{
  "_id": "6a12a953acfb0bcc41d15cd5",
  "Package": "nonlinearTseries",
  "Type": "Package",
  "Title": "Nonlinear Time Series Analysis",
  "Version": "0.3.2",
  "Date": "2026-3-18",
  "Maintainer": "Constantino A. Garcia <constantino.garciama@ceu.es>",
  "Authors@R": "c(person(\"Constantino A.\", \"Garcia\",\nemail = \"constantino.garciama@ceu.es\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Gunther\", \"Sawitzki\",\nrole = \"ctb\"))",
  "Description": "Functions for nonlinear time series analysis. This package\npermits the computation of the most-used nonlinear\nstatistics/algorithms including generalized correlation\ndimension, information dimension, largest Lyapunov exponent,\nsample entropy and Recurrence Quantification Analysis (RQA),\namong others. Basic routines for surrogate data testing are\nalso included. Part of this work was based on the book\n\"Nonlinear time series analysis\" by Holger Kantz and Thomas\nSchreiber (ISBN: 9780521529020).",
  "License": "GPL-3",
  "URL": "https://github.com/constantino-garcia/nonlinearTseries",
  "BugReports": "https://github.com/constantino-garcia/nonlinearTseries/issues",
  "Copyright": "ANN library is copyright University of Maryland and Sunil\nArya and David Mount. R wrapper is based on the ANN library,\ncopyright Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.\nSee file COPYRIGHT for details",
  "VignetteBuilder": "knitr",
  "RoxygenNote": "7.3.3",
  "Encoding": "UTF-8",
  "Config/pak/sysreqs": "libssl-dev",
  "Repository": "https://constantino-garcia.r-universe.dev",
  "Date/Publication": "2026-03-25 14:34:49 UTC",
  "RemoteUrl": "https://github.com/constantino-garcia/nonlineartseries",
  "RemoteRef": "HEAD",
  "RemoteSha": "061c464de087970dff10b269596cfd1c657a308b",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-24 07:22:23 UTC",
    "User": "root"
  },
  "Author": "Constantino A. Garcia [aut, cre],\nGunther Sawitzki [ctb]",
  "MD5sum": "ebe3116fb1f4953d465f535d4addc382",
  "_user": "constantino-garcia",
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  "_created": "2026-05-24T07:22:23.000Z",
  "_published": "2026-05-24T07:31:31.742Z",
  "_distro": "noble",
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      "package": "Matrix",
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  "_tags": [],
  "_topics": [
    "chaos",
    "chaotic-systems",
    "nonlinear-dynamics",
    "nonlinear-time-series",
    "time-series",
    "openblas",
    "cpp"
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    "name": "Constantino Antonio García Martínez"
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  "_homeurl": "https://github.com/constantino-garcia/nonlineartseries",
  "_realowner": "constantino-garcia",
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  "_releases": [
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      "version": "0.1",
      "date": "2013-08-29"
    },
    {
      "version": "0.2",
      "date": "2013-12-15"
    },
    {
      "version": "0.2.1",
      "date": "2014-01-30"
    },
    {
      "version": "0.2.2",
      "date": "2015-06-03"
    },
    {
      "version": "0.2.3",
      "date": "2015-07-25"
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    {
      "version": "0.2.4",
      "date": "2018-08-10"
    },
    {
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      "date": "2018-09-23"
    },
    {
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      "date": "2019-02-21"
    },
    {
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      "date": "2019-11-23"
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      "date": "2020-03-22"
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      "date": "2020-05-03"
    },
    {
      "version": "0.2.10",
      "date": "2020-06-13"
    },
    {
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      "date": "2021-05-12"
    },
    {
      "version": "0.2.12",
      "date": "2022-03-30"
    },
    {
      "version": "0.3.0",
      "date": "2024-04-01"
    },
    {
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      "date": "2024-09-23"
    },
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      "date": "2026-03-23"
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  ],
  "_exports": [
    "buildTakens",
    "cliffordMap",
    "contourLines",
    "corrDim",
    "corrMatrix",
    "dfa",
    "divergence",
    "divTime",
    "embeddingDims",
    "estimate",
    "estimateEmbeddingDim",
    "FFTsurrogate",
    "findAllNeighbours",
    "fixedMass",
    "fluctuationFunction",
    "gaussMap",
    "getContourLines",
    "henon",
    "ikedaMap",
    "infDim",
    "keenanTest",
    "logisticMap",
    "logRadius",
    "lorenz",
    "maxLyapunov",
    "mcleodLiTest",
    "mutualInformation",
    "neighbourSearch",
    "nlOrder",
    "nonlinearityTest",
    "nonLinearNoiseReduction",
    "nonLinearPrediction",
    "plotLocalScalingExp",
    "poincareMap",
    "radius",
    "recurrencePlot",
    "rossler",
    "rqa",
