The question as to why I personally started using R for MVPA is easy: I started before MVPA packages were available, so I had to write my own scripts, and I prefer scripting in R (then and now). Whether to keep using my own scripts or switch to pyMVPA (or some other package) is something I reconsider occasionally.
A very big reason to an established package is that it's a known quantity: coding bugs have hopefully been caught, and analyses can be reproduced. Some packages are more open (and have more stringent tests) than others, but in general, the more eyes that have studied the code and tried the routines, the better. This need for openness was one of my motivations for starting this blog: to post bits of code and detailed methods descriptions. I think the more code and details we share (blog, OSF, github, whatever), the better, regardless of what software we use (and I wish code was hosted by journals, but that's another issue).
I'm a very, very big fan of using R for statistical analyses, and of knitr (sweave and RMarkdown are also viable options in R) for documenting the various analyses, results, impressions, and decisions as the research progresses (see my demo here), regardless of the program that generated the raw data. My usual workflow is to switch to knitr once an analysis reaches the "what happened?" stage, regardless of the program that generated the data being analyzed (e.g., I have knitr files summarizing the motivation, procedure, and calculating results from cvMANOVA analyses run in MATLAB). Python has the iPython Notebook, which is sort of similar to knitr (I don't think as aesthetically pleasing, but that's a matter of taste);
All neuroimaging (and psychology, neuroscience, ...) graduate students should expect to learn a proper statistical analysis language, by which I mostly mean R, with MATLAB and python coming in as secondary options. In practice, if you have proficiency in one of these programs you can use the others as needed (the syntax isn't that different), or have them work together (e.g., calling MATLAB routines from R; calling R functions from python). The exact same MVPA can be scripted in all three languages (e.g., read in NIfTI images, fit a linear SVM, write the results into a file), and I don't see that any one of the three languages is clearly best or worst. MATLAB has serious licensing issues (and expense); python dependencies can be a major headache, but which program is favored seems to go more with field (engineers for MATLAB, statisticians for R) and personal preference than intrinsic qualities.
So, what should a person getting started with MVPA use? I'd say an R, python, or MATLAB-based package/set of scripts, with which exact one depending on (probably most important!) what your colleagues are using, personal preference and experience (e.g, if you know python in and out, try pyMVPA), and what software you're using for image preprocessing (e.g., if SPM, try PRoNTO). Post-MVPA (and non-MVPA) investigations will likely involve R at some point (e.g., for fitting mixed models or making publication-quality graphs), since it has the most comprehensive set of functions (statisticians favor R), but that doesn't mean everything needs to be run in R.
But don't start from scratch; use existing scripts/programs/functions as much as possible. You should mostly be writing code for analysis-specific things (e.g., the cross-validation scheme, which subjects are patients, which ROIs to include), not general things (like reading NIfTI images, training a SVM, fitting a linear model). Well-validated functions exist for those more general things (e.g., oro.nifti, libsvm); use them.