owl: Generalized Linear Models Regularized with the Sorted L1-Norm

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.

Package details

AuthorJohan Larsson [aut, cre] (<https://orcid.org/0000-0002-4029-5945>), Jonas Wallin [aut] (<https://orcid.org/0000-0003-0381-6593>), Malgorzata Bogdan [ctb] (code adapted from 'SLOPE'), Ewout van den Berg [ctb] (code adapted from 'SLOPE'), Chiara Sabatti [ctb] (code adapted from 'SLOPE'), Emmanuel Candes [ctb] (code adapted from 'SLOPE'), Evan Patterson [ctb] (code adapted from 'SLOPE'), Weijie Su [ctb] (code adapted from 'SLOPE'), Jerome Friedman [ctb] (code adapted from 'glmnet'), Trevor Hastie [ctb] (code adapted from 'glmnet'), Rob Tibshirani [ctb] (code adapted from 'glmnet'), Balasubramanian Narasimhan [ctb] (code adapted from 'glmnet'), Noah Simon [ctb] (code adapted from 'glmnet'), Junyang Qian [ctb] (code adapted from 'glmnet')
MaintainerJohan Larsson <johan.larsson@stat.lu.se>
URL https://github.com/jolars/owl https://jolars.github.io/owl
Package repositoryView on CRAN
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owl documentation built on Feb. 11, 2020, 5:09 p.m.