cmmclaug
commented
3 years ago Submitting Authors:
- Santiago Rugeles (@ansarusc)
- Neel Phaterpekar (@nphaterp)
- Daniel Ortiz (@danielon-5)
- Craig McLaughlin (@cmmclaug)
Package Name: aridanalysis
One-Line Description of Package: DRY out your regression analysis!
Repository Link: https://github.com/UBC-MDS/aridanalysis_py
Version submitted: 0.4.0
Editor: Tiffany Timbers (@ttimbers)
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD
Description
As Data Scientists, being able to perform Exploratory Data Analysis as well as Regression Analysis are paramount to the process of analyzing trends in data. Moreover, following the DRY (Do Not Repeat Yourself) principle is regarded as a majority priority for maximizing code quality. Yet, often times Data Scientists facing these tasks will start the entire process from scratch, wasting both time and effort while compromising code quality. The aridanalysis package strives to remedy this problem by giving users an easy-to-implement EDA function alongside 3 robust statistical tests that will simplify these analytical processes and produce an easy to read interpretation of the input data. Users will no longer have to write many lines of code to explore their data effectively.
Scope
- Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [x] Data munging
- [ ] Data deposition
- [ ] Reproducibility
- [ ] Geospatial
- [ ] Education
- [x] Data visualization*
* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.
-
Explain how the and why the package falls under these categories (briefly, 1-2 sentences):
- This aridanalysis package falls under data munging because we provide functions to perform exploratory data and produce regression analysis models given provided data.
- The aridanalysis package is considered a data visualization package because we provide an
arid_edafunction to produce a number of data visualizations given the data.
-
Who is the target audience and what are scientific applications of this package?
- The target audience is machine learning enthusiasts looking to expand upon Sci-kit Learn models to explore inferential questions with their R style statistical models.
-
Are there other Python packages that accomplish the same thing? If so, how does yours differ?
- Yes, the statsmodels package provides a large library of R style statistical models and functions. Our package differs in that we have focused and simplified the interface while also providing an associated Sci-Kit Learn model to leverage both predictive and inferential model examples.
-
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or
@tagthe editor you contacted:
Technical checks
For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:
- [x] does not violate the Terms of Service of any service it interacts with.
- [x] has an OSI approved license.
- [x] contains a README with instructions for installing the development version.
- [x] includes documentation with examples for all functions.
- [x] contains a vignette with examples of its essential functions and uses.
- [x] has a test suite.
- [x] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
Publication options
- [ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:
JOSS Checks
- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?
This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.
- [x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.
Code of conduct
- [x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.
P.S. *Have feedback/comments about our review process? Leave a comment here
danielon-5
Dropping template here:
Submitting Author: Name (@github_handle)
Package Name: One-Line Description of Package: Repository Link:
Version submitted:
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD
Description
Scope
* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.
Explain how the and why the package falls under these categories (briefly, 1-2 sentences):
Who is the target audience and what are scientific applications of this package?
Are there other Python packages that accomplish the same thing? If so, how does yours differ?
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or
@tagthe editor you contacted:Technical checks
For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:
Publication options
JOSS Checks
- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?
This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.
Code of conduct
P.S. *Have feedback/comments about our review process? Leave a comment here
Editor and Review Templates
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