How do Rpy2, pyrserve and PypeR compare?

I would like to access R from within a Python program. I am aware of Rpy2, pyrserve and PypeR.

What are the advantages or disadvantages of these three options?

I know one of the 3 better than the others, but in the order given in the question:

rpy2:

• C-level interface between Python and R (R running as an embedded process)
• R objects exposed to Python without the need to copy the data over
• Conversely, Python's numpy arrays can be exposed to R without making a copy
• Low-level interface (close to the R C-API) and high-level interface (for convenience)
• In-place modification for vectors and arrays possible
• R callback functions can be implemented in Python
• Possible to have anonymous R objects with a Python label
• Python pickling possible
• Full customization of R's behavior with its console (so possible to implement a full R GUI)
• MSWindows with limited support

pyrserve:

• native Python code (will/should/may work with CPython, Jython, IronPython)
• use R's Rserve
• advantages and inconveniences linked to remote computation and to RServe

pyper:

• native Python code (will/should/may work with CPython, Jython, IronPython)
• use of pipes to have Python communicate with R (with the advantages and inconveniences linked to it)

edit: Windows support for rpy2