While I must admit I never worked with R (so I don't know what incanter is lacking), I do know that whatever problem needs solving, it won't be hard to solve in Clojure. Two specific advantages of Clojure / Incanter are the clojure-inherent concurrency stuff plus access to all Java libs.
In other words, yes, it will be hard to solve in Clojure. It was hard to solve in other lisps, it was hard to solve in fortran, and it will be hard to solve in any other language you pick... unless someone else has already done the heavy lifting.
This isn't the sort of thing you whip up in any language, good numerical analysis libraries are typically often decades of work by people with specialized backgrounds.
e.g.
`gbm` is pretty much a C++ project.
`randomForest` now appears to contain a fair bit of R code, but it still has the (original?) fortran routines for tree construction, as well as C code wrapping the fortran.
https://github.com/harrysouthworth/gbm http://cran.r-project.org/web/packages/randomForest/index.ht...
My comment was really addressing "it won't be hard to solve in Clojure.". Some things are just hard to get right, period.
I am not saying "every statistical problem can be solved efficiently, numerically stable, ... in clojure" (and I realize you may read my comment like that), but rather most of the critical stuff is provided by incanter and the rest you can do yourself in clojure.
As a side note: Incanter also has some fortran dependencies, sot he critical stuff is also not done in clojure.