Roadmap for release 0.6.0

[raphaelvallat]

The following issues should be addressed:

• anderson() boolean returned value is inverted #308 ❗
• ensure compatibility with numpy 2.0 #444
• Feature request: add confidence interval to pg.wilcoxon() #153
• Adding eta squared parametric ci to pingouin.compute_esci #253
• Naming of return-values for T-tests etc #208
• Return Confidence Interval for nonparametric Mann Whitney U Test #225
• Get residuals from anova #118

The following PR should be merged:

• Add confidence interval for MWU #226
• Add new function for pairwise T-tests between columns of a dataframe (pingouin.ptests) #291
• Pandas-friendly column names #443

The following dependencies should be updated:

• Scikit-learn <1.1.2 (revert temporary fix in Use scikit-learn<1.1.0 #278)

The following deprecation should be made:

[Federico2111]

Seems like a good new release. Is there any time line? When will these upgrades be made?
Thanks

[raphaelvallat]

Hi @Federico2111,

I have had no time to work on Pingouin in the last few months, and I anticipate this will continue at least until Q1 2023. I therefore cannot promise a timeline for this new release.

[musicinmybrain]

The following dependencies should be updated:

* [ ]  Scikit-learn <1.1.2 (revert temporary fix in [Use scikit-learn<1.1.0 #278](https://github.com/raphaelvallat/pingouin/pull/278))

This can be checked off, since #300 is merged and released in 0.5.3.

[Federico2111]

Any update about the progress of this new release? Any help needed?

[raphaelvallat]

Thanks @Federico2111 — unfortunately at the moment I only have time for urgent bug fixes, not for implementing new features. So it's unlikely that version 0.6.0 is coming anytime soon; however I'd like to release version 0.5.4 with some minor improvements and bugfixes (e.g. CI tests) in the coming weeks.

[Federico2111]

Thanks @raphaelvallat.
By CI tests, you mean also the parametric Confidence Interval around eta squared that I had suggested?
#253

[raphaelvallat]

No sorry I meant the unit tests (aka continuous integration or CI), see GitHub Actions: https://github.com/raphaelvallat/pingouin/actions

[Federico2111]

Any update? :-)

[raphaelvallat]

I recently released a minor version that included bugfixes and some improvements: https://github.com/raphaelvallat/pingouin/releases/tag/v0.5.4

I cannot say when the next release will be, but likely not in the coming weeks. Feel free to submit PRs for what you consider to be the most important/urgent tasks. Thanks

[FlorinAndrei]

@raphaelvallat What's happening with the site https://pingouin-stats.org/ ? I see the text, but all the styling is broken. I've tried Shift-reload, no luck. Using latest Chrome on Win10.

[raphaelvallat]

@FlorinAndrei #400, I'll investigate and try to fix soon

[Federico2111]

Thanks @raphaelvallat
My suggestion was #253 which is to add the parametric confidence interval around the eta squared effect size. I provided some documentation about it. This feature would complete the pingouin.compute_esci library, where parametric ci's are calculated for other effect sizes, but not for eta squared: https://pingouin-stats.org/build/html/generated/pingouin.compute_esci.html

[turkalpmd]

I have implemented some of the adjustment methods mentioned in this article (https://scholarworks.umass.edu/pare/vol20/iss1/8/) in Python. If deemed appropriate, I can create a PR.

[raphaelvallat]

I have implemented some of the adjustment methods mentioned in this article (https://scholarworks.umass.edu/pare/vol20/iss1/8/) in Python. If deemed appropriate, I can create a PR.

@turkalpmd Can you please first open an issue to describe exactly what new or updated methods could be implemented based on this paper?

[raphaelvallat]

@Federico2111 I'm trying to move away from a system in which I implement external requests for features, towards a more sustainable model where people actually implement and submit modifications in pull requests. Furthermore, this is not a highly-requested feature so I have not considered it a high-priority item. Have you made your own implementation of this feature with Python?

[turkalpmd]

I have implemented some of the adjustment methods mentioned in this article (https://scholarworks.umass.edu/pare/vol20/iss1/8/) in Python. If deemed appropriate, I can create a PR.

@turkalpmd Can you please first open an issue to describe exactly what new or updated methods could be implemented based on this paper?

Here, I've opened the issue you mentioned, @raphaelvallat

[Federico2111]

Hi @raphaelvallat,
I have not implemented that feature (#253) in my Python scripts. If it is not implemented in the pingouin library, I would consider studying the literature and writing the code in my scripts. Currently pingouin.compute_esci calculates the parametric confidence intervals around the most commonly used effect sizes in one-way anova. Eta squared is the most commonly used effect size in two-way anova, which is why I think it would complete the features of the library and would be a beneficial addition.

[Federico2111]

Any news or timeline?
Can we help in some way?

[remrama]

Hi @Federico2111, Pingouin is mostly dependent on community efforts to push new features/fixes through. So regarding a timeline, this version will get pushed when the checklist at the top of this Roadmap/Issue is complete. As you can see, there is more work to do.

Can we help in some way?

Yes, see the checklist at the top for detailed descriptions of where help is needed.