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Simulation smoothing involves drawing latent state variables in discrete-time state–space models from their conditional distribution given parameters and data as defined by McCausland, Miller, & Pelletier (2011). This tool greatly facilitates Bayes... [Read more...]

Introduction It is no secret that sparse matrix operations are faster in C++ than in R. RcppArmadillo and RcppEigen do a great job copying sparse matrices from R to C++ and back again. But note the word “copy”. Every time RcppArmadillo converts an ... [Read more...]

Introduction Character encodings can be tricky and frustrating to deal with. Several newer languages such as Go or Julia default to native UTF-8 on all platforms, greatly facilitating and easing use of characters with languages other than English on ... [Read more...]

Introduction GNU Guile is a programming language “designed to help programmers create flexible applications that can be extended by users or other programmers with plug-ins, modules, or scripts”. It is an extension language platform, and contains an e... [Read more...]

Introduction When we are using R6 objects and want to introduce some C++ code in our project, we may also want to interact with these objects using Rcpp. With this objective in mind, the key to interacting with R6 objects is that they are essentially... [Read more...]

Introduction With Rcpp attributes Rcpp modules (described in the Rcpp vignettes) it is easy to expose C++ classes and functions to R. This note describes how to use classes exported by modules in conjunction with functions exported using Rcpp attributes through the use of RCPP_EXPOSED* macros. In the following ... [Read more...]

Sometimes one needs to mimic the exact behavior of R’s Distributions within C++ code. The incredible Rcpp team has provided access to these distributions through Rmath.h (in the R:: namespace), as well as through the Rcpp:: namespace where there can ... [Read more...]

Introduction Often we need to have optional arguments in R of Rcpp functions with default values. Sometimes, the default value for the optional parameters is set to be NULL. Rcpp provides the Nullable to set default value as to be R_NilValue ... [Read more...]

Introduction Rcpp provides the DataFrame class which enables us to pass data.frame object between C++ and R. DataFrame objects are key to R and used very widely. They also provide the basis from which two key packages extend them. One of these, the tibble package, operates in a similar ... [Read more...]

Introduction Roxygen2 is a convenient way to document functions in R packages. Based on the Doxygen model, it parses relevant information from the comments and generates the corresponding man/*.Rd files. The major strength of this model – besides no... [Read more...]

This article demonstrates using the RcppParallel package to aggregate to an output vector. It extends directly from previous demonstrations of single-valued aggregation, through providing necessary details to enable aggregation to a vector, or by exten... [Read more...]

Introduction Besides outstanding support for dense matrices, the Armadillo library also provides a great way to manipulate sparse matrices in C++. However, the performance characteristics of dealing with sparse matrices may be surprising if one is only familiar with dense matrices. This is a collection of observations on getting best ... [Read more...]

Introduction Rcpp has an elegant mechanism of exception handling whereby C++ exceptions are automatically translated to errors in R. For most projects, the Rcpp::stop wrapper (in conjunction with the BEGIN_RCPP and END_RCPP macros automatically inser... [Read more...]

Introduction The RcppArrayFire package provides an interface from R to and from the ArrayFire library, an open source library that can make use of GPUs and other hardware accelerators via CUDA or OpenCL. The official R bindings expose ArrayFire data structures as S4 objects in R, which would require a ... [Read more...]

Introduction In the quest for ever faster code, one generally begins exploring ways to integrate C++ with R using Rcpp. This post provides an example of multiple implementations of a European Put Option pricer. The implementations are done in pure R, pure Rcpp using some Rcpp sugar functions, and then ... [Read more...]

RcppProgress is a tool to help you monitor the execution time of your C++ code, by providing a way to interrupt the execution inside the C++ code, and also to display a progress bar indicative of the state of your computation. Additionally, it is com... [Read more...]

When writing an R package wrapping some C++ data structures, using Rcpp Modules is a convenient option. After exposing a class to R, it can be used to easily create new instances of that class. As an example, let us look at the Uniform class of the Rcpp Modules vignette: // ... [Read more...]

Motivation: More Precise Timestamps R has excellent facilities for dealing with both dates and datetime objects. For datetime objects, the POSIXt time type can be mapped to POSIXct and its representation of fractional seconds since the January 1, 1970 “epoch” as well as to the broken-out list representation in POSIXlt. Many add-on ... [Read more...]

Sitting on top of R’s external pointers, the RcppXPtr class provides a powerful and generic framework for Passing user-supplied C++ functions to a C++ backend. This technique is exploited in the RcppDE package, an efficient C++ based implementation of the DEoptim package that accepts optimisation objectives as both R ... [Read more...]