[nothis]

I just wonder if there's rationality behind not changing this (by either switching to a different language, fixing it or, hardcore mode, create a new one).

I know from a very, very different field that you often have to deal with decade(s) old technology because your employee/professor/etc is just used to it and, 15 years ago, it simply was the best option. I guess it's an equation that puts time spent learning its quirks vs time saved using a more sensible tool. While tiresome, "learning" might actually be the faster way to get things done. But it also carries so much ballast that, if there's a better alternative, must waste millions of frust-hours (and actual errors), especially for newcomers who could just as well learn a new tool - faster.

[wanderfowl]

I started in SPSS. This is so much better. And although it drives me nuts, I still spend the other half of the time working in R feeling like a goddamned wizard. So even if it takes an hour and a half some days to figure out how to turn a list of unique factors into a list for lapplying (or something else stupid), it's not bad enough.

Also, remember that I'm the R geek around. Whether it's the best tool for the job or not, in my field, R is the lingua franca for stats. I could swear off R and move to Python Stats, but I'd still be supporting R among colleagues and friends. It's hard enough to convince folks who grew up in SAS to move to R, let alone to learn Python.

Finally, I'll have a hard time convincing editors that some weird-ass python implementation of GAMs or LMER is kosher when they're barely OK with the idea of GAMs. Reviewer two is, shall we say, technologically conservative.