benchmark.pls           Comparison of model selection criteria for
                        Partial Least Squares Regression.
benchmark.regression    Comparison of Partial Least Squares Regression,
                        Principal Components Regression and Ridge
                        Regression.
coef.plsdof             Regression coefficients
compute.lower.bound     Lower bound for the Degrees of Freedom
dA                      Derivative of normalization function
dnormalize              Derivative of normalization function
dvvtz                   First derivative of the projection operator
first.local.minimum     Index of the first local minimum.
information.criteria    Information criteria
kernel.pls.fit          Kernel Partial Least Squares Fit
krylov                  Krylov sequence
linear.pls.fit          Linear Partial Least Squares Fit
normalize               Normalization of vectors
pcr                     Principal Components Regression
pcr.cv                  Model selection for Princinpal Components
                        regression based on cross-validation
pls.cv                  Model selection for Partial Least Squares based
                        on cross-validation
pls.dof                 Computation of the Degrees of Freedom
pls.ic                  Model selection for Partial Least Squares based
                        on information criteria
pls.model               Partial Least Squares
plsdof-package          Degrees of Freedom and Statistical Inference
                        for Partial Least Squares Regression
ridge.cv                Ridge Regression.
tr                      Trace of a matrix
vcov.plsdof             Variance-covariance matrix
vvtz                    Projectin operator
