DChaos: Chaotic Time Series Analysis

Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour from empirical time series data. One of the main features of chaos is the well known initial value sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify the initial value sensitive property estimating the Lyapunov exponents. The DChaos package provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos based on the Lyapunov exponent in order to know if the data generating process behind time series behave chaotically or not.

Version: 0.1-6
Imports: xts, zoo, outliers, nnet, pracma, sandwich
Published: 2021-02-10
Author: Julio E. Sandubete [aut, cre], Lorenzo Escot [aut]
Maintainer: Julio E. Sandubete <jsandube at ucm.es>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: TimeSeries
CRAN checks: DChaos results

Downloads:

Reference manual: DChaos.pdf
Package source: DChaos_0.1-6.tar.gz
Windows binaries: r-devel: DChaos_0.1-6.zip, r-release: DChaos_0.1-6.zip, r-oldrel: DChaos_0.1-6.zip
macOS binaries: r-release (arm64): DChaos_0.1-6.tgz, r-release (x86_64): DChaos_0.1-6.tgz, r-oldrel: DChaos_0.1-6.tgz
Old sources: DChaos archive

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