More information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page.

/code/rcpp | permanent link

Rcpp 0.8.5
A new release (now at version 0.8.5) of Rcpp is now on CRAN and in Debian.

This release constitutes a quick follow-up to the last release 0.8.4 which we got out just before CRAN closed for summer vacations. Some fixes were made right after last release: two harmless warnings from the help file parser of the development version of R are now addressed, and we stopped using shell expansions in the Makefile snippets. We also added to some internal speedups we discovered while prepapring the talk about RProtoBuf for last week's useR! meeting.

The NEWS entry follows below:

0.8.5   2010-07-25

    o   speed improvements. Vector::names, RObject::slot have been improved
        to take advantage of R API functions instead of callbacks to R

    o   Some small updates to the Rd-based documentation which now points to 
        content in the vignettes.  Also a small formatting change to suppress
        a warning from the development version of R.

    o   Minor changes to Date() code which may reenable SunStudio builds

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

useR 2010 at NIST in Gaithersburg
This past week, the annual R user conference useR! 2010 took place at the National Institute of Standards and Technology (NIST) in Gaithersburg, MD (which is a tad northwest of Washington, DC). Kate Mullen and her team of local organizers did a truly tremendous job in putting together a very smooth conference attended by almost 500 people. It is always nice to meet so many other R contributors and users in person. And needless to say it's also just plain fun to hang out with these folks.

As at the preceding useR! 2008 in Dortmund and useR! 2009 in Rennes, I presented a three-hour tutorial on high-performance computing with R. This covers scripting/automation, profiling, vectorisation, interfacing compiled code, parallel computing and large-memory approaches. The slides, as well as a condensed 2-up version, are now on my presentations page.

On Wednesday, Romain and I had a chance to talk about recent work on Rcpp, our R and C++ integration. Thursday, we followed up with a presentation on RProtoBuf -- a project integrating Google's Protocol Buffers with R which much to our delight already seems to be in use at Google itself! It was quite fun to do these two talks jointly with Romain. But my other coauthor Khanh had to be at a conference related to his actual PhD work. So on Friday it was just me to give a presentation about RQuantLib which brings QuantLib to R.

Slides from all these talks have now been added to my presentations page. I will also upload them via the conference form so that they can be part of the conference's collection of presentations which should be forthcoming.

/computers/R | permanent link

Rcpp 0.8.4
Romain and I wrapped up release 0.8.4 of Rcpp last Friday. However, given the time of year, it only appeared on CRAN this morning, and then only after some prodding as CRAN processing is more or less closed this week and probably next.

This release builds upon release 0.8.3. Highlights include changes to the sugar framework for highly expressive C++ constructs which gained new vector function as well as a first set of matrix function. As well, unit tests have been reorganised in such a way that we end up with a lot fewer compilations (but of several files at once) which reaps significant speed gains. Date calculation now use the same mktime() function R itself uses (and which comes from Arthur Olson's tzone library). The NEWS entry follows below:

0.8.4   2010-07-09

    o   new sugar vector functions: rep, rep_len, rep_each, rev, head, tail,
        diag
        
    o   sugar has been extended to matrices: The Matrix class now extends the 
        Matrix_Base template that implements CRTP. Currently sugar functions 
        for matrices are: outer, col, row, lower_tri, upper_tri, diag

    o   The unit tests have been reorganised into fewer files with one call
        each to cxxfunction() (covering multiple tests) resulting in a
        significant speedup

    o   The Date class now uses the same mktime() replacement that R uses
        (based on original code from the timezone library by Arthur Olson)
        permitting wide dates ranges on all operating systems

    o   The FastLM example has been updated, a new benchmark based on the
        historical Longley data set has been added

    o   RcppStringVector now uses std::vector internally
    
    o   setting the .Data slot of S4 objects did not work properly

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

Rcpp 0.8.3
A new version 0.8.3 of Rcpp is now CRAN and in Debian.

It comes about three weeks after the 0.8.2 release. And even though we promised to concentrate on documentation, it contains a raft of new features:

• The addition of what we dub Rcpp sugar: some syntactic sugar based on clever use of expression templates that lets us write C++ expression as neatly and compactly as vectorised R expressions (while getting C++ speed!!). More on that below.
• New classes Date and Datetime with internal representations just like R's Date and POSIXct classes, plus vector versions DateVector and DatetimeVector which behave just like STL vectors. With that, the new API is now feature-complete compared to the older 'classic' API.
• Rcpp Modules (our 'not unlike Boost::Python' feature introduced in version 0.8.1) can now expose public data members
• A new API class InternalFunction which can expose C++ functions even without R modules.

The main thing here is Rcpp sugar for which we also have a new (seventh !!) vignette Rcpp-sugar. As a quick example, consider this simple C++ function that takes two vectors from R and creates a new one conditional on the relative values:

export "C" SEXP foo( SEXP xs, SEXP ys) {
    Rcpp::NumericVector x(xs), y(ys);
    int n = x.size();
    Rcpp::NumericVector res( n );
    double xd = 0.0, yd = 0.0 ;
    for( int i=0; i<n; i++){
        xd = x[i];
        yd = y[i];
        if( xd < yd ){
            res[i] = xd * xd ;
        } else {
            res[i] = -( yd * yd);
        }
    }
    return res ;
}

extern "C" SEXP foo( SEXP xs, SEXP ys){
    Rcpp::NumericVector x(xs), y(xs);
    return ifelse( x < y, x*x, -(y*y));
}

The full NEWS entry for this release follows below:

0.8.3   2010-06-27

    o	This release adds Rcpp sugar which brings (a subset of) the R syntax
        into C++. This supports : 
         - binary operators : <,>,<=,>=,==,!= between R vectors
         - arithmetic operators: +,-,*,/ between compatible R vectors
         - several functions that are similar to the R function of the same name:
        abs, all, any, ceiling, diff, exp, ifelse, is_na, lapply, pmin, pmax, 
        pow, sapply, seq_along, seq_len, sign
        
        Simple examples :
        
          // two numeric vector of the same size
          NumericVector x ;
          NumericVector y ;
          NumericVector res = ifelse( x < y, x*x, -(y*y) ) ;
        
          // sapply'ing a C++ function
          double square( double x ){ return x*x ; }
          NumericVector res = sapply( x, square ) ;
        
        Rcpp sugar uses the technique of expression templates, pioneered by the 
        Blitz++ library and used in many libraries (Boost::uBlas, Armadillo). 
        Expression templates allow lazy evaluation of expressions, which 
        coupled with inlining generates very efficient code, very closely 
        approaching the performance of hand written loop code, and often
        much more efficient than the equivalent (vectorized) R code.
        
        Rcpp sugar is curently limited to vectors, future releases will 
        include support for matrices with sugar functions such as outer, etc ...
        
        Rcpp sugar is documented in the Rcpp-sugar vignette, which contains
        implementation details.

    o   New helper function so that "Rcpp?something" brings up Rcpp help

    o   Rcpp Modules can now expose public data members

    o   New classes Date, Datetime, DateVector and DatetimeVector with proper
        'new' API integration such as as(), wrap(), iterators, ...

    o   The so-called classic API headers have been moved to a subdirectory
        classic/ This should not affect client-code as only Rcpp.h was ever
        included.

    o   RcppDate now has a constructor from SEXP as well

    o   RcppDateVector and RcppDatetimeVector get constructors from int
        and both const / non-const operator(int i) functions
        
    o   New API class Rcpp::InternalFunction that can expose C++ functions
    	to R without modules. The function is exposed as an S4 object of 
    	class C++Function

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

RcppArmadillo 0.2.3
The minor bug-fix release 0.2.3 of RcppArmadillo went to CRAN this morning.

It adds a tiny bit of configuration to permit Sun Studio / suncc to successfully build the package. There is no code change, and no configuration change for the other platforms. Thanks for Brian Ripley for additional testing, and of course for running those build instances (and everything else he does) for the R project, and to Conrad Sanderson as upstream author of the Armadillo C++ library for linear algebra.

As usual, more information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page.

/code/rcpp | permanent link

Beancounter minor bug fix release 0.8.10
A few month after the 0.8.9 release, we have another small update to beancounter. This version fixes a minor infelicity in the manual page (thanks to an Ubuntu and then Debian bug report) as well as a small coding bug where 'USD' was hard-coded when the user-defined default home currency should have been used.

The new version is now in Debian, at CPAN and on my beancounter page here. Enjoy!

/code/beancounter | permanent link

Rcpp 0.8.2
A bug fix release Rcpp version 0.8.2 is now on CRAN and Debian. It contains some fixes for Sun compiler, but no user-visible changes and complements the Rcpp 0.8.1 release made Tuesday. Our thanks to Brian Ripley for help with the portability tests for that platform. It will be good to get an autobuilder for pre-emptive testing for that platform too as we make quite some use of the win-builder servive in Dortmund.

