Hi Jose,

Most kernel density estimators are straight forward to implement using the
tools from the gsl statistics package.  I've written a simple one for a
current project using gsl functions.  Here's the
source<http://github.com/cboettig/structured-populations/blob/master/src/kde.c>and
header<http://github.com/cboettig/structured-populations/blob/master/src/kde.h>files.
 The links point to my github project page in case you want to see
how I'm calling the kerneldensity.   Will probably expand it eventually,
would like to hear what you settle on.    hope this helps,

-Carl

On Fri, May 7, 2010 at 7:57 AM, Jose Carlos Rubio Ballester <
josecrubi...@gmail.com> wrote:

> Hi,
>
> Does anyone know how can I train an arbitrary density distribution from
> training data using a non-parametric method, like a Kernel Density
> Estimator?. I see that the former "Goose" library has this feature:
>
> - Kernel density estimation using Epanechnikov, Biweight, Triweight,
> Gaussian and Uniform kernels.
>
> However, I don't see anything like that in the GSL documentation. I wonder
> if this algorithm is still in the library, and in that case, where can I
> find information about how to use it?
>
> Thanks!
> _______________________________________________
> Help-gsl mailing list
> Help-gsl@gnu.org
> http://lists.gnu.org/mailman/listinfo/help-gsl
>



-- 
Carl Boettiger
Population Biology, UC Davis
http://two.ucdavis.edu/~cboettig
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