Package: fastMatMR 1.2.5

Rohit Goswami

fastMatMR: High-Performance Matrix Market File Operations

An interface to the 'fast_matrix_market' 'C++' library, this package offers efficient read and write operations for Matrix Market files in R. It supports both sparse and dense matrix formats. Peer-reviewed at ROpenSci (<https://github.com/ropensci/software-review/issues/606>).

Authors:Rohit Goswami [aut, cre], Ildiko Czeller [rev], Adam Lugowski [ctb]

fastMatMR_1.2.5.tar.gz
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fastMatMR.pdf |fastMatMR.html✨
fastMatMR/json (API)
NEWS

# Install 'fastMatMR' in R:
install.packages('fastMatMR', repos = c('https://ropensci.r-universe.dev', 'https://cloud.r-project.org'))

Reviews:rOpenSci Software Review #606

Bug tracker:https://github.com/ropensci/fastmatmr/issues

Pkgdown site:https://docs.ropensci.org

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:fastMatMR (archived)

Conda:

cpp17matrix-marketmatrix-market-formatr-cpp11cpp

3.92 score 5 stars 11 scripts 38 downloads 9 exports 1 dependencies

Last updated 1 years agofrom:9146e5de53 (on main). Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 14 2025
R-4.5-win-x86_64OKMar 14 2025
R-4.5-mac-x86_64OKMar 14 2025
R-4.5-mac-aarch64OKMar 14 2025
R-4.5-linux-x86_64OKMar 14 2025
R-4.4-win-x86_64OKMar 14 2025
R-4.4-mac-x86_64OKMar 14 2025
R-4.4-mac-aarch64OKMar 14 2025
R-4.4-linux-x86_64OKMar 14 2025
R-4.3-win-x86_64OKMar 14 2025
R-4.3-mac-x86_64OKMar 14 2025
R-4.3-mac-aarch64OKMar 14 2025

Exports:fmm_to_matfmm_to_sparse_Matrixfmm_to_vecintmat_to_fmmintvec_to_fmmmat_to_fmmsparse_Matrix_to_fmmvec_to_fmmwrite_fmm

Dependencies:cpp11

Benchmarking Sparse Matrix Market Read Operations

Rohit Goswami

Rendered fromfmm_read_bench.Rmdusingknitr::rmarkdownon Mar 14 2025.

Last update: 2023-11-03
Started: 2023-09-02

Benchmarking Sparse Matrix Market Write Operations

Rohit Goswami

Rendered fromfmm_write_bench.Rmdusingknitr::rmarkdownon Mar 14 2025.

Last update: 2023-11-03
Started: 2023-08-31

Getting Started

Rohit Goswami

Rendered fromfastMatMR.Rmdusingknitr::rmarkdownon Mar 14 2025.

Last update: 2023-11-03
Started: 2023-10-24

Citation

To cite package ‘fastMatMR’ in publications use:

Goswami R (2025). fastMatMR: High-Performance Matrix Market File Operations. R package version 1.2.5, https://github.com/ropensci/fastmatmr.

Corresponding BibTeX entry: 

  @Manual{,
    title = {fastMatMR: High-Performance Matrix Market File
      Operations},
    author = {Rohit Goswami},
    year = {2025},
    note = {R package version 1.2.5},
    url = {https://github.com/ropensci/fastmatmr},
  }

Readme and manuals

fastMatMR fastMatMR

About

fastMatMR provides R bindings for reading and writing to Matrix Market files using the high-performance fast_matrix_market C++ library (version 1.7.4).

Why?

Matrix Market files are crucial to much of the data-science 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.

The target audience and scientific applications primarily include data scientists or researchers developing numerical methods who may wish to either test standard NIST (National Institute of Standards and Technology) 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, being able to use the matrix market file format, means it is easier to interface R analysis with those in Python (e.g. SciPy uses the same underlying C++ library). These files can also be used with the Tensor Algebra Compiler (TACO).

Features

  • Extended Support: fastMatMR supports standard R vectors, matrices, as well as Matrix sparse objects.

  • Performance: The package is a thin wrapper around one of the fastest C++ libraries for reading and writing .mtx files.

  • Correctness: Unlike Matrix, roundtripping with NA and NaN values works by coercing to NaN instead of to arbitrarily high numbers.

We have vignettes for both read and write operations to demonstrate the performance claims.

Alternatives and statement of need
  • The Matrix package allows reading and writing sparse matrices in the .mtx (matrix market) format.
    • However, for .mtx files, it can only handles sparse matrices for writing and reading.
    • Round-tripping (writing and subsequently reading) data with NA and NaN values produces arbitrarily high numbers instead of preserving NaN / handling NA

Installation

CRAN

For the latest CRAN version:

install.packages("fastMatMR")
R-Universe

For the latest development version of fastMatMR:

install.packages("fastMatMR",
                 repos = "https://ropensci.r-universe.dev")
Development Git

For the latest commit, one can use:

# install.packages("devtools")
# devtools::install_github("ropensci/fastMatMR")
install.packages("fastMatMR", repos = c('https://ropensci.r-universe.dev', 'https://cloud.r-project.org'))

Quick Example

library(fastMatMR)
spmat <- Matrix::Matrix(c(1, 0, 3, 2), nrow = 2, sparse = TRUE)
write_fmm(spmat, "sparse.mtx")
fmm_to_sparse_Matrix("sparse.mtx")

The resulting .mtx file is language agnostic, and can even be read back in python as an example:

pip install fast_matrix_market
python -c 'import fast_matrix_market as fmm; print(fmm.read_array_or_coo("sparse.mtx"))'
((array([1., 3., 2.]), (array([0, 0, 1], dtype=int32), array([0, 1, 1], dtype=int32))), (2, 2))
python -c 'import fast_matrix_market as fmm; print(fmm.read_array("sparse.mtx"))'
array([[1., 3.],
       [0., 2.]])

Similarly, fastMatMR supports writing and reading from other R objects (e.g. standard R vectors and matrices), as seen in the getting started vignette.

Contributing

Contributions are very welcome. Please see the Contribution Guide and our Code of Conduct.

License

This project is licensed under the MIT License.

Logo

The logo was generated via a non-commercial use prompt on hotpot.ai, both blue, and green, as a riff on the NIST Matrix Market logo. The text was added in a presentation software (WPS Presentation). Hexagonal cropping was accomplished in a hexb compatible design using hexsticker.