The full NEWS entry for this release follows below:

0.8.2   2010-06-09

    o   Bug-fix release for suncc compiler with thanks to Brian Ripley for
        additional testing.

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

RcppArmadillo 0.2.2
Following the Rcpp 0.8.1 release we made yesterday, we released RcppArmadillo release 0.2.2 this morning. RcppArmadillo uses Rcpp (and some 'glue' code) to provide a transparent interface from R to Conrad Sanderson's impressive Armadillo library for linear algebra.

This release works well with the most recent inline release 0.3.5. One can now employ inlined R code as we generalized how/which headers are included and how library / linking information is added thanks a plugin mechanism. This is the first RcppArmadillo version to provide such a plugin, We also updated the included Armadillo headers to its most recent release 0.9.10, added some more operators and provide a utility function RcppArmadillo:::CxxFlags() to provide include directory information on the fly.

An example of the direct inline approach for the fastLm function:

library(inline)
library(RcppArmadillo)

src <- '
	Rcpp::NumericVector yr(ys);			// creates Rcpp vector from SEXP
	Rcpp::NumericMatrix Xr(Xs);			// creates Rcpp matrix from SEXP
	int n = Xr.nrow(), k = Xr.ncol();

	arma::mat X(Xr.begin(), n, k, false);   	// reuses memory and avoids extra copy
	arma::colvec y(yr.begin(), yr.size(), false);

	arma::colvec coef = arma::solve(X, y);      	// fit model y ~ X
	arma::colvec res = y - X*coef;			// residuals

	double s2 = std::inner_product(res.begin(), res.end(), res.begin(), double())/(n - k);
							// std.errors of coefficients
	arma::colvec std_err = arma::sqrt(s2 * arma::diagvec( arma::inv(arma::trans(X)*X) ));

	return Rcpp::List::create(Rcpp::Named("coefficients") = coef,
				  Rcpp::Named("stderr")       = std_err,
				  Rcpp::Named("df")           = n - k
				  );
'

fun <- cxxfunction(signature(ys="numeric", Xs="numeric"), src, plugin="RcppArmadillo")

This creates a compiled function fun which, by using Armadillo, regresses a vector ys on a matrix Xs (just how the fastLmPure() function in the package does) --- yet is constructed on the fly using cxxfunction from inline.

More information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page.

/code/rcpp | permanent link

Rcpp 0.8.1
Early this morning I sent Rcpp version 0.8.1 off to CRAN and Debian. In the meantime, Romain has already provided a very nice blog post about it.

There are a few fairly visible new things in this release. As we want to focus the next few minor releases on completing the documentation, we started by adding a total of four (!!) new vignettes:

• Rcpp-package showing how to write your own package using Rcpp,
• Rcpp-FAQ addressing several frequently asked questions,
• Rcpp-modules discussing how to expose C++ functions and modules with ease using an idea borrowed from Boost::Python, and
• Rcpp-extending detailing the steps needed to extend Rcpp with user-provided or third-party classes,

The most interesting new feature is what we call Rcpp modules and is modeled after Boost::Python. This makes it pretty easy to expose C++ functions and classes to R -- without having to write glue code. This is pretty new and may change a tad over the coming releases, but it is also quite exciting.

Other changes concern more improvements for use of inline which should now allow packages like our RcppArmadillo to be used with it, and some bug fixes. The full NEWS entry for this release follows below:

0.8.1   2010-06-08

    o   This release adds Rcpp modules. An Rcpp module is a collection of
        internal (C++) functions and classes that are exposed to R. This
        functionality has been inspired by Boost.Python.
        
        Modules are created internally using the RCPP_MODULE macro and
        retrieved in the R side with the Module function. This is a preview 
        release of the module functionality, which will keep improving until
        the Rcpp 0.9.0 release. 

        The new vignette "Rcpp-modules" documents the current feature set of
        Rcpp modules.
        
    o   The new vignette "Rcpp-package" details the steps involved in making a
        package that uses Rcpp.

    o   The new vignette "Rcpp-FAQ" collects a number of frequently asked
        questions and answers about Rcpp.

    o   The new vignette "Rcpp-extending" documents how to extend Rcpp
        with user defined types or types from third party libraries. Based on
        our experience with RcppArmadillo
        
    o   Rcpp.package.skeleton has been improved to generate a package using 
        an Rcpp module, controlled by the "module" argument

    o   Evaluating a call inside an environment did not work properly
        
    o   cppfunction has been withdrawn since the introduction of the more
        flexible cxxfunction in the inline package (0.3.5). Rcpp no longer
        depends on inline since many uses of Rcpp do not require inline at
        all. We still use inline for unit tests but this is now handled
        locally in the unit tests loader runTests.R. 

        Users of the now-withdrawn function cppfunction can redefine it as:
        
           cppfunction <- function(...) cxxfunction( ..., plugin = "Rcpp" )

    o   Support for std::complex was incomplete and has been enhanced.

    o   The methods XPtr::getTag and XPtr::getProtected are deprecated, 
        and will be removed in Rcpp 0.8.2. The methods tag() and prot() should
        be used instead. tag() and prot() support both LHS and RHS use. 

    o   END_RCPP now returns the R Nil values; new macro VOID_END_RCPP
        replicates prior behabiour
        

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

inline 0.3.5
Yesterday morning, Romain pushed inline release 0.3.5 to CRAN.

This is some ways a continuation of the 0.3.4 release I had made in December. That release had opened the door for the wide use of inline in our Rcpp package. And just how Rcpp has grown, we now have needs beyond the initial change. See the post on Romain's blog for details, but in a nutshell we are now gaining

• cxxfunction which extends cfunction further for C++ use and, among other things, adds a plugin system we can use from RcppArmadillo to permit use of inline
• package.skeleton which makes it easy to carry a function that one has prototyped with inline over into its own package -- and how to do that was a question at my most recent Rcpp talk in Vienna), and
• getDynLib which Romain will use to great effect in the next version of Rcpp to provide something not unlike Boost::Python. Stay tuned!

Last but not least, our thanks to Oleg Sklyar for letting us extend his amazing inline package for use by Rcpp.

/code/inline | permanent link

Bike The Drive 2010
Memorial Day weekend is also time for the annual Bike The Drive in Chicago. This time only half the household got up bright and early and enjoyed Lakeshore Drive free of cars. A highly recommended event.

/sports/cycling | permanent link

JPM Chase Corporate Challenge 2010
It's Memorial Day weekend so it was time for the Chicago's JP Morgan Chase Corporate Challenge on Thursday. The weather was glorious, the usual 20-some thousand runners participated and a good time was had. Work had arranged for a nice tent, food, music --- and a bunch of people showed up and enjoyed it. Nice one.

This time we all got chip-timing via a small (rfid ?) strip tagged to back of the bib number. Which is handy as I managed to not stop my time by hand correctly. Given that I am still nursing a sore Achilles tendon and don't train well or much, the time of 23:51 (or 6:48 min/mile) was ok compared to the other seven previous times I have run this.

/sports/running | permanent link

WU Wien presentations
Last week I had the opportunity to spend a few days at the Institute for Statistics and Mathematics of the WU Vienna / Wirtschaftsuniversitaet Wien. On Thursday, I gave a seminar on Rcpp and RInside introducing all the recent work with Romain on making R and C++ integration easier. Both (compact) handout and (full) presentation slides are now posted alongside the other presentations.

On Friday, I also gave an informal lecture / tutorial / workshop to some of the Stats and Finance Ph.D. students, drawing largely from the section on parallel computing of the most recent Introduction to High-Performance Computing with R tutorial.

My sincere thanks to Kurt Hornik and Stefan Theussl for the invite -- it was a great trip, notwithstanding the mostly unseasonally cold and wet weather.

/computers/R | permanent link

RcppArmadillo 0.2.0 (and 0.2.1)
With the Rcpp 0.8.0 release on Monday, Romain, Doug and I were able to follow-up with a new RcppArmadillo release. RcppArmadillo uses Rcpp (and a few dozen lines of 'glue') to provide a transparent interface from R to Conrad Sanderson's impressive Armadillo library for linear algebra.

