[PRE REVIEW]: MFDFA: Multifractal Detrended Fluctuation Analysis in Python

[whedon]

Submitting author: @LRydin (Leonardo Rydin Gorjão)
Repository: https://github.com/LRydin/MFDFA
Version: 0.2
Editor: @dfm
Reviewer: Pending

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[whedon]

Attempting to check references...

[whedon]

Attempting PDF compilation. Reticulating splines etc...

[whedon]

OK DOIs

- 10.1016/S0378-4371(02)01383-3 is OK
- 10.3389/fphys.2012.00141 is OK
- 10.1103/PhysRevE.49.1685 is OK
- 10.1103/PhysRevE.64.011114 is OK
- 10.1103/PhysRevA.44.2730 is OK
- 10.1088/1742-5468/2006/02/p02003 is OK
- 10.1007/s00382-019-04965-0 is OK
- 10.1038/20924 is OK
- 10.2478/s11534-009-0058-0 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[whedon]

👉 Check article proof 📄 👈

[labarba]

👋 @dfm — I believe this submission is one you could handle as editor? Have look please. We understand of course that these days will be slow and you might be on holiday. Let us know, and we can assign you for handling this after the New Year.

[dfm]

@labarba: sounds good. I'm happy to edit this in the new year!

[labarba]

@whedon assign @dfm as editor

[whedon]

OK, the editor is @dfm

[dfm]

@LRydin: After a pre-review of your submission, and an assessment by the editorial board at large, we find that it does not meet our submission requirements. In particular, while the software seems useful, it falls under the 'Minor utility' category, and we therefore decided not to put it through review.

Thank you for considering JOSS as a venue for your software, and I hope you'll submit other software with research application in the future.

[LRydin]

Dear @dfm. Thank you for the reply. Althought this might seem uncommon, I hope you can take the following under consideration:
Althought the python package might seem "thin", the applicability of MFDFA is extensive. I refer here mainly to the work of Espen Ihlen, Introduction to multifractal detrended fluctuation analysis in Matlab, which has gained a lot of attention over these last 7 years (over 400 citations). This paper explains extensively the use of DFA and MFDFA. The introduction to MFDFA is briliant, but the matlab code is not flexible.
Secondly I point out that this method is not a minor utility. Take for example the recent publication A simple decomposition of European temperature variability capturing the variance from days to a decade, which is entirelly based on applying MFDFA to temperature data. This to point out that MFDFA is not a minor utility, but a extensive method with applications in various fields. There are entire publications (see paper) based solely on DFA analysis of data, such as Earth-quake time distributions, heart-rate variability...

It is not my intension in this paper to present an introduction (the paper by Espen Ihlhen and the original paper by Kandelhardt do so), but to write an efficient python code, accessible across fields of study. Please take this into consideration when evaluating this package as a "minor utility".

I hope you can take this into consideration in re-evaluating my submission. I am submitting this package to JOSS to obtain a thorought analysis of the code itself, which admittedly could see some improvement.

Best regards,
Leonardo Rydin

[dfm]

@LRydin: The editorial board has reconsidered and we stand by our initial assessment. The classification of "minor utility" is not a statement about whether or not a piece of software is useful. Instead, we're assessing on the scope of the library (in this case, a single method of <50 lines of code, based on an existing implementation in MATLAB) and whether or not that justifies a JOSS publication.

Speaking for myself, I do believe that this software seems useful and I recommend asking that users cite the Zenodo DOI so that you can track its usage. You could also contribute the method as a pull request to an existing library (statsmodels, for example) to receive a code review and broaden the accessibility of this work.

Thanks again for considering JOSS!