{
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  "Package": "msqrob2",
  "Title": "Robust statistical inference for quantitative LC-MS proteomics",
  "Version": "1.21.0",
  "Authors@R": "c(\nperson(given = \"Lieven\",\nfamily = \"Clement\",\nrole = c(\"aut\", \"cre\"),\nemail = \"lieven.clement@ugent.be\",\ncomment = c(ORCID = \"0000-0002-9050-4370\")),\nperson(given = \"Laurent\",\nfamily = \"Gatto\",\nrole = c(\"aut\"),\nemail = \"laurent.gatto@uclouvain.be\",\ncomment = c(ORCID = \"0000-0002-1520-2268\")),\nperson(family = \"Crook\",\ngiven = \"Oliver M.\",\nemail = \"oliver.crook@stats.ox.ac.uk\",\ncomment = c(ORCID = \"0000-0001-5669-8506\"),\nrole = \"aut\"),\nperson(given = \"Adriaan\",\nfamily = \"Sticker\",\nemail = \"adriaan.sticker@ugent.be\",\nrole = \"ctb\"),\nperson(given = \"Ludger\",\nfamily = \"Goeminne\",\nemail = \" ludgergoeminne@gmail.com\",\nrole = \"ctb\"),\nperson(given = \"Milan\",\nfamily = \"Malfait\",\nemail = \"milan.malfait94@gmail.com\",\ncomment = c(ORCID = \"0000-0001-9144-3701\"),\nrole = \"ctb\"),\nperson(given = \"Stijn\",\nfamily = \"Vandenbulcke\",\nemail = \"vandenbulcke.stijn@gmail.com\",\nrole = \"aut\")\n)",
  "Description": "msqrob2 provides a robust linear mixed model framework for\nassessing differential abundance in MS-based Quantitative\nproteomics experiments. Our workflows can start from raw\npeptide intensities or summarised protein expression values.\nThe model parameter estimates can be stabilized by ridge\nregression, empirical Bayes variance estimation and robust\nM-estimation. msqrob2's hurde workflow can handle missing data\nwithout having to rely on hard-to-verify imputation\nassumptions, and, outcompetes state-of-the-art methods with and\nwithout imputation for both high and low missingness. It builds\non QFeature infrastructure for quantitative mass spectrometry\ndata to store the model results together with the raw data and\npreprocessed data.",
  "License": "Artistic-2.0",
  "Collate": "'msqrob-framework.R' 'allGenerics.R' 'calculateNormFactors.R'\n'accessors.R' 'msqrob.R' 'msqrob-utils.R' 'StatModel-methods.R'\n'hypothesisTest-methods.R' 'msqrob-methods.R'\n'msqrobAggregate.R' 'topFeatures.R' 'data.R' 'msqrobQB.R'\n'msqrobHurdle-methods.R' 'collect-plot-results.R'",
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  "RoxygenNote": "7.3.3",
  "biocViews": "Proteomics, Metabolomics, MassSpectrometry,\nDifferentialExpression, MultipleComparison, Regression,\nExperimentalDesign, Software, ImmunoOncology, Normalization,\nTimeCourse, Preprocessing",
  "URL": "https://github.com/statOmics/msqrob2",
  "BugReports": "https://github.com/statOmics/msqrob2/issues",
  "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libuv1-dev\nlibxml2-dev libssl-dev zlib1g-dev",
  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:56:09 UTC",
  "RemoteUrl": "https://github.com/bioc/msqrob2",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-30 09:40:33 UTC",
    "User": "root"
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  "Author": "Lieven Clement [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-9050-4370>),\nLaurent Gatto [aut] (ORCID: <https://orcid.org/0000-0002-1520-2268>),\nOliver M. Crook [aut] (ORCID: <https://orcid.org/0000-0001-5669-8506>),\nAdriaan Sticker [ctb],\nLudger Goeminne [ctb],\nMilan Malfait [ctb] (ORCID: <https://orcid.org/0000-0001-9144-3701>),\nStijn Vandenbulcke [aut]",
  "Maintainer": "Lieven Clement <lieven.clement@ugent.be>",
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    "getCoef",
    "getContrast",
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    "getModel",
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    "msqrobAggregate",
    "msqrobCollect",
    "msqrobGlm",
    "msqrobHurdle",
    "msqrobLm",
    "msqrobLmer",
    "msqrobQB",
    "nfLogMedian",
    "nfLogMedianOfRatios",
    "plotVolcano",
    "show",
    "smallestUniqueGroups",
    "StatModel",
    "topFeatures",
    "varContrast"
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      "title": "Example data for 100 proteins",
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      "fields": [],
      "table": false,
      "tojson": false
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      "page": "dot-computeNfLogMedian",
      "title": "Helper function to calculate sample-specific normalization factors on the log2 scale using conventional median normalisation",
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        ".computeNfLogMedian"
      ]
    },
    {
      "page": "dot-computeNfLogMedianOfRatios",
      "title": "Helper function to calculate sample-specific normalization factors",
      "topics": [
        ".computeNfLogMedianOfRatios"
      ]
    },
    {
      "page": "createPairwiseContrasts",
      "title": "Construct all contrasts for all pairwise comparisons between all levels of a factor",
      "topics": [
        "createPairwiseContrasts"
      ]
    },
    {
      "page": "statModelMethods",
      "title": "Methods for StatModel class",
      "topics": [
        "getContrast",
