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