What is it about?
The Matrix Market (.mtx) format is a standard way to store sparse and dense matrices, widely used for sharing benchmark data between languages. The existing R packages for reading and writing .mtx files were slow and had correctness issues with certain matrix types. fastMatMR uses a C++ backend to handle the parsing and serialization, exposed to R through a clean interface. Read and write speeds improve by 1-2 orders of magnitude compared to existing R packages. The package handles all Matrix Market format variants (coordinate, array, real, integer, complex, pattern) and passes the full Matrix Market test suite. The package is on CRAN and integrates with R's standard matrix classes, including sparse matrices from the Matrix package.
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Why is it important?
Data exchange between R and Python is a recurring friction point in scientific workflows. Both languages have strong Matrix Market support on the Python side (scipy.io), but the R side lagged behind in both speed and correctness. fastMatMR closes that gap. Loading a large sparse matrix that previously took minutes now takes seconds. The correctness fixes matter for reproducibility: the old packages silently mishandled certain symmetric or pattern matrices. The package went through rOpenSci peer review, which ensures documentation quality and API consistency beyond what CRAN checks alone provide.
Perspectives
I built fastMatMR because I kept running into the same problem in my own work: moving matrix data between R and Python was painfully slow. The existing R packages either crashed on large files or silently produced wrong results for edge cases. The C++ backend was the obvious solution. R's Rcpp ecosystem makes this straightforward, and the Matrix Market format is simple enough that the parser fits in a few hundred lines. Most of the development time went into edge cases and testing against the full NIST Matrix Market collection. The rOpenSci review process improved the package substantially, especially the documentation and error handling.
Rohit Goswami
University of Iceland
Read the Original
This page is a summary of: fastMatMR: High-Performance Matrix Market File Operations, November 2023, The R Foundation,
DOI: 10.32614/cran.package.fastmatmr.
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