[SciPy-Dev] Boost for stats

[Neal Becker]

I have been using pybind11 (and it's predecessor before it,
boost::python) to package c++ code for python use for many years,
including some of boost libraries.
pybind11 is easy to use and is much better than e.g., cython for
packaging c++ code.  pybind11 is also header-only.

I would also like to call attention for anyone interested in
scientific software and c++ to a wonderful library (header-only),
xtensor
https://xtensor.readthedocs.io/en/latest/

On Mon, Feb 15, 2021 at 7:15 AM Hans Dembinski <hans.dembinski at gmail.com> wrote:
>
>
> > On 15. Feb 2021, at 08:26, Andrew Nelson <andyfaff at gmail.com> wrote:
> >
> > My questions would be:
> >
> > - how portable is the boost code in general?
>
> It is very portable. The core goal of Boost is to offer implementations with quality and portability on par with the C++ standard library implementations. Non-portable extensions are sometimes used to speed up things, but there is always a standard compliant vanilla version. In practice, maintainers test portability with CI on Windows, OSX, Linux, using various versions of gcc, clang, msvc, intel, see e.g.
>
> https://github.com/boostorg/math/blob/develop/.github/workflows/ci.yml
>
> and the Boost build farm from the days before free CI for OSS was easily available,
>
> https://www.boost.org/development/tests/master/developer/move.html
>
> Not all compilers/platforms are fully compliant, of course. Boost uses workarounds to combat that and submits bug reports on the compiler bug trackers.
>
> > - how easy is it to install the library.
>
> As Nicholas mentioned, Boost.Math (and Boost.Histogram) is header-only, so it is sufficient to include the headers.
>
> Best regards,
> Hans
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