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ipflasso

Integrative Lasso with Penalty Factors

v1.1 · Dec 10, 2019 · GPL

Description

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.

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CRAN incoming feasibility

Maintainer: ‘Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Anne-Laure",
                      family = "Boulesteix",
                      role = c("aut", "cre"),
                      email = "boulesteix@ibe.med.uni-muenchen.de"),
               person(given = "Mathias",
                      family = "Fuchs",
                      role = "aut"),
               person(given = "Gerhard",
                      family = "Schulze",
                      role = "aut"))
as necessary.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Anne-Laure",
                      family = "Boulesteix",
                      role = c("aut", "cre"),
                      email = "boulesteix@ibe.med.uni-muenchen.de"),
               person(given = "Mathias",
                      family = "Fuchs",
                      role = "aut"),
               person(given = "Gerhard",
                      family = "Schulze",
                      role = "aut"))
as necessary.

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Anne-Laure",
                      family = "Boulesteix",
                      role
...[truncated]...
              person(given = "Mathias",
                      family = "Fuchs",
                      role = "aut"),
               person(given = "Gerhard",
                      family = "Schulze",
                      role = "aut"))
as necessary.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Anne-Laure",
                      family = "Boulesteix",
                      role
...[truncated]...
              person(given = "Mathias",
                      family = "Fuchs",
                      role = "aut"),
               person(given = "Gerhard",
                      family = "Schulze",
                      role = "aut"))
as necessary.

Reverse Dependencies (2)

Dependency Network

Dependencies Reverse dependencies glmnet survival priorityelasticnet prioritylasso ipflasso

Version History

new 1.1 Mar 10, 2026
updated 1.1 ← 0.2 diff Dec 9, 2019
updated 0.2 ← 0.1 diff May 9, 2019
new 0.1 Nov 23, 2015