NlinTS: An R Package For Causality Detection in Time Series

The causality is an important concept that is widely studied in the literature, and has several applications, especially when modelling dependencies within complex data, such as multivariate time series. In this article, we present a theoretical description of methods from the NlinTS package, and we focus on causality measures. The package contains the classical Granger causality test. To handle non-linear time series, we propose an extension of this test using an artificial neural network. The package includes an implementation of the Transfer entropy, which is also considered as a non linear causality measure based on information theory. For discrete variables, we use the classical Shannon Transfer entropy, while for continuous variables, we adopt the k-nearest neighbors approach to estimate it.

Youssef Hmamouche

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

NlinTS, vars, lmtest, RTransferEntropy, timeSeries, Rcpp

CRAN Task Views implied by cited packages

TimeSeries, Finance, Econometrics, HighPerformanceComputing, MissingData, NumericalMathematics, SocialSciences


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For attribution, please cite this work as

Hmamouche, "The R Journal: NlinTS: An R Package For Causality Detection in Time Series", The R Journal, 2020

BibTeX citation

  author = {Hmamouche, Youssef},
  title = {The R Journal: NlinTS: An R Package For Causality Detection in Time Series},
  journal = {The R Journal},
  year = {2020},
  note = {},
  doi = {10.32614/RJ-2020-016},
  volume = {12},
  issue = {1},
  issn = {2073-4859},
  pages = {21-31}