pipeML/DESCRIPTION at main · VeraPancaldiLab/pipeML · GitHub

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Package: pipeML
Title: A flexible and modular machine learning framework designed to support leakage-free model training through custom cross-validation fold construction
Version: 0.0.1
Authors@R: c(
    person("Marcelo", "Hurtado", , "marcelo.hurtado@inserm.fr", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0004-6712-0864")),
    person("Vera", "Pancaldi", , "vera.pancaldi@inserm.fr", role = "aut",
           comment = c(ORCID = "0000-0002-7433-624X")))
Description: pipeML enables users to recompute global dataset features independently within each cross-validation fold, ensuring strict separation between training and test data, while preserving compatibility with a wide range of machine learning algorithms for both classification and survival tasks.
License: GPL (>= 3)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Imports:
    doParallel,
    dplyr,
    foreach,
    caret,
    ggplot2,
    reshape2,
    grDevices,
    parallel,
    purrr (>= 1.0.2),
    stats,
    tibble,
    tidyr,
    Boruta,
    survival,
    survminer,
    fastshap,
    dials,
    parsnip,
    rsample,
    workflows,
    tune,
    yardstick
Remotes:
    VeraPancaldiLab/multideconv,
Suggests:
    testthat (>= 3.0.0),
    knitr,
    matlib,
    C50,
    randomForest,
    glmnet,
    xgboost,
    kernlab,
    rmarkdown,
    tidyverse,
    tidymodels,
    cowplot,
    recipes,
    censored,
    flexsurv,
    coin,
    aorsf,
    WGCNA
LazyData: true
LazyDataCompression: bzip2
VignetteBuilder: knitr
Depends:
    R (>= 4.3)
Config/testthat/edition: 3
URL: https://verapancaldilab.github.io/pipeML