High-Performance Open-Source Archive
Fits a univariate-guided sparse regression (lasso), by a two-stage
procedure. The first stage fits p separate univariate models to the
response. The second stage gives more weight to the more important
univariate features, and preserves their signs. Conveniently, it returns
an objects that inherits from class glmnet, so that all of
the methods for glmnet are available. See doi:10.48550/arXiv.2501.18360 for details.
To install the uniLasso R package directly from github, run the following in R:
library(devtools)
install_github(repo="trevorhastie/uniLasso")
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