interpolateCoefficients

beta

<p>a list generated from <code><a rd-options="=interpolatePenalty" href="/link/interpolatePenalty()?package=owl&version=0.1.1&to=%3DinterpolatePenalty" data-mini-rdoc="=interpolatePenalty::interpolatePenalty()">interpolatePenalty()</a></code></p>

interpolation_list

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>)
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.

Johan Larsson

Generalized Linear Models Regularized with the Sorted L1-Norm

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

interpolateCoefficients function

<p>a list generated from <code><a rd-options='=interpolatePenalty' href='interpolatePenalty()'>interpolatePenalty()</a></code></p>

Interpolate coefficients — interpolateCoefficients

Interpolate coefficients

interpolateCoefficients: Interpolate coefficients

Description

Usage

Arguments

Value