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  "Title": "Chaotic Time Series Analysis",
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  "Author": "Julio E. Sandubete [aut, cre], Lorenzo Escot [aut]",
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  "Description": "Chaos theory has been hailed as a revolution of thoughts\nand attracting ever increasing attention of many scientists\nfrom diverse disciplines. Chaotic systems are nonlinear\ndeterministic dynamic systems which can behave like an erratic\nand apparently random motion. A relevant field inside chaos\ntheory and nonlinear time series analysis is the detection of a\nchaotic behaviour from empirical time series data. One of the\nmain features of chaos is the well known initial value\nsensitivity property. Methods and techniques related to test\nthe hypothesis of chaos try to quantify the initial value\nsensitive property estimating the Lyapunov exponents. The\nDChaos package provides different useful tools and efficient\nalgorithms which test robustly the hypothesis of chaos based on\nthe Lyapunov exponent in order to know if the data generating\nprocess behind time series behave chaotically or not.",
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