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
crossvalidation 
pls.cv()

Model selection for Partial Least Squares based on crossvalidation 
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 
plsdofpackage

Degrees of Freedom and Statistical Inference for Partial Least Squares
Regression 
ridge.cv()

Ridge Regression. 
tr()

Trace of a matrix 
vcov(<plsdof>)

Variancecovariance matrix 
vvtz()

Projectin operator 