This post shows how to serialize a c++ object to the raw vector in R and deserialize it with the help of Rcereal and BH. First, please install the Rcpp, Rcereal, and BH from CRAN and enable the support of C++11 via Sys.setenv("PKG_CXXFLAGS"="-std=c++11"). We can ...

Introduction This post is a simple introduction to Rcpp for disease ecologists, epidemiologists, or dynamical systems modelers - the sorts of folks who will benefit from a simple but fully-working example. My intent is to provide a complete, self-contained introduction to modeling with Rcpp. My hope is that this model ...

Motivation The purpose of this gallery post is several fold: to demonstrate the use of the new and improved C++-level implementation of R’s sample() function (see here) to demonstrate the Gallery’s new support for images in contributed posts to demonstrate the usefulness of SIR for updating posterior ...

Users new to the Rcpp family of functionality are often impressed with the performance gains that can be realized, but struggle to see how to approach their own computational problems. Many of the most impressive performance gains are demonstrated with seemingly advanced statistical methods, advanced C++–related constructs, or both. ...

The bigmemory package allows users to create matrices that are external to R, stored either in RAM or on disk, allowing them to be bigger than the system RAM, and allowing them to be shared across R sessions. While these objects are defined by the big.matrix class in R, ...

The RcppParallel package includes high level functions for doing parallel programming with Rcpp. For example, the parallelFor function can be used to convert the work of a standard serial “for” loop into a parallel one. This article describes using RcppParallel to compute pairwise distances for each row in an input ...

The RcppParallel package includes high level functions for doing parallel programming with Rcpp. For example, the parallelReduce function can be used aggreggate values from a set of inputs in parallel. This article describes using RcppParallel to parallelize the inner-product example previously posted to the Rcpp Gallery. Serial Version First the ...

The RcppParallel package includes high level functions for doing parallel programming with Rcpp. For example, the parallelReduce function can be used aggreggate values from a set of inputs in parallel. This article describes using RcppParallel to sum an R vector. Serial Version First a serial version of computing the sum ...

The RcppParallel package includes high level functions for doing parallel programming with Rcpp. For example, the parallelFor function can be used to convert the work of a standard serial “for” loop into a parallel one. This article describes using RcppParallel to transform an R matrix in parallel. Serial Version First ...

The Chain Ladder method is an actuarial technique used for projecting incurred insurance claims to their ultimate loss values. The data exists as claims triangles where the claims for each accounting year increments down the rows and the claims for eac...

Introduction This post provides a brief introduction to calling Python from R through Rcpp. The official Python documentation explains how to embed python into C/C++ applications. Moreover, the Boost.Python library provides seamless interoperability between C++ and the Python programming language. Similarlly, Rcpp provides interoperability between C++ and R. ...

Iterating over a sparse vector Consider the following vector:

idx1 <span><-</span> <span>c</span><span>(</span><span>2L</span><span>,</span> <span>0L</span><span>,</span> <span>4L</span><span>,</span> <span>0L</span><span>,</span> <span>7L</span><span>)</span>

A sparse representation of this vector will tell that at entries 1,3,5 (or at entries 0,2,4 if we are 0-based) we will find the values 2,4,7. Using Eigen via RcppEigen we can obtain the coercion with .sparseView(). We can iterate over all elements (...

Currie, Durban and Eilers write: Data with an array structure are common in statistics, and the design or regression matrix for analysis of such data can often be written as a Kronecker product. Factorial designs, contingency tables and smoothing of data on multidimensional grids are three such general classes of ...

We want to do matrix multiplication for 3 cases: dense times dense sparse times dense for sparse matrices of class dgCMatrix sparse times dense for sparse matrices of class indMatrix, using R’s Matrix package for sparse matrices in R and RcppArmadillo for C++ linear algebra:

<span>// [[Rcpp::depends(RcppArmadillo)]]</span>
<span>#include <RcppArmadillo.h></span>
<span>using</span> <span>namespace</span> <span>Rcpp</span> <span>;</span>

