Dear R-Users,
I'm searching for somebody who can support me or even likes to
collaborate with
me in setting up an R-package for "constrained maximim log-likelihood"
parameter
estimation.
For example fitting the parameters of a MA(1)-APARCH(1,1) model for a
time series
of 17'000 points (e.g. the famous Ding-Granger-Engle mode) takes about
10 minutes
with the existing optimization algorithms available under R.
Modern state of the art algorithms, like SQP algorithms as implemented
in Gauss,
Matlab, Ox, take about a few seconds. I tested this finding with a free
constrained
SQP solver written in FORTRAN under R and found these results confirmed. I
got the results in a few seconds instead of a few minutes!
Now I'm looking for a collegue who has the experience in implementing
FORTRAN
Optimization Code in R, calling the objective function and optionally
gradient and
hessian from R functions. I have already inspected a lot of Fortran, C,
and R sources
from the base package, but I didn't succeed so far with a reasonable effort.
Many thanks in advance
Diethelm Wuertz
