Readers of this list might be interested in the following commenta about R.
In a recent report, by Michael N. Mitchell http://www.ats.ucla.edu/stat/technicalreports/ says about R: "Perhaps the most notable exception to this discussion is R, a language for statistical computing and graphics. R is free to download under the terms of the GNU General Public License (see http://www.r-project. org/). Our web site has resources on R and I have tried, sometimes in great earnest, to learn and understand R. I have learned and used a number of statistical packages (well over 10) and a number of programming languages (over 5), and I regret to say that I have had enormous diffculties learning and using R. I know that R has a great fan base composed of skilled and excellent statisticians, and that includes many people from the UCLA statistics department. However, I feel like R is not so much of a statistical package as much as it is a statistical programming environment that has many new and cutting edge features. For me learning R has been very diffcult and I have had a very hard time finding answers to many questions about using it. Since the R community tends to be composed of experts deeply enmeshed in R, I often felt that I was missing half of the pieces of the puzzle when reading information about the use of R { it often feels like there is an assumption that readers are also experts in R. I often found the documentation for R quite sparse and many essential terms or constructs were used but not defined or cross-referenced. While there are mailing lists regarding R where people can ask questions, there is no offcial "technical support". Because R is free and is based on the contributions of the R community, it is extremely extensible and programmable and I have been told that it has many cutting edge features, some not available anywhere else. Although R is free, it may be more costly in terms of your time to learn, use, and obtain support for it. My feeling is that R is much more suited to the sort of statistician who is oriented towards working very deeply with it. I think R is the kind of package that you really need to become immersed in (like a foreign language) and then need to use on a regular basis. I think that it is much more diffcult to use it casually as compared to SAS, Stata or SPSS. But by devoting time and effort to it would give you access to a programming environment where you can write R programs and collaborate with others who are also using R. Those who are able to access its power, even at an applied level, would be able to access tools that may not be found in other packages, but this might come with a serious investment of time to suffciently use R and maintain your skills with R." Kjetil [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
