Rcpp 0.7.8

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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<int RTYPE> 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<int RTYPE> 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<int RTYPE> that represents a column of a 
    	matrix. (similar to Rcpp::Row<int RTYPE>). 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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