This paper describes CXXR, a project to refactor the R interpreter from C into C++, with a view to making the internals of the interpreter more readily accessible to researchers and developers.

The continued growth of CRAN is a testament to the increasing number of developers engaged in R development. But far fewer researchers have experimented with the R interpreter itself. The code of the interpreter, written for the most part in C, is structured in a way that will be foreign to students brought up with object-oriented programming, and the available documentation, though giving a general understanding of how the interpreter works, does not really enable a newcomer to start modifying the code with any confidence. The CXXR project is progressively refactoring the interpreter into C++, whilst all the time preserving existing functionality. By restructuring the code into tightly encapsulated and carefully documented classes, CXXR aims to open up the interpreter to more ready experimentation by statistical computing researchers.

This paper focuses on the example task of providing, as a package, a new type of data vector, and shows how CXXR greatly facilitates such a task by internal changes to the structure of the interpreter, and by offering a higher-level interface for packages to exploit. WIREs Comput Stat 2013, 5:181–189. doi: 10.1002/wics.1251

Linear discriminant analysis (LDA) is among the most classical classification techniques, while it continues to be a popular and important classifier in practice. However, the advancement of science and technology brings the new challenge of high-dimensional datasets, where the dimension can be in thousands. In such datasets, LDA is inapplicable. Recently, statisticians have devoted many efforts to creating high-dimensional LDA methods. These methods typically perform variable selection via regularization techniques. Various theoretical results, algorithms, and empirical results support the application of these methods. In this review, we provide a brief description of difficulties in extending LDA and present some successful proposals. WIREs Comput Stat 2013, 5:190–197. doi: 10.1002/wics.1257

Conflict of interest: The author has declared no conflicts of interest for this article.

We describe a method of fusing, visualizing, and exploring multiple registered images called the image grand tour (IGT). The IGT is based on the grand tour idea for exploring high-dimensional data sets. In this article, we provide information on the general grand tour and show how this can be adapted to explore many linear combinations of the registered images in search of interesting structure and information. We provide several examples where the IGT is used for remote sensing, the search for rock art pictographs, and sensor fusion. WIREs Comput Stat 2013, 5:198–206. doi: 10.1002/wics.1253

This article is a U.S. Government work, and as such, is in the public domain in the United States of America.

Conflict of interest: The author has declared no conflicts of interest for this article.

In this article, we review a Markov chain Monte Carlo (MCMC) algorithm for performing conditional inference in contingency tables in the presence of partial information using Markov bases, a key tool arising from the area known as algebraic statistics. We review applications of this algorithm to the problems of conditional exact tests, ecological inference, and disclosure limitation and illustrate how these problems fall naturally in the setting of inference with partial information. We also discuss some issues associated with computing Markov bases which are needed as an input to the algorithm. WIREs Comput Stat 2013, 5:207–218. doi: 10.1002/wics.1256

Conflict of interest: The authors have declared no conflicts of interest for this article.

There has been a renewed interest in developing more flexible statistical distributions in recent decades. A major milestone in the methods for generating statistical distributions is undoubtedly the system of differential equation approach. There is a recent renewed interest in generating skewed distributions. Generally speaking, the methods developed prior to 1980s may be summarized into three categories: (1) method of differential equation, (2) method of transformation, and (3) quantile method. Techniques developed since 1980s may be categorized as ‘methods of combination’ for the reason that these methods attempt to combine existing distributions into new distributions or adding new parameters to an existing distribution. This article discusses five general methods of combination and their variations. These five are (1) method of generating skew distributions, (2) method of adding parameters (e.g., exponentiation), (3) beta generated method, (4) transformed-transformer method, and (5) composite method. WIREs Comput Stat 2013, 5:219–238. doi: 10.1002/wics.1255

Conflict of interest: The authors have declared no conflicts of interest for this article.

This review article will explore the innovative and popular theme of engineering modeling and simulation, predominantly in the manufacturing industry and cybersecurity world, citing severe challenges, advantages and time- and budget saving solutions and its future. The power of simulation is not an exaggeration but an understatement. The favorable outcomes since the advent of digital computers and software revolution could not have been achieved, especially without the multiple benefits of statistical simulation, which underlies the widespread use of modeling and simulation in engineering and sciences, stretching from A (Astronomy) to Z (Zoology). This refers not only to research findings in verifying a certain piece of theory, such as that of the recently discovered Higgs Boson, but in testing new products to innovate new discoveries so as to make our universe a more peaceful place by modeling and simulating the future projects and taking precautions before disasters occur. The review explores a cross section of engineering modeling and simulation practices illustrating a window of numerical examples. WIREs Comput Stat 2013, 5:239–266. doi: 10.1002/wics.1254

Conflict of interest: The author has declared no conflicts of interest for this article.

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