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  "Title": "Scale Space Multiresolution Analysis of Random Signals",
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  "Date": "2019-12-10",
  "Authors@R": "c(\nperson(\"Thimo\", \"Schuster\", email = \"thimo.schuster@gmail.com\", role = c(\"aut\")),\nperson(\"Roman\", \"Flury\", email = \"roman.flury@math.uzh.ch\", role = c(\"cre\", \"aut\")),\nperson(\"Leena\", \"Pasanen\", email = \"leena.pasanen@oulu.fi\", role = \"ctb\"),\nperson(\"Reinhard\", \"Furrer\", email = \"reinhard.furrer@math.uzh.ch\", role = \"ctb\"))",
  "Maintainer": "Roman Flury <roman.flury@math.uzh.ch>",
  "Description": "A method for the multiresolution analysis of spatial\nfields and images to capture scale-dependent features. mrbsizeR\nis based on scale space smoothing and uses differences of\nsmooths at neighbouring scales for finding features on\ndifferent scales. To infer which of the captured features are\ncredible, Bayesian analysis is used. The scale space\nmultiresolution analysis has three steps: (1) Bayesian signal\nreconstruction. (2) Using differences of smooths,\nscale-dependent features of the reconstructed signal can be\nfound. (3) Posterior credibility analysis of the differences of\nsmooths created. The method has first been proposed by\nHolmstrom, Pasanen, Furrer, Sain (2011)\n<DOI:10.1016/j.csda.2011.04.011>. Matlab code is available\nunder <http://cc.oulu.fi/~lpasanen/MRBSiZer/>.",
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  "URL": "https://github.com/romanflury/mrbsizeR,\nhttps://romanflury.github.io/mrbsizeR/",
  "Repository": "https://romanflury.r-universe.dev",
  "Date/Publication": "2019-12-10 12:37:18 UTC",
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        "mrbsizer"
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      "topics": [
        "CImap"
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    },
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    },
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    },
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      "page": "mrbsizeR",
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      "topics": [
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        "mrbsizeR"
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      "topics": [
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    },
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      "topics": [
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    },
    {
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      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/romanflury/actions/runs/26941821522"
    }
  ]
}