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NlinTS (version 1.4.5)

Models for Non Linear Causality Detection in Time Series

Description

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) , and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy , and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors . 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.

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Version

Install

install.packages('NlinTS')

Monthly Downloads

336

Version

1.4.5

License

GNU General Public License

Maintainer

Youssef Hmamouche

Last Published

February 2nd, 2021

Functions in NlinTS (1.4.5)

entropy_cont

Continuous entropy
varmlp

Artificial Neural Network VAR (Vector Auto-Regressive) model using a MultiLayer Perceptron, with the sigmoid activation function. The optimization algorithm is based on the stochastic gradient descent.
te_disc

Discrete Transfer Entropy
NlinTS-package

Models for non-linear causality detection in time series.
causality.test

The Granger causality test
df.test

Augmented Dickey_Fuller test
mi_cont

Continuous Mutual Information
entropy_disc

Discrete Entropy
nlin_causality.test

A non linear Granger causality test
te_cont

Continuous Transfer Entropy
mi_disc

Discrete multivariate Mutual Information
mi_disc_bi

Discrete bivariate Mutual Information