ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration
Author(s)
Type
Journal Article
Abstract
Summary:ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements.
Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html.
Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html.
Date Issued
2011-01-13
Date Acceptance
2010-12-07
Citation
Bioinformatics, 2011, 27 (4), pp.587-588
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Start Page
587
End Page
588
Journal / Book Title
Bioinformatics
Volume
27
Issue
4
Copyright Statement
© The Author(s) 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor
Biotechnology and Biological Sciences Research Council (BBSRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000287246000023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
BB/C519670/1
G0600609
G0801056B
G1002319
Subjects
Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
Publication Status
Published