libPLS: An integrated library for partial least squares regression and linear discriminant analysis

• Published in: Chemometrics and intelligent laboratory systems Vol. 176; pp. 34 - 43
• Main Authors: Li, Hong-Dong, Xu, Qing-Song, Liang, Yi-Zeng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.05.2018
• Subjects: Elastic component regression; Model population analysis; Outlier detection; Partial least squares; Variable selection; Outlier detection; Elastic component regression; Model population analysis; Partial least squares; Variable selection
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2018.03.003

Partial least squares (PLS) have gained wide applications especially in chemometrics, metabolomics/metabonomics as well as bioinformatics. Here, we present libPLS, a library that integrates not only basic PLS modeling algorithms but also advanced and/or recently developed methods on model assessment, outlier detection, and variable selection. This package is featured in a set of Model Population Analysis (MPA)-type approaches that have not been integrated into a single package yet and thus functionally complement existing toolboxes. libPLS provides an integrated platform for developing PLS regression and/or linear discriminant analysis (PLS-LDA) models. It is written in MATLAB and freely available at www.libpls.net. •Provide an integrated library for partial least squares regression and discriminant analysis.•Featured in model population analysis approaches.•Contain a series of versatile variable selection methods.