Mixed Models in R: Mind the Gap

The linear mixed model ecosystem in R consists of over 80 libraries that either construct and solve mixed model equations or helper packages the process the results from mixed model analysis. These libraries provide a patchwork of overlapping and unique functionality regarding the fundamental structure of mixed models: allowable distributions, nested and crossed random effects, heterogenous error structures and other facets. No single library has all possible functionality enabled. These libraries vary greatly in accessory functions enabled, such as conducting ANOVA, accessing the BLUPs and other standard model output such as the log likelihood, residuals, and variances. The only common features guaranteed in these packages are that they (1) conduct a mixed model, and (2) if the package is on CRAN, have a method to print the output from a mixed model function call. Additionally, each library has its own unique syntax for allowable function arguments. This patchwork of packages makes it very challenging for statisticians to conduct mixed model analysis and to teach others how to run mixed models in R. There have been recent attempts to create a common function call for mixed models with the library ‘multilevelmod’ and a common way to access accessory information with ‘broom.mixed’. These meta-libraries are enormously helpful, but they are incomplete, addressing less than 10 mixed models packages in total. As a community of agricultural statisticians and statistical consultants, what should we do about this? The goal of this talk/discussion is to identify gaps in mixed model analysis in R and discuss options and our capacity to address those gaps.