StabilizedRegression: Stabilizing Regression and Variable Selection

Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2019) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.

Version: 1.0
Depends: R (≥ 3.5)
Imports: MASS, R6, glmnet, corpcor, ggplot2, ggrepel
Published: 2020-03-13
Author: Niklas Pfister [aut, cre], Evan Williams [ctb]
Maintainer: Niklas Pfister <np at math.ku.dk>
BugReports: https://github.com/NiklasPfister/StabilizedRegression-R/issues
License: GPL-3
NeedsCompilation: no
CRAN checks: StabilizedRegression results

Documentation:

Reference manual: StabilizedRegression.pdf

Downloads:

Package source: StabilizedRegression_1.0.tar.gz
Windows binaries: r-devel: StabilizedRegression_1.0.zip, r-release: StabilizedRegression_1.0.zip, r-oldrel: StabilizedRegression_1.0.zip
macOS binaries: r-release (arm64): StabilizedRegression_1.0.tgz, r-release (x86_64): StabilizedRegression_1.0.tgz, r-oldrel: StabilizedRegression_1.0.tgz

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