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: R. Dennis Cook, Liliana M. Forzani, Diego R. Tomassi
Title: LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
Abstract: We introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.

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Paper: LDR: A Package for Likelihood-Based Sufficient Dimension Reduction     Download PDF (Downloads: 4327)
Supplements:
LDR.zip: MATLAB source package Download (Downloads: 650; 939KB)
v39i03.m: MATLAB example code from the paper Download (Downloads: 674; 3KB)
digits.txt: Example data Download (Downloads: 766; 923KB)
nap.txt: Example data Download (Downloads: 764; 6KB)
DOI: 10.18637/jss.v039.i03

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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.