Package: MIRL
Type: Package
Title: Multiple Imputation Random Lasso for Variable Selection with
        Missing Entries
Version: 1.0
Author: Ying Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall
Maintainer: Ying Liu <summeryingl@gmail.com>
Description: Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. 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.
License: GPL-2
Depends: glmnet,mice,MASS,boot
NeedsCompilation: no
Packaged: 2018-04-11 15:54:39 UTC; summe
Repository: CRAN
Date/Publication: 2018-04-11 16:42:22 UTC
