Replies: 2 comments 4 replies
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Hey! Here are some comments:
Yes, this usually means API stability. But there is no rush! For scikit-learn, this took years ;)
Yes! Especially if we can organize the plan in issues with labels (e.g., good first issue) and release milestones. We use GitHub Projects in other projects, which works quite well with a simple Kanban board. For example, we could create such projects for core methods, documentation and code style. |
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Missed this when it first started; Yeah |
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If and when should we bump PyFixest's version to
1.0.0
, to signal API stability? Is there actual value in doing so?I believe that PyFixest's
feols()
andfepois()
APIs are mature - I do not plan to introduce any changes that go beyond adding new function arguments. This is not 100% true for all the methods: e.g. the naming conventions are somewhat inconsistent.ritest()
asks forreps
, wildboottest wants to haveB
, and confint wantsnboot
to specify the number of iterations. There is also inconsistent usage ofalpha
vslevels
across functions. Here I think I should soft-deprecate some arguments in favor of consistent naming.I'd also have to review the Difference-in-Differences APIs, for example, the
event_study()
function requires an update regarding it's handling of dates.One strategy could consist of going through all the methods & to unify (and soft deprecate) naming conventions. Before releasing 1.0.0, I'd also like to implement the numpy API Apoorva @apoorvalal has suggested.
Did I miss anything? And is there even value in releasing a 1.0.0 version? More generally, would there be value in sketching out a development plan? @juanitorduz
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