Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm or, equivalently, ordered weighted L1-norm (OWL). There is support for ordinary least-squares regression, binomial regression, multinomial regression, and poisson regression, as well as both dense and sparse predictor matrices. In addition, the package features predictor screening rules that enable efficient solutions to high-dimensional problems.
You can install the development version from GitHub with:
# install.packages("remotes") remotes::install_github("jolars/owl")
owl uses semantic versioning.
Please note that the ‘owl’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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