Hi, Making it easier to properly cite relevant papers is something I would also really like to see addressed!
I am a bit concerned that most people wouldn't want or wouldn't be able to install an external program, though. For this reason, I think the ideal solution should be web based. This could for example take the form of a sphinx plugin for easily integrating with the project's documentation. We could maintain a BibTeX file and reference BibTeX entries from within the documentation. The sphinx plugin would make it easier to find relevant citations from various places in the documentation (class reference, user guide). One difficulty, though, is that the relevant citations in scikit-learn estimators often depends on constructor options. For example, in LinearSVC, the paper to cite is not the same whether we use dual=True or dual=False, penalty="l1" or penalty="l2", etc. On Fri, Aug 28, 2015 at 1:36 AM, Yaroslav Halchenko <s...@onerussian.com> wrote: > > 1. natively within the code base (that is what we have done to PyMVPA in > [1]). > Idea is that such "native support" should not impact original project to > any degree -- duecredit dependency would remain optional and code must > not puke > if something in duecredit fails > 2. and through "injections" -- decorating corresponding functions upon > corresponding > module import, that is what we have done so far > for some references within numpy, scipy, scikit-learn. > Another less intrusive solution would be to add citation annotations to docstrings. Do you have a mechanism for reusing the same citation in several places? Mathieu
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