Comparison of Mixed Models Packages

Thank you so much for all your work on mixed models throughout the R ecosystem!

I found these pages as a great summary of how mixed models are available within R. One thing that I have not found is a resource comparing the features of the various R packages for fitting mixed models. I wonder if I'm missing it somewhere that you're aware of or if it may be a good fit here?

As a specific example, I need to fit a linear mixed model with different factor levels and heteroskedastic residual error by crossing the factor levels. (To ensure that I'm being clear here, I have treatment effects for treatments A and B measured daily on days 1 to 5 and treatment effects for treatments C and D measured daily on days 1 to 7; days are treated categorically; and I need to estimate unstructured covariance with treatment by day and a random effect on the intercept.)

I know that I can do unstructured residual error with `nlme::lme()` and that I can have the `treament*day` interaction for random effects with `lme4::lmer()`, but I don't see an easy way to do both. Looking at various resources that you have helpfully compiled over time, I see that I can beat `lme4::lmer()` into submission (😄) for the unstructured residual covariance matrix (though it's not immediately clear how to use the result) that you wrote at https://bbolker.github.io/mixedmodels-misc/notes/varmats.html.

So, the question/ask is: Do you know of a table that identifies the features of mixed effects modeling packages for R?