Multiple Imputation Random Lasso for Variable Selection with Missing Entries

Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) . It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.


Reference manual

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1.0 by Ying Liu, a year ago

Browse source code at

Authors: Ying Liu , Yuanjia Wang , Yang Feng , Melanie M. Wall

Documentation:   PDF Manual  

GPL-2 license

Depends on glmnet, mice, MASS, boot

See at CRAN