    "sampleEntropy",
    "sampleEntropyFunction",
    "sinaiMap",
    "spaceTimePlot",
    "surrogateTest",
    "thresholdTest",
    "timeAsymmetry",
    "timeAsymmetry2",
    "timeLag",
    "tsayTest",
    "windowSizes"
  ],
  "_help": [
    {
      "page": "buildTakens",
      "title": "Build the Takens' vectors",
      "topics": [
        "buildTakens"
      ]
    },
    {
      "page": "cliffordMap",
      "title": "Clifford map",
      "topics": [
        "cliffordMap"
      ]
    },
    {
      "page": "contourLines",
      "title": "Obtain the contour lines of the space time plot.",
      "topics": [
        "contourLines"
      ]
    },
    {
      "page": "corrDim",
      "title": "Correlation sum, correlation dimension and generalized correlation dimension (order q >1).",
      "topics": [
        "corrDim",
        "corrMatrix.corrDim",
        "embeddingDims.corrDim",
        "estimate.corrDim",
        "nlOrder.corrDim",
        "plot.corrDim",
        "plotLocalScalingExp.corrDim",
        "radius.corrDim"
      ]
    },
    {
      "page": "corrMatrix",
      "title": "Returns the correlation sums stored in the _corrDim_ object",
      "topics": [
        "corrMatrix"
      ]
    },
    {
      "page": "dfa",
      "title": "Detrended Fluctuation Analysis",
      "topics": [
        "dfa",
        "estimate.dfa",
        "fluctuationFunction.dfa",
        "plot.dfa",
        "windowSizes.dfa"
      ]
    },
    {
      "page": "divergence",
      "title": "Returns the rate of divergence of close trajectories needed for the maximum Lyapunov exponent estimation.",
      "topics": [
        "divergence"
      ]
    },
    {
      "page": "divTime",
      "title": "Returns the time in which the divergence of close trajectories was computed in order to estimate the maximum Lyapunov exponent.",
      "topics": [
        "divTime"
      ]
    },
    {
      "page": "embeddingDims",
      "title": "Get the embedding dimensions used for compute a chaotic invariant.",
      "topics": [
        "embeddingDims"
      ]
    },
    {
      "page": "estimate",
      "title": "Estimate several chaotic invariants using linear regression",
      "topics": [
        "estimate"
      ]
    },
    {
      "page": "estimateEmbeddingDim",
      "title": "Estimate the embedding dimension",
      "topics": [
        "estimateEmbeddingDim"
      ]
    },
    {
      "page": "FFTsurrogate",
      "title": "Generate surrogate data using the Fourier transform",
      "topics": [
        "FFTsurrogate"
      ]
    },
    {
      "page": "findAllNeighbours",
      "title": "neighbour search",
      "topics": [
        "findAllNeighbours"
      ]
    },
    {
      "page": "fixedMass",
      "title": "fixed mass",
      "topics": [
        "fixedMass"
      ]
    },
    {
      "page": "fluctuationFunction",
      "title": "Returns the fluctuation function obtained in a DFA and represented by a _dfa_ object.",
      "topics": [
        "fluctuationFunction"
      ]
    },
    {
      "page": "gaussMap",
      "title": "Gauss map",
      "topics": [
        "gaussMap"
      ]
    },
    {
      "page": "getContourLines",
      "title": "Obtain the contour lines of the space time plot.",
      "topics": [
        "getContourLines"
      ]
    },
    {
      "page": "henon",
      "title": "Henon map",
      "topics": [
        "henon"
      ]
    },
    {
      "page": "ikedaMap",
      "title": "Ikeda map",
      "topics": [
        "ikedaMap"
      ]
    },
    {
      "page": "infDim",
      "title": "Information dimension",
      "topics": [
        "embeddingDims.infDim",
        "estimate.infDim",
        "fixedMass.infDim",
        "infDim",
        "logRadius.infDim",
        "plot.infDim",
        "plotLocalScalingExp.infDim"
      ]
    },
    {
      "page": "keenanTest",
      "title": "Keenan's test",
      "topics": [
        "keenanTest"
      ]
    },
    {
      "page": "logisticMap",
      "title": "Logistic map",
      "topics": [
        "logisticMap"
      ]
    },
    {
      "page": "logRadius",
      "title": "Obtain the the average log(radius) computed on the information dimension algorithm.",
      "topics": [
        "logRadius"
      ]
    },
    {
      "page": "lorenz",
      "title": "Lorenz system",
      "topics": [
        "lorenz"
      ]
    },
    {
      "page": "maxLyapunov",
      "title": "Maximum lyapunov exponent",
      "topics": [
        "divergence.maxLyapunov",
        "divTime.maxLyapunov",
        "embeddingDims.maxLyapunov",
        "estimate.maxLyapunov",
        "maxLyapunov",
        "plot.maxLyapunov"
      ]
    },
    {
      "page": "mcleodLiTest",
      "title": "McLeod-Li test",
      "topics": [
        "mcleodLiTest"
      ]
    },
    {
      "page": "mutualInformation",
      "title": "Average Mutual Information (AMI)",
      "topics": [
        "as.numeric.mutualInf",
        "mutualInformation",
        "plot.mutualInf",
        "[.mutualInf",
        "[[.mutualInf"
      ]
