R Consortium call for funding
Lize and Manuel brought up covariance structures and speed, so I wanted to let you all know that the glmmTMB developers have been working towards these goals (https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html <https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html>). It?s also easy to add additional structures to glmmTMB. We could use some help testing the covariance structures (https://github.com/glmmTMB/glmmTMB/issues/344 <https://github.com/glmmTMB/glmmTMB/issues/344>). We recently found a bug that could cause problems in models with multiple types of covariance structures, but it has been fixed if you install the development (i.e. Github) version of lme4 and the fix_covstruct_order2 branch of glmmTMB (https://github.com/glmmTMB/glmmTMB/tree/fix_covstruct_order2 <https://github.com/glmmTMB/glmmTMB/tree/fix_covstruct_order2>). These should both be on CRAN soon. In my opinion, the biggest need for improvement is to provide predictions and coefficients with confidence intervals on a meaningful scale when a nonlinear link function is used. This comes up repeatedly on this list (e.g. earlier this month https://stat.ethz.ch/pipermail/r-sig-mixed-models/2018q3/027237.html). The solution will probably involve marginalizing over random effects, but non-parametric bootstrapping while resampling the levels of random effects could also be useful. cheers, Mollie
On 26Sep 2018, at 12:52, Manuel Ramon <m.ramon.fernandez at gmail.com> wrote: I totally agree with you, Ben. I have to admit that all the tidyverse world has suppose a great improvement in the way I work with data, but in the end, almost all my analyses conclude with the nlme/lme4 packages. So I think it is worth investing funds and time on it. As suggested by others, the inclusion of the variance functions from nlme would be very useful. Also, some of the capabilities of the mixed.models in Julia language in terms of computation time and data size would be very welcome, but this latter it is probably very difficult (almost impossible) given that they are to different platforms. In any case, thanks for the initiative and I hope it will go ahead. Regards, Manuel On Tue, Sep 25, 2018 at 11:00 PM Ben Bolker <bbolker at gmail.com> wrote:
https://www.r-consortium.org/announcement/2018/09/25/fall-2018-isc-call-for-proposals "What can you do to improve the R ecosystem and how can the R Consortium help you do it?" The mixed-model ecosystem is admittedly a small part of the R ecosystem, but I (biasedly) think it's an important one. If people have ideas & opinions about how a chunk of money on the order of $10,000 could be valuably spent to improve the mixed-model ecosystem in a way that would be appealing to a very broad audience of useRs, please discuss. The deadline for submitting a proposal is midnight PST, Sunday October 31, 2018. cheers Ben Bolker
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