Outliers in glmmTMB

From R mixed models list:

I don't know about packages that will directly work with glmmTMB objects, but computing Cook's distances can be easily done by hand. Let b be the vector with the estimated fixed effects from the model and V(b) the corresponding var-cov matrix. You can extract these with fixef() and vcov() from your model. Now leave out either a single observation or a cluster of observations (e.g., all observations corresponding to an individual) and let b_{-i} denote the estimated fixed effects when fitting the data to this subset of the dataset. Then Cook's distances is simply

D_i = (b - b_{-i})' V(b)^{-1} (b - b_{-i})

Now rinse and repeat for every i, which is easily done in a loop. It might take a while to complete depending on how complex your model is.

Some might compute D_i with V(b_{-i})^{-1} in place of V(b)^{-1}. Can be done easily at the same time, so you could do both and compare.

No files, tests, or entry points are named. Start by reviewing how the package handles model-specific performance metrics and how glmmTMB objects expose fixef() and vcov(); clarify the supported outlier method and the expected validation before implementation.