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Alcampopiano / hypothesize

Robust statistics in Python
https://alcampopiano.github.io/hypothesize/
BSD 3-Clause "New" or "Revised" License
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Software paper issues - JOSS review #6

Closed martinagvilas closed 4 years ago

martinagvilas commented 4 years ago

This issue outlines some minor issues with the software paper as part of the review for JOSS.

  1. [x] Since the JOSS guidelines require "a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience" it would be nice to have a short definition of what a robust statistical method is before mentioning the advantages it has over other approaches.

  2. [x] When mentioning the R library created by Wilcox, I believe the following citation should be added: https://link.springer.com/article/10.3758/s13428-019-01246-w

  3. [x] When describing the target audience of Hypothesize, I would add that this toolbox does assume knowledge on how to perform robust analyses. The description of the methods in the API or manipulating the method's parameters assumes familiarity with concepts and acronyms used in the field (e.g. FWE, MOM). This is by no means a criticism of the toolbox per se, it is just a suggestion to better clarify who are the target users of Hypothesize.

Alcampopiano commented 4 years ago

Hello @martinagvilas,

Thank you very much for these helpful suggestions. Please feel free to reopen this issue if you feel that that I have not fully addressed your suggestions.

I have addressed the above points as follows:

  1. ... In general, robust hypothesis testing uses techniques which minimize the effects of violating standard statistical assumptions. ...

    It then goes on to talk specifically about the advantages of using the trimmed mean and bootstrapping (as written originally).

  2. I referenced Wilcox's collection of functions (referred to as "WRS") rather than Mair's derived R library (referred to as "WRS2") as Hypothesize is based off of Wilcox's original collection (in terms of API and code). Mair's library is fantastic but it has a different API, does not include all of Wilcox's functions, and I'm unsure how or if it will diverge from Wilcox's original collection.

  3. ... It is, however, assumed that users have a basic understanding of the concepts and terms related to robust hypothesis testing (e.g., trimmed mean and bootstrapping).

Please let me know if these additions are sufficient.