I haven't had a chance to play with it extensively but I have a basic
implementation:
https://github.com/PyMVPA/PyMVPA/blob/master/mvpa2/misc/dcov.py
which still lacks statistical assessment, but provides dCov, dCor values

and yes -- it is "inherently multivariate", but since also could be
useful if applied in a univariate fashion, you can see those uv bool
arguments.  Implementation as is is very memory hungry (even for
multivariate case... which shouldn't be the case)

On Fri, 15 Jun 2012, Satrajit Ghosh wrote:

>    hi yarik,

>      hm... interesting -- and there is no comparison against "minimizing
>      independence"? e.g. dCov measure
>      [1]http://en.wikipedia.org/wiki/Distance_correlation which is really
>      simple
>      to estimate and as intuitive as a correlation coefficient

>    thanks for bringing up dCov. have you had a chance to play with it (their
>    R package) [1] /try to implement it from their paper [2]. it seems like
>    dCov, from the paper doesn't apply to 2 vectors, but it can compare the
>    covariance of two datasets, where n_samples>1. their code also allows you
>    to enter two distance matrices into dCor and dCov and dCor(x,x) !=
>    dCor(x).
>    i would love if somebody can explain to me how dCor, dCov can be used for
>    2 random vectors.
>    all that said, i started implementing dCorr/dCov for sklearn and stopped
>    as i couldn't match my output with the R code (i didn't actually look at
>    the source there) and it seemed it wasn't going to be related to comparing
>    two samples.
>    cheers,
>    satra
>    [1]�[2]http://cran.r-project.org/web/packages/energy/index.html
>    [2]�[3]http://arxiv.org/pdf/0803.4101.pdf
>    �
-- 
Yaroslav O. Halchenko
Postdoctoral Fellow,   Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
WWW:   http://www.linkedin.com/in/yarik        

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