NlinTS: Models for Non Linear Causality Detection in Time Series
Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>, and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.
| Version: |
1.4.5 |
| Depends: |
Rcpp |
| Imports: |
methods, timeSeries, Rdpack |
| LinkingTo: |
Rcpp |
| Published: |
2021-02-02 |
| Author: |
Youssef Hmamouche [aut, cre] |
| Maintainer: |
Youssef Hmamouche <hmamoucheyussef at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GNU General Public License] |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| In views: |
TimeSeries |
| CRAN checks: |
NlinTS results |
Documentation:
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