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On Sat, Feb 6, 2021 at 9:30 AM Michael Baudin <michael...@gmail.com
> I develop the OpenTURNS library for uncertainty quantification. We recently had a discussion (at https://github.com/statsmodels/statsmodels/pull/7254
) about using OpenTURNS features to provide copulas algorithms in statsmodels.
> It appears that the LGPL licence of OpenTURNS might an issue. I do not understand this particular point: can't a MIT software use a LGPL one?
LGPL allows us to use it as a library, but not to read the code, and
not to copy parts of it.
It is a major advantage of the MIT/BSD-3 dominance in this area of
Python packages, that we can read and copy each others code.
Patsy has splines that can be used in formulas, but does not provide
the extra functionality for penalized splines, and the information in
the formula is to vague to work with it directly.
When we added GAM, penalized splines for GLM, we copied part of
patsy's spline code and addjusted and added to what we needed for
It's still convenient for users to use patsy's spline in the formulas,
but for GAM we needed our own.
scipy and numpy.random are providing most of the distribution
functionality for us.
However, for models we need more, e.g. derivatives, or a different
parameterization. So for the core part of models, we have our own
version of Poisson, Binomial and similar, but delegate to scipy for
the rest, cdf, ppf, ....
On the other hand, numpy and pandas are used by statsmodels as
libraries because there is less direct overlap in functionality.
Still, it's sometimes easier to look at the code in how something is
implemented, and it makes life easier for developers that work on
several packages if those are license compatible.
> If OpenTURNS was MIT, what would the features that might be useful for statsmodels? I think that the probabilistic modeling (distributions, copula, nonparametric methods, etc...) would be useful, and higher level algorithms as well (HDR, parameter estimation, etc...). Is this correct?
I try to answer later, need to go offline soon
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