Adaptive Group-Regularized Logistic Elastic Net Regression

Allows the user to incorporate multiple sources of co-data (e.g., previously obtained p-values, published gene lists, and annotation) in the estimation of a logistic regression model to enhance predictive performance and feature selection, as described in Münch, Peeters, van der Vaart, and van de Wiel (2018) .

Travis-CI Build Status R package for better prediction by use of co-data



If you encounter any problems while using the package, or if you have any suggestions for additions/improvements, please don't hesitate to contact the author.

NOTE: the VIGNETTE is included in the package and contains two mehtylation data examples.


Münch, M.M., Peeters, C.F.W., van der Vaart, A.W., and van de Wiel, M.A. (2018). Adaptive group-regularized logistic elastic net regression. arXiv:1805.00389v1 [stat.ME].


This is the first version, so no changes are available

Reference manual

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0.0.1 by Magnus M. Münch, 3 years ago

Browse source code at

Authors: Magnus M. Münch

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, glmnet, Iso, pROC

Suggests knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

See at CRAN