Submission: easymlr (R)

[hellosakshi]

---

name: easymlr about: Use this template to submit software for review

---

Submitting Author:

• Lara habashy (@larahabashy)
• Ifeanyi Anene (@IfyAnene7)
• Sakshi Jain (@hellosakshi)
• Zhenrui (Eric) Yu (@yzr1996)

Repository: https://github.com/UBC-MDS/easymlr Version submitted: Editor: Tiffany Timbers(@ttimbers ) Reviewers: TBD

Archive: TBD Version accepted: TBD

---

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

Package: easymlr
Title: Easy machine learning in r
Version: 0.0.0.9000
Authors@R: 
    c(person(given = "Ifeanyi",
           family = "Anene",
           role = c("aut"),
           email = "ifyanene@student.ubc.ca"
           ),
    person(given = "Lara",
           family = "Habashy",
           role = c("aut"),
           email = "laraahabashy@gmail.com"
           ),
    person(given = "Sakshi",
           family = "Jain",
           role = c("aut"),
           email = "hellosakshi@gmail.com"
           ),
    person(given = "Zhenrui",
           family = "Yu",
           role = c("aut", "cre"),
           email = "yuzhenrui1996@gmail.com"))
Description: In this pacakge, there are four functions, EDA, miss_data, baseline_fun and feature_select.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Suggests: 
    testthat (>= 2.0.0),
    covr,
    knitr,
    rmarkdown
Config/testthat/edition: 2
URL: https://github.com/UBC-MDS/easymlr
BugReports: https://github.com/UBC-MDS/easymlr/issues
Imports: 
    caret,
    MASS,
    rlist,
    e1071,
    dplyr,
    magrittr
Depends: 
    tidyverse
VignetteBuilder: knitr

Scope

• Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):

  • [ ] data retrieval
  • [ ] data extraction
  • [ ] data munging
  • [ ] data deposition
  • [ ] workflow automation
  • [ ] version control
  • [ ] citation management and bibliometrics
  • [ ] scientific software wrappers
  • [ ] field and lab reproducibility tools
  • [ ] database software bindings
  • [ ] geospatial data
  • [ ] text analysis
  • [x] Exploratory data analysis
  • [x] Feature selection
  • [x] Missing data imputation
  • [x] Model selection

• Explain how and why the package falls under these categories (briefly, 1-2 sentences): It explores data and impute missing values. Also, it helps in feature selection and helps in selecting the model.

• Who is the target audience and what are scientific applications of this package? The target audience of this package includes anyone who has the requirements to clean data and build machine learning model. For instance, students interested in machine learning might be the target audience. Also, data scientists, data engineers, statisticians can be possible target users.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category? As per our knowledge, there is no package in R to accomplish the same thing. There are four main components of our R package and each of the functions has its innovation points. For example, our eda_analysis function gives users a sense of the whole distribution summary of the raw data. In addition to EDA, our model helps in filling the missing values, in feature selection and in selecting the best model.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

• If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.

Technical checks

Confirm each of the following by checking the box.

• [x] I have read the guide for authors and rOpenSci packaging guide.

This package:

• [x] does not violate the Terms of Service of any service it interacts with.
• [X] has a CRAN and OSI accepted license.
• [x] contains a README with instructions for installing the development version.
• [x] includes documentation with examples for all functions, created with roxygen2.
• [x] contains a vignette with examples of its essential functions and uses.
• [x] has a test suite.
• [x] has continuous integration, including reporting of test coverage using services such as Travis CI, Coveralls and/or CodeCov.

Publication options

• [ ] Do you intend for this package to go on CRAN?

• [ ] Do you intend for this package to go on Bioconductor?

• [ ] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

- [ ] The package is novel and will be of interest to the broad readership of the journal. - [ ] The manuscript describing the package is no longer than 3000 words. - [ ] You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see [MEE's Policy on Publishing Code](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/journal-resources/policy-on-publishing-code.html)) - (*Scope: Do consider MEE's [Aims and Scope](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/aims-and-scope/read-full-aims-and-scope.html) for your manuscript. We make no guarantee that your manuscript will be within MEE scope.*) - (*Although not required, we strongly recommend having a full manuscript prepared when you submit here.*) - (*Please do not submit your package separately to Methods in Ecology and Evolution*)

Code of conduct

• [x] I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.