asmbPLS-package         Predicting and Classifying Patient Phenotypes
                        with Multi-Omics Data
asmbPLS.cv              Cross-validation for asmbPLS to find the best
                        combinations of quantiles for prediction
asmbPLS.example         Example data for asmbPLS algorithm
asmbPLS.fit             asmbPLS for block-structured data
asmbPLS.predict         Using an asmbPLS model for prediction of new
                        samples
asmbPLSDA.cv            Cross-validation for asmbPLS-DA to find the
                        best combinations of quantiles for
                        classification
asmbPLSDA.example       Example data for asmbPLS-DA algorithm
asmbPLSDA.fit           asmbPLS-DA for block-structured data
asmbPLSDA.predict       Using an asmbPLS-DA model for classification of
                        new samples
asmbPLSDA.vote.fit      asmbPLS-DA vote model fit
asmbPLSDA.vote.predict
                        Using an asmbPLS-DA vote model for
                        classification of new samples
mbPLS.fit               mbPLS for block-structured data
meanimp                 Mean imputation for the survival time
plotCor                 Graphical output for the asmbPLS-DA framework
plotPLS                 PLS plot for asmbPLS-DA
plotRelevance           Relevance plot for asmbPLS-DA
quantileComb            Create the quantile combination set for asmbPLS
                        and asmbPLS-DA
to.categorical          Converts a class vector to a binary class
                        matrix
