[Rd] Philosophy behind converting Fortran to C for use in R
wdunlap at tibco.com
Tue Jun 6 23:34:25 CEST 2017
Here are three reasons for converting Fortran code, especially older
Fortran code, to C:
1. The C-Fortran interface is not standardized. Various Fortran compilers
pass logical and character arguments in various ways. Various Fortran
compilers mangle function and common block names in variousl ways. You can
avoid that problem by restricting R to using a certain Fortran compiler,
but that can make porting R to a new platform difficult.
2. By default, variables in Fortran routines are not allocated on the
stack, but are statically allocated, making recursion hard.
3. New CS graduates tend not to know Fortran.
(There are good reasons for not translating as well, risk and time being
the main ones.)
On Tue, Jun 6, 2017 at 1:27 PM, Avraham Adler <avraham.adler at gmail.com>
> This is not a question about a bug or even best practices; rather I'm
> trying to understand the philosophy or theory as to why certain
> portions of the R codebase are written as they are. If this question
> is better posed elsewhere, please point me in the proper direction.
> In the thread about the issues with the Tukey line, Martin said :
> > when this topic came up last (for me) in Dec. 2014, I did spend about 2
> days work (or more?)
> > to get the FORTRAN code from the 1981 - book (which is abbreviated the
> "ABC of EDA")
> > from a somewhat useful OCR scan into compilable Fortran code and then
> > wrote an R interface function found problems…
> I have seen this in the R source code and elsewhere, that native
> Fortran is converted to C via f2c and then run as C within R. This is
> notwithstanding R's ability to use Fortran, either directly through
> .Fortran()  or via .Call() using simple helper C-wrappers .
> I'm curious as to the reason. Is it because much of the code was
> written before Fortran 90 compilers were freely available? Does it
> help with maintenance or make debugging easier? Is it faster or more
> likely to compile cleanly?
> Thank you,
>  https://stat.ethz.ch/pipermail/r-devel/2017-May/074363.html
>  Such as kmeans does for the Hartigan-Wong method in the stats package
>  Such as the mvtnorm package does
> R-devel at r-project.org mailing list
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