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

[Phillip Alday]

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

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
> 
-----BEGIN PGP SIGNATURE-----
Version: GnuPG v2.0.22 (GNU/Linux)

iQEcBAEBAgAGBQJUP4SEAAoJEH6E4TigDpMcqbwH/jGQO84Dc5C5xZkWKPuoBqzu
THaoJnkacieeCMFt1FF2z8wlHVlYDWNqkIWzSuE0fzTASywH+AG5Dj/D5KPdT78/
1M6LAAN1HiEdKztC5wa1ceAzlE2EyOHoQFSvSxLtxl/PmEZj/BmODaMRBPJHUkeN
ZxwR4y0V9/DdEtRtFeddnETg6YzFnITZh6r3cqXSp83McSx6MAcI3NXfTKtjQVbW
/hrW1KKacngo1o48INDfocEddbuUNKx4bT3DzN0iwLSoHRhGKADQr8VEZjtbJXvK
SVk5PFu/en9szvcdPd2HV/2plAE0weepTmf1gvce8C7Oxn20369drqxsTZrrtgw=
=cuZ2
-----END PGP SIGNATURE-----