Revolution Analytics is proud to announce the latest update to our enhanced, production-grade distribution of R, Revolution R Enterprise. This update expands the range of supported computation platforms, adds new Big Data predictive models, and updates to the latest stable release of open source R (2.14.2), which improves performance of the R interpreter by about 30%.
This release expands the range of big-data statistical analysis with support for Generalized Linear Models (GLM). Logistic (Binomial) Poisson, Gamma and Tweedie models are all supported with a high-performance C++ implementation, and you can also model any distribution in the GLM family with a custom link function written in R. Big Data GLM has been a common request from many of our customers, and beta testers have been blown away by the speed of the implementation. For example here's an example of a Tweedie regression on 8.5 million insurance claims in less than 2 and a half minutes (skip ahead to 1:10 for the demo):
We can make these computations fast by streaming data through the CPU instead of loading it all at once (which also means no data-size limitations), and by distributing the computations across multiple processors on a server or multiple nodes in a cluster. Revolution R Enterprise 6.0 now supports Linux-based clusters running IBM's Platform LSF grid software, in addition to clusters running Microsoft HPC Server. And with HPC server, you can now run big-data analytics in the Azure Cloud, as shown in this demo.
One other new feature relates to accessing data. Revolution R Enterprise has always included our XDF data format: a high-performance local filestore for Big Data in R. Making a local copy of data in XDF is a very useful if you plan to analyze the same data several times, and want to avoid the network latency of copying the data over and over. But if you don't need a local copy, you can now use the RevoScaleR data step and big-data algorithms on the source data (SAS, SPSS, ASCII or ODBC) directly. (By the way, you don't need a SAS or SPSS license to access data files in those formats, which is useful if you've switched to an R-only environment.) For example, here's an analysis of commuter behavior from 2010 NYC Community Health Survey working directly from the source SAS file:
If you're already a subscriber to Revolution R Enterprise, you'll receive an email with upgrade instructions today. (If you'd like to become a subscriber, click here and we'll be happy to help.) As always, academic users can download and use the latest version free of charge.
For more information about Revolution R Enterprise, follow the link below. And if you try out 6.0, let us know what you think in the comments.
Revolution Analytics: Revolution R Enterprise