Sorted L1 Penalized Estimation

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.


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0.4.0 by Johan Larsson, 2 months ago,

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Authors: Johan Larsson [aut, cre] , Jonas Wallin [aut] , Malgorzata Bogdan [aut] , Ewout van den Berg [aut] , Chiara Sabatti [aut] , Emmanuel Candes [aut] , Evan Patterson [aut] , Weijie Su [aut] , 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') , Akarsh Goyal [ctb] , Jakub Kała [ctb] , Krystyna Grzesiak [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports foreach, lattice, Matrix, methods, Rcpp

Suggests caret, covr, glmnet, ggplot2, stringr, scales, tidyr, dplyr, bench, knitr, rmarkdown, spelling, testthat

Linking to Rcpp, RcppArmadillo

See at CRAN