This new release offers a number of key improvements:

• Headers-only: given that Armadillo is a C++ template library, we now ship its headers in the package. In the previous release, we required Armadillo to be built as a library. As this Armadillo library mostly provides things we get from R for free (such as access to Blas, where available), we can do without it and stick to templates-only. The upside is that the usage requirements for RcppArmadillo have become much simpler: R, a C++ compiler and Rcpp. In practice, this also means that Windows users will now get pre-built binaries via CRAN
• Update to Armadillo 0.9.8: We added the headers from Conrad's most recent release.
• The fastLm() function is now generic and provides a default and formula interface just like lm() along with standard methods print, summary and predict. The documentation is enhanced as well and now contains an example of a rank-deficient model matrix where the non-pivoting scheme of fastLm() fails.
• Doug Bates joined Romain and myself as an author of RcppArmadillo

While we had tested this quite rigourously, the combination of some last minute changes that were meant to be stylistic-only, some troubles with the tests and builds at CRAN that were not apparent in all our tests (hint: do not yet use dynamic help features referencing other packages even if you have a Depends: on them) and an upcoming travel deadline meant that we missed a gotcha on Windows---so release 0.2.1 had to follow a few hours after the short-lived 0.2.0.

More information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page.

/code/rcpp | permanent link

Rcpp 0.8.0
Romain and I are happy to announce the release of Rcpp version 0.8.0. It has been uploaded to CRAN. A Debian upload is delayed until the now-required inline package is accepted into Debian. The source package is also available from here.

This release brings a number of changes that are detailed below. Of particular interest may be the much more robust treatment of exceptions, the new classes for data frames and formulae, and the availability of the new helper function cppfunction for use with inline. Also of note is the new support for the 'LinkingTo' directive with which packages using Rcpp will get automatic access to the header files.

An announcement email went to the r-packages list (ETH Zuerich, Gmane); Romain also blogged about the release.

The full NEWS entry for this release follows below:

0.8.0   2010-05-17

    o   All Rcpp headers have been moved to the inst/include directory,
	allowing use of 'LinkingTo: Rcpp'. But the Makevars and Makevars.win
	are still needed to link against the user library.

    o   Automatic exception forwarding has been withdrawn because of
	portability issues (as it did not work on the Windows platform).
	Exception forwarding is still possible but is now based on explicit
	code of the form:
	
	  try {
	    // user code
	  } catch( std::exception& __ex__){ 
	    forward_exception_to_r( __ex___ ) ;
	  }
	
	Alternatively, the macro BEGIN_RCPP and END_RCPP can use used to enclose
	code so that it captures exceptions and forward them to R. 
	
	  BEGIN_RCPP
	  // user code
	  END_RCPP
	
    o   new __experimental__ macros 
	
	The macros RCPP_FUNCTION_0, ..., RCPP_FUNCTION_65 to help creating C++
	functions hiding some code repetition: 
	
	  RCPP_FUNCTION_2( int, foobar, int x, int y){
	    return x + y ;
	  }

	The first argument is the output type, the second argument is the 
	name of the function, and the other arguments are arguments of the C++ 
	function. Behind the scenes, the RCPP_FUNCTION_2 macro creates 
	an intermediate function compatible with the .Call interface and handles 
	exceptions
	
	Similarly, the macros RCPP_FUNCTION_VOID_0, ..., RCPP_FUNCTION_VOID_65 
	can be used when the C++ function to create returns void. The generated
	R function will return R_NilValue in this case.
	
	  RCPP_FUNCTION_VOID_2( foobar, std::string foo ){
	    // do something with foo
	  }

	  
	The macro RCPP_XP_FIELD_GET generates a .Call compatible function that
	can be used to access the value of a field of a class handled by an 
	external pointer. For example with a class like this: 
	
	  class Foo{
		public:
			int bar ;
	  }
	
	  RCPP_XP_FIELD_GET( Foo_bar_get, Foo, bar ) ;
	  
	RCPP_XP_FIELD_GET will generate the .Call compatible function called
	Foo_bar_get that can be used to retrieved the value of bar.
	
	
	The macro RCPP_FIELD_SET generates a .Call compatible function that 
	can be used to set the value of a field. For example:
	
	  RCPP_XP_FIELD_SET( Foo_bar_set, Foo, bar ) ;
	
	generates the .Call compatible function called "Foo_bar_set" that 
	can be used to set the value of bar
	
	
	The macro RCPP_XP_FIELD generates both getter and setter. For example
	
	  RCPP_XP_FIELD( Foo_bar, Foo, bar )
	  
	generates the .Call compatible Foo_bar_get and Foo_bar_set using the 
	macros RCPP_XP_FIELD_GET and RCPP_XP_FIELD_SET previously described
	
	  
	The macros RCPP_XP_METHOD_0, ..., RCPP_XP_METHOD_65 faciliate 
	calling a method of an object that is stored in an external pointer. For 
	example: 
	
	  RCPP_XP_METHOD_0( foobar, std::vector , size )
	
	creates the .Call compatible function called foobar that calls the
	size method of the std::vector class. This uses the Rcpp::XPtr<
	std::vector > class.
	
	The macros RCPP_XP_METHOD_CAST_0, ... is similar but the result of
	the method called is first passed to another function before being
	wrapped to a SEXP.  For example, if one wanted the result as a double
	
	   RCPP_XP_METHOD_CAST_0( foobar, std::vector , size, double )
	
	The macros RCPP_XP_METHOD_VOID_0, ... are used when calling the
	method is only used for its side effect.
	
	  RCPP_XP_METHOD_VOID_1( foobar, std::vector, push_back ) 
	
	Assuming xp is an external pointer to a std::vector, this could
	be called like this :
	
	  .Call( "foobar", xp, 2L )
	
    o	Rcpp now depends on inline (>= 0.3.4)
	
    o   A new R function "cppfunction" was added which invokes cfunction from
	inline with focus on Rcpp usage (enforcing .Call, adding the Rcpp
	namespace, set up exception forwarding). cppfunction uses BEGIN_RCPP
	and END_RCPP macros to enclose the user code

    o	new class Rcpp::Formula to help building formulae in C++
    
    o   new class Rcpp::DataFrame to help building data frames in C++
	
    o   Rcpp.package.skeleton gains an argument "example_code" and can now be
	used with an empty list, so that only the skeleton is generated. It
	has also been reworked to show how to use LinkingTo: Rcpp
	
    o   wrap now supports containers of the following types: long, long double,
	unsigned long, short and unsigned short which are silently converted
	to the most acceptable R type.
	
    o	Revert to not double-quote protecting the path on Windows as this
	breaks backticks expansion used n Makevars.win etc
        
    o   Exceptions classes have been moved out of Rcpp classes,
	e.g. Rcpp::RObject::not_a_matrix is now Rcpp::not_a_matrix

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

R Project and Google Summer of Code: Welcome to our students!
A few hours ago, I sent the following to both the R development list and the informal R / GSoC list:

Date: Mon, 26 Apr 2010 15:27:29 -0500
To: R Development List 
CC: gsoc-r 
Subject: R and the Google Summer of Code 2010 -- Please welcome our new students!
From: Dirk Eddelbuettel 

Earlier today Google finalised student / mentor pairings and allocations for
the Google Summer of Code 2010 (GSoC 2010).  The R Project is happy to
announce that the following students have been accepted:

   Colin Rundel, "rgeos - an R wrapper for GEOS", mentored by Roger Bivand of
      the Norges Handelshoyskole, Norway

   Ian Fellows, "A GUI for Graphics using ggplot2 and Deducer", mentored by
      Hadley Wickham of Rice University, USA

   Chidambaram Annamalai, "rdx - Automatic Differentiation in R", mentored by
      John Nash of University of Ottawa, Canada

   Yasuhisa Yoshida, "NoSQL interface for R", mentored by Dirk Eddelbuettel,
      Chicago, USA

   Felix Schoenbrodt, "Social Relations Analyses in R", mentored by Stefan
      Schmukle, Universitaet Muenster, Germany

   Details about all proposals are on the R Wiki page for the GSoC 2010 at
   http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2010

The R Project is honoured to have received its highest number of student
allocations yet, and looks forward to an exciting Summer of Code.  Please
join me in welcoming our new students.

At this time, I would also like to thank all the other students who have
applied for working with R in this Summer of Code. With a limited number of
available slots, not all proposals can be accepted -- but I hope that those
not lucky enough to have been granted a slot will continue to work with R and
towards making contributions within the R world. 

I would also like to express my thanks to all other mentors who provided for
a record number of proposals.  Without mentors and their project ideas we
would not have a Summer of Code -- so hopefully we will see you again next
year. 

Regards,  

Dirk (acting as R/GSoC 2010 admin)

/computers/misc | permanent link

Boston Marathon 2010
This Monday saw the 114th running of the Boston Marathon. Under near-ideal conditions with some sunny skies, some clouds and moderate temperatures in the high 40s to low 50s, a course record as well as US Mens record were set. Having run this race in 2007 and in 2009 (with 2008 in London), I knew the difficult course and was aiming to run it more evenly. So no breakdown on those famous hills! But without too much fire in my belly over the winter, training was somewhat minimal: one 20 miler, two 18 milers. Friends and co-runners were joking that I had turned my trusted 'run less, run faster' program by the FIRST lab into my own 'run less, run lesser' program. And then were a nagging achilles tendon and a little strain from the underwhelming recent half-marathon so that I was really trying to hold back and not go out too fast on the initial portion which is mostly downhill.

