Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 176, Issue -, Pages 34-43Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2018.03.003
Keywords
Partial least squares; Elastic component regression; Outlier detection; Variable selection; Model population analysis
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Funding
- start-up funding from Central South University [10200-502041004]
- National Natural Science Foundation of China [61702556, 11271374]
- Mathematics and Interdisciplinary Sciences Project of Central South University
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Partial least squares (PIS) 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.
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