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<std::string> internally
    
    o   setting the .Data slot of S4 objects did not work properly

• 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,

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<t>::getTag and XPtr<t>::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
        

• 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!

• 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

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<int> , size )
	
	creates the .Call compatible function called foobar that calls the
	size method of the std::vector<int> class. This uses the Rcpp::XPtr<
	std::vector<int> > 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<int> , 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<int>, push_back ) 
	
	Assuming xp is an external pointer to a std::vector<int>, 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

// -*- 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);