doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.">

priorityelasticnet: Comprehensive Analysis of Multi-Omics Data Using an Offset-Based Method (original) (raw)

Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) <doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.

Version: 0.2.0
Depends: R (≥ 3.5.0)
Imports: survival, glmnet, utils, checkmate, shiny, tidyr, dplyr, caret, pROC, PRROC, plotrix, ggplot2, magrittr, tibble, broom, cvms, glmSparseNet
Suggests: ipflasso, rlang, knitr, rmarkdown
Published: 2025-01-19
DOI: 10.32614/CRAN.package.priorityelasticnet
Author: Laila Qadir Musib [aut, cre], Eunice Carrasquinha [aut], Helena Mouriño [aut]
Maintainer: Laila Qadir Musib
License: GPL-3
NeedsCompilation: no
CRAN checks: priorityelasticnet results

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