owl

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.

Installation

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("jolars/owl")

Versioning

owl uses semantic versioning.

Code of conduct

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.

Copy Link

Version

Down Chevron

Install

install.packages('owl')

Monthly Downloads

85

Version

0.1.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Last Published

February 11th, 2020

Functions in owl (0.1.1)