Package: POMA
Title: User-friendly Workflow for Omics Data Analysis
Version: 1.6.0
Authors@R: 
    c(person(given = "Pol",
             family = "Castellano-Escuder",
             role = c("aut", "cre"),
             email = "polcaes@gmail.com",
             comment = c(ORCID = "0000-0001-6466-877X"))
             )
Description: 
    A structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploration, 
    and statistical analysis of omics datasets. The main aim of POMA is to enable a flexible data cleaning and 
    statistical analysis processes in one comprehensible and user-friendly R package. This package also has a Shiny app 
    version that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny.
License: GPL-3
Encoding: UTF-8
LazyData: true
biocViews: MassSpectrometry, Metabolomics, Proteomics, Software,
        StatisticalMethod, Visualization, Preprocessing, Normalization,
        ReportWriting
Imports: broom, caret, ComplexHeatmap, dplyr, e1071, ggplot2, ggrepel,
        glasso (>= 1.11), glmnet, impute, knitr, limma, magrittr,
        mixOmics, randomForest, RankProd (>= 3.14), rmarkdown,
        SummarizedExperiment, tibble, tidyr, vegan
Suggests: BiocStyle, covr, ggraph, patchwork, plotly, tidyverse,
        testthat (>= 2.3.2)
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2
Depends: R (>= 4.0)
VignetteBuilder: knitr
URL: https://github.com/pcastellanoescuder/POMA
BugReports: https://github.com/pcastellanoescuder/POMA/issues
git_url: https://git.bioconductor.org/packages/POMA
git_branch: RELEASE_3_15
git_last_commit: 2ed1380
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-26
NeedsCompilation: no
Packaged: 2022-04-27 00:30:24 UTC; biocbuild
Author: Pol Castellano-Escuder [aut, cre]
    (<https://orcid.org/0000-0001-6466-877X>)
Maintainer: Pol Castellano-Escuder <polcaes@gmail.com>
Built: R 4.2.0; ; 2022-04-27 09:54:06 UTC; windows
