After Microsoft acquired Revolution Analytics and its implementation of the R language for statistics and scientific work, worry abounded. Would Microsoft -- even the "new" Microsoft with its expanded overtures to open source -- make R into a closed source offering? Or perhaps change its licensing to more deeply integrate it into Microsoft's proprietary world?
The answer to both questions appears to be no ... so far. The most recent version of Revolution R Open, 3.2.2, is now known as Microsoft R Open and is available for download along with all of its source code.
Revolution R Open stands as one of the flagship implementations of R, so Microsoft's ownership of it gives de facto control over the way R is used by a good chunk of its base. But as with previous versions of the product, it's still offered under the terms of the GPLv2 license.
A blog post released yesterday by Joseph Sirosh, corporate vice president of Microsoft Data Group, touted the advantages of using Microsoft R Open over other implementations of the language. Most of the described features are holdovers from before Microsoft closed its acquisition of Revolution Analytics, such as "multi-threaded processor optimized computations provided by Intel MKL (Math Kernel Libraries)," which are meant to accelerate math operations, "especially in matrix oriented computations."
Aside from reaffirming its commitment to working on the software itself, Microsoft also declared further support for the community of "data scientists, statisticians, and now enterprises" around R. "Microsoft has pledged its support for the R Project by being one of the founding members of the R Consortium," said Sirosh.
A spokesperson for Microsoft further clarified that "as part of Microsoft's commitment to open source and the R community, we maintain Microsoft R Open distribution under the GPLv2." When asked if the new Microsoft R Server was based on the same source code as R Open, however, the answer was less straightforward: "Microsoft R Server extends the benefits of Open Source R and provides enterprise-grade support, security, scalability, and parallelization of key algorithms and models through ScaleR, ConnectR, and DistributeR."