Package: msqrob2
Title: Robust statistical inference for quantitative LC-MS proteomics
Version: 1.18.0
Authors@R: c(
      person(given = "Lieven",
           family = "Clement",
           role = c("aut", "cre"),
           email = "lieven.clement@ugent.be",
           comment = c(ORCID = "0000-0002-9050-4370")),
      person(given = "Laurent",
             family = "Gatto",
             role = c("aut"),
             email = "laurent.gatto@uclouvain.be",
             comment = c(ORCID = "0000-0002-1520-2268")),
      person(family = "Crook",
             given = "Oliver M.",
       		   email = "oliver.crook@stats.ox.ac.uk",
       		   comment = c(ORCID = "0000-0001-5669-8506"),
       		   role = "aut"),
      person(given = "Adriaan",
             family = "Sticker",
             email = "adriaan.sticker@ugent.be",
             role = "ctb"),
      person(given = "Ludger",
             family = "Goeminne",
             email = " ludgergoeminne@gmail.com",
             role = "ctb"),
      person(given = "Milan",
             family = "Malfait",
             email = "milan.malfait94@gmail.com",
             comment = c(ORCID = "0000-0001-9144-3701"),
             role = "ctb"),
      person(given = "Stijn",
             family = "Vandenbulcke",
             email = "vandenbulcke.stijn@gmail.com",
             role = "aut")
      )
Description: msqrob2 provides a robust linear mixed model framework for
        assessing differential abundance in MS-based Quantitative
        proteomics experiments. Our workflows can start from raw
        peptide intensities or summarised protein expression values.
        The model parameter estimates can be stabilized by ridge
        regression, empirical Bayes variance estimation and robust
        M-estimation. msqrob2's hurde workflow can handle missing data
        without having to rely on hard-to-verify imputation
        assumptions, and, outcompetes state-of-the-art methods with and
        without imputation for both high and low missingness. It builds
        on QFeature infrastructure for quantitative mass spectrometry
        data to store the model results together with the raw data and
        preprocessed data.
Depends: R (>= 4.1), QFeatures (>= 1.1.2)
Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS,
        limma, SummarizedExperiment, MultiAssayExperiment, codetools
Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR,
        sessioninfo, rmarkdown, testthat, tidyverse, plotly, MsDataHub,
        MSnbase, matrixStats, MsCoreUtils, covr
License: Artistic-2.0
Collate: 'msqrob-framework.R' 'allGenerics.R' 'accessors.R' 'msqrob.R'
        'msqrob-utils.R' 'StatModel-methods.R'
        'hypothesisTest-methods.R' 'msqrob-methods.R'
        'msqrobAggregate.R' 'topFeatures.R' 'data.R' 'msqrobQB.R'
        'msqrobHurdle-methods.R'
Encoding: UTF-8
VignetteBuilder: knitr
Roxygen: list(markdown=TRUE)
RoxygenNote: 7.3.2
biocViews: Proteomics, MassSpectrometry, DifferentialExpression,
        MultipleComparison, Regression, ExperimentalDesign, Software,
        ImmunoOncology, Normalization, TimeCourse, Preprocessing
URL: https://github.com/statOmics/msqrob2
BugReports: https://github.com/statOmics/msqrob2/issues
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libxml2-dev
        libssl-dev zlib1g-dev
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2025-10-29 15:09:38 UTC
RemoteUrl: https://github.com/bioc/msqrob2
RemoteRef: RELEASE_3_22
RemoteSha: eb5e42c8b967b542575023c98015f37683405b0f
NeedsCompilation: no
Packaged: 2026-02-03 08:51:00 UTC; root
Author: Lieven Clement [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-9050-4370>),
  Laurent Gatto [aut] (ORCID: <https://orcid.org/0000-0002-1520-2268>),
  Oliver M. Crook [aut] (ORCID: <https://orcid.org/0000-0001-5669-8506>),
  Adriaan Sticker [ctb],
  Ludger Goeminne [ctb],
  Milan Malfait [ctb] (ORCID: <https://orcid.org/0000-0001-9144-3701>),
  Stijn Vandenbulcke [aut]
Maintainer: Lieven Clement <lieven.clement@ugent.be>
Built: R 4.5.2; ; 2026-02-03 08:53:17 UTC; windows
