Seamless R and C++ Integration.zip

## Rcpp: Seamless R and C++ Integration [](https://github.com/RcppCore/Rcpp/actions?query=workflow%3Aci) [](https://www.gnu.org/licenses/gpl-2.0.html) [](https://cran.r-project.org/package=Rcpp) [](https://cran.r-project.org/package=Rcpp) [](https://app.codecov.io/github/RcppCore/Rcpp?branch=master) [](https://packages.debian.org/sid/r-cran-rcpp) [](https://github.com/RcppCore/Rcpp) [](https://www.r-pkg.org/pkg/Rcpp) [](https://www.r-pkg.org/pkg/Rcpp) [](https://cran.r-project.org/package=Rcpp) [](https://cran.r-project.org/package=Rcpp) [](https://cran.r-project.org/package=Rcpp) [](https://stackoverflow.com/questions/tagged/rcpp) [](https://dx.doi.org/10.18637/jss.v040.i08) [](https://www.amazon.com/gp/product/1461468671/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1461468671&linkCode=as2&tag=rcpp-20&linkId=3P5LNUWOAQ2YMEJ6) [](https://dx.doi.org/10.1080/00031305.2017.1375990) ### Synopsis The [Rcpp package](https://cran.r-project.org/package=Rcpp) integrates R and C++ via R functions and a (header-only) C++ library. All underlying R types and objects, _i.e._, everything a `SEXP` represents internally in R, are matched to corresponding C++ objects. This covers anything from vectors, matrices or lists to environments, functions and more. Each `SEXP` variant is automatically mapped to a dedicated C++ class. For example, numeric vectors are represented as instances of the `Rcpp::NumericVector` class, environments are represented as instances of `Rcpp::Environment`, functions are represented as `Rcpp::Function`, etc ... The [Rcpp-introduction](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-introduction.pdf) vignette (now published as a [TAS paper](https://doi.org/10.1080/00031305.2017.1375990); an [earlier introduction](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-jss-2011.pdf) was also published as a [JSS paper](https://doi.org/10.18637/jss.v040.i08) provides a good entry point to Rcpp as do the [Rcpp website](https://www.rcpp.org), the [Rcpp page](https://dirk.eddelbuettel.com/code/rcpp.html) and the [Rcpp Gallery](https://gallery.rcpp.org). Full documentation is provided by the [Rcpp book](https://www.amazon.com/gp/product/1461468671/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1461468671&linkCode=as2&tag=rcpp-20&linkId=3P5LNUWOAQ2YMEJ6). Other highlights: - The conversion from C++ to R and back is driven by the templates `Rcpp::wrap` and `Rcpp::as` which are highly flexible and extensible, as documented in the [Rcpp-extending](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-extending.pdf) vignette. - Rcpp also provides Rcpp modules, a framework that allows exposing C++ functions and classes to the R level. The [Rcpp-modules](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-modules.pdf) vignette details the current set of features of Rcpp-modules. - Rcpp includes a concept called Rcpp sugar that brings many R functions into C++. Sugar takes advantage of lazy evaluation and expression templates to achieve great performance while exposing a syntax that is much nicer to use than the equivalent low-level loop code. The [Rcpp-sugar](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-sugar.pdf) gives an overview of the feature. - Rcpp attributes provide a high-level syntax for declaring C++ functions as callable from R and automatically generating the code required to invoke them. Attributes are intended to facilitate both interactive use of C++ within R sessions as well as to support R package development. Attributes are built on top of Rcpp modules and their implementation is based on previous work in the inline package. See the [Rcpp-atttributes](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-attributes.pdf) vignettes for more details. ### Documentation The package ships with ten pdf vignettes, including a [recent introduction to Rcpp](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-introduction.pdf) now published as a [paper in TAS](https://doi.org/10.1080/00031305.2017.1375990) (and as a [preprint in PeerJ](https://peerj.com/preprints/3188/)). Also available is an [earlier introduction](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-jss-2011.pdf) which was published as a [JSS paper](https://doi.org/10.18637/jss.v040.i08). Among the other vignettes are the [Rcpp FAQ](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-FAQ.pdf) and the introduction to [Rcpp Attributes](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-attributes.pdf). Additional documentation is available via the [Rcpp book](https://www.amazon.com/gp/product/1461468671/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1461468671&linkCode=as2&tag=rcpp-20&linkId=3P5LNUWOAQ2YMEJ6) by Eddelbuettel (2013, Springer); see 'citation("Rcpp")' for details. ### Performance Rcpp follows the C++ motto of _"you pay only for what you use"_ and imposes no _run-time_ performance penalty: Rcpp outperforms related packages in direct comparison, see for example [this repo for details](https://github.com/eddelbuettel/ldlasb). Compile-time performance can be tuned by selecting components. But it is also worth noting that use of [`ccache`](https://ccache.dev/) will (strongly) dominate all such possible component choices, we have [previously recommended](https://dirk.eddelbuettel.com/blog/2017/11/27/) its use. ### Examples The [Rcpp Gallery](https://gallery.rcpp.org) showcases over one hundred fully documented and working examples. The [package RcppExamples](https://cran.r-project.org/package=RcppExamples) contains a few basic examples covering the core data types. A number of examples are included, as are well over one thousand unit tests which provide additional usage examples. An earlier version of Rcpp, containing what we now call the 'classic Rcpp API' was written during 2005 and 2006 by Dominick Samperi. This code has been factored out of Rcpp into the package RcppClassic, and it is still available for code relying on the older interface. New development should always use this Rcpp package instead. Other usage examples are provided by packages using Rcpp. As of early July 2023, there are 2712 [CRAN](https://cran.r-project.org) packages using Rcpp (corresponding to 13.7% of all packages, and 59.5% of packages containing compiled code), a further 250 [BioConductor](https://www.bioconductor.org) packages in its current release as well as an unknown number of GitHub, Bitbucket, R-Forge, ... repositories using Rcpp. All these packages provide usage examples for Rcpp. The package is in widespread use and has been downloaded over 72.3 million times (per the partial logs from the cloud mirrors of CRAN). ### Installation #### CRAN Rcpp released on CRAN are carefully tested and curat