Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression

Documentation for package ‘plsdof’ version 0.1-1

plsdof-package	Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression
aic	Akaike information criterion
bic	Bayesian information criterion
coef.plsdof	Regression coefficients
dA	Derivative of normalization function
dnormalize	Derivative of normalization function
dvvtz	First derivative of the projection operator
gmdl	Generalized minimum description length
information.criteria	Information criteria
kernel.pls	Kernel Partial Least Squares
kernel.pls.cv	Cross-validation for Kernel Partial Least Squares
kernel.pls.fit	Kernel Partial Least Squares fit
kernel.pls.ic	Model selection for Kernel Partial Least Squares based on information criteria
linear.pls	Linear Partial Least Squares
myrange.X	Scaling of predictor variables
normalize	Normalization of vectors
plsdof	Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression
vcov.plsdof	Variance-covariance matrix
vvtz	Projectin operator
where.max	Compute argmax of a vector
X2kernel	Computation of the kernel matrix