The Rcpp package simplifies integrating C++ code with R by providing a consistent C++ class hierarchy that maps various R objects to dedicated C++ classes. It enables seamless object interchange between R and C++ using flexible and extensible concepts, including support for C++ Standard Template Library idioms. Rcpp allows C++ code to be compiled, linked, and loaded on the fly or via packages, and offers flexible error and exception handling. It significantly lowers the barrier for programmers to combine C++ with R. Rcpp provides a modern API for accessing, extending, or modifying R objects in C++. This API is a rewritten and extended version of an earlier API, now deprecated in favor of the new API. The new API features lightweight encapsulation of R objects in C++ classes, automatic garbage collection, code inlining, data interchange between R and C++, and error handling. Examples demonstrate the benefits of using Rcpp over the traditional R API. The Rcpp package includes several examples and is available on CRAN. It has a history of development starting in 2005, with a significant redesign in 2009. Rcpp supports various use cases, including adding C++-based functions, calling functions from other libraries, and embedding R in C++ applications. The Rcpp API includes classes for handling R objects, such as RObject, NumericVector, and CharacterVector. These classes simplify data interchange between R and C++ using functions like wrap() and as(). The API also supports function calls, including using the Rcpp::Environment and Language classes, and provides a way to call R functions from C++. Rcpp also supports the use of Standard Template Library algorithms and provides efficient implementations for tasks like convolution. It offers performance benefits over traditional R API methods, with the fastest implementation using Rcpp sugar, which provides R-like syntax in C++. Rcpp is actively developed, with ongoing work on improving interoperability between R and C++. Key developments include 'Rcpp sugar' for R-like syntax in C++ and 'Rcpp modules' for exposing C++ functions and classes at the R level. The Rcpp package simplifies the integration of compiled C++ code with R, reducing the complexity of code and improving readability, maintainability, and reuse without significant performance penalties. It provides a lightweight API with minimal overhead, making it a valuable tool for R developers.The Rcpp package simplifies integrating C++ code with R by providing a consistent C++ class hierarchy that maps various R objects to dedicated C++ classes. It enables seamless object interchange between R and C++ using flexible and extensible concepts, including support for C++ Standard Template Library idioms. Rcpp allows C++ code to be compiled, linked, and loaded on the fly or via packages, and offers flexible error and exception handling. It significantly lowers the barrier for programmers to combine C++ with R. Rcpp provides a modern API for accessing, extending, or modifying R objects in C++. This API is a rewritten and extended version of an earlier API, now deprecated in favor of the new API. The new API features lightweight encapsulation of R objects in C++ classes, automatic garbage collection, code inlining, data interchange between R and C++, and error handling. Examples demonstrate the benefits of using Rcpp over the traditional R API. The Rcpp package includes several examples and is available on CRAN. It has a history of development starting in 2005, with a significant redesign in 2009. Rcpp supports various use cases, including adding C++-based functions, calling functions from other libraries, and embedding R in C++ applications. The Rcpp API includes classes for handling R objects, such as RObject, NumericVector, and CharacterVector. These classes simplify data interchange between R and C++ using functions like wrap() and as(). The API also supports function calls, including using the Rcpp::Environment and Language classes, and provides a way to call R functions from C++. Rcpp also supports the use of Standard Template Library algorithms and provides efficient implementations for tasks like convolution. It offers performance benefits over traditional R API methods, with the fastest implementation using Rcpp sugar, which provides R-like syntax in C++. Rcpp is actively developed, with ongoing work on improving interoperability between R and C++. Key developments include 'Rcpp sugar' for R-like syntax in C++ and 'Rcpp modules' for exposing C++ functions and classes at the R level. The Rcpp package simplifies the integration of compiled C++ code with R, reducing the complexity of code and improving readability, maintainability, and reuse without significant performance penalties. It provides a lightweight API with minimal overhead, making it a valuable tool for R developers.