Interface to the 'C++' Library 'Pf'

Builds and runs 'c++' code for classes that encapsulate state space model, particle filtering algorithm pairs. Algorithms include the Bootstrap Filter from Gordon et al. (1993) , the generic SISR filter, the Auxiliary Particle Filter from Pitt et al (1999) , and a variety of Rao-Blackwellized particle filters inspired by Andrieu et al. (2002) . For more details on the 'c++' library 'pf', see Brown (2020) .

C R Bytecode Compiler

Implements a bytecode compiler for 'R' in 'C', targeting functional parity with the base 'compiler' package, while focusing on better performance. The architecture mostly mirrors the original 'GNU-R' compiler as described in < https://homepage.cs.uiowa.edu/~luke/R/compiler/compiler.pdf>.

'OpenGL Mathematics' 'C++' Headers

Provides the header-only 'OpenGL Mathematics' (GLM) 'C++' library for numerical computations in graphics and geometry < https://github.com/g-truc/glm>. Includes vector and matrix types, coordinate transformations, quaternions, and geometric functions following the conventions of the 'OpenGL Shading Language'.

C++ Header Files for Stan

The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

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.

Matching Algorithms in R and C++

Computes matching algorithms quickly using Rcpp. Implements the Gale-Shapley Algorithm to compute the stable matching for two-sided markets, such as the stable marriage problem and the college-admissions problem. Implements Irving's Algorithm for the stable roommate problem. Implements the top trading cycle algorithm for the indivisible goods trading problem.

Distribution of the 'BayesX' C++ Sources

'BayesX' performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the 'BayesX' command line tool for easy installation. A convenient R interface is provided in package R2BayesX.

Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

'R' Access to the 'tskit C' API

'Tskit' enables efficient storage, manipulation, and analysis of ancestral recombination graphs (ARGs) using succinct tree sequence encoding. The tree sequence encoding of an ARG is described in Wong et al. (2024) , while `tskit` project is described in Jeffrey et al. (2026) . See also < https://tskit.dev> for project news, documentation, and tutorials. 'Tskit' provides 'Python', 'C', and 'Rust' application programming interfaces (APIs). The 'Python' API can be called from 'R' via the 'reticulate' package to load and analyse tree sequences as described at < https://tskit.dev/tutorials/tskitr.html>. 'RcppTskit' provides 'R' access to the 'tskit C' API for cases where the 'reticulate' option is not optimal; for example, high-performance or low-level work with tree sequences. Currently, 'RcppTskit' provides a limited set of 'R' functions because the 'Python' API and 'reticulate' already covers most needs.

Translates an R Function to a C++ Function

Enable translation of a tiny subset of R to C++. The user has to define a R function which gets translated. For a full list of possible functions check the documentation. After translation an R function is returned which is a shallow wrapper around the C++ code. Alternatively an external pointer to the C++ function is returned to the user. The intention of the package is to generate fast functions which can be used as ode-system or during optimization.