{
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  "Date": "2023-09-01",
  "Author": "Richard A. Bilonick <rabilonick@gmail.com>",
  "Maintainer": "Richard A. Bilonick <rabilonick@gmail.com>",
  "Title": "Accuracy and Precision of Measurements",
  "Description": "N>=3 methods are used to measure each of n items. The data\nare used to estimate simultaneously systematic error (bias) and\nrandom error (imprecision). Observed measurements for each\nmethod or device are assumed to be linear functions of the\nunknown true values and the errors are assumed normally\ndistributed. Pairwise calibration curves and plots can be\neasily generated. Unlike the 'ncb.od' function, the 'omx'\nfunction builds a one-factor measurement error model using\n'OpenMx' and allows missing values, uses full information\nmaximum likelihood to estimate parameters, and provides both\nlikelihood-based and bootstrapped confidence intervals for all\nparameters, in addition to Wald-type intervals.",
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  "Packaged": {
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    "User": "root"
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  "Repository": "https://rabilon.r-universe.dev",
  "Date/Publication": "2023-08-29 14:30:47 UTC",
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    "email": "rabilonick@gmail.com",
    "login": "rabilon",
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      "package": "graphics",
      "role": "Imports"
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      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "utils",
      "role": "Imports"
    },
    {
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      "role": "Imports"
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  "_assets": [
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    "manual.pdf"
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  "_realowner": "rabilon",
  "_cranurl": false,
  "_releases": [
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      "date": "2006-02-08"
    },
    {
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      "date": "2011-08-04"
    },
    {
      "version": "2.0.1",
      "date": "2012-03-19"
    },
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      "date": "2015-11-03"
    },
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      "date": "2023-08-29"
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    "cplot",
    "errors.cb",
    "errors.nb",
    "errors.ncb",
    "lrt",
    "merror.pairs",
    "mle",
    "mle.se2",
    "ncb.od",
    "omx",
    "panel.merror",
    "precision.grubbs.cb.pd",
    "precision.grubbs.ncb.od",
    "precision.mle.ncb.od",
    "process.sd",
    "process.var.mle",
    "process.var.mle.jaech.err",
    "sigma_mle"
  ],
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      "title": "PM 2.5 Concentrations from SCAMP Collocated Samplers",
      "object": "pm2.5",
      "file": "pm2.5.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
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        "ws.conc.1",
        "ms.conc.2",
        "ws.conc.2",
        "frm"
      ],
      "rows": 77,
      "table": true,
      "tojson": true
    },
    {
      "name": "redshift",
      "title": "Spectroscopic and Photometric Galaxy Redshift Measurements",
      "object": "redshift",
      "file": "redshift.rda",
      "class": [
        "data.frame"
      ],
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        "z_fink",
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        "z_pfor",
        "z_salv",
        "z_wikl",
        "z_wuyt"
      ],
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      "table": true,
      "tojson": true
    }
  ],
  "_help": [
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      "page": "alpha.beta.sigma",
      "title": "Build an alpha-beta-sigma Matrix for Use with the cplot Function",
      "topics": [
        "alpha.beta.sigma"
      ]
    },
    {
      "page": "beta.bar",
      "title": "Compute the estimates of betas.",
      "topics": [
        "beta.bar"
      ]
    },
    {
      "page": "cb.pd",
      "title": "Compute accuracy estimates and maximum likelihood estimates of precision for the constant bias measurement error model using paired data.",
      "topics": [
        "cb.pd"
      ]
    },
    {
      "page": "cplot",
      "title": "Scatter plot of observations for a pair of devices with calibration curve.",
      "topics": [
        "cplot"
      ]
    },
    {
      "page": "errors.cb",
      "title": "Extracts the estimated measurement errors assuming there is a constant bias and using the original data.",
      "topics": [
        "errors.cb"
      ]
    },
    {
      "page": "errors.nb",
      "title": "Extracts the estimated measurement errors assuming there is no bias and using the original data.",
      "topics": [
        "errors.nb"
      ]
    },
    {
      "page": "errors.ncb",
      "title": "Extracts the estimated measurement errors assuming there is a nonconstant bias and using the original data values.",
      "topics": [
        "errors.ncb"
      ]
    },
    {
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      "title": "Likelihood ratio test for all betas equalling one.",
      "topics": [
        "lrt"
      ]
    },
    {
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      "title": "A modified '\"pairs\"' plot with all axes haveing the same range.",
      "topics": [
        "merror.pairs"
      ]
    },
    {
      "page": "mle",
      "title": "Compute maximum likelihood estimates of precision.",
      "topics": [
        "mle"
      ]
    },
    {
      "page": "mle.se2",
      "title": "Compute squared standard errors for imprecision estimates for the constant bias model using paired data.",
      "topics": [
        "mle.se2"
      ]
    },
    {
      "page": "ncb.od",
      "title": "Compute accuracy estimates and maximum likelihood estimates of precision for the nonconstant bias measurement error model using original data.",
      "topics": [
        "ncb.od"
      ]
    },
    {
      "page": "omx",
      "title": "Compute full information maximum likelihood estimates of accuracy and precision for the nonconstant bias measurement error model using 'OpenMx'.",
      "topics": [
        "omx"
      ]
    },
    {
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      "title": "Draw diagonal line (line of equality) on merror.pairs plots",
      "topics": [
        "panel.merror"
      ]
    },
    {
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      "title": "PM 2.5 Concentrations from SCAMP Collocated Samplers",
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      ]
    },
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      "page": "precision.grubbs.cb.pd",
      "title": "Computes Grubbs' method of moments estimators of precision for the constant bias model using paired differences.",
      "topics": [
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      ]
    },
    {
      "page": "precision.grubbs.ncb.od",
      "title": "Computes Grubbs' method of moments estimators of precision for the nonconstant bias model using original data values.",
      "topics": [
        "precision.grubbs.ncb.od"
      ]
    },
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      "title": "Computes iterative approximation to mle precision estimates for nonconstant bias model using original data.",
      "topics": [
        "precision.mle.ncb.od"
      ]
    },
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      "title": "Compute process standard deviation",
      "topics": [
        "process.sd"
      ]
    },
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      "page": "process.var.mle",
      "title": "Compute process variance.",
      "topics": [
        "process.var.mle"
      ]
    },
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      "page": "process.var.mle.jaech.err",
      "title": "Compute process variance but with minor error in Jaech Fortran code.",
      "topics": [
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      ]
    },
    {
      "page": "redshift",
      "title": "Spectroscopic and Photometric Galaxy Redshift Measurements",
      "topics": [
        "redshift"
      ]
    },
    {
      "page": "sigma_mle",
      "title": "Computes the ith iteration for computing the squared imprecision estimates.",
      "topics": [
        "sigma_mle"
      ]
    }
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