I would say that in the current state of Julia, you won’t miss Stan since Stan.jl does a great job.
You won’t miss knitr because Weave.jl is great.
You won’t miss regular expressions because Julia’s string macros are more powerful than anything you’d imagine. You definitely won’t miss Rcpp because that entire idea is eliminated: you just write Julia code and and get the C++ speed free. However, if you were using it to wrap a C++ package, you can use Cxx.jl:
And MixedModels.jl is written by the same guy who wrote the main R statistical packages:
As for the others, some who have come to Julia don’t think there’s quite a replacement to ggplot yet. Gadfly.jl uses a grammar of graphics syntax, but it’s limited. Plots.jl is a great plotting library, but it doesn’t use grammar of graphics except in some experimental addon:
Also, DataTables/DataFrames are in flux right now:
Hope that’s a good overview of what to expect at this current stage. Julia, the core language itself, is much more developed than you’d probably expect, and the package ecosystem has many packages which are already more expansive than what you’d find anywhere else (especially in scientific computing, like optimization, differential equations (
), and linear algebra). However, I think those two points (and maybe a few others) are where we are still lacking some.
However, the point I’d like to end on is that, in Julia, I find it so easy to write efficient code that in many cases packages aren’t necessary in ways they are in other languages.