Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Luigi Augugliaro, Angelo Mineo, Ernst C. Wit
Title: dglars: An R Package to Estimate Sparse Generalized Linear Models
Abstract: dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013), developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013), and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012). The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.

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Paper: dglars: An R Package to Estimate Sparse Generalized Linear Models     Download PDF (Downloads: 3943)
dglars_1.0.5.tar.gz: R source package Download (Downloads: 226; 2MB)
v59i08.R: R example code from the paper Download (Downloads: 283; 37KB)

DOI: 10.18637/jss.v059.i08

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.