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Additional models to support with summ #16
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Suggestions are welcome. Hopefully soon I'll better document to outsiders how to make a |
How about adding the support for packages supporting mixed models with specified residual covariance, like nlme glmmTMB, glmPQL? lme4 cannot do that, which eliminates it from the analysis of longitudinal trials, where the assumptions made by lme4 are totally unrealistic. |
I will look into that, @some-r-user. I had in the past looked into |
It might be cool to see support for |
Hello everybody, Best, |
It would be great if you could support |
My general philosophy goes like this:
lavaan
, which can be endlessly complicated and used for very different purposes)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 beyondsummary
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 ifsumm
can add benefit since refitting models isn't feasible.stanreg
— less concern about variation than withbrmsfit
, but "added value" concern remainspolr
— 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 forordinal
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 innlme
?)glmmTMB
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