        "getContrast,StatModel-method",
        "StatModel-method",
        "statModelMethods",
        "varContrast",
        "varContrast,StatModel-method"
      ]
    },
    {
      "page": "statModelAccessors",
      "title": "Accessor functions for StatModel class",
      "topics": [
        "getCoef",
        "getCoef,StatModel-method",
        "getDF",
        "getDF,StatModel-method",
        "getDfPosterior",
        "getDfPosterior,StatModel-method",
        "getFitMethod",
        "getFitMethod,StatModel-method",
        "getModel",
        "getModel,StatModel-method",
        "getSigma",
        "getSigma,StatModel-method",
        "getSigmaPosterior",
        "getSigmaPosterior,StatModel-method",
        "getVar",
        "getVar,StatModel-method",
        "getVarPosterior",
        "getVarPosterior,StatModel-method",
        "getVcovUnscaled",
        "getVcovUnscaled,StatModel-method",
        "statModelAccessors"
      ]
    },
    {
      "page": "hypothesisTest",
      "title": "Parameter estimates, standard errors and statistical inference on differential expression analysis",
      "topics": [
        "hypothesisTest",
        "hypothesisTest,QFeatures-method",
        "hypothesisTest,SummarizedExperiment-method",
        "hypothesisTestHurdle",
        "hypothesisTestHurdle,QFeatures-method",
        "hypothesisTestHurdle,SummarizedExperiment-method"
      ]
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      "page": "makeContrast",
      "title": "Make contrast matrix",
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        "makeContrast"
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    },
    {
      "page": "msqrob",
      "title": "Methods to fit msqrob models with ridge regression and/or random effects using lme4",
      "topics": [
        "msqrob",
        "msqrob,QFeatures-method",
        "msqrob,SummarizedExperiment-method"
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    },
    {
      "page": "msqrobAggregate",
      "title": "Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein.",
      "topics": [
        "msqrobAggregate",
        "msqrobAggregate,QFeatures-method",
        "msqrobAggregate,SummarizedExperiment-method"
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      "page": "msqrobCollect",
      "title": "Function to collect the inference tables generated by the msqrob2 statistical inference workflow.",
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        "msqrobCollect"
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    },
    {
      "page": "msqrobGlm",
      "title": "Function to fit msqrob models to peptide counts using glm",
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        "msqrobGlm"
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    },
    {
      "page": "msqrobHurdle",
      "title": "Function to fit msqrob hurdle models",
      "topics": [
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        "msqrobHurdle,QFeatures-method",
        "msqrobHurdle,SummarizedExperiment-method"
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      "title": "Function to fit msqrob models using lm and rlm",
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      "title": "Function to fit msqrob models with ridge regression and/or random effects using lme4",
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      "title": "Function to fit msqrob models to peptide counts using glm",
      "topics": [
        "msqrobQB",
        "msqrobQB,QFeatures-method",
        "msqrobQB,SummarizedExperiment-method"
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      "title": "Methods to computes sample-specific normalization factors on the log scale using conventional median summarisation.",
      "topics": [
        "nfLogMedian",
        "nfLogMedian,matrix-method",
        "nfLogMedian,QFeatures-method",
        "nfLogMedian,SummarizedExperiment-method"
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        "nfLogMedianOfRatios",
        "nfLogMedianOfRatios,matrix-method",
        "nfLogMedianOfRatios,QFeatures-method",
        "nfLogMedianOfRatios,SummarizedExperiment-method"
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      "page": "data",
      "title": "Example data for 100 proteins",
      "topics": [
        "data",
        "pe"
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      "page": "plotVolcano",
      "title": "Volcano plot",
      "topics": [
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      "title": "Smallest unique protein groups",
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    },
    {
      "page": "StatModel",
      "title": "The StatModel class for msqrob",
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        "show,StatModel-method",
        "StatModel",
        "StatModel-class"
      ]
    },
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      "page": "topTable",
      "title": "Toplist of DE proteins, peptides or features",
      "topics": [
        "topFeatures"
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