Which seems to have worked. My pace was more even, and I conserved some energy and made it past the last of the hills around mile 21 without walking a single step while staying a few second under a 8 min/mile average pace. But then around mile 23 and 24 I had two sharp short cramps which forced me to walk. Interestingly enough, Bob Richards writes about cramps as a main theme for many runners in this year's race. Maybe the wind and temperature combined with the hills to get us after all! Anyway, I ended up with 3:29:14 which, at a 7:59 pace, is right between the 2007 and 2009 results and quite decent given the circumstances.

And of course the weekend as whole was again a hoot even if I had only a short stay of around 30 hours in Boston given our R/Finance conference on Friday and Saturday. We'll see if I will manage to qualify once more for next year.

/sports/running | permanent link

R / Finance 2010 presentations
Last Friday and Saturday the second R / Finance conference took place in Chicago on the UIC campus.

As a co-organizer, it was a great pleasure to see so many users of R in Finance---from both industry and academia---come to Chicago to discuss and share recent work. There is a lot going on, and it is always good to exchange ideas with others sharing the same infrastructure. Participants appeared to enjoy the conference. My thanks to everybody who helped to put it together, from the local committee to the helping hands at UIC and of course the sponsors.

I just put my slides from the Extending and Embedding R with C++ tutorial preceding the conference, as well as the RQuantLib: Interfacing QuantLin from R presentation (with Khanh Nguyen), up onto my presentations page. I do have a usb-drive with all conference presentations and will provide them via the R / Finance site in a few days.

The only truly sour note is the fact that several presenters from Europe had their travels schedules turned upside down by the disruption to international air travel caused by the Icelandic volcano eruption and the resulting ash clouds. While we are glad to have had them for a little longer in Chicago, we understand that they are getting eager to return home. I hope this extended stay in the Windy City does not take away from the overall usefulness of the trip.

/computers/R | permanent link

Rcpp 0.7.12
A new bug fix versions 0.7.12 of Rcpp is awaiting inclusion into CRAN and Debian. It is also available from here.

This is another bug-fix version related solely to a build failure on Windows. Trying to protect paths with spaces has the side-effect of breaking backticks use, which unfortunately is already in use by a number of package that since broke during CRAN autobuilds. No other changes were made.

As always, full details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

Video of UCLA / LA RUG talk on R and C++ integration
Thanks to the efforts of the tireless R User Group organizers Szilard Pafka (in Los Angeles, recording the talk) and Drew Conway (in New York, converting and organising hosting), there is now a video and slide combo of my recent talk about Rcpp and RInside at UCLA and the Los Angeles R Users Group.

Thanks also to David Smith (at the REvolutions blog) and Drew Conway (at his blog) for spreading the word about the presentation video and slides -- quite a few folks have come to my presentations page to get them.

/computers/R | permanent link

UCLA and LA RUG talks on R and C++ integration
We spent last week in the LA area and had a generally good time out west. I was able to sneak in two talks and a group discussion, thanks to the help by Jan de Leeuw (and everybody at UCLA's Stats department) as well as by Szilard Pafka representing the LA R User's Group. Pdf files for the slides for the talks are now on my presentations page in both a compact handout and presentation slide version (where the content is identical; if in doubt use the first file).

The talks centered around R and C++ integration using both Rcpp and RInside and summarise where both projects stand after all the recent work Romain and I put in over the last few months. The presentations went fairly well; I received some favourable comments.

Szilard and the R User Group had also suggested a group discussion about CRAN, its growth and how to maximise its usefulness. Given my CRANberries feed, my work on the CRAN Task Views for Empirical Finance and High-Performance Computing with R as well as our cran2deb binary package generator, I had some views and ideas that helped frame the discussion which turned out to very useful and informed. So maybe we should do this User Group thing in Chicago too!

Special thanks to Jan de Leeuw and Szilard Pafka for organising the meeting, talks and discussion.

/computers/R | permanent link

Finance::YahooQuote 0.24
Having espoused rule number one in regression testing in the post about yesterday's bug fix upload 0.23, we can now add rule number zero: Do not introduce a new error by omitting the trailing semicolon. I guess it shows that I don't really program in Perl anymore.

Anyway, a new version 0.24 of Finance::YahooQuote which addresses the issue that required upload 0.23 yesterday is now in the Debian queue and on CPAN and my local yahooquote page. This time it may even work. A big thanks to the CPAN Testers for getting me reports on this one too.

/computers/linux/debian/packages | permanent link

Rcpp 0.7.11
A new versions 0.7.11 of Rcpp is awaiting inclusion into CRAN and Debian. It is also available from here.

This version fixes a somewhat serious bug uncovered by Doug Bates when working with vectors of strings. We also added a few new accessor functions as well as a new convenience function create that is particularly useful for creating (possibly named) list objects that are returned to R.

Here is the full NEWS entry for this release:

0.7.11	2010-03-26

    o	Vector<> gains a set of templated factory methods "create" which
        takes up to 20 arguments and can create named or unnamed vectors.
        This greatly facilitates creating objects that are returned to R. 

    o	Matrix now has a diag() method to create diagonal matrices, and
        a new constructor using a single int to create square matrices

    o	Vector now has a new fill() method to propagate a single value
	
    o	Named is no more a class but a templated function. Both interfaces
        Named(.,.) and Named(.)=. are preserved, and extended to work also on 
        simple vectors (through Vector<>::create) 
	
    o	Applied patch by Alistair Gee to make ColDatum more robust
    
    o	Fixed a bug in Vector that caused random behavior due to the lack of
        copy constructor in the Vector template

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

Finance::YahooQuote 0.23
Rule number one in regression testing is to not depend on volatile data. Which I seem to have violated in file t/02simple.t in the Perl package Finance::YahooQuote.

Which lead the automated Perl test scripts to remind me for a few days now that the full company name for symbol IBM no longer corresponded to what I had encoded. Not really a bug, but a failure in tests anyway.

So without further ado: a new version 0.23 of Finance::YahooQuote which addressed this issue is now in the Debian queue and on CPAN and my local yahooquote page.

/computers/linux/debian/packages | permanent link

RInside release 0.2.2
The shiny new 0.2.2 release of RInside has just been uploaded to CRAN; it should hit mirrors tommorow. Sources are also at my RInside page.

RInside is a set of convenience classes to facilitate embedding of R inside of C++ applications. It works particularly well with Rcpp and now depends on it.

This is the first release since version 0.2.1 in early January. Romain and I made numerous changes to Rcpp in the meantime. With this release, RInside is starting to catch up by taking advantage of many new automatic (templated) type converters. We have updated the existing examples, and added several new ones. These are all visibile directly via the Doxygen-generated documentation under the Files heading. Two examples are also shown directly on the RInside page.

Also added are new examples showing how to use RInside to embed R inside C++ applications using MPI for parallel computing. This was contributed via two examples files by Jianping Hua, and we reworked the examples slightly (and added two variants that use MPI's C++ API).

As it is so short, here is the basic 'Hello, World' example now showing the simpler Rcpp-based variable assignment:

// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4;  tab-width: 8; -*-
//
// Simple example showing how to do the standard 'hello, world' using embedded R
//
// Copyright (C) 2009 Dirk Eddelbuettel
// Copyright (C) 2010 Dirk Eddelbuettel and Romain Francois
//
// GPL'ed

#include <RInside.h>                    // for the embedded R via RInside

int main(int argc, char *argv[]) {

    RInside R(argc, argv);              // create an embedded R instance

    R["txt"] = "Hello, world!\n";	// assign a char* (string) to 'txt'

    R.parseEvalQ("cat(txt)");           // eval the init string, ignoring any returns

    exit(0);
}

One minor setback is that the examples currently segfault on Windows. That may be an issue with linking and class instantiation or something related. Romain and I focus much more on Linux and OS X, so this has not gotten a lot of attention. Debugging help would be appreciated.

/code/rinside | permanent link

2010 March Madness Half Marathon in Cary
The annual March Madness Half Marathon in Cary took place this morning. This is both one of Chicagoland's 'early races' to start the season as well as the classic Boston preparation due to the hilly course. I have now run this consecutively for six years (see 2005. 2006, 2007, 2008, and 2009).

As for the race conditions, we had fantastic weather all week with temperatures up to the sixties and then all of a sudden a forecast of rain, snow and even sleet for the weekend. Luckily, and while yesterday was sucky, today was allright or better. A little chilly and damp, but neither rain nor snow --- or even wind. So the conditions were good, with the course challenging as usual.

