I've built a relatively large negative binomial GLMM (~50000 observations, 10 covariates in conditional model, 1 covariate as zero-inflation model, 3 random intercepts (650, 26, 26 levels in each group respectively), and a number of random slopes). The model was fitted with the package
glmmTMB in R.
I'm trying to build confidence intervals for my covariates, and I'm trying to figure out the advantages of building them by likelihood profile vs bootstrapping. Is there a reason to use one over the other?