I've been recently learning about mixed effects models (e.g. via
Fitzmaurice, Laird, and Ware 's book *Applied Longitudinal Analysis*) as
well as Bayesian hierarchical models (e.g. via Gelman and Hill's book *Data
Analysis Using Regression and Multilevel/Hierarchical Models*)

One curious thing I've noticed: The Bayesian literature tends to emphasize
that their models can handle covariates at multiple level of analysis. For
example, if the clustering is by person, and each person is measured in
multiple "trials," then the Bayesian hierarchical models can investigate
the main effects of covariates both at the subject and trial level, as well
as interactions across "levels."

However, I have not seen these kinds of models in the textbooks introducing
frequentist methods.

I'm not sure if this is a coincidence, or an example of where Bayesian
methods can do "more complicated things." Is it possible to use mixed
effects models (e.g. the lme4 or nlme packages in the R statistical
software) to investigate interactions of fixed effect covariates across
"levels" of analysis?

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