Implementation and derivation of "Variational Bayesian inference for a nonlinear forward model." [Chappell et al. 2008] for arbitrary, user-defined model errors.
The name of the branch is misleading, I only added the building of a package that could be uploaded to pypi. If we agree on doing this, the next step would be to use the pypi installation to build a conda package (it also works without pypi, but then everyone is forced to use conda). if we want to have both, then I think it is easier to have a consistent approach. As suggested by @TTitscher it is probably a good idea to automate this (in particular having a script that uploads it to pypi with every new label added in github), but that would be a next step.
The name of the branch is misleading, I only added the building of a package that could be uploaded to pypi. If we agree on doing this, the next step would be to use the pypi installation to build a conda package (it also works without pypi, but then everyone is forced to use conda). if we want to have both, then I think it is easier to have a consistent approach. As suggested by @TTitscher it is probably a good idea to automate this (in particular having a script that uploads it to pypi with every new label added in github), but that would be a next step.