The race itself went fine. I ran more or less steadily, never had to stop but was not particularly fast at 1:39:38 or a pace of 7:36.3. I had aimed for beating 1:40, had missed that target by miles 4 to 6 and was about 10 or 15 seconds behind but managed to get a negative split on the second half of the course to reach that goal. Which is nice, but the time is still the slowest I've ever run that race, and my slowest half-marathon since 2004.

Training had been sluggish all winter. Oddly enough, already in last year's post I stated pretty much the same and feared that Boston may become tough --- which it did. But this year may well be a lot worse as I had no spring in my step all winter long. No fire in the belly for training will make for a long race. We'll see how it goes. Four weeks to go.

/sports/running | permanent link

R Project selected for the Google Summer of Code 2010
Earlier today, Google announced the list of accepted mentor organizations for the Google Summer of Code 2010 (GSoC 2010). And we are happy to report that the R Project is once again a participating organization (and now for the third straight year) joining a rather august group of open source projects from around the globe.

An R Wiki page had been created and serves as the central point of reference for the R Project and the GSoC 2010. It contains a list of project ideas, currently counting eleven and spanning everything from research-oriented topics (such as spatial statistics or automatic differentiation) to R community-support (regarding CRAN statistics and the CRANtastic site) to extensions (NoSQL, RPy2 data interfaces, Rserve browser integration) and more. I also just created a mailing list gsoc-r@googlegroups.com where prospective students and mentors can exchange ideas and discuss. As for other details, the Google Summer of Code 2010 site has most of the answers, and we will try to keep R-related information on the aforementioned R Wiki page.

/computers/misc | permanent link

Rcpp 0.7.10
Versions 0.7.7 to 0.7.9 of Rcpp contained a bug: protecting paths with quotes was supposed to help with Windows builds, but did the opposite at least in 'backticks mode' for getting path and/or library information. Using the shQuote() function instead helped. Our thanks to the tireless R-on-Windows maintainer Uwe Ligges for an earlier heads-up about the problem. So another quick bug-fix release 0.7.10 is now in Debian and should be on CRAN some time tomorrow.

We also put two small improvements in, see the full NEWS entry for this release:

0.7.10  2010-03-15

    o	new class Rcpp::S4 whose constructor checks if the object is an S4 object
	
    o	maximum number of templated arguments to the pairlist function, 
	the DottedPair constructor, the Language constructor and the 
	Pairlist constructor has been updated to 20 (was 5) and a script has been
	added to the source tree should we want to change it again

    o   use shQuotes() to protect Windows path names (which may contain spaces)

As always, even fuller details are in Rcpp Changelog page and 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

/code/rcpp | permanent link

Rcpp 0.7.9
Version 0.7.8 of Rcpp, released just a few days ago contained a nasty bug or two which we noticed when trying to built the initial release of RcppArmadillo on 64-bit platforms.

So a quick bug-fix release 0.7.9 is now in Debian and should be on CRAN shortly.

The full NEWS entry for this release follows:

0.7.9   2010-03-12

    o	Another small improvement to Windows build flags

    o	bugfix on 64 bit platforms. The traits classes (wrap_type_traits, etc)
	used size_t when they needed to actually use unsigned int

    o	fixed pre gcc 4.3 compatibility. The trait class that was used to 
	identify if a type is convertible to another had too many false positives
	on pre gcc 4.3 (no tr1 or c++0x features). fixed by implementing the 
	section 2.7 of "Modern C++ Design" book. 

As always, even fuller details are in Rcpp Changelog page and 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: First version number corrected to 0.7.8.

/code/rcpp | permanent link

RcppArmadillo 0.1.0
Besides the new RcppExamples, another new package RcppArmadillo got spun out of Rcpp with the recent release 0.7.8 of Rcpp.

Romain and I already had an example of a simple but fast linear model fit using the (very clever) Armadillo C++ library by Conrad Sanderson. In fact, I had used this as a motivational example of why Rcpp rocks in a recent talk to the ACM chapter at U of Chicago which, thanks to David Smith at REvo, got some further exposure.

Now this example is more refined as further glue got added. Given that both Armadillo and Rcpp make use of C++ templates, the actual amount of code in RcppArmadillo is not that large: just over 200 lines in a header file, and a little less for some testing accessor and example functions in a source file. And this makes for some really nice example code: the 'fast regression' example becomes this (where I simply removed two blocks with conditional on the Armadillo version):

#include <RcppArmadillo.h>

extern "C" SEXP fastLm(SEXP ys, SEXP Xs) {

    Rcpp::NumericVector yr(ys);			// creates Rcpp vector from SEXP
    Rcpp::NumericMatrix Xr(Xs);			// creates Rcpp matrix from SEXP
    int n = Xr.nrow(), k = Xr.ncol();

    arma::mat X(Xr.begin(), n, k, false);   	// reuses memory and avoids extra copy
    arma::colvec y(yr.begin(), yr.size(), false);

    arma::colvec coef = solve(X, y);            // fit model y ~ X
    arma::colvec resid = y - X*coef; 		// residuals
    double sig2 = arma::as_scalar( trans(resid)*resid/(n-k) );
    						// std.error of estimate
    arma::colvec stderrest = sqrt( sig2 * diagvec( arma::inv(arma::trans(X)*X)) );

    Rcpp::Pairlist res(Rcpp::Named( "coefficients", coef),
                       Rcpp::Named( "stderr", stderrest));
    return res;
}

No extra copies! Armadillo instantiates directly from the underlying R objects for the vector and matrix, solves the regression equations, computes the standard error of the estimates and returns the two vectors. Leaving us to write about eleven lines of code. Moreover, as Armadillo is well designed and uses template meta-programming to avoid extra copies (see these lecture notes for details), it is about as efficient as it can be (and will use Atlas or other BLAS where available).

And, this is just one example. Rcpp should be suitable for other C++ libraries, and provides an easy to use seamless interface between C++ and R.

However, we should note that (at about the last minute) we found out about some unit test failures in OS X as well as some issues in a Debian chroot -- cran2deb ran into some build issues on i386 and amd64 in the testing chroot even this 'it all works' swimmingly on our Debian, Ubuntu and Fedora build environments. A follow-up with fixes for either Rcpp and/or RcppArmadillo appears likely.

Update: The build issues seems to be with 64-bit systems and everything appears cool in 32-bit.

/code/rcpp | permanent link

RcppExamples 0.1.0
Version 0.1.0 of RcppExamples, a simple demo package for Rcpp should appear on CRAN some time tomorrow.

As mentioned in the post about release 0.7.8 of Rcpp, Romain and I carved this out of Rcpp itself to provide a cleaner separation of code that implements our R / C++ interfaces (which remain in Rcpp) and code that illustrates how to use it --- which is now in RcppExamples. This also provides an easier template for people wanting to use Rcpp in their packages as it will be easier to wrap one's head around the much smaller RcppExamples package.

A simple example (using the newer API) may illustrate this:

#include <Rcpp.h>

RcppExport SEXP newRcppVectorExample(SEXP vector) {

    Rcpp::NumericVector orig(vector);			// keep a copy (as the classic version does)
    Rcpp::NumericVector vec(orig.size());		// create a target vector of the same size

    // we could query size via
    //   int n = vec.size();
    // and loop over the vector, but using the STL is so much nicer
    // so we use a STL transform() algorithm on each element
    std::transform(orig.begin(), orig.end(), vec.begin(), sqrt);

    Rcpp::Pairlist res(Rcpp::Named( "result", vec),
                       Rcpp::Named( "original", orig));

    return res;
}

With essentially five lines of code, we provide a function that takes any numeric vector and returns both the original vector and a tranformed version---here by applying a square root operation. Even the looping along the vector is implicit thanks to the generic programming idioms of the Standard Template Library.

Nicer still, even on misuse, exceptions get caught cleanly and we get returned to the R prompt without any explicit coding on the part of the user:

R> library(RcppExamples)
Loading required package: Rcpp
R> print(RcppVectorExample( 1:5, "new" )) # select new API
$result
[1] 1.000 1.414 1.732 2.000 2.236

$original
[1] 1 2 3 4 5

R> RcppVectorExample( c("foo", "bar"), "new" )
Error in RcppVectorExample(c("foo", "bar"), "new") :
  not compatible with INTSXP
R>

There is also analogous code for the older API in the package, but it is about three times as long, has to loop over the vector and needs to set up the execption handling explicitly.

As of right now, RcppExamples does not document every class but it should already provide a fairly decent start for using Rcpp. And many more actual usage examples are ... in the over two-hundred unit tests in Rcpp.

Update: Now actually showing new rather than classic API.

/code/rcpp | permanent link

Rcpp 0.7.8
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.

