libPLS: An Integrated Library for Partial Least Squares Regression and Discriminant Analysis
1
College of Chemistry and Chemical Engineering, Central South University, Changsha, China
2
School of Mathematics and Statistics, Central South University, Changsha, China
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Science, Statistics
- Keywords
- outlier detection, Partial least squares, model population analysis, variable selection
- Copyright
- © 2014 Li et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Cite this article
- 2014. libPLS: An Integrated Library for Partial Least Squares Regression and Discriminant Analysis. PeerJ PrePrints 2:e190v1 https://doi.org/10.7287/peerj.preprints.190v1
Abstract
Partial least squares (PLS) have gained wide applications especially in chemometrics, metabolomics/metabonomics as well as bioinformatics. To our knowledge, an integrated PLS library that include not only basic PLS modeling algorithms but also advanced and/or recently developed methods on model assessment, outlier detection and variable selection is in lack. Here we present libPLS which provides an integrated platform for developing PLS regression and/or discriminant analysis (PLS-DA) models. This library is written in MATLAB and freely available at www.libpls.net.
Author Comment
An easy-to-use library for building PLS or PLS-DA models for analyzing chemical or biological data.