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fixed typo in DESCRIPTION, finally added README test from DESCRIPTION and edited/expanded
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DESCRIPTION

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Package: Rcpp
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Title: Seamless R and C++ Integration
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Version: 0.10.6.3
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Version: 0.10.6.4
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Date: 2013-11-14
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Authors@R: c(
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person("Dirk", "Eddelbuettel", role = c("aut", "cre"), email = "edd@debian.org" ),
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URL: http://www.rcpp.org, http://dirk.eddelbuettel.com/code/rcpp.html, https://github.com/RcppCore/Rcpp
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License: GPL (>= 2)
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BugReports: https://github.com/RcppCore/Rcpp/issues
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MailingList: Please send questions and comments regarding Rcpp to rcpp-devel@lists.r-forge.r-project.or
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MailingList: Please send questions and comments regarding Rcpp to rcpp-devel@lists.r-forge.r-project.org

README.md

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Rcpp
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====
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Rcpp: Seamless R and C++ Integration
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====================================
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[![Build Status](https://travis-ci.org/RcppCore/Rcpp.png)](https://travis-ci.org/RcppCore/Rcpp)
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The Rcpp package provides R functions as well as a C++ library
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which facilitate the integration of R and C++
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R data types (`SEXP`) are matched to C++ objects in a class hierarchy. All R
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types are supported (vectors, functions, environment, etc ...) and each
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type is mapped to a dedicated class. For example, numeric vectors are
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represented as instances of the Rcpp::NumericVector class, environments are
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represented as instances of Rcpp::Environment, functions are represented as
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Rcpp::Function, etc ...
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The
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[Rcpp-introduction](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-introduction.pdf)
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vignette (also published as [in JSS in 2011](http://www.jstatsoft.org/v40/i08/)) provides a good
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entry point to Rcpp, as does the [Rcpp website](http://www.rcpp.org) and the
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[Rcpp page](http://dirk.eddelbuettel.com/code/rcpp.html).
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Conversion from C++ to R and back is driven by the templates `Rcpp::wrap`
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and `Rcpp::as` which are highly flexible and extensible, as documented
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in the [Rcpp-extending](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-extending.pdf) vignette.
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Rcpp also provides Rcpp modules, a framework that allows exposing
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C++ functions and classes to the R level. The [Rcpp-modules]((http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-modules.pdf) vignette
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details the current set of features of Rcpp-modules.
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Rcpp includes a concept called Rcpp sugar that brings many R functions
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into C++. Sugar takes advantage of lazy evaluation and expression templates
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to achieve great performance while exposing a syntax that is much nicer
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to use than the equivalent low-level loop code. The [Rcpp-sugar]((http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-sugar.pdf)
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gives an overview of the feature.
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Rcpp attributes provide a high-level syntax for declaring C++
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functions as callable from R and automatically generating the code
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required to invoke them. Attributes are intended to facilitate both
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interactive use of C++ within R sessions as well as to support R
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package development. Attributes are built on top of Rcpp modules and
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their implementation is based on previous work in the inline package.
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See the [Rcpp-atttributes]((http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-attributes.pdf) vignettes for more details.
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Many examples are included, and over 800 unit tests in over 400 unit
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test functions provide additional usage examples.
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An earlier version of Rcpp, containing what we now call the 'classic Rcpp
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API' was written during 2005 and 2006 by Dominick Samperi. This code has
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been factored out of Rcpp into the package RcppClassic, and it is still
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available for code relying on the older interface. New development should
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always use this Rcpp package instead.
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Additional documentation is available via the [JSS paper](http://www.jstatsoft.org/v40/i08/) by Eddelbuettel and
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Francois (2011, JSS) paper and the [book](http://www.rcpp.org/book) by Eddelbuettel (2013, Springer);
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see 'citation("Rcpp")' for details.

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