{
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  "Package": "metabomxtr",
  "Type": "Package",
  "Title": "A package to run mixture models for truncated metabolomics data\nwith normal or lognormal distributions",
  "Version": "1.46.0",
  "Date": "2022-08-17",
  "Author": "Michael Nodzenski, Anna Reisetter, Denise Scholtens",
  "Maintainer": "Michael Nodzenski <michael.nodzenski@gmail.com>",
  "Description": "The functions in this package return optimized parameter\nestimates and log likelihoods for mixture models of truncated\ndata with normal or lognormal distributions.",
  "License": "GPL-2",
  "biocViews": "ImmunoOncology, Metabolomics, MassSpectrometry",
  "Config/pak/sysreqs": "cmake",
  "Repository": "https://bioc-release.r-universe.dev",
  "Date/Publication": "2026-04-28 12:39:29 UTC",
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    "User": "root"
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    "name": "Bioconductor",
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        "aldohexose",
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        "disaccharide.2",
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        "heptadecane",
        "nonadecane",
        "tetradecanedioic.acid",
        "pentadecanoic.acid",
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        "methyl.heptadecanoate",
        "hydrocarbon",
        "deoxyhexose",
        "glucose",
        "pentose.sugar",
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        "beta.sitosterol",
        "X1.5.anhydroglucitol",
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        "campesterol",
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    {
      "page": "metabomxtr-package",
      "title": "A package to run mixture models on truncated normal or lognormal data",
      "topics": [
        "metabomxtr-package",
        "metabomxtr"
      ]
    },
    {
      "page": "addBatchMeans",
      "title": "A function to append batch specific mean metabolite abundances to data frames containing raw values.",
      "topics": [
        "addBatchMeans"
      ]
    },
    {
      "page": "addOutlierInfo",
      "title": "A function to determine whether specific metabolite observations are outliers.",
      "topics": [
        "addOutlierInfo"
      ]
    },
    {
      "page": "allMissingLevels",
      "title": "A function to determine whether metabolite levels are present for at most one level of a categorical variable.",
      "topics": [
        "allMissingLevels"
      ]
    },
    {
      "page": "anyMissingLevels",
      "title": "A function to determine whether any level of a categorical variable has completely missing outcome (metabolite) data",
      "topics": [
        "anyMissingLevels"
      ]
    },
    {
      "page": "euMetabCData",
      "title": "A sample data set of truncated metabolomics data.",
      "topics": [
        "euMetabCData"
      ]
    },
    {
      "page": "euMetabData",
      "title": "A sample data set of truncated metabolomics data.",
      "topics": [
        "euMetabData"
      ]
    },
    {
      "page": "idMissingLevels",
      "title": "A function to determine the levels of a categorical variable with completely missing outcome data.",
      "topics": [
        "idMissingLevels"
      ]
    },
    {
      "page": "metabdata",
      "title": "A sample data set of truncated metabolomics data",
      "topics": [
        "metabdata"
      ]
    },
    {
      "page": "metabplot",
      "title": "A function to plot metabolite abundance before and after normalization.",
      "topics": [
        "metabplot"
      ]
    },
    {
      "page": "mixnorm",
      "title": "A function to perform per-metabolite batch normalization using a mixture model with batch-specific thresholds and run order correction if desired.",
      "topics": [
        "mixnorm"
      ]
    },
    {
      "page": "mxtrmod",
      "title": "A function to return optimized parameter estimates and the negative log-likelihood of mixture models for truncated normal or lognormal data",
      "topics": [
        "mxtrmod"
      ]
    },
    {
      "page": "mxtrmodLL",
      "title": "A function to return the negative log-likelihood of mixture models of truncated, normal or lognormal data",
      "topics": [
        "mxtrmodLL"
      ]
    },
    {
      "page": "mxtrmodLRT",
      "title": "A function to run likelihood ratio tests on full vs. reduced mixture models",
      "topics": [
        "mxtrmodLRT"
      ]
    },
    {
      "page": "mxtrmodstart",
      "title": "A function to generate starting parameter estimates for the optimization of mixture model parameters",
      "topics": [
        "mxtrmodstart"
      ]
    },
    {
      "page": "removeAllMissingCatVar",
      "title": "A function to remove categorical variables with insufficient data to estimate effects from mixture models",
      "topics": [
        "removeAllMissingCatVar"
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    },
    {
      "page": "removeMissingLevels",
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      "topics": [
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      ]
    },
    {
      "page": "runMxtrmod",
      "title": "A function to return optimized parameter estimates and the negative log-likelihood of mixture models for truncated normal or lognormal data",
      "topics": [
        "runMxtrmod"
      ]
    },
    {
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      "title": "~~ Methods for Function 'xdesign' ~~",
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        "xdesign,data.frame-method",
        "xdesign,ExpressionSet-method",
        "xdesign,matrix-method",
        "xdesign-methods"
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        "yvals,ExpressionSet,character-method",
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        "yvals-methods"
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