This library provides a whole set of easy-to-use functions for building partial least squares (PLS) regression (PLSR) and discriminant analysis (PLS-DA) models as well as predictive performance evaluation. Towards building a reliable model, we also implemented a number of commonly used outlier detection and variable selection methods that can be used to "clean" your data by removing potential outliers and using only a sub-set of selected variables.

libPLS_1.6\axisrange.m
..........\carspls.m
..........\carspls_simplify.m
..........\carsplslda.m
..........\classplot2.m
..........\corn_m51.mat
..........\databin.m
..........\demo_PLS_Discriminant_Analysis.m
..........\demo_PLS_Regression.m
..........\DM2.mat
..........\histfitnew.m
..........\ks.m
..........\ldapinv.m
..........\Mannual_libPLS.pdf
..........\matlab.mat
..........\mcs.m
..........\mcuvepls.m
..........\mcuveplslda.m
..........\mwpls.m
..........\plotcars.m
..........\plotcars_plslda.m
..........\plotlda.m
..........\plotmcs.m
..........\plotspa.m
..........\pls.m
..........\pls_nipals.m
..........\plscv.m
..........\plsdcv.m
..........\plslda.m
..........\plsldacv.m
..........\plsldamccv.m
..........\plsldaval.m
..........\plsmccv.m
..........\plsrdcv.m
..........\plsval.m
..........\pretreat.m
..........\randomfrog_pls.m
..........\randomfrog_plslda.m
..........\roccurve.m
..........\sesp.m
..........\simuin.m
..........\spa.m
..........\tp.m
..........\vip.m
libPLS_1.6

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