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I see two possible approaches:

1. Embed an R interpreter into your application, e.g. using the Rinside package. Then you can call the R function that wraps around FederovOpt.
2. The FederovOpt function wraps around some normal C functions that do the actual work. It’s main task is to interface these C functions with R. If you are not using R otherwise you could also extract these C functions and use them directly.

Which solution to use? If you need other R functionality, 1. might be easier. Have a look at the examples that come with Rinside. You probably have to stick with mingw, preferably from Rtools. If this is the only R functionality you need, 2. Is probably the way to go. If you make the code independent of R, you can use VS. There are examples where something similar was done, e.g. https://github.com/zhanxw/libMvtnorm. In both cases you have to abide to the GPL.

To make option two more explicit I made a simple test:

1. Download FederovOpt.c and wheeler.h.
2. Remove all #includes of R specific headers.
3. Add #include and #defines for TRUE and FALSE.
4. Remove the R specific functions ProgAlloc, ProgDealloc and FederovOpt
5. Remove R specific function calls R_CheckUserInterrupt(), GetRNGstate() and PutRNGstate().
6. Replace unif_rand() with rand() / (RAND_MAX + 1.0).

The resulting file can be compiled with gcc without error or warning. One could even link it without the need for R specific libraries when one adds a main(). In there you would allocate memory, call FederovOptimize and free the memory again.

BTW, the RNG is of course much worse than R's RNG, but might be sufficient for this task. With C++11 you have of course better possibilities available.