/code/rcpp | permanent link

R and Sudoku solvers: Plus ca change...
Christian Robert blogged about a particularly heavy-handed solution to last Sunday's Sudoku puzzle in Le Monde. That had my symapthy as I like evolutionary computing methods, and his chart is rather pretty. From there, this spread on to the REvolutions blogs where David Smith riffed on it, and showed the acual puzzle. That didn't stop things as Christian blogged once more about it, this time welcoming his post-doc Robin Ryder who posts a heavy analysis on all this that is a little much for me at this time of day.

But what everybody seems to be forgetting is that R has had a Sudoku solver for years, thanks to the sudoku package by David Brahm and Greg Snow which was first posted four years ago. What comes around, goes around.

With that, and about one minute of Emacs editing to get the Le Monde puzzle into the required ascii-art form, all we need to do is this:

R> library(sudoku)
R> s <- readSudoku("/tmp/sudoku.txt")
R> s
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
 [1,]    8    0    0    0    0    1    2    0    0
 [2,]    0    7    5    0    0    0    0    0    0
 [3,]    0    0    0    0    5    0    0    6    4
 [4,]    0    0    7    0    0    0    0    0    6
 [5,]    9    0    0    7    0    0    0    0    0
 [6,]    5    2    0    0    0    9    0    4    7
 [7,]    2    3    1    0    0    0    0    0    0
 [8,]    0    0    6    0    2    0    1    0    9
 [9,]    0    0    0    0    0    0    0    0    0
R> system.time(solveSudoku(s))
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
 [1,]    8    4    9    6    7    1    2    5    3
 [2,]    6    7    5    2    4    3    9    1    8
 [3,]    3    1    2    9    5    8    7    6    4
 [4,]    1    8    7    4    3    2    5    9    6
 [5,]    9    6    4    7    8    5    3    2    1
 [6,]    5    2    3    1    6    9    8    4    7
 [7,]    2    3    1    8    9    4    6    7    5
 [8,]    4    5    6    3    2    7    1    8    9
 [9,]    7    9    8    5    1    6    4    3    2
   user  system elapsed
  5.288   0.004   5.951
R>

Just in case we needed another illustration that it is hard to navigate the riches and wonders that is CRAN...

/computers/R | permanent link

U of C ACM talk
Fellow GSoC mentor and local ACM masterminder Borja Sotomayor had invited me a few months ago to give a talk at the ACM chapter at the University of Chicago. Today was the day, and the slides from the 50-minutes talk on R and extending R with Rcpp are now on my presentations page.

/computers/R | permanent link

Rcpp 0.7.7
Version 0.7.7, a shiny new bug fix release of Rcpp, our set of R / C++ interface classes, just arrived on CRAN and in Debian. The Language class had a real bug leading to this new release just two days after 0.7.6.

0.7.7	2010-02-14

    o	new template classes Rcpp::unary_call and Rcpp::binary_call
    	that facilitates using R language calls together 
    	with STL algorithms.
    	
    o	fixed a bug in Language constructors taking a string as their
    	first argument. The created call was wrong.

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. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page

/code/rcpp | permanent link

Rcpp 0.7.6
The new 0.7.6 release of Rcpp, our set of R / C++ interface classes, is now at CRAN and Debian. This comes just a few days after 0.7.5 as we had made a mistake in Makefile.win which is now fixed. A few other things sneaked in while were at it, see the snippet from the NEWS file below or look at Romain's blog where he highlights named-based indexing in vectors and the addition of iterator as well as begin() and end() members that now allow the use of STL algorithms on our R objects which is nifty.

The changes are summarised below in the NEWS file snippet, more details are in the ChangeLog as well.

0.7.6   2010-02-12

    o   SEXP_Vector (and ExpressionVector and GenericVector, a.k.a List) now
	have methods push_front, push_back and insert that are templated

    o   SEXP_Vector now has int- and range-valued erase() members

    o   Environment class has a default constructor (for RInside)

    o   SEXP_Vector_Base factored out of SEXP_Vector (Effect. C++ #44)

    o   SEXP_Vector_Base::iterator added as well as begin() and end()
        so that STL algorithms can be applied to Rcpp objects

    o   CharacterVector gains a random access iterator, begin() and end() to
	support STL algorithmsl; iterator dereferences to a StringProxy

    o   Restore Windows build; successfully tested on 32 and 64 bit;

    o   Small fixes to inst/skeleton files for bootstrapping a package

    o   RObject::asFoo deprecated in favour of Rcpp::as

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. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page

/code/rcpp | permanent link

Rcpp 0.7.5
A new release of our Rcpp R / C++ interface classes is now out, the version number is 0.7.5. It comes on the heels of the release 0.7.4 and keeps with our semi-frantic schedule of releases every ten or so days going. The package is now on CRAN and Debian, and mirrors start to get the new versions. As before, my local page provides more details and Romain's blog is always worth watching too.

The changes are summarised below in the NEWS file snippet, more details are in the ChangeLog as well.

0.7.5	2010-02-08

    o 	wrap has been much improved. wrappable types now are :
    	- primitive types : int, double, Rbyte, Rcomplex, float, bool
    	- std::string
    	- STL containers which have iterators over wrappable types:
    	  (e.g. std::vector, std::deque, std::list, etc ...). 
    	- STL maps keyed by std::string, e.g std::map
    	- classes that have implicit conversion to SEXP
    	- classes for which the wrap template if fully or partly specialized
    	This allows composition, so for example this class is wrappable: 
    	std::vector< std::map > (if T is wrappable)
    	
    o 	The range based version of wrap is now exposed at the Rcpp::
    	level with the following interface : 
    	Rcpp::wrap( InputIterator first, InputIterator last )
    	This is dispatched internally to the most appropriate implementation
    	using traits

    o	a new namespace Rcpp::traits has been added to host the various
    	type traits used by wrap

    o 	The doxygen documentation now shows the examples

    o 	A new file inst/THANKS acknowledges the kind help we got from others

    o	The RcppSexp has been removed from the library.
    
    o 	The methods RObject::asFoo are deprecated and will be removed
    	in the next version. The alternative is to use as.

    o	The method RObject::slot can now be used to get or set the 
    	associated slot. This is one more example of the proxy pattern
    	
    o	Rcpp::VectorBase gains a names() method that allows getting/setting
    	the names of a vector. This is yet another example of the 
    	proxy pattern.
    	
    o	Rcpp::DottedPair gains templated operator<< and operator>> that 
    	allow wrap and push_back or wrap and push_front of an object
    	
    o	Rcpp::DottedPair, Rcpp::Language, Rcpp::Pairlist are less
    	dependent on C++0x features. They gain constructors with up
    	to 5 templated arguments. 5 was choosed arbitrarily and might 
    	be updated upon request.
    	
    o	function calls by the Rcpp::Function class is less dependent
    	on C++0x. It is now possible to call a function with up to 
    	5 templated arguments (candidate for implicit wrap)
    	
    o	added support for 64-bit Windows (thanks to Brian Ripley and Uwe Ligges)

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. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page

/code/rcpp | permanent link

R / Finance 2010 Open for Registration
The annoucement below went out to R-SIG-Finance earlier today. For information is as usual the the R / Finance 2010 page:

Now open for registrations:

R / Finance 2010: Applied Finance with R
April 16 and 17, 2010
Chicago, IL, USA

The second annual R / Finance conference for applied finance using R, the premier free software system for statistical computation and graphics, will be held this spring in Chicago, IL, USA on Friday April 16 and Saturday April 17.

Building on the success of the inaugural R / Finance 2009 event, this two-day conference will cover topics as diverse as portfolio theory, time-series analysis, as well as advanced risk tools, high-performance computing, and econometrics. All will be discussed within the context of using R as a primary tool for financial risk management and trading.

Invited keynote presentations by Bernhard Pfaff, Ralph Vince, Mark Wildi and Achim Zeileis are complemented by over twenty talks (both full-length and 'lightning') selected from the submissions. Four optional tutorials are also offered on Friday April 16.

R / Finance 2010 is organized by a local group of R package authors and community contributors, and hosted by the International Center for Futures and Derivatives (ICFD) at the University of Illinois at Chicago.

Conference registration is now open. Special advanced registration pricing is available, as well as discounted pricing for academic and student registrations.

More details and registration information can be found at the website at
http://www.RinFinance.com

For the program committee:

Gib Bassett, Peter Carl, Dirk Eddelbuettel, John Miller,
Brian Peterson, Dale Rosenthal, Jeffrey Ryan

See you in Chicago in April!

/computers/R | permanent link

RProtoBuf 0.1-0
Romain uploaded our first release of RProtoBuf to CRAN yesterday. RProtoBuf provides bindings for GNU R to the Google Protobuf implementation. Google Protobuf is (and I quote) a way of encoding structured data in an efficient yet extensible format that is used for almost all internal RPC protocols and file formats at Google.

