STOPES: Selection Threshold Optimized Empirically via Splitting

A variable selection procedure for low to moderate size linear regressions models. This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model.

Version:	0.1
Imports:	changepoint, glmnet, MASS
Published:	2019-06-14
Author:	Marinela Capanu, Mihai Giurcanu, Colin Begg, and Mithat Gonen
Maintainer:	Marinela Capanu <capanum at mskcc.org>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	STOPES results

Documentation:

Reference manual:

STOPES.pdf

Downloads:

Package source:	STOPES_0.1.tar.gz
Windows binaries:	r-devel: STOPES_0.1.zip, r-release: STOPES_0.1.zip, r-oldrel: STOPES_0.1.zip
macOS binaries:	r-release (arm64): STOPES_0.1.tgz, r-oldrel (arm64): STOPES_0.1.tgz, r-release (x86_64): STOPES_0.1.tgz, r-oldrel (x86_64): STOPES_0.1.tgz

Linking:

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