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

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