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Li, Hong-Dong; Xu, Qing-Song; Liang, Yi-Zeng
Chemometrics and intelligent laboratory systems, 05/2018, Letnik: 176Journal Article
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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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