Date accepted: 2023-10-30
Submitting Author Name: Rohit Goswami
Submitting Author Github Handle: @HaoZeke
Repository: https://github.com/HaoZeke/fastMatMR
Version submitted:
Submission type: Standard
Editor: @maelle
Reviewers: @osorensen, @czeildi

Archive: TBD
Version accepted: TBD
Language: en

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

Package: fastMatMR
Title: "fastMatMR: High-Performance Matrix Market File Operations in R"
Version: 1.0.0.0
Authors@R:
    person("Rohit", "Goswami", , "rgoswami@ieee.org", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-2393-8056"))
Description: "fastMatMR is an R package offering high-performance read and write operations for Matrix Market files. It acts as a thin wrapper around the 'fast_matrix_market' C++ library, offering speed and extended support for Matrix Market formats. Unlike other R packages, fastMatMR supports not just sparse matrices but also dense vectors and matrices. This makes it a versatile choice for dealing with .mtx files in R."
License: MIT + file LICENSE
SystemRequirements: C++17
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
LinkingTo:
    cpp11
Suggests:
    ggplot2,
    knitr,
    Matrix,
    microbenchmark,
    rmarkdown,
    testthat (>= 3.0.0)
URL: https://github.com/HaoZeke/fastMatMR
BugReports: https://github.com/HaoZeke/fastMatMR/issues
Config/testthat/edition: 3
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
  • data validation and testing
  • workflow automation
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

• Explain how and why the package falls under these categories (briefly, 1-2 sentences):

The matrix market exchange formats are crucial to much of the ecosystem. The fastMatMR package focuses on high-performance read and write operations for Matrix Market files, serving as a key tool for data extraction in computational and data science pipelines.

• Who is the target audience and what are scientific applications of this package?

Data scientists who might want to load and test the NIST matrices which include:

comparative studies of algorithms for numerical linear algebra, featuring nearly 500 sparse matrices from a variety of applications, as well as matrix generation tools and services.

Additionally, this makes its simpler to interface to scipy and the rest of the data science ecosystem. This also includes working with the Tensor Algebra Compiler (TACO).

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

The Matrix package in R can perform similar operations but only for sparse matrices. The fastMatMR package not only provides enhanced performance but also extends support to dense matrices and vectors in base R, thus offering a more versatile solution.

We have both read and write performance vignettes backing up the claims made.

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

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

• Explain reasons for any pkgcheck items which your package is unable to pass.

The package passes pkcheck: ropensci/fastMatMR#18, though the review bot disagrees :)

Technical checks

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions, created with roxygen2.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, including reporting of test coverage.

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