DESCRIPTION

Package: dar
Title: Differential Abundance Analysis by Consensus
Version: 1.1.2
Date: 2023-09-21
Authors@R: 
    c(person(given = "Francesc",
             family = "Catala-Moll",
             role = c("aut", "cre"),
             email = "fcatala@irsicaixa.es",
             comment = c(ORCID = "0000-0002-2354-8648"))
    )
Description: Differential abundance testing in microbiome data challenges both 
    parametric and non-parametric statistical methods, due to its sparsity, high 
    variability and compositional nature. Microbiome-specific statistical 
    methods often assume classical distribution models or take into account 
    compositional specifics. These produce results that range within the 
    specificity vs sensitivity space in such a way that type I and type II error 
    that are difficult to ascertain in real microbiome data when a single method 
    is used. Recently, a consensus approach based on multiple differential 
    abundance (DA) methods was recently suggested in order to increase robustness. 
    With dar, you can use dplyr-like pipeable sequences of DA methods and then 
    apply different consensus strategies. In this way we can obtain more reliable 
    results in a fast, consistent and reproducible way.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(roclets = c("collate", "namespace", "rd", "roxytest::testthat_roclet",
    "roxyglobals::global_roclet"), markdown = TRUE)
URL: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar,
    https://microbialgenomics-irsicaixaorg.github.io/dar/
BugReports: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar/issues
biocViews: Software, Sequencing, Microbiome, Metagenomics, MultipleComparison, Normalization
Imports: 
    cli,
    ComplexHeatmap,
    crayon,
    dplyr,
    generics,
    ggplot2,
    glue,
    gplots,
    heatmaply,
    magrittr,
    methods,
    mia,
    phyloseq,
    purrr,
    readr,
    rlang (>= 0.4.11),
    scales,
    stringr,
    tibble,
    tidyr,
    UpSetR,
    waldo
Suggests: 
    ALDEx2,
    ANCOMBC,
    apeglm,
    ashr,
    Biobase,
    corncob,
    covr,
    DESeq2,
    devtools,
    furrr,
    future,
    knitr,
    lefser,
    limma,
    Maaslin2,
    metagenomeSeq,
    microbiome,
    rmarkdown,
    roxygen2,
    roxyglobals,
    roxytest,
    rstatix,
    SummarizedExperiment,
    TreeSummarizedExperiment,
    testthat (>= 3.0.0)
Config/testthat/edition: 3
Depends: 
    R (>= 4.4.0)
LazyData: false
Collate: 
    'recipe-class.R'
    'aldex2.R'
    'ancom.R'
    'bake.R'
    'corncob.R'
    'dar-package.R'
    'data.R'
    'deseq2.R'
    'filter_by_abundance.R'
    'filter_by_prevalence.R'
    'filter_by_rarity.R'
    'filter_by_variance.R'
    'filter_taxa.R'
    'globals.R'
    'lefse.R'
    'maaslin2.R'
    'metagenomeseq.R'
    'misc.R'
    'phyloseq_qc.R'
    'pkg_check.R'
    'plot_methods.R'
    'rarefaction.R'
    'read_data.R'
    'steps_and_checks.R'
    'subset_taxa.R'
    'utils-pipe.R'
    'utils-tidy-eval.R'
    'utils.R'
    'wilcox.R'
VignetteBuilder: knitr
Config/roxyglobals/filename: globals.R
Config/roxyglobals/unique: TRUE
Config/testthat/parallel: true
RoxygenNote: 7.3.2