Tools for Working with Posterior Distributions

Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) .

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.)

'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.

'C++' Standard Template Library Containers

Use 'C++' Standard Template Library containers interactively in R. Includes sets, unordered sets, multisets, unordered multisets, maps, unordered maps, multimaps, unordered multimaps, stacks, queues, priority queues, vectors, deques, forward lists, and lists.

Generate Port Files for C Libraries

Generate port files for foreign function interfaces to C libraries by parsing C-family header files with 'CastXML'.

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.

C++ Standard Library Vectors in R

Allows the creation and manipulation of C++ std::vector's in R.

Capture Hi-C Analysis Engine

Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.

Allow Access to the 'Dlib' C++ Library

Interface for 'Rcpp' users to 'dlib' < http://dlib.net> which is a 'C++' toolkit containing machine learning algorithms and computer vision tools. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. This package allows R users to use 'dlib' through 'Rcpp'.

C++ Implementations of Phylogenetic Cladogenesis Calculations

Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information.