[R-sig-ME] Julia vs nlme vs lme4 implementation of fitting linear mixed models

[W Robert Long]

Thanks Phillip

I've seen the glmm wikidot pages. I've also seen some of the code 
comparisons between lme4 and julia on github.  I've also seen the book 
by Pinheiro and Bates about nlme and the recent paper on lme4 at
http://arxiv.org/abs/1406.5823
[this paper gives some hints about what is done differently in julia, 
but I would really like more detail if possible]

I'm interested in the differences in internal implementation, rather 
than the differences that a typical user of the packages would be 
concerned with.

Thanks
Rob



On 16/10/2014 09:40, Phillip Alday wrote:
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> There's a bit on the FAQ under "Which R packages (functions) fit GLMMs?":
>
> http://glmm.wikidot.com/faq
>
> whihc is fleshed out on this page:
>
> http://glmm.wikidot.com/pkg-comparison
>
> And check out this question on StackOverflow:
>
> http://stats.stackexchange.com/questions/5344/how-to-choose-nlme-or-lme4-r-library-for-mixed-effects-models
>
>
> Those pages only discuss R packages, for the Julia package(s), you
> should check out the Julia package from Doug Bates, which has examples
> worked as parallels to the lme4 examples:
>
> https://github.com/dmbates/MixedModels.jl
>
> If I recall correctly, he also has an iPython Notebook with a more
> involved technical examination of mixed models in Julia, but I can't
> find the link at the moment.
>
> Best,
> Phillip
>
>
> On 16.10.2014 10:31, W Robert Long wrote:
>> What are the resources that compare how linear and generalised
>> linear mixed models are fitted in julia, lme4 and nlme in terms of
>> the how they differ in their implementation, and what
>> advantages/disadvantages each has. I'm asking about the theoretical
>> and computational issues rather than comparing speeds for any
>> particular dataset/model.
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
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