Colon                   Gene expression data from Alon et al. (1999)
Ecoli                   Ecoli gene expression and connectivity data
                        from Kao et al. (2003)
SRBCT                   Gene expression data from Khan et al. (2001)
TFA.estimate            Prediction of Transcription Factor Activities
                        using PLS
gsim                    GSIM for binary data
gsim.cv                 Determination of the ridge regularization
                        parameter and the bandwidth to be used for
                        classification with GSIM for binary data
leukemia                Gene expression data from Golub et al. (1999)
logit.spls              Classification procedure for binary response
                        based on a logistic model, solved by a
                        combination of the Ridge Iteratively Reweighted
                        Least Squares (RIRLS) algorithm and the
                        Adaptive Sparse PLS (SPLS) regression
logit.spls.cv           Cross-validation procedure to calibrate the
                        parameters (ncomp, lambda.l1, lambda.ridge) for
                        the LOGIT-SPLS method
logit.spls.stab         Stability selection procedure to estimate
                        probabilities of selection of covariates for
                        the LOGIT-SPLS method
matrix.heatmap          Heatmap visualization for matrix
mgsim                   GSIM for categorical data
mgsim.cv                Determination of the ridge regularization
                        parameter and the bandwidth to be used for
                        classification with GSIM for categorical data
mrpls                   Ridge Partial Least Square for categorical data
mrpls.cv                Determination of the ridge regularization
                        parameter and the number of PLS components to
                        be used for classification with RPLS for
                        categorical data
multinom.spls           Classification procedure for multi-label
                        response based on a multinomial model, solved
                        by a combination of the multinomial Ridge
                        Iteratively Reweighted Least Squares
                        (multinom-RIRLS) algorithm and the Adaptive
                        Sparse PLS (SPLS) regression
multinom.spls.cv        Cross-validation procedure to calibrate the
                        parameters (ncomp, lambda.l1, lambda.ridge) for
                        the multinomial-SPLS method
multinom.spls.stab      Stability selection procedure to estimate
                        probabilities of selection of covariates for
                        the multinomial-SPLS method
pls.lda                 Classification with PLS Dimension Reduction and
                        Linear Discriminant Analysis
pls.lda.cv              Determination of the number of latent
                        components to be used for classification with
                        PLS and LDA
pls.regression          Multivariate Partial Least Squares Regression
pls.regression.cv       Determination of the number of latent
                        components to be used in PLS regression
plsgenomics-deprecated
                        Deprecated function(s) in the 'plsgenomics'
                        package
preprocess              preprocess for microarray data
rpls                    Ridge Partial Least Square for binary data
rpls.cv                 Determination of the ridge regularization
                        parameter and the number of PLS components to
                        be used for classification with RPLS for binary
                        data
sample.bin              Generates covariate matrix X with correlated
                        block of covariates and a binary random reponse
                        depening on X through a logistic model
sample.cont             Generates design matrix X with correlated block
                        of covariates and a continuous random reponse Y
                        depening on X through gaussian linear model
                        Y=XB+E
sample.multinom         Generates covariate matrix X with correlated
                        block of covariates and a multi-label random
                        reponse depening on X through a multinomial
                        model
spls                    Adaptive Sparse Partial Least Squares (SPLS)
                        regression
spls.cv                 Cross-validation procedure to calibrate the
                        parameters (ncomp, lambda.l1) of the Adaptive
                        Sparse PLS regression
spls.stab               Stability selection procedure to estimate
                        probabilities of selection of covariates for
                        the sparse PLS method
stability.selection     Stability selection procedure to select
                        covariates for the sparse PLS, LOGIT-SPLS and
                        multinomial-SPLS methods
stability.selection.heatmap
                        Heatmap visualization of estimated
                        probabilities of selection for each covariate
variable.selection      Variable selection using the PLS weights
