable Length Markov Chain -- Models

Functions, Classes & Methods for estimation, prediction,
and simulation (bootstrap) of VLMC -- Variable Length Markov
Chain -- Models

Variable Length Markov Chains ('VLMC') Models
Functions, Classes & Methods for estimation, prediction, and
simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.

Variable-Lag Time Series Causality Inference Framework
person(given = "Chainarong",
family = "Amornbunchornvej",
role = c("aut", "cre"),
email = "grandca@gmail.com",
comment =c(ORCID = "0000-0003-3131-0370"))
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) <https://www.cs.uic.edu/~elena/pubs/amornbunchornvej-dsaa19.pdf> when referring to this package in publications.
R (>= 3.5.0), dtw, tseries, RTransferEntropy

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.