Additional models to support with summ

My general philosophy goes like this:

* The model's output needs to be relatively predictable (this is why I have not supported `lavaan`, which can be endlessly complicated and used for very different purposes)
* The model should be regression or similar — `summ` will not handle other kinds of input, like data.frames or the like. `skimr` is a package that does those things well.
* `summ` should be able to offer added value above and beyond `summary`

With that said, models I definitely plan to support are:

* `lme`

Still thinking about/auditing:

* `brmsfit` — worried about variation in output due to wide variety of options, unsure if `summ` can add benefit since refitting models isn't feasible.
* `stanreg` — less concern about variation than with `brmsfit`, but "added value" concern remains
* `polr` — Need to look more closely at the interface, make sure I know enough to make a good summary. Need to think about how to plot predictions from these models (same goes for `ordinal` package models), but that isn't essential.

Checklist of models I plan to add barring complications as I implement them (and outside contributors may feel free to do a pull request for one of these):

* [ ] `lme` (others in `nlme`?)
* [ ] `glmmTMB`

Start by reviewing the existing `summ` support and the model interfaces for `lme` and `glmmTMB`; the issue also identifies `brmsfit`, `stanreg`, and `polr` as models needing evaluation. Done means implementing support for a selected planned model and resolving its checklist item, while confirming that the output is predictable and adds value beyond `summary`.

Written by the indexing model from the issue text.