Rcpp 0.7.8

March 9, 2010

(This article was first published on dirk.eddelbuettel, and kindly contributed to R-bloggers)

Version 0.7.8 of the Rcpp
R / C++ interface classes is now on CRAN and in Debian. As of right now. Debian has
already built packages for eight more architectures; and CRAN has built the
Windows binary. Oh, and cran2deb
had Debian packages for ‘testing’ before I was done with the blog entry.

This is a minor feature release based on a over three weeks of changes that
are summarised below in the extract from the NEWS file. Some noteworthy
highlights are

  • something that isn’t there: we have split most of the example code and their manual pages off into a new package RcppExamples which can now be released given that 0.7.8 is out
  • another new package RcppArmadillo will also be forthcoming shortly: it shows how to use Rcpp with Conrad Sanderson’s excellent Armadillo C++ library for linear algebra; this required some internal code changes to seamlessly pass data from R via Rcpp to Armadillo and back;
  • there is a new example fastLm using Armadillo for faster (than lm() or lm.fit()) linear model fits
  • yet more internal improvements to the class hierarchy as detailed below; more support for STL iterators and algorithms;
  • more build fixes; paths with spaces in the name should now be tolerated
  • and last but not least a new introduction / overview vignette based on a just-submitted paper on Rcpp.

The full NEWS entry for this release follows:

0.7.8 2010-03-09 o	All vector classes are now generated from the same template class Rcpp::Vector where RTYPE is one of LGLSXP, RAWSXP, STRSXP, INTSXP, REALSXP, CPLXSXP, VECSXP and EXPRSXP. typedef are still available : IntegerVector, ... All vector classes gain methods inspired from the std::vector template : push_back, push_front, erase, insert o	New template class Rcpp::Matrix deriving from Rcpp::Vector . These classes have the same functionality as Vector but have a different set of constructors which checks that the input SEXP is a matrix. Matrix<> however does/can not guarantee that the object will allways be a matrix. typedef are defined for convenience: Matrix is IntegerMatrix, etc... o	New class Rcpp::Row that represents a row of a matrix of the same type. Row contains a reference to the underlying Vector and exposes a nested iterator type that allows use of STL algorithms on each element of a matrix row. The Vector class gains a row(int) method that returns a Row instance. Usage examples are available in the runit.Row.R unit test file o	New class Rcpp::Column that represents a column of a matrix. (similar to Rcpp::Row ). Usage examples are available in the runit.Column.R unit test file o	The Rcpp::as template function has been reworked to be more generic. It now handles more STL containers, such as deque and list, and the genericity can be used to implement as for more types. The package RcppArmadillo has examples of this o new template class Rcpp::fixed_call that can be used in STL algorithms such as std::generate. o	RcppExample et al have been moved to a new package RcppExamples; src/Makevars and src/Makevars.win simplified accordingly o	New class Rcpp::StringTransformer and helper function Rcpp::make_string_transformer that can be used to create a function that transforms a string character by character. For example Rcpp::make_string_transformer(tolower) transforms each character using tolower. The RcppExamples package has an example of this. o	Improved src/Makevars.win thanks to Brian Ripley o	New examples for 'fast lm' using compiled code: - using GNU GSL and a C interface - using Armadillo (http://arma.sf.net) and a C++ interface Armadillo is seen as faster for lack of extra copying o	A new package RcppArmadillo (to be released shortly) now serves as a concrete example on how to extend Rcpp to work with a modern C++ library such as the heavily-templated Armadillo library o	Added a new vignette 'Rcpp-introduction' based on a just-submitted overview article on Rcpp 

As always, even fuller details are in the ChangeLog on the
Rcpp page which also
leads to the downloads, the browseable
doxygen docs
and zip files of doxygen output for the standard formats.
A local directory has
source and documentation too. Questions, comments etc should go to the
rcpp-devel mailing list
off the R-Forge page

Update: Two links corrected.

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