Given an outcome vector and model matrix, this function finds the submodel(s) minimizing the Akaike (1973, 1974) information criterion (AIC), a corrected version thereof (Sugiura, 1978; Hurvich and Tsai, 1989), and the Bayesian information criterion (BIC; Schwarz, 1978).
A model selection criterion proposed by Reiss et al. (2012), which employs cross-validation to estimate the overoptimism associated with the best candidate model of each size.
This function inputs a table of models produced by scoremods, picks out the best models according to a specified information criterion, and (optionally) generates a graphical representation of these models.
Model selection by an extended information criterion (EIC), based on nonparametric bootstrapping, was introduced by Ishiguro et al. (1997). This function implements the extension by Reiss et al. (2012) to adaptive linear model selection.
Resampling methods for adaptive linear model selection. These can be thought of as extensions of the Akaike information criterion that account for searching among candidate models. A number of functions in the package depend crucially on the leaps package, whose authors are gratefully acknowledged.
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