RcppArmadillo 15.6.0-1 on CRAN: New Upstream Minor
Armadillo is a powerful and expressive C++ template library for linear algebra and scientific computing. It aims towards a good balance between speed and ease of use, has a syntax deliberately close to Matlab, and is useful for algorithm development directly in C++, or quick conversion of research code into production environments. RcppArmadillo integrates this library with the R environment and language–and is widely used by (currently) 1331 other packages on CRAN, downloaded 48.5 million times (per the partial logs from the cloud mirrors of CRAN), and the CSDA paper (preprint / vignette) by Conrad and myself has been cited 727 times according to Google Scholar.
This versions updates to the 15.6.0 upstream Armadillo release made
yesterday. It extends solver options for poorly conditioned systems, and
brings some updates and extension to the cube data type.
For this release, we once again ran the usual complete
reverse-dependency check which came back spotless, and did CRAN so no
email exchange needed despite nearly 1300 reverse dependencies (but it
ended up taking more than a single business day). Still, automation can
be helpful when used with a well-maintained software stack. The package
has also already been updated for Debian, built for r2u and r-universe, and
will build shortly at CRAN for
the different binary releases.
All changes since the last CRAN release follow.
Changes in RcppArmadillo version 15.6.0-1 (2026-09-07)
Upgraded to Armadillo release 15.6.0 (Medium Roast Cortado)
Expanded
solve()withsolve_opts::scale_threshoption to widen detection of poorly conditioned systemsExpanded
trans()and.t()to handle cubesAdded
permute()to rearrange dimensions of cubes (generalised transpose)Added
cubemul()for batched matrix multiplication of cube slices
Courtesy of my CRANberries, there is a diffstat report relative to previous release. More detailed information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the Rcpp R-Forge page.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.
