The fitted log-likelihood, with df (parameter count) and nobs attributes attached so base
AIC()/BIC() work directly off it.
Usage
# S3 method for class 'corehydro_fit'
logLik(object, ...)Details
fit_gmm() is method-of-moments: GeneralizedMethodOfMoments computes no likelihood
surface, so a GMM fit reports NA for $log_likelihood, $aic and $bic, and logLik()
returns NA_real_ (still carrying df/nobs) so base AIC()/BIC() come back a
self-explanatory NA. The GMM analogue of a likelihood-based goodness-of-fit summary is the J-statistic
overidentification diagnostic already on the fit (fit$j_stat/fit$j_stat_pval; see
fit_gmm()), which is also what print() on a corehydro_fit shows in place of the
log-likelihood line for a GMM fit.