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Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) <doi:10.48550/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: | 1.1 |
| Depends: | R (≥ 3.5) |
| Imports: | MASS, R6, glmnet, corpcor, ggplot2, ggrepel |
| Published: | 2022-06-30 |
| 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 |
| Reference manual: | StabilizedRegression.pdf |
| Package source: | StabilizedRegression_1.1.tar.gz |
| Windows binaries: | r-devel: StabilizedRegression_1.1.zip, r-release: StabilizedRegression_1.1.zip, r-oldrel: StabilizedRegression_1.1.zip |
| macOS binaries: | r-release (arm64): StabilizedRegression_1.1.tgz, r-oldrel (arm64): StabilizedRegression_1.1.tgz, r-release (x86_64): StabilizedRegression_1.1.tgz, r-oldrel (x86_64): StabilizedRegression_1.1.tgz |
| Old sources: | StabilizedRegression archive |
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