@strengejacke I think I'll need your help for the marginal section, the different types of marginalization etc. it's quite blurry in my head to be honest ^^

@easystats/core-team remaining bits:

• add details about types of marginalization
• Drawbacks/benefits of both backends
• Polishing

emmeans relies on the delta method for non-linear transformations when those are done as part of the estimation.

@DominiqueMakowski where do you want my input, and what should it include? I must admit I have 0 familiarity with modelbased - I use marginaleffects, emmeans and ggeffects directly for plotting and estimation, so I have no idea what would be relevant here.

I must admit I have 0 familiarity

Your torment over you can make the switch now 😛

Mostly if you could take a look at the technical details section that talks about the backends

I must admit I have 0 familiarity with modelbased

modelbased currently covers classical predict() (non-focal held constant at representative values) or estimates marginal means (non-focal averaged/weighted), the latter using emmeans resp. marginaleffects via estimate_means(). estimate_means() should return identical results for both emmeans and marginaleffects backends, i.e. currently we support marginaleffects by mimicking the emmeans-approach of marginalzation.