<span>arma</span><span>::</span><span>mat</span> <span>matmult_sp</span><span>(</span><span>const</span> <span>arma</span><span>::</span><span>sp_mat</span> <span>X</span><span>,</span> <span>const</span> <span>arma</span><span>::</span><span>mat</span> <span>Y</span><span>){</span>
    <span>arma</span><span>::</span><span>mat</span> <span>ret</span> <span>=</span> <span>X</span> <span>*</span> <span>Y</span><span>;</span>
    <span>return</span> <span>ret</span><span>;</span>
<span>};</span>
<span>arma</span><span>::</span><span>mat</span> <span>matmult_dense</span><span>(</span><span>const</span> <span>arma</span><span>::</span><span>mat</span> <span>X</span><span>,</span> <span>const</span> <span>arma</span><span>::</span><span>mat</span> <span>Y</span><span>){</span>
    <span>arma</span><span>::</span><span>mat</span> <span>ret</span> <span>=</span> <span>X</span> <span>*</span> <span>Y</span><span>;</span>
    <span>return</span> <span>ret</span><span>;</span>
<span>};</span>
<span>arma</span><span>::</span><span>mat</span> <span>matmult_ind</span><span>(</span><span>const</span> <span>SEXP</span> <span>Xr</span><span>,</span> <span>const</span> <span>arma</span><span>::</span><span>mat</span> <span>Y</span><span>){</span>
    <span>// pre-multiplication with index matrix is a permutation of Y's rows: </span>
    <span>arma</span><span>::</span><span>uvec</span> <span>perm</span> <span>=</span>  <span>as</span><span><</span><span>S4</span><span>></span><span>(</span><span>Xr</span><span>).</span><span>slot</span><span>(</span><span>"perm"</span><span>);</span>
    <span>arma</span><span>::</span><span>mat</span> <span>ret</span> <span>=</span> <span>Y</span><span>.</span><span>rows</span><span>(</span><span>perm</span> <span>-</span> <span>1</span><span>);</span>
    <span>return</span> <span>ret</span><span>;</span>
<span>};</span>

<span>//[[Rcpp::export]]</span>
<span>arma</span><span>::</span><span>mat</span> <span>matmult_cpp</span><span>(</span><span>SEXP</span> <span>Xr</span><span>,</span> <span>const</span> <span>arma</span><span>::</span><span>mat</span> <span>Y</span><span>)</span> <span>{</span>
    <span>if</span> <span>(</span><span>Rf_isS4</span><span>(</span><span>Xr</span><span>))</span> <span>{</span>
        <span>if</span><span>(</span><span>Rf_inherits</span><span>(</span><span>Xr</span><span>,</span> <span>"dgCMatrix"</span><span>))</span> <span>{</span>
            <span>return</span> <span>matmult_sp</span><span>(</span><span>as</span><span><</span><span>arma</span><span>::</span><span>sp_mat</span><span>></span><span>(</span><span>Xr</span><span>),</span> <span>Y</span><span>)</span> <span>;</span>
        <span>}</span> <span>;</span>
        <span>if</span><span>(</span><span>Rf_inherits</span><span>(</span><span>Xr</span><span>,</span> <span>"indMatrix"</span><span>))</span> <span>{</span>
            <span>return</span> <span>matmult_ind</span><span>(</span><span>Xr</span><span>,</span> <span>Y</span><span>)</span> <span>;</span> 
        <span>}</span> <span>;</span>
        <span>stop</span><span>(</span><span>"unknown class of Xr"</span><span>)</span> <span>;</span>
    <span>}</span> <span>else</span> <span>{</span>
        <span>return</span> <span>matmult_dense</span><span>(</span><span>as</span><span><</span><span>arma</span><span>::</span><span>mat</span><span>></span><span>(</span><span>Xr</span><span>),</span> <span>Y</span><span>)</span> <span>;</span>
    <span>}</span> 
<span>}</span>

Set up test cases: {{...

Rcpp 0.11.1 has introduced flexible subsetting for Rcpp vectors. Subsetting is implemented for the Rcpp vector types through the [ operator, and intends to mimic R’s [ operator for most cases. We diverge from R’s subsetting semantics in a few important ways: For integer and numeric vectors, 0-based indexing is performed, ...

There is a lot of literature and debate on how to rank candidates under preferential voting systems. Two of the methods used to determine winners are those based on some form of Borda count and those based on some form of Condorcet method. Many students of politics and voting systems ...

Rcpp can be used to convert basic R data types to and from Boost.Geometry models. In this example we take a matrix of 2d-points and convert it into a Boost.Geometry polygon. We then compute the convex hull of this polygon using a Boost.Geometry function boost::geometry::convex_...