owl-package: owl: Generalized Linear Models Regularized with the Sorted...

In jolars/prague: Generalized Linear Models Regularized with the Sorted L1-Norm

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>) or, equivalently, ordered weighted L1-norm (OWL). 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)

Other contributors:

• Malgorzata Bogdan (code adapted from 'SLOPE') [contributor]

• Ewout van den Berg (code adapted from 'SLOPE') [contributor]

• Chiara Sabatti (code adapted from 'SLOPE') [contributor]

• Emmanuel Candes (code adapted from 'SLOPE') [contributor]

• Evan Patterson (code adapted from 'SLOPE') [contributor]

• Weijie Su (code adapted from 'SLOPE') [contributor]

• 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]

See Also

Useful links:

• https://github.com/jolars/owl

• https://jolars.github.io/owl

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

jolars/prague documentation built on March 4, 2020, 7:13 p.m.