Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. 2015). 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.
Maintainer: Johan Larsson johanlarsson@outlook.com (ORCID)
Authors:
Jonas Wallin jonas.wallin@stat.lu.se (ORCID)
Malgorzata Bogdan
Ewout van den Berg
Chiara Sabatti
Emmanuel Candes
Evan Patterson
Weijie Su
Jakub Kała
Krystyna Grzesiak
Michal Burdukiewicz (ORCID)
Other contributors:
Jerome Friedman (code adapted from 'glmnet') [contributor]
Trevor Hastie (code adapted from 'glmnet') [contributor]
Rob Tibshirani (code adapted from 'glmnet') [contributor]
Balasubramanian Narasimhan (code adapted from 'glmnet') [contributor]
Noah Simon (code adapted from 'glmnet') [contributor]
Junyang Qian (code adapted from 'glmnet') [contributor]
Akarsh Goyal [contributor]
Useful links: