Stabilizing Regression and Variable Selection

Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2019) . 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.


Reference manual

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1.0 by Niklas Pfister, a year ago

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Authors: Niklas Pfister [aut, cre] , Evan Williams [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports MASS, R6, glmnet, corpcor, ggplot2, ggrepel

See at CRAN