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: 1.3.8
Depends: Rcpp
Imports: methods, timeSeries, Rdpack
LinkingTo: Rcpp
Published: 2020-01-15
Author: Youssef Hmamouche [aut, cre], Sylvain Barthelemy [cph]
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

Downloads:

Reference manual: NlinTS.pdf
Package source: NlinTS_1.3.8.tar.gz
Windows binaries: r-devel: NlinTS_1.3.8.zip, r-release: NlinTS_1.3.8.zip, r-oldrel: NlinTS_1.3.8.zip
macOS binaries: r-release: NlinTS_1.3.8.tgz, r-oldrel: NlinTS_1.3.8.tgz
Old sources: NlinTS archive

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