A function calculating Akaike's Information Criterion (AIC) based on the log-likelihood
value extracted from logLik.cmp, according to the formula
-2log-likelihood + knpar, where npar represents the number of parameters
in the fitted model, and k=2 for the usual AIC or k=log(n) (n being
the number of observations) for the so-called BIC (Bayesian Information Criterion).
# S3 method for cmp
AIC(object, ..., k = 2)an object class 'cmp' object, obtained from a call to glm.cmp
other arguments passed to or from other methods (currently unused).
numeric: the penalty per parameter to be used; the default k = 2 is the classical AIC.
A numeric value with the corresponding AIC (or BIC, or ..., depends on k).
When comparing models fitted by maximum likelihood to the same data, the smaller the AIC or BIC, the better the fit.