Hi Brian and Uwe,
Given my package passes all checks but I have received no further responses
since your initial reply, I assume you are no longer interested in the package
Rigroup-0.84.0. Your concerns about my chosen license being too vague "GPL
| LGPL" is in direct contradiction to what your own documents say about not
changing your license after a package has been accepted at CRAN. This is the
same license this package has had since it was first accepted in 2006 ("GPL
or LGPL your choice"). Your additional requirement was that I explain why
I did not reply to your January e-mail thereby forcing you to do "extra
work to archive the package" was already met on this very list. As I
explained back in March, I never received it (and no Uwe that does not mean I am
claiming you never sent it - just that I did not receive it!). Not quite the
"thank you for contributing to R" I hoped for, but after 7 years or
so, it is unfortunately what I expected...
Therefore this is to let you know that I am hereby withdrawing my package
Rigroup-0.84.0 from CRAN consideration. You can obviously keep using the
previous (now-archived) version under its license. But since I will no longer
be supporting the package and as its author I ask you to remove all versions
from CRAN. This is of course your choice given the original license Rigroup was
released under. If anyone's package depends on Rigroup, please feel free to
absorb it into your own packages in any way you want under any GPL *, BSD, LGPL
* licenses.
One thing the developers of R might want to consider is to add the very basic
optimization that Rigroup uses to base R so that it can better handle very very
large datasets (ala BigData) more effieciently. That is assuming there is not
some other package that does this that I am unaware of or that a similar
capability hasn't already been added to R-3.X since 2006.
The primary idea is simple and time worn ...
Given a large unsorted vector of data whose elements have been assigned to n
groups and whose group membership is represented by a second vector of equal
length whoses values are members of {1,...,n}, you can easily calculate multiple
group statistics for all n groups in just one pass through the unsorted data
vector by using the group membership as an index into one or multiple vectors
of statistics such as count, sum, max, min, all, any, average, second moment,
variance standard deviation, higher moments, etc .... Since this is meant for
very large vectors, it was implemented in C for speed.
For very very large unsorted data vectors, this one pass approach of using the
group membership as an offset into potentially multiple "statistics
vectors" is much much faster than trying to sort, or index, or subset the
large unsorted data vector using the typical approaches of R and then
calculating the stats and will work even if the group membership indicators do
not span the set. This is all that Rigroup did. Pretty much just common sense
to any old programmer who wanted to build portfolios and calculate basic
portfolio stats but who has to deal with large amounts of data. Perhaps
sometime over the last 7 years, you have already added something similar, if so,
please ignore this.
Given my withdrawl of this package I will also be removing myself from the
R-devel mailing list so if you want to contact me for any reason please CC me
directly (hopefully the sympatico.ca spam filter will not lose too many of you)!
It was an interesting 7 years. Good luck to you all.
Take care,
Kevin
