C/C++ Source Code to Trigger Address and Undefined Behaviour Sanitizers

Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). As an alternative, pre-built Docker containers such as the Rocker container 'r-devel-san' or the multi-purpose container 'r-debug' can be used. This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. The code samples are based on the examples from the Address Sanitizer Wiki at < https://github.com/google/sanitizers/wiki>.

HTTP and WebSocket Server Library

Provides low-level socket and protocol support for handling HTTP and WebSocket requests directly from within R. It is primarily intended as a building block for other packages, rather than making it particularly easy to create complete web applications using httpuv alone. httpuv is built on top of the libuv and http-parser C libraries, both of which were developed by Joyent, Inc. (See LICENSE file for libuv and http-parser license information.)

'Rapidxml' C++ Header Files

Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.

Advanced and Fast Data Transformation

A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'. For a concise overview of the package see Krantz (2026) .

Split, Combine and Compress PDF Files

Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the 'qpdf' C++ library < https://qpdf.sourceforge.io/> and does not require any command line utilities. Note that 'qpdf' does not read actual content from PDF files: to extract text and data you need the 'pdftools' package.

A 'C++20' API for R

A header-only 'C++20' API for manipulating R data structures from 'C++'. Provides 'C++20' concepts specific to R, custom scalar and vector classes with built-in NA handling, automatic object protection, 'SIMD' (single-instruction-multiple-data), parallelisation, and a streamlined system for registering 'C++' functions, including templates, to R. Full API reference and documentation are available at < https://nicchr.github.io/cppally/>.

Builds the 'TinyCC' Command-Line Interface and Library for 'C' Scripting in 'R'

Builds the 'TinyCC' (Tiny 'C' Compiler) command-line interface and library for package use in 'R'. The package compiles 'TinyCC' from source and provides R functions to interact with the compiler. 'TinyCC' can be used for header preprocessing, just-in-time compilation of 'C' code in 'R', and lightweight 'C' scripting workflows.

Solve Erlang-C Model

Provides a set of functions to solve Erlang-C model. The Erlang C formula was invented by the Danish Mathematician A.K. Erlang and is used to calculate the number of advisors and the service level.

'Rcpp' Meets 'C++' Arrays

Interoperability between 'Rcpp' and the 'C++11' array and tuple types. Linking to this package allows fixed-length 'std::array' objects to be converted to and from equivalent R vectors, and 'std::tuple' objects converted to lists, via the as() and wrap() functions. There is also experimental support for 'std::span' from 'C++20'.

Testing for Change in C-Statistic

Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) .