The proposal is excellent! A mixed models task view is long overdue, I suspect in part since it's a rather substantial task. So thank you for tackling this.
One thing that immediately comes to mind is location-scale models, like Hedeker's stuff (e.g., https://doi.org/10.18637/jss.v052.i12) except that MIXREGLS isn't an R package (but could link to it under Other/Links: https://voices.uchicago.edu/hedeker/mixwild_mixregls/). But glmmTMB
allows this via the dispformula
argument for the error variance / dispersion parameter. I am not aware of any packages that also allow scale modeling of other variance components, but one might cobble something together via OpenMx
. In any case, I think location-scale models should fall under Specialized models and glmmTMB
should be mentioned there.
Under Missing values, I would also mention mice
there since it can do some multilevel imputation stuff. Also, JointAI
and mdmb
should be mentioned here as packages that go beyond the mice capabilities for mixed effects models.
Also, package mbest
should be added. It fits hierarchical models using moment-based estimation. Could mention this under the Frequentist packages (it also does GLMMs), or alternatively under Specialized models but it's not really a different model per se, just an alternative to maximum likelihood estimation.
Since the group of people working with mixed effects models overlaps with people who may want to do model selection via information-theoretic methods, it might be worth mentioning packages like glmulti
and MuMIn
as well.