MIRL (1.0)

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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.

Maintainer: Ying Liu
Author(s): Ying Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall

License: GPL-2

Uses: boot, glmnet, MASS, mice

Released over 1 year ago.



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