RcppArmadillo 0.4.400.0

August 19, 2014
By

(This article was first published on Thinking inside the box , and kindly contributed to R-bloggers)

After two pre-releases in the last few days, Conrad finalised a new Armadillo version 4.400 today. I had kept up with the pre-releases, tested twice against all eighty (!!) CRAN dependents of RcppArmadillo and have hence uploaded RcppArmadillo 0.4.400.0 to CRAN and into Debian.

This release brings a number of new upstream features which are detailed below. As included is s bugfix for sparse matrix creation at the RcppArmadillo end which was found by the ASAN tests at CRAN --- which are similar to the sanitizers tests I recently blogged. I was able to develop and test the fix in the very docker r-devel-san images I had written about which was nice. Special thanks also to Ryan Curtin for help with the fix.

Changes in RcppArmadillo version 0.4.400.0 (2014-08-19)

  • Upgraded to Armadillo release Version 4.400 (Winter Shark Alley)

    • added gmm_diag class for statistical modelling using Gaussian Mixture Models; includes multi-threaded implementation of k-means and Expectation-Maximisation for parameter estimation

    • added clamp() for clamping values to be between lower and upper limits

    • expanded batch insertion constructors for sparse matrices to add values at repeated locations

    • faster handling of subvectors by dot()

    • faster handling of aliasing by submatrix views

  • Corrected a bug (found by the g++ Address Sanitizer) in sparse matrix initialization where space for a sentinel was allocated, but the sentinel was not set; with extra thanks to Ryan Curtin for help

  • Added a few unit tests for sparse matrices

Courtesy of CRANberries, there is also a diffstat report for the most recent release. As always, more detailed information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

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