Function reference • plsdof

All functions
`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` `plsdof`	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

Reference