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)
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
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
preprocess              preprocess for microarray data
rirls.spls              Classification by Ridge Iteratively Reweighted
                        Least Squares followed by Adaptive Sparse PLS
                        regression for binary response
rirls.spls.tune         Tuning parameters (ncomp, lambda.l1,
                        lambda.ridge) for Ridge Iteratively Reweighted
                        Least Squares followed by Adaptive Sparse PLS
                        regression for binary response, by K-fold
                        cross-validation
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 design matrix X with correlated block
                        of covariates and a binary random reponse
                        depening on X through logit 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
spls.adapt              Classification by Ridge Iteratively Reweighted
                        Least Squares followed by Adaptive Sparse PLS
                        regression for binary response
spls.adapt.tune         Tuning parameters (ncomp, lambda.l1) for
                        Adaptive Sparse PLS regression for continuous
                        response, by K-fold cross-validation
variable.selection      Variable selection using the PLS weights
