DFbetas for identifying influential participants - linear mixed models

Hello everyone,

I have used the package influence.me to determine whether I had influential participants using dfbetas. Yet, this package takes forever to run and I just can’t wait another month for it to run. I tried to use it in Julia with RCall to see if it would help, but so far it doesn’t seem to be helping. I guess if you use slow code in a different interface it just behaves as it would normally.
Does anyone know of a package in Julia that runs dfbetas for linear mixed effects models? I know of LinRegOutliers but that one is just for linear regression.

Thank you!

You are quite correct that calling an R package through a different interface is unlikely to make it run faster.

I don’t know of any Julia packages for evaluating dfbetas for linear mixed models. Generally the MixedModels.jl package outperforms lme4 for fitting linear mixed models. If it was known how influence.MEis performing the calculations it may be possible to emulate it with a model fit by MixedModels. There is already a method for StatsModels.influence for a LinearMixedModel object. I think it should be much faster than the R method but still it is not blazingly fast when applied to large models.