SLOPE
R
SLOPE is a package for Sorted L-One Penalized Estimation
Citation
BibTeX citation:
@misc{larsson2022,
author = {Johan Larsson and Jonas Wallin and Małgorzata Bogdan and
Ewout van den Berg and Chiara Sabatti and Emmanuel Candés and Evan
Patterson and Weijie Su and Jakub Kała and Krystyna Grzesiak and
Michal Burdukiewicz and Akarsh Goyal},
title = {SLOPE},
date = {2022-06-09},
url = {https://jolars.github.io/qualpalr},
langid = {en},
abstract = {Efficient implementations for Sorted L-One Penalized
Estimation (SLOPE): generalized linear models regularized with the
sorted L1-norm (Bogdan et al. (2015) \textless
doi:10/gfgwzt\textgreater). Supported models include ordinary
least-squares regression, binomial regression, multinomial
regression, and Poisson regression. Both dense and sparse predictor
matrices are supported. In addition, the package features predictor
screening rules that enable fast and efficient solutions to
high-dimensional problems.}
}
For attribution, please cite this work as:
Johan Larsson, Jonas Wallin, Małgorzata Bogdan, Ewout van den Berg,
Chiara Sabatti, Emmanuel Candés, Evan Patterson, et al. 2022.
“SLOPE.” R. https://jolars.github.io/qualpalr.