SLOPE-package: SLOPE: Sorted L1 Penalized Estimation

In SLOPE: Sorted L1 Penalized Estimation

SLOPE: Sorted L1 Penalized Estimation

Description

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. (2015) <doi:10/gfgwzt>). 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.

Author(s)

Maintainer: Johan Larsson [e-mail] (ORCID)

Authors:

• Jonas Wallin [e-mail] (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]

See Also

Useful links:

• https://jolars.github.io/SLOPE/

• https://github.com/jolars/SLOPE

• Report bugs at https://github.com/jolars/SLOPE/issues

SLOPE documentation built on June 10, 2022, 1:05 a.m.