DESCRIPTION

Package: fuseMLR
Type: Package
Title: Fusing Machine Learning in R
Version: 0.0.1
Authors@R: c(person(given = c("Cesaire", "J."),
                      family = c("K.", "Fouodo"),
                      role = c("aut", "cre"),
                      email = "cesaire.kuetefouodo@uni-luebeck.de")
            )
Maintainer: Cesaire J. K. Fouodo <cesaire.kuetefouodo@uni-luebeck.de>
Description: Recent technological advances have enable the simultaneous collection
    of multi-omics data i.e., different types or modalities of molecular data, 
    presenting challenges for integrative prediction modeling due to the heterogeneous,
    high-dimensional nature and possible missing modalities of some individuals. 
    We introduce this package for late integrative prediction modeling, enabling 
    modality-specific variable selection and prediction modeling, followed by the 
    aggregation of the modality-specific predictions to train a final meta-model. 
    This package facilitates conducting late integration predictive modeling in a 
    systematic, structured, and reproducible way.
License: GPL-3
Encoding: UTF-8
Imports: 
    R6,
    stats,
    digest
Suggests: 
    testthat (>= 3.0.0),
    UpSetR (>= 1.4.0),
    caret,
    ranger,
    glmnet,
    Boruta,
    knitr,
    rmarkdown,
    pROC,
    checkmate
Config/testthat/edition: 3
Depends: R (>= 3.6.0)
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
Collate: 
    'Data.R'
    'HashTable.R'
    'Lrner.R'
    'Model.R'
    'PredictData.R'
    'PredictLayer.R'
    'PredictMetaLayer.R'
    'Predicting.R'
    'Target.R'
    'TestData.R'
    'TestLayer.R'
    'TestMetaLayer.R'
    'Testing.R'
    'TrainData.R'
    'TrainLayer.R'
    'TrainMetaLayer.R'
    'Training.R'
    'VarSel.R'
    'bestLayerLearner.R'
    'cobra.R'
    'createCobraPred.R'
    'createDif.R'
    'createLoss.R'
    'createWeights.R'
    'multi_omics.R'
    'predict.bestLayerLearner.R'
    'predict.cobra.R'
    'weightedMeanLearner.R'
    'predict.weightedMeanLearner.R'
    'testingFunctions.R'
    'trainingFunctions.R'
VignetteBuilder: knitr, rmarkdown
BugReports: https://github.com/imbs-hl/fuseMLR/issues