Package: normScore
Title: Evaluation and Ranking of Normalization Methods for Proteomics
        Data
Version: 0.99.1
Authors@R: c( 
    person(
        given = "Julia",
        family = "García Currás",
        role = c("aut", "cre"),
        email = "julia.gcurras@udc.es",
        comment = c(ORCID = "0009-0002-6354-5035"),
    ), 
    person(
        "Axencia Galega de Innovación (GAIN), Xunta de Galicia",
        role = "fnd",
        comment = "Industrial Doctorate Grant 2022-2026, Ref. 23_IN606D_2022_2707220"
    )
    )
Description: Provides tools to evaluate and rank normalization methods
        for omics datasets using a composite score derived from
        multiple performance metrics. The package is designed to
        support systematic benchmarking and comparison of normalization
        strategies across datasets and experimental settings. It also
        includes utilities for summarizing results and visualizing
        normalization performance.
License: GPL-2
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
Depends: R (>= 4.5)
Imports: stats, ggplot2, ggpubr, boot, MASS, rlang, withr
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), limma, NormalyzerDE,
        SummarizedExperiment, S4Vectors, methods, BiocStyle,
        BiocManager
VignetteBuilder: knitr
BuildVignettes: true
Config/testthat/edition: 3
biocViews: Software, Proteomics, Normalization, QualityControl,
        Preprocessing, MultipleComparison
URL: https://github.com/juliagcurras/normScore,
        https://juliagcurras.github.io/normScore/
BugReports: https://https://github.com/juliagcurras/normScore/issues
Config/roxygen2/version: 8.1.0
Config/pak/sysreqs: cmake make libicu-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-08-25 11:30:18 UTC
RemoteUrl: https://github.com/bioc/normScore
RemoteRef: HEAD
RemoteSha: 38e861691626485d5e0fff3ef1e94b9b388fae37
NeedsCompilation: no
Packaged: 2026-10-11 10:34:05 UTC; root
Author: Julia García Currás [aut, cre] (ORCID:
    <https://orcid.org/0009-0002-6354-5035>),
  Axencia Galega de Innovación (GAIN), Xunta de Galicia [fnd] (Industrial
    Doctorate Grant 2022-2026, Ref. 23_IN606D_2022_2707220)
Maintainer: Julia García Currás <julia.gcurras@udc.es>