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[–]zip117 2 points3 points ago


You're right, there is one main one main procedure for doing HPDs in SAS called PROC MCMC, which comes with a very detailed 286 page user's guide with lots of examples and graphics. Everything that the procedure does is well documented through equations and references to peer-reviewed literature.

This single procedure, which accounts for most of the bayesian capabilities in SAS, is compared to a single R function in this article. The comparison cannot and should not be made.

I'm tired of these ridiculous adversarial comparisons between these two programs. The SAS system is designed first and foremost for high volume data processing, on zSeries mainframes for instance, and second for business analytics. It is excellent, high-quality software for this purpose and there is nothing comparable. DATA step programming is somewhat unwieldy compared to the R language for general data management, but feels completely natural when you need high-throughput I/O. See: Program Data Vector (PDV).

R is an excellent general technical computing environment. Setting aside the statistics capabilities for a moment, the closest comparison would probably be MATLAB. With few exceptions, it is an in-memory application which gives you a lot more flexibility to manipulate your data with matrix/vector operations, for applications like raster image analysis. One downside is documentation - very few packages come with high-quality cohesive documentation as SAS procedures do. You often only find out how a package works through a mix of reference manuals, vignettes and source code.

Different programs for different purposes. I use both almost every day and gladly pay for both: by contributing packages to CRAN, and by paying license fees to SAS for their expertise in high-throughput data processing.