interpolatePenalty

penalty

internal

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.

Johan Larsson

SLOPE

Sorted L1 Penalized Estimation

Jonas Wallin

Malgorzata Bogdan

Ewout van den Berg

Chiara Sabatti

Emmanuel Candes

Evan Patterson

Weijie Su

Jerome Friedman

Trevor Hastie

Rob Tibshirani

Balasubramanian Narasimhan

Noah Simon

Junyang Qian

Akarsh Goyal

interpolatePenalty function

Interpolate penalty values — interpolatePenalty

Interpolate penalty values

interpolatePenalty: Interpolate penalty values

Description

Usage

Arguments

Value