Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19427
Title: lmSubsets: Exact Variable-Subset Selection in Linear Regression for R
Authors: Hofmann, Marc 
Gatu, Cristian 
Kontoghiorghes, Erricos John 
Colubi, Ana 
Zeileis, Achim 
Major Field of Science: Natural Sciences
Field Category: Mathematics
Keywords: Best-subset regression;Linear regression;Model selection;R;Variable selection
Issue Date: Apr-2020
Source: Journal of Statistical Software, 2020, vol. 93, no. 3
Volume: 93
Issue: 3
Journal: Journal of Statistical Software 
Abstract: An R package for computing the all-subsets regression problem is presented. The proposed algorithms are based on computational strategies recently developed. A novel algorithm for the best-subset regression problem selects subset models based on a pre-determined criterion. The package user can choose from exact and from approximation algorithms. The core of the package is written in C++ and provides an efficient implementation of all the underlying numerical computations. A case study and benchmark results illustrate the usage and the computational efficiency of the package.
URI: https://hdl.handle.net/20.500.14279/19427
ISSN: 15487660
DOI: 10.18637/jss.v093.i03
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : University of Oviedo 
Cyprus University of Technology 
University of Iasi 
University of London 
Universität Innsbruck 
Appears in Collections:Άρθρα/Articles

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