Am I in the wrong stats universe?
I work in agriculture and our bread and butter is designed experiments intended to be analyzed with ANOVA or as mixed-effect models. The most common packages I use for analysis are agricolae and nlme. Sometimes I can just use base stats (
lm), but it's often not sufficient.
I use a tidy workflow, but haven't found a great way to mix anything beyond
lm into my code. I find ways to do it, but not great ways. In the end, I seldom have a nice table I can share with a non-R colleague. There are also a couple fringe packages out there that I am excited about (broom.mixed, nls.multstart, but don't have a ton of support at this time.
When what I'm doing is not 1) incorporated in the Tidyverse, 2) contained in any other well-supported packages, 3) found as popular (or even answered) questions on StackOverflow, I start to feel like maybe it's because everyone else is taking a different approach? Or did the ag stats/ANOVA world just get left behind?
I've worked quite a bit with predictive models, but have recently needed to return to a lot of split-plots, strip-plots, split-split plots etc. and I'm wondering if I should just really dig into mixed-effect models or if there is something out there that I am missing?