RProtoBuf had a funny start. I had blogged about the 12 hour passage from proof of concept to R-Forge project following the ORD session hackfest in October. What happened next was as good. Romain emailed within hours of the blog post and reminded me of a similar project that is part of Saptarshi Guha's RHIPE R/Hadoop implementation. So the three of us--Romain, Saptarshi and I---started emailing and before long it becomes clear that Romain is both rather intrigued by this (whereas Saptarshi has slightly different needs for the inner workings of his Hadoop bindings) and was able to devote some time to it. So the code kept growing and growing at a fairly rapid clip. Til that stopped as we switched to working feverishly on Rcpp to both support the needs of this project, and to implement ideas we had while working on this. That now lead to the point where Rcpp is maturing in terms of features, so we will probably have time come back to more work on RProtoBuf to take advantage of the nice templated autoconversions we now have in Rcpp. Oddly enough, the initial blog post seemed to anticipate changes in Rcpp.

Anyway -- RProtoBuf is finally here and it already does a fair amount of magic based of code reflection using the proto files. The Google documentation has a simple example of a 'person' entry in an 'addressbook' which, when translated to R, goes like this:

R> library( RProtoBuf )                      ## load the package
R> readProtoFiles( "addressbook.proto" )     ## acquire protobuf information
R> bob <- new( tutorial.Person,              ## create new object
+   email = "bob@example.com",
+   name = "Bob",
+   id = 123 )
R> writeLines( bob$toString() )              ## serialize to stdout
name: "Bob"
id: 123
email: "bob@example.com"

R> bob$email                                 ## access and/or override
[1] "bob@example.com"
R> bob$id <- 5
R> bob$id
[1] 5

R> serialize( bob, "person.pb" )             ## serialize to compact binary format

There is more information at the RProtoBuf page, and we already have a draft package vignette, a 'quick' overview vignette and a unit test summary vignette.

More changes should be forthcoming as Romain and I find time to code them up. Feedback is as always welcome.

/code/rprotobuf | permanent link

Rcpp 0.7.4
Yesterday, and about nine days after release 0.7.3 of Rcpp (a set of R / C++ interface classes), Romain and I released version 0.7.4. It has been uploaded to CRAN and Debian, and mirrors should already have new versions. As before, my local page is also available for downloads and some more details.

The release once again combines a number of necessary fixes with numerous new features:

• Building on OS X did not support multi-arch, and we are grateful for Simon who once again came to the rescue. Things should be fine now. The big take-away is that under no circumstances, include either a file configure or src/Makefile if you want multi-arch builds for free. As Rcpp is effectively a library to be used by other packages, this mattered.
• We added a file NEWS from which I include the relevant section below.
• Much more code re-organisation and enhancement making passage of various C++ types even easier -- see the NEWS entry below.
• More unit tests, now including ones for the 'old Rcpp API'.

Lastly, we had a remaining Windows build issue. Also, Brian Ripley and Uwe Ligges kindly sent us a small patch supporting the new Windows 64-bit builds using the new MinGW 64-bit compiler for Windows -- so release 0.7.5 may follow in due course.

The NEWS file entry for release 0.7.4 is as follows:

0.7.4	2010-01-30

    o	matrix matrix-like indexing using operator() for all vector 
    	types : IntegerVector, NumericVector, RawVector, CharacterVector
    	LogicalVector, GenericVector and ExpressionVector. 

    o	new class Rcpp::Dimension to support creation of vectors with 
    	dimensions. All vector classes gain a constructor taking a 
    	Dimension reference.

    o	an intermediate template class "SimpleVector" has been added. All
    	simple vector classes are now generated from the SimpleVector 
    	template : IntegerVector, NumericVector, RawVector, CharacterVector
    	LogicalVector.

    o	an intermediate template class "SEXP_Vector" has been added to 
    	generate GenericVector and ExpressionVector.

    o	the clone template function was introduced to explicitely
    	clone an RObject by duplicating the SEXP it encapsulates.

    o	even smarter wrap programming using traits and template
        meta-programming using a private header to be include only
        RcppCommon.h

    o 	the as template is now smarter. The template now attempts to 
    	build an object of the requested template parameter T by using the
    	constructor for the type taking a SEXP. This allows third party code
    	to create a class Foo with a constructor Foo(SEXP) to have 
    	as for free.

    o	wrap becomes a template. For an object of type T, wrap uses
    	implicit conversion to SEXP to first convert the object to a SEXP
    	and then uses the wrap(SEXP) function. This allows third party 
    	code creating a class Bar with an operator SEXP() to have 
    	wrap for free.

    o	all specializations of wrap :  wrap, wrap< vector >
    	use coercion to deal with missing values (NA) appropriately.

    o	configure has been withdrawn. C++0x features can now be activated
    	by setting the RCPP_CXX0X environment variable to "yes".

    o	new template r_cast to facilitate conversion of one SEXP
    	type to another. This is mostly intended for internal use and 
    	is used on all vector classes

    o	Environment now takes advantage of the augmented smartness
    	of as and wrap templates. If as makes sense, one can 
    	directly extract a Foo from the environment. If wrap makes
    	sense then one can insert a Bar directly into the environment. 
    	Foo foo = env["x"] ;  /* as is used */
	Bar bar ;
	env["y"] = bar ;      /* wrap is used */    	

    o	Environment::assign becomes a template and also uses wrap to 
    	create a suitable SEXP

    o	Many more unit tests for the new features; also added unit tests
        for older API

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. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page

/code/rcpp | permanent link

Rcpp 0.7.3
A quick nine days after release 0.7.2 of Rcpp, our R / C++ interface classes, Romain and I are happy to roll out a new version 0.7.3. It has been uploaded to CRAN and Debian, and mirrors should have the new versions shortly. As before, my local page is also available for downloads and some more details.

This release combines a number of under-the-hood fixes and enhancements with one bug fix:

• The Rcpp:::LdFlags() helper function to dynamically provide linker options for packages using Rcpp now defaults to static linking on OS X as well. For installation from source dynamic linking always worked, but not for binary installation (as e.g. from CRAN). As on the other platforms, this default can be overridden. Thanks to the phylobase team for patient help in tracking this down.
• Accessing various types via [] should now be faster due to some enhancements in the internal representations.
• configure now has a command-line option (as well as an environment variable) to select support for the draft of the upcoming C++0x standard.
• A new function Rcpp.package.skeleton(), modelled after package.skeleton() in R itself, helps to set up a new package with support for using Rcpp.
• A number of other minor tweaks and improvements...

As always, full 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. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page

/computers/linux/debian/packages | permanent link

RQuantLib 0.3.2 released
A new version of RQuantLib (a package combining the quantitative analytics of QuantLib with the R statistical computing environment and language) is now out at CRAN and in Debian (where it depends on the 1.0.0 beta of QuantLib that is currently in the NEW queue with its new library version). This RQuantLib release works with either the current release 0.9.9 as well as with the just-released first beta of QuantLib 1.0.0.

This versions brings a few cleanups due to minor Rcpp changes (in essence: we now define the macro R_NO_REMAP before including R's headers and this separate non-namespaced functions like error() or length() out into prefixed-versions Rf_error() and Rf_length() which is a good thing).

It also adds a number of calendaring and holiday utilities that Khanh just added: tests for weekend, holiday, endOfMonth as well dayCount, date advancement and year fraction functions commonly used in fixed income.

Full changelog details, examples and more details about this package are at my RQuantLib page.

/computers/linux/debian/packages | permanent link

Rcpp 0.7.2
Not even two weeks after the Rcpp 0.7.1 release, Romain and I have a new one to present: Rcpp 0.7.2. It has been uploaded to CRAN and Debian, and the respective package management systems should carry them around in the next few hours. As always, the local page is also available for download too.

A lot of the momentum for the new API is continuing, thanks in large part to Romain. A number of new classes have been added, and existing ones have been enhanced. There are more unit tests than ever, and more documentation. We have better build support (with g++ version detection so that we can add some C++0x support where available) and a new examples sub-directory.

We did take one toy away, though. The Doxygen-generated docs were getting so big that we decided to keep them out of the source tarball. (And arguably, they are also too volatile.) We still have the browseable html docs as well as the pdf version (now at over 300 pages!). And we added zip archives of the docs in html, latex, and man format for download.

As always, full details are in the ChangeLog on the Rcpp page. Questions, comments etc: bring them to the rcpp-devel mailing list off the R-Forge page

/computers/linux/debian/packages | permanent link

Review of 'Computational Statistics: An Introduction to R' in JSS
Somehow missed during the the end-of-year switchover was the fact that my review of Guenther Sawitzki's Computational Statistics: An Introduction to R (CRC / Chapman \& Hall, 2009) is now up on the Journal of Statistical Software website.