    },
    {
      "page": "neighbourSearch",
      "title": "neighbour search",
      "topics": [
        "neighbourSearch"
      ]
    },
    {
      "page": "nlOrder",
      "title": "Get the order of the nonlinear chaotic invariant.",
      "topics": [
        "nlOrder"
      ]
    },
    {
      "page": "nonlinearityTest",
      "title": "Nonlinearity test",
      "topics": [
        "nonlinearityTest"
      ]
    },
    {
      "page": "nonLinearNoiseReduction",
      "title": "Nonlinear noise reduction",
      "topics": [
        "nonLinearNoiseReduction"
      ]
    },
    {
      "page": "nonLinearPrediction",
      "title": "Nonlinear time series prediction",
      "topics": [
        "nonLinearPrediction"
      ]
    },
    {
      "page": "plotLocalScalingExp",
      "title": "Plot local scaling exponents",
      "topics": [
        "plotLocalScalingExp"
      ]
    },
    {
      "page": "poincareMap",
      "title": "Poincare map",
      "topics": [
        "poincareMap"
      ]
    },
    {
      "page": "radius",
      "title": "Get the radius of the neighborhoods used for the computations of a chaotic invariant.",
      "topics": [
        "radius"
      ]
    },
    {
      "page": "recurrencePlot",
      "title": "Recurrence Plot",
      "topics": [
        "recurrencePlot"
      ]
    },
    {
      "page": "rossler",
      "title": "Rossler system",
      "topics": [
        "rossler"
      ]
    },
    {
      "page": "rqa",
      "title": "Recurrence Quantification Analysis (RQA)",
      "topics": [
        "rqa"
      ]
    },
    {
      "page": "sampleEntropy",
      "title": "Sample Entropy (also known as Kolgomorov-Sinai Entropy)",
      "topics": [
        "embeddingDims.sampleEntropy",
        "estimate.sampleEntropy",
        "nlOrder.sampleEntropy",
        "plot.sampleEntropy",
        "radius.sampleEntropy",
        "sampleEntropy",
        "sampleEntropyFunction.sampleEntropy"
      ]
    },
    {
      "page": "sampleEntropyFunction",
      "title": "Returns the sample entropy function h_q(m,r) used for the computations of the sample entropy.",
      "topics": [
        "sampleEntropyFunction"
      ]
    },
    {
      "page": "sinaiMap",
      "title": "Sinai map",
      "topics": [
        "sinaiMap"
      ]
    },
    {
      "page": "spaceTimePlot",
      "title": "Space Time plot",
      "topics": [
        "contourLines.spaceTimePlot",
        "getContourLines.spaceTimePlot",
        "plot.spaceTimePlot",
        "spaceTimePlot"
      ]
    },
    {
      "page": "surrogateTest",
      "title": "Surrogate data testing",
      "topics": [
        "surrogateTest"
      ]
    },
    {
      "page": "thresholdTest",
      "title": "Threshold nonlinearity test",
      "topics": [
        "thresholdTest"
      ]
    },
    {
      "page": "timeAsymmetry",
      "title": "Time Reversibility statistic",
      "topics": [
        "timeAsymmetry"
      ]
    },
    {
      "page": "timeAsymmetry2",
      "title": "Time Reversibility statistic",
      "topics": [
        "timeAsymmetry2"
      ]
    },
    {
      "page": "timeLag",
      "title": "Estimate an appropiate time lag for the Takens' vectors",
      "topics": [
        "timeLag"
      ]
    },
    {
      "page": "tsayTest",
      "title": "Tsay's test",
      "topics": [
        "tsayTest"
      ]
    },
    {
      "page": "windowSizes",
      "title": "Returns the window sizes used for DFA in a _dfa_ object.",
      "topics": [
        "windowSizes"
      ]
    }
  ],
  "_readme": "https://github.com/constantino-garcia/nonlineartseries/raw/HEAD/README.md",
  "_rundeps": [
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    "rlang",
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      "homepage": "https://www.openblas.net/",
      "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)"
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      "shlib": "libstdc++",
      "package": "libstdc++6",
      "source": "gcc",
      "version": "14.2.0-4ubuntu2~24.04.1",
      "name": "c++",
      "homepage": "http://gcc.gnu.org/",
      "description": "GNU Standard C++ Library v3"
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  ],
  "_vignettes": [
    {
      "source": "nonlinearTseries_quickstart.Rmd",
      "filename": "nonlinearTseries_quickstart.html",
      "title": "nonlinearTseries Quickstart",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Data: Lorenz system",
        "Taken's embedding theorem",
        "Lyapunov exponent and dimensions",
        "Correlation dimension",
        "Sample entropy",
        "Maximum Lyapunov exponent",
        "Surrogate data testing",
        "Other functionality included in the package"
      ],
      "created": "2016-08-11 08:00:34",
      "modified": "2021-05-12 11:06:44",
      "commits": 3
    }
  ],
  "_score": 9.000009415402307,
  "_indexed": true,
  "_nocasepkg": "nonlineartseries",
  "_universes": [
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