Cross-validation | Examples | Polynomial regression for the Auto data | Using cv() | Comparing competing models | Logistic regression for the Mroz data | Replicating cross-validation | Manipulating "cv" and related objects | Parallel computations | References

A preliminary example | Polynomial regression for the Auto data revisited: meta cross-validation | Mroz's logistic regression revisited | Cross-validating choice of transformations in regression | Selecting both transformations and predictors[^Venables] | References

Adding a cost criterion | Adding a model class not covered by the default cv() method | Independently sampled cases | Calling cvCompute() | Mixed-effects models | Adding a model-selection procedure | References

Efficient computations for linear and generalized linear models | Computation of the bias-corrected CV criterion and confidence intervals | Why the complement of AUC isn't a casewise CV criterion | The values of $\mathrm{cv}(\widehat{y}_i, y_i)$ that express AUC as a sum of casewise values are solutions of equation (\ref{eq:cw}), which can be written as solutions of the following system of $2^{n_v}$ linear simultaneous equations in $2n_v$ unknowns:\begin{equation}\label{eq:lin}\tag{2}(\mathbf{U} -\mathbf{Y}) \mathbf{c}_0 + \mathbf{Y} \mathbf{c}_1 | References

Example: The High-School and Beyond data | Example: Contrasting cluster-based and case-based CV | Example: Crossed random effects | References