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.

Package details

AuthorYoussef Hmamouche [aut, cre], Sylvain Barthelemy [cph]
MaintainerYoussef Hmamouche <>
LicenseGNU General Public License
Package repositoryView on CRAN
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NlinTS documentation built on July 1, 2020, 7:17 p.m.