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  "Date": "2020-08-04",
  "Title": "Multivariate Likelihood Ratio Calculation and Evaluation",
  "Authors@R": "c(person(\"David\", \"Lucy\", role = \"aut\"),\nperson(\"James\", \"Curran\", role = c(\"aut\", \"cre\"),\nemail = \"j.curran@auckland.ac.nz\"),\nperson(\"Agnieszka\", \"Martyna\", role = \"aut\",\nemail = \"ag.rzepecka@gmail.com\"))",
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  "Description": "Functions for calculating and evaluating likelihood ratios\nfrom uni/multivariate continuous observations. The package\nincludes the two-level functions to calculate the LR assuming\nmultivariate normality, and another with drops this assumption\nand uses a multivariate kernel density estimate. The package\nalso contains code to perform empirical cross entropy (ECE)\ncalibration of likelihood ratios. The LR functions are based\nprimarily on Aitken, C.G.G. and Lucy, D. (2004)\n<doi:10.1046/j.0035-9254.2003.05271.x>, \"Evaluation of trace\nevidence in the form of multivariate data,\" Journal of the\nRoyal Statistical Society: Series C (Applied Statistics), 53:\n109-122. The ECE functions are based primarily on D. Ramos and\nJ. Gonzalez-Rodrigues, (2008) \"Cross-entropy analysis of the\ninformation in forensic speaker recognition,\" in Proc. IEEE\nOdyssey, Speaker Lang. Recognit. Workshop.",
  "License": "GPL (>=2)",
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  "Roxygen": "list(markdown = TRUE)",
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  "Repository": "https://jmcurran.r-universe.dev",
  "Date/Publication": "2020-08-16 23:49:35 UTC",
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    "User": "root"
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  "Author": "David Lucy [aut],\nJames Curran [aut, cre],\nAgnieszka Martyna [aut]",
  "Maintainer": "James Curran <j.curran@auckland.ac.nz>",
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    "message": "Merge pull request #1 from mdedonno1337/calc.ece.method\n\nAdd the \"method\" parameter to the calc.ece function",
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    "two.level.normal.LR"
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        "fragment",
        "logNaO",
        "logMgO",
        "logAlO",
        "logSiO",
        "logKO",
        "logCaO",
        "logFeO"
      ],
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      "table": true,
      "tojson": true
    }
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    {
      "page": "calc.ece",
      "title": "Empirical cross-entropy (ECE) calculation",
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        "calc.ece"
      ]
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    {
      "page": "calibrate.set",
      "title": "Calculate the calibrated set of idea LRs",
      "topics": [
        "calibrate.set"
      ]
    },
    {
      "page": "comparison",
      "title": "comparison: A package for computing likelihood ratios for univariate and multivariate evidence.",
      "topics": [
        "comparison"
      ]
    },
    {
      "page": "glass",
      "title": "Glass composition data for seven elements from 200 glass items.",
      "topics": [
        "glass"
      ]
    },
    {
      "page": "plot.ece",
      "title": "An S3 plot method for objects of class 'ece'",
      "topics": [
        "plot.ece"
      ]
    },
    {
      "page": "two.level.comparison.items",
      "title": "Create a 'compitem' object.",
      "topics": [
        "two.level.comparison.items"
      ]
    },
    {
      "page": "two.level.components",
      "title": "Compute integrated means and covariances",
      "topics": [
        "two.level.components"
      ]
    },
    {
      "page": "two.level.density.LR",
      "title": "Calculate the likelihood ratio using multivariate KDEs",
      "topics": [
        "two.level.density.LR"
      ]
    },
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      "title": "Likelihood ratio calculation using Lindley's approach",
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        "two.level.lindley.LR"
      ]
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      "title": "Likelihood ratio calculation - normal",
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      ]
    }
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