/computers/R | permanent link

RInside release 0.2.1
The shiny new 0.2.1 release of RInside, a set of convenience classes to facilitate embedding of R inside of C++ applications, just went out to CRAN; sources are also at my RInside page

This is a maintenance release building on the recent 0.2.0 release which added Windows support (provided you use the Rtools toolchain for Windows). In this release, we changed the startup initialization so that interactive() comes out FALSE (just as we had done for littler just yesterday) and with that no longer call Rf_KillAllDevices() from the destructor as we may not have had devices in the first place. A few minor things were tweaked around the code organisation and build process, see the ChangeLog for details.

The new release should hit CRAN mirrors tomorrow, and is (as always) available from my machine too.

/computers/linux/debian/packages | permanent link

littler 0.1.3
A new littler release (now at 0.1.3) just went out of the door this evening.

littler provides r (pronounced littler), a shebang / scripting / quick eval / pipelining front-end to the the R language and system.

This version adds a few minor behind-the-scenes improvements:

• interactive() now evaluates to false as you'd expect in a non-interactive scripting front-end. To restore the previous behaviour, new switches -i or --interactive have been added.
• Some of the 'cleanup' functionality described in Section 8.1.2 on 'Setting R callbacks' from the R Extension manual have been adopted.
• Example scripts install.r and update.r received an update based on lessons learned from the R 2.10.0 roll-out and package rebuilding.
• A few build issues were improved, a minor manual page formatting bug was fixed.

As usual, our code is available via our svn archive or from tarballs off my littler page and the local directory here. A fresh package is in Debian's incoming queue and will hit mirrors shortly.

/computers/linux/debian/packages | permanent link

Rcpp 0.7.1
Two weeks after the Rcpp 0.7.0 release, Romain and I are happy to announce release 0.7.1 of Rcpp. It is currently in the incoming section of CRAN and has been accepted into Debian. Mirrors will catch up over the next few days, in the meantime the local page is available for download too.

A lot has changed under the hood since 0.7.0, and this is the first release that really reflects many of Romain's additions. Some of the changes are

• A new base class Rcpp::RObject that replaces RcppSexp (which is still provided for compatibility); it provides basic R object handling and other new classes derive from it.
• Rcpp::RObject has real simple wrappers for object creation and a SEXP operator for transfer back to R that make simple interfaces even easier.
• New classes Rcpp::Evaluator and Rcpp::Environment for expression evaluation and R environment access, respectively.
• A new class Rcpp::XPtr for external pointer access and management.
• Enhanced exception handling: exception can be trapped at the R even outside of try/catch blocks, see Romain's blog post for more.
• Namespace support with the addition of a Rcpp namespace; we will be incremental in phasing this in keeping compatibility with the old interface
• Unit test for most all of the above via use of the RUnit package, and several new examples.
• Inline support has been removed and replaced with a Depends: on inline (>= 0.3.4) as our patch is now part of the current inline package as mentioned <here.

/computers/linux/debian/packages | permanent link

inline 0.3.4 released
Oleg has updated the inline package to version 0.3.4 which is now on CRAN. It is includes my patch for both Rcpp support as well as extended header / library options for PKG_CPPFLAGS, PKG_CXXFLAGS, and PKG_LIBS which I had mentioned recently here and here. There is also a brand-new and shiny R-Forge project for it.

A new release of Rcpp will probably follow in due course as we can now depend on this new inline version; Romain has also put nice new code in around exceptions and we may add some more on external pointers. Good times ...

/computers/linux/debian/packages | permanent link

Beancounter minor bug fix release 0.8.9
Another small update to beancounter and just like the previous release due to changes in Perl's Date::Manip. It seems that Date_Calc now really wants a reference to store error codes, so we supply one. I also changed the FX code to use OandA ad there are often issues with Yahoo's FX data. The version 0.8.9 is now in Debian, at CPAN and on my beancounter page here.

/computers/linux/debian/packages | permanent link

RInside release 0.2.0
RInside, a set of convenience classes to facilitate embedding of R inside of C++ applications, was updated to release 0.2.0 over the weekend. Sources as well as binary packages are at CRAN; sources are also at my RInside page

The biggest news is that we now support builds on Windows -- if and only if you use the R toolchain -- more on that below. Since I had first released RInside people had emailed about support on Windows. However, Richard Holbrey deserves special mention as he actually sat down and tried to build it. Over a few email exchanges we made decent progress, but stalled. More recently, I sat down and had another go and lo and behold, it works. This required a few #ifdef statements here and there, as well as a function setenv() which Windows does not have -- Richard kindly helped here and borrowed most of the code from R itself.

Now, to make this plain: you need gcc (as this is how R itself is built). This gcc compiler is the native compiler on Linux and OS X. But on Windows this requires installing the Rtools compiled by Duncan Murdoch as detailed in the The Windows Toolset appendix to the R Installation manual. Do not, I repeat, do not report an error if you fail to build this with another compiler as this is not supported.

That said, if you have the proper tools, things are peachy. Install the package, change into the examples directory installed with it and say make (or on Windows: make -f Makefile.win). That's it -- now run the examples (which on Windows will most likely require setting the environment variable R_HOME). Now you can run simple examples like the one from the previous blog post or any of the other ones.

Happy embedding!

/computers/linux/debian/packages | permanent link

Rcpp and inline example
Following up on yesterday's Rcpp 0.7.0 release, here is a quick example of why the new feastures can be so useful.

The following R code defines a character variable gslrng. This variable contains a short C++ code segment, which is then transformed by the function cfunction into a function of two arguments assigned to funx:

    ## now use Rcpp to pass down a parameter for the seed, and a vector size
    gslrng <- '
       int seed = RcppSexp(s).asInt();
       int len = RcppSexp(n).asInt();

       gsl_rng *r;
       gsl_rng_env_setup();
       std::vector<double> v(len);

       r = gsl_rng_alloc (gsl_rng_default);

       gsl_rng_set (r, (unsigned long) seed);
       for (int i=0; i<len; i++) {
           v[i] = gsl_rng_get (r);
       }
       gsl_rng_free(r);

       return RcppSexp(v).asSexp();
    '

    ## turn into a function that R can call
    ## compileargs redundant on Debian/Ubuntu as gsl headers are found anyway
    funx <- cfunction(signature(s="numeric", n="numeric"),
                      gslrng,
                      includes="#include <gsl/gsl_rng.h>",
                      Rcpp=TRUE,
                      cppargs="-I/usr/include",
                      libargs="-lgsl -lgslcblas")
    print(funx(0, 5))

The signature argument to cfunction defines two variables s and n -- which the C++ function then reads in from R and converts to two integers seed and len. seed is used to initialize the random-number generator, and len draws are then taken and stored in the STL vector v which returned at the end.

As the R level, we now have a function of two arguments returning a vector of RNG draws of the given lenth and using the given seed.

Also note how we tell cfunction to add the GSL include line, specify that we want to compile and link against Rcpp and provide -I and -L arguments to compile and link with the GSL. (The include statement is not needed as the compiler would have found them in /usr/include anyway, but it shows how to set this if needed.)

Finally, we simply call our freshly compiled, linked and loaded C++ function with arguments zero for the seed and five for the length, and print the results vector returned to R from C++.

/computers/linux/debian/packages | permanent link

Rcpp 0.7.0
A new release of Rcpp awaits inclusion on CRAN and has also been sent as a .deb to Debian. It will hit mirrors over the next few days, in the meantime you can get it here as well.

This release has a couple new features :

• inline support: the cfunction from Oleg Sklyar's excellent inline package has been imported and adapted. This means simple C++ programs can be defined in an R character vector and passed to cfunction which will create a complete file that it then compiles, links and loads --- giving you access to compiled C++ code right from the R prompt without having to worry about compiler flags, linker options, ... Better still, we extended this to not only support Rcpp but any external library via addtional header / linker arguments that will be passed to R via the PKG_CPPFLAGS, PKG_CXXFLAGS and PKG_LIBS environment variables.
• this even works on Windoze (if you have the Rtools installed as detailed in the Windows Toolset appendix to the R Installation manual) in exactly the same way. And no folks, that still does NOT mean you can use Visual Whatever -- R really requires MinGW as the links in this parapgraph document very plainly. But if and when you have the tools, R's remarkable consistency across operating systems allows you to use Rcpp and inline in pretty much the same way.
• A handful of new examples for the inline support have been added.
• A new type RcppSexp for simple int, double or std::string scalars as well as vectors; this is particularly useful for the inline support.
• This also completes the source code reorginsation: every class now has its own header and implementation file
• Last but not least, the package has been relicensed from LGPL-2.1 (or later) to GPL 2 (or later).

/computers/linux/debian/packages | permanent link