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[REVIEW]: simstudy: Illuminating research methods through data generation #2763
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Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. @gagolews, @brunaw it looks like you're currently assigned to review this paper 🎉. Due to the challenges of the COVID-19 pandemic, JOSS is currently operating in a "reduced service mode". You can read more about what that means in our blog post. ⭐ Important ⭐ If you haven't already, you should seriously consider unsubscribing from GitHub notifications for this (https://github.com/openjournals/joss-reviews) repository. As a reviewer, you're probably currently watching this repository which means for GitHub's default behaviour you will receive notifications (emails) for all reviews 😿 To fix this do the following two things:
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PDF failed to compile for issue #2763 with the following error: Can't find any papers to compile :-( |
@whedon generate pdf from branch joss-submission |
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@whedon check references from branch joss-submission |
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@gagolews, @brunaw: Thanks for agreeing to review. Please carry out your review in this issue by updating the checklist above and giving feedback in this issue. The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. If possible create issues (and cross-reference) in the submission's repository to avoid too specific discussions in this review thread. If you have any questions or concerns please let me know. |
I personally appreciate the creation of this package, since I have had to come up with my own data simulation procedures several times, for many different contexts. I think this package should be accepted because it will help lots of researchers with the same issues. Comments and suggestions about the code & content/documentation of the package are below. Package content/documentation
Package code
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@brunaw Thank you for your review and your positive recommendation! I will try to address each point you have brought up
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@assignUser Thank you for the quick reply, and I apologize if some of my comments weren't clear.
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@brunaw Arguments: I feel that with all of the vignettes and targeted documentation we have this covered as a specific example for Install: I just found the solution for this issue. This is a recently solved problem where devtools/remotes was assuming the default branch to be "master" which was fixed by r-lib/remotes#510 but is not on cran yet: https://remotes.r-lib.org/news/index.html |
@whedon generate pdf |
PDF failed to compile for issue #2763 with the following error: Can't find any papers to compile :-( |
@whedon generate pdf from branch joss-submission |
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@brunaw As for the grammar of the vignettes - were there any that were particularly egregious? I will certainly go through all of them - as you see there are quite a few - but it would help if you found that particular ones need special attention. |
@brunaw |
The package may be useful to some researchers and students, in cases where generation of data following some typical models Paper — remarks:
Vignettes — remarks:
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@brunaw re: simulating correlated data - see https://en.wikipedia.org/wiki/Copula_(probability_theory) and https://cran.r-project.org/web/packages/copula/index.html |
@gagolews Thanks for your feedback - will make the editing changes. I agree with you regarding 1e - we may make that change in a future iteration, though there are some "formulas" where there is no standard R formulation. The following (clearly nonsensical) snippet shows three distributions where the standard formulas don't really apply: library(simstudy)
d <- defData(varname = "x", formula = "0;1", dist = "uniform")
d <- defData(d, varname ="y", formula = "-2+x;-1 + 0.5*x",
dist = "categorical", link = "logit")
d <- defData(d, varname = "z", formula = "x|0.5 + y|0.5", dist = "mixture")
set.seed(5)
genData(10, d)
#> id x y z
#> 1: 1 0.2002145 3 0.2002145
#> 2: 2 0.6852186 3 3.0000000
#> 3: 3 0.9168758 3 0.9168758
#> 4: 4 0.2843995 2 0.2843995
#> 5: 5 0.1046501 2 0.1046501
#> 6: 6 0.7010575 1 0.7010575
#> 7: 7 0.5279600 3 0.5279600
#> 8: 8 0.8079352 3 0.8079352
#> 9: 9 0.9565001 3 3.0000000
#> 10: 10 0.1104530 1 1.0000000 With respect to 2a, I agree that copulas are useful in generating correlated data. Indeed, that is what we are doing in |
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@mikldk I now recommend the paper be accepted for publication. |
@whedon set 10.5281/zenodo.4134675 as archive |
OK. 10.5281/zenodo.4134675 is the archive. |
@whedon set v0.2.2 as version |
OK. v0.2.2 is the version. |
@whedon generate pdf |
@whedon accept |
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👋 @openjournals/joss-eics, this paper is ready to be accepted and published. Check final proof 👉 openjournals/joss-papers#1870 If the paper PDF and Crossref deposit XML look good in openjournals/joss-papers#1870, then you can now move forward with accepting the submission by compiling again with the flag
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@whedon accept deposit=true |
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🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦 |
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Any issues? Notify your editorial technical team... |
@gagolews, @brunaw - many thanks for your reviews here and to @mikldk for editing this submission ✨ @assignUser - your paper is now accepted into JOSS ⚡🚀💥 |
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Submitting author: @assignUser (Jacob Wujciak-Jens)
Repository: https://github.com/kgoldfeld/simstudy/
Version: v0.2.2
Editor: @mikldk
Reviewer: @gagolews, @brunaw
Archive: 10.5281/zenodo.4134675
Due to the challenges of the COVID-19 pandemic, JOSS is currently operating in a "reduced service mode". You can read more about what that means in our blog post.
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Review checklist for @gagolews
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Review checklist for @brunaw
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