goldilocks

[nzgwynn]

Submitting Author Name: gwynn gebeyehu Submitting Author Github Handle: !--author1-->@nzgwynn<!--end-author1-- Repository: https://github.com/nzgwynn/goldilocks Submission type: Pre-submission Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

The Goldilocks package implements the theoretical permutation
    approach developed in this article is an interactive Shiny application. 
    This package allows researchers to randomize cluster randomized trials with 
    confidence by visualizing the distribution of average differences in the 
    treatment and control arms in practice randomizations. The application also 
    allows researchers to randomize a trial and download the resultant 
    randomizations. The Goldilocks application is deployed on the web put some 
    researchers may prefer an R package.

Scope

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):

  Data Lifecycle Packages

  • [ ] data retrieval

  • [ ] data extraction

  • [ ] data munging

  • [ ] data deposition

  • [ ] data validation and testing

  • [x] workflow automation

  • [ ] version control

  • [ ] citation management and bibliometrics

  • [ ] scientific software wrappers

  • [ ] field and lab reproducibility tools

  • [ ] database software bindings

  • [ ] geospatial data

  • [ ] text analysis

    Statistical Packages

  • [ ] Bayesian and Monte Carlo Routines

  • [ ] Dimensionality Reduction, Clustering, and Unsupervised Learning

  • [ ] Machine Learning

  • [ ] Regression and Supervised Learning

  • [ ] Exploratory Data Analysis (EDA) and Summary Statistics

  • [ ] Spatial Analyses

  • [ ] Time Series Analyses

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of: @maurolepore suggested here that this may fit into a workflow package as it is not statistical software.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

• Who is the target audience and what are scientific applications of this package? This packages allows anyone randomizing clusters (schools, hospitals, nursing homes, etc...) to randomize with some confidence that balance will be achieved in chosen variables. It is a GUI that allows them to upload data, practice randomizing, then randomize.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category? I do not know of any other packages that do this.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research? There is no personal data anywhere in this package.

• Any other questions or issues we should be aware of?: Thank you for the time you are taking to look at this package.

[maurolepore]

Dear @nzgwynn,

Again, thanks for your submission.

Sorry to confuse you. What I tried to say here is that:

1. We might consider it a "workflow" package under our statistical-software peer-review process (not as a "workflow" package under the standard peer review process).

2. We expect the package to first meet the guidelines for statistical software before we can consider it for review.