With DimensionalData everything is strongly typed, and we've adopted that for Dataset and InferenceData as well, where the underlying storage is a NamedTuple. One of the consequences of this is that if a user adds a variable to a group, then the resulting InferenceData now has a new type, and so there are frequent delays due to JIT compiling.

It would be nice if we could figure out a workaround for this. DataFrames seems to do so by having the underling storage be an OrderedCollections.LittleDict. I'm guessing DataFrames maintains efficiency when operating on columns/rows by using function barriers everywhere. We could do something similar. This would also allow InferenceData and Dataset to be modified in-place. Type inferrability for efficiency is likely only critical when operating on variables themselves.