Rcpp 1.0.2: Small Polish
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The second maintenance release of Rcpp, following up on the 10th anniversary and the 1.0.0. release, was prepared last Saturday and released to both the Rcpp drat repo and CRAN. Following all the manual inspection (including a false positive result from reverse dependencies), it has finally arrived on CRAN earlier today. The corresponding Debian package was also uploaded, and binaries have since been built.
Just like for Rcpp 1.0.1, we have a four month gap between releases which seems appropriate given both the changes still being made (see below) and the relative stability of Rcpp. It still takes work to release this as we run multiple extensive sets of reverse dependency checks so maybe one day we will switch to six month cycle.
Rcpp has become the most popular way of enhancing GNU R with C or C++ code. As of today, 1713 packages on CRAN depend on Rcpp for making analytical code go faster and further, along with 176 in BioConductor. Per the (partial) logs of CRAN downloads, we have had over one million downloads a month following the previous release.
This release features a number of different pull requests by four different contributors as detailed below.
Changes in Rcpp version 1.0.2 (2019-07-20)
Changes in Rcpp API:
Changes in Rcpp Attributes:
Changes in Rcpp Sugar:
Changes in Rcpp Deployment:
- Travis CI unit tests are now always running irrespective of the package version (Dirk in #954).
Changes in Rcpp Documentation:
Thanks to CRANberries, you can also look at a diff to the previous release. 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.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.