By Dirk Eddelbuettel
Rcpp is the glue that binds the facility and flexibility of R with the rate and potency of C++. With Rcpp, the move of information among R and C++ is almost seamless, and high-performance statistical computing is ultimately available to such a lot R users. Rcpp may be a part of each statistician's toolbox. -- Michael Braun, MIT Sloan college of Management
"Seamless R and C++ integration with Rcpp" is just a superb book. For an individual who makes use of C/C++ and R, it really is an fundamental resource. The writing is outstanding. an important bonus is the part on functions. This part covers the matrix applications Armadillo and Eigen and the GNU medical Library in addition to RInside which helps you to use R inside of C++. those functions are what such a lot people want to know to actually do medical programming with R and C++. i like this ebook. -- Robert McCulloch, college of Chicago sales space tuition of Business
Rcpp is now thought of a vital package deal for anyone doing critical computational examine utilizing R. Dirk's booklet is a wonderful spouse and takes the reader from a steady advent to extra complicated purposes through quite a few examples and potency improving gem stones. The ebook is filled with all you've gotten ever desired to find out about Rcpp, its cousins (RcppArmadillo, RcppEigen .etc.), modules, package deal improvement and sugar. total, this booklet is a must have in your shelf. -- Sanjog Misra, UCLA Anderson institution of Management
The Rcpp package deal represents a big step forward for medical computations with R. With only a few traces of C++ code, one has R's information buildings without problems handy for additional computations in C++. as a result, high-level numerical programming may be made in C++ nearly as simply as in R, yet usually with a considerable pace achieve. Dirk is a vital individual in those advancements, and his booklet takes the reader from the 1st fragile steps directly to utilizing the entire Rcpp equipment. a really prompt booklet! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg collage, Denmark
"Seamless R and C ++ Integration with Rcpp" presents the 1st finished creation to Rcpp. Rcpp has turn into the main widely-used language extension for R, and is deployed through over one-hundred various CRAN and BioConductor programs. Rcpp allows clients to move scalars, vectors, matrices, record or whole R items from side to side among R and C++ conveniently. This brings the intensity of the R research framework including the facility, velocity, and potency of C++.
Dirk Eddelbuettel has been a contributor to CRAN for over a decade and keeps round twenty packages. he's the Debian/Ubuntu maintainer for R and different quantitative software program, edits the CRAN job perspectives for Finance and High-Performance Computing, is a co-founder of the once a year R/Finance convention, and an editor of the magazine of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.
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Additional info for Seamless R and C++ Integration with Rcpp (Use R!)
The first, as, is used to convert the incoming argument xs from SEXP to integer. Call(). 4 Using Inline The next steps are to compile these two functions, to link them into a so-called shared library (which can be loaded at run-time by a system such as R), and to actually load it. These three steps do sound a little tedious and labor-intensive, and they are. So it is at this point that we introduce another very powerful helper: the inline package (Sklyar et al. 2012). inline, written mostly by Oleg Sklyar, brings an idea to R which has been used with other dynamically extensible scripting languages.
Few languages besides R are this expressive and powerful for working with data. A key aspect of the internal implementation of R is that its own core interpreter and extension mechanism are implemented in the C language. C is often used for system programming as it is reasonably lean and fast, yet also very portable and easily available on most hardware platforms. A key advantage of C is that it is extensible via external libraries and modules. R takes full advantage of this, and so does the Rcpp extension featured in this book.
10 Fibonacci number in C++ via memoization We define a very simple C++ class Fib with three elements: • A constructor which is called once upon initialization. • A single public member function which computes Fn . • A private data vector holding the memoization values. So this example provides a first glance at using classes in C++ code. In the actual wrapper function, we simply instantiate an object f of the class Fib and then invoke the member function to compute the given Fibonacci number. 8 A Third R Solution Naturally, we can also compute Fn using an iterative approach.