Package: flowml
Type: Package
Title: A Backend for a 'nextflow' Pipeline that Performs
        Machine-Learning-Based Modeling of Biomedical Data
Version: 0.1.3
Authors@R: c(
    person("Sebastian", "Malkusch",
      email = "sebastian.malkusch@boehringer-ingelheim.com",
      role = c("aut", "cre"),
      comment = c(ORCID = "0000-0001-6766-140X")
    ),
    person("Kolja", "Becker",
      email = "kolja.becker@boehringer-ingelheim.com",
      role = c("aut"),
      comment = c(ORCID = "0000-0001-8282-5329")
    ),
    person("Alexander", "Peltzer",
      email = "alexander.peltzer@boehringer-ingelheim.com",
      role = c("ctb"),
      comment = c(ORCID = "0000-0002-6503-2180")
    ),
    person("Neslihan", "Kaya",
      email = "neslihan.kaya@boehringer-ingelheim.com",
      role = c("ctb"),
      comment = c(ORCID = "0000-0002-0213-3072")
    ),
    person(
      "Boehringer Ingelheim Ltd.",
      role = c("cph", "fnd")
    )
  )
Maintainer: Sebastian Malkusch <sebastian.malkusch@boehringer-ingelheim.com>
Description: Provides functionality to perform machine-learning-based modeling in a computation pipeline.
    Its functions contain the basic steps of machine-learning-based knowledge discovery workflows,
    including model training and optimization, model evaluation, and model testing.
    To perform these tasks, the package builds heavily on existing machine-learning packages,
    such as 'caret' <https://github.com/topepo/caret/> and associated packages.
    The package can train multiple models, optimize model hyperparameters by performing a grid search
    or a random search, and evaluates model performance by different metrics.
    Models can be validated either on a test data set, or in case of a small sample size
    by k-fold cross validation or repeated bootstrapping.
    It also allows for 0-Hypotheses generation by performing permutation experiments.
    Additionally, it offers methods of model interpretation and item categorization
    to identify the most informative features from a high dimensional data space.
    The functions of this package can easily be integrated into computation pipelines
    (e.g. 'nextflow' <https://www.nextflow.io/>) and hereby improve scalability,
    standardization, and re-producibility in the context of machine-learning.
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/Boehringer-Ingelheim/flowml
BugReports: https://github.com/Boehringer-Ingelheim/flowml/issues
Imports: ABCanalysis, caret, data.table, dplyr, fastshap, furrr,
        future, magrittr, optparse, parallel, purrr, R6, readr, rjson,
        rlang, rsample, stats, stringr, tibble, tidyr, utils, vip
RoxygenNote: 7.2.3
Collate: 'fml_resampler.R' 'fml_resample.R' 'fml_format_response.R'
        'fml_parser.R' 'fml_bootstrap.R' 'fml_categorize.R'
        'fml_example.R' 'fml_globals.R' 'fml_grids.R' 'fml_interpret.R'
        'fml_train.R' 'fml_validate.R'
Suggests: ada, adabag, arm, bartMachine, bst, C50, caTools, class,
        Cubist, e1071, earth, elasticnet, evtree, fastICA, foreach,
        frbs, gam, gbm, ggplot2, glmnet, h2o, hda, ipred, keras,
        kernlab, kknn, klaR, knitr, kohonen, lars, leaps, LiblineaR,
        LogicReg, MASS, Matrix, mboost, mda, mgcv, monomvn, neuralnet,
        nnet, nnls, pamr, partDSA, party, partykit, penalized, pls,
        plyr, proxy, quantregForest, randomForest, ranger, rFerns,
        rmarkdown, rpart, rrcov, rrcovHD, RSNNS, RWeka, sda, shapviz,
        spls, superpc, VGAM, xgboost
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2024-02-16 08:55:02 UTC; malkusch
Author: Sebastian Malkusch [aut, cre] (<https://orcid.org/0000-0001-6766-140X>),
  Kolja Becker [aut] (<https://orcid.org/0000-0001-8282-5329>),
  Alexander Peltzer [ctb] (<https://orcid.org/0000-0002-6503-2180>),
  Neslihan Kaya [ctb] (<https://orcid.org/0000-0002-0213-3072>),
  Boehringer Ingelheim Ltd. [cph, fnd]
Repository: CRAN
Date/Publication: 2024-02-16 10:40:02 UTC
Built: R 4.2.3; ; 2024-02-18 06:50:47 UTC; unix
