Multiple Imputation Random Lasso for Variable Selection with
Missing Entries
Description
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.