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  "Title": "Time Series Costationarity Determination",
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  "Date": "2023-09-06",
  "Authors@R": "c(person(\"Guy\", \"Nason\", role=c(\"aut\", \"cre\"),\nemail=\"g.nason@imperial.ac.uk\"),\nperson(\"Alessandro\", \"Cardinali\", role=c(\"aut\", \"ctb\")))",
  "Description": "Contains functions that can determine whether a time\nseries is second-order stationary or not (and hence evidence\nfor locally stationarity). Given two non-stationary series\n(i.e. locally stationary series) this package can then discover\ntime-varying linear combinations that are second-order\nstationary. Cardinali, A. and Nason, G.P. (2013)\n<doi:10.18637/jss.v055.i01>.",
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  "Author": "Guy Nason [aut, cre], Alessandro Cardinali [aut, ctb]",
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      "page": "costat-package",
      "title": "Computes localized autocovariance and searches for costationary solutions to bivariate time series.",
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        "costat"
      ]
    },
    {
      "page": "AntiAR",
      "title": "Undo autoreflection action for an EWS object (wd stationary)",
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        "AntiAR"
      ]
    },
    {
      "page": "BootTOS",
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        "BootTOS"
      ]
    },
    {
      "page": "COEFbothscale",
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        "COEFbothscale"
      ]
    },
    {
      "page": "coeftofn",
      "title": "Convert wavelet coefficients for two time-varying functions into two functions with respect to time.",
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      ]
    },
    {
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      "title": "Perform running mean smoothing of an EWS object",
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      ]
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      ]
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      "title": "Given two time series find some time-varying linear combinations that are stationary.",
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      ]
    },
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