This generic function calculates the small-sample corrected Akaike information criterion,
for one or several
fitted model objects for which a log-likelihood value can be obtained,
according to the formula *-2*log-likelihood +
2*npar*(nobs/(nobs-npar-2))*, where *npar* represents the
number of parameters and *nobs* the number of
observations in the fitted model.

1 |

`object` |
An object of a suitable class for the AICc to be
calculated - usually a |

`...` |
Some methods for this generic function may take additional, optional arguments. At present none do. |

Returns a numeric value with the corresponding AICc.

Maarten Speekenbrink

Hurvich, C.M. and Tsai, C.L. (1989) "Regression and Time Series Model Selection in Small Samples", *Biometrika*,
**76**, 297-307.

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