SLOPE: Sorted L1 Penalized Estimation
Johan Larsson, Jonas Wallin, Małgorzata Bogdan, E. van den Berg, Chiara Sabatti, Emmanuel J. Candès, Evan Patterson, Weijie Su, Jakub Kała, Krystyna Grzesiak, Mathurin Massias, Quentin Klopfenstein, Michal Burdukiewicz · 2015
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.