R internally uses 32-bit integers for indexing (though this may change).
For this and other reasons these external objects with specialized purposes
(larger-than-RAM, shared memory) simply can't behave exactly as R objects.
Best case, some R functions will work.  Others would simply break.  Others
would perhaps work if the problem is small enough, but would choke in the
creation of temporary objects in memory.

I understand your sentiment, but it isn't that easy.  If you are
interested, however, we do provide examples of authoring functions in C++
which can work interchangeably on both matrix and big.matrix objects.

Jay

 Hi Jay,
>
> I have a question about your reply.
>
> You mentioned that "the more serious problem is that you can't expect to
> run just any R function on a big.matrix (or on an ff object, if you check
> out ff for some nice features).  "
>
> I am confused why the packages could not communicate with each other. I
> understand that maybe for some programming or statistical reasons, one
> package need its own "class" so that specific algorithm can be implemented.
> However, R as a statistical programming environment, one of its advantages
> is the abundance of the packages under R structure. If different packages
> generate different kinds of object and can not be recognized and used for
> further analysis by other packages, then each package would appears to be
> similar with the normal independent software, e.g., SAS, MATLAB... then
> this could reduce the whole R ability for handling complicated analysis
> situation.
>
> This is just a general thought.
>
> Thank you very much.
>
> ------------------------------
> ya
>
> --
John W. Emerson (Jay)
Associate Professor of Statistics
Department of Statistics
Yale University
http://www.stat.yale.edu/~jay

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