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  "Title": "Integrative Lasso with Penalty Factors",
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  "Date": "2019-12-10",
  "Author": "Anne-Laure Boulesteix, Mathias Fuchs, Gerhard Schulze",
  "Maintainer": "Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>",
  "Description": "The core of the package is cvr2.ipflasso(), an extension\nof glmnet to be used when the (large) set of available\npredictors is partitioned into several modalities which\npotentially differ with respect to their information content in\nterms of prediction. For example, in biomedical applications\npatient outcome such as survival time or response to therapy\nmay have to be predicted based on, say, mRNA data, miRNA data,\nmethylation data, CNV data, clinical data, etc. The clinical\npredictors are on average often much more important for outcome\nprediction than the mRNA data. The ipflasso method takes this\nproblem into account by using different penalty parameters for\npredictors from different modalities. The ratio between the\ndifferent penalty parameters can be chosen from a set of\noptional candidates by cross-validation or alternatively\ngenerated from the input data.",
  "License": "GPL",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-27 10:29:14 UTC",
    "User": "root"
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  "Repository": "https://boulesteix.r-universe.dev",
  "Date/Publication": "2019-12-10 14:10:02 UTC",
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