miselect: Variable Selection for Multiply Imputed Data

Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) <doi:10.1080/10618600.2022.2035739>. They, by construction, force selection of the same variables across multiply imputed data. 'miselect' also provides cross validated variants of these methods.

Version:	0.9.2
Depends:	R (≥ 3.5.0)
Suggests:	mice, knitr, rmarkdown, testthat
Published:	2024-03-05
DOI:	10.32614/CRAN.package.miselect
Author:	Michael Kleinsasser [cre], Alexander Rix [aut], Jiacong Du [aut]
Maintainer:	Michael Kleinsasser <biostat-cran-manager at umich.edu>
License:	GPL-3
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	miselect results

Documentation:

Reference manual:	miselect.pdf
Vignettes:	miselect

Downloads:

Package source:	miselect_0.9.2.tar.gz
Windows binaries:	r-devel: miselect_0.9.2.zip, r-release: miselect_0.9.2.zip, r-oldrel: miselect_0.9.2.zip
macOS binaries:	r-release (arm64): miselect_0.9.2.tgz, r-oldrel (arm64): miselect_0.9.2.tgz, r-release (x86_64): miselect_0.9.2.tgz, r-oldrel (x86_64): miselect_0.9.2.tgz
Old sources:	miselect archive

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