Dear R-programmers,
I am trying out certain methods in R, and the statistics require me to
calculate n-(sample size) dimensional equations. They are not really very
hard to solve - my home-brew implentation of Newton-Raphson in R succeeds
most of time with simulated data. (Note that I am assured of a unique
solution by theory). Problem comes in with real data, for which I should
really implement a good line search (convergence issues). Being lazy, i
would like to link to the GSL routines which are of course faster and more
reliable.
My question is should i use the C - GSL routines or the Python ones in
NumPy? My major concern is the portability of the code i write: really dont
want users to have to install a bunch of software just to use my package.
(Im looking at Windows here)
Alternatively, should i just hack out the code (fsolve) and put it in my
package?
Thanks for the advice,
Mohit Dayal
Applied Statistics & Computing Lab
ISB
[[alternative HTML version deleted]]
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
