interpolatePenalty

internal

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.

Johan Larsson

SLOPE

Sorted L1 Penalized Estimation

Jonas Wallin

Malgorzata Bogdan

Ewout van den Berg

Chiara Sabatti

Emmanuel Candes

Evan Patterson

Weijie Su

Jakub Kała

Krystyna Grzesiak

Mathurin Massias

Quentin Klopfenstein

Michal Burdukiewicz

Jerome Friedman

Trevor Hastie

Rob Tibshirani

Balasubramanian Narasimhan

Noah Simon

Junyang Qian

interpolatePenalty function

<dl><dt>penalty</dt>
<dd>current penalty sequence</dd>
<dt>x</dt>
<dd>new sequence</dd></dl>

Arguments

Jerome Friedman, Trevor Hastie, Rob Tibshirani, and Noah Simon

Author

Interpolate penalty values — interpolatePenalty

<dl>

<dt>penalty</dt>
<dd>current penalty sequence</dd>


<dt>x</dt>
<dd>new sequence</dd>

</dl>

Interpolate penalty values

interpolatePenalty: Interpolate penalty values

Description

Usage

Value

Arguments

Author