Programming Languages and Compilers Commons^™

Multiple Factor Analysis For Contingency Tables In The Factominer Package, Belchin Kostov, Mónica Bécue-Bertaut, François Husson

The R Journal

We present multiple factor analysis for contingency tables (MFACT) and its implementation in the FactoMineR package. This method, through an option of the MFA function, allows us to deal with multiple contingency or frequency tables, in addition to the categorical and quantitative multiple tables already considered in previous versions of the package. Thanks to this revised function, either a multiple contingency table or a mixed multiple table integrating quantitative, categorical and frequency data can be tackled.

The FactoMineR package (Lê et al., 2008; Husson et al., 2011) offers the most commonly used principal component methods: principal component analysis (PCA), correspondence …

Generalized Simulated Annealing For Global Optimization: The Gensa Package, Yang Xiang, Sylvain Gubian, Brain Suomela, Julia Hoeng

The R Journal

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve determination of the global minimum of multidimensional functions. R packages for different stochastic methods such as genetic algorithms and differential evolution have been developed and successfully used in the R community. Based on Tsallis statistics, the R package GenSA was developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. In this paper we provide a brief introduction to the R package and demonstrate its utility by solving a non-convex portfolio optimization problem in finance and the Thomson problem …

R Foundation News, Kurt Hornik

The R Journal

New Benefactors

Quartz, Bio, Switzerland

New supporting Institutions

Institute for Geoinformatics, Westfälische Wilhelms-Universität Münster, Germany

News From The Bioconductor Project, Bioconductor Team

The R Journal

Bioconductor 2.12 was released on 3 October 2012. It is compatible with R 3.0.1, and consists of 671 software packages and more than 675 up-to-date annotation packages. The release includes 65 new software packages, and enhancements to many others. Descriptions of new packages and updated NEWS files provided by current package maintainers are at http://bioconductor.org/news/bioc_2_12_release/.

Conference Review: The 6th Chinese R Conference, Jing Leng, Jingjing Guan

The R Journal

The 6th Chinese R Conference (Beijing session) was held in the Sinology Pavilion of Renmin University of China (RUC), Beijing, from May 18th to 19th, 2013. The conference was orga nized by the “Capital of Statistics” (COS, http://cos.name), an online statistical community in China. It was sponsored and co-organized by the Center for Applied Statistics of RUC, the School of Statistics of RUC, and the Business Intelligence Research Center of Peking University

Possible Directions For Improving Dependency Versioning In R, Jeroen Ooms

The R Journal

One of the most powerful features of R is its infrastructure for contributed code. The built-in package manager and complementary repositories provide a great system for development and exchange of code, and have played an important role in the growth of the platform towards the de-facto standard in statistical computing that it is today. However, the number of packages on CRAN and other repositories has increased beyond what might have been foreseen, and is revealing some limitations of the current design. One such problem is the general lack of dependency versioning in the infrastructure. This paper explores this problem in …

Beadarrayfilter: An R Package To Filter Beads, Anyiawung Chiara Forcheh, Geert Verbeke, Adetayo Kasim, Dan Lin, Ziv Shkedy, Willem Talloen, Hinrich W.H. Göhlmann, Lieven Clement

The R Journal

Microarrays enable the expression levels of thousands of genes to be measured simultaneously. However, only a small fraction of these genes are expected to be expressed under different experimental conditions. Nowadays, filtering has been introduced as a step in the microarray pre-processing pipeline. Gene filtering aims at reducing the dimensionality of data by filtering redundant features prior to the actual statistical analysis. Previous filtering methods focus on the Affymetrix platform and can not be easily ported to the Illumina platform. As such, we developed a filtering method for Illumina bead arrays. We developed an R package, beadarrayFilter, to implement …

Ggmap: Spatial Visualization With Ggplot2, David Kahle, Hadley Wickham

The R Journal

In spatial statistics the ability to visualize data and models super imposed with their basic social landmarks and geographic context is in valuable. ggmap is a new tool which enables such visualization by combining the spatial information of static maps from Google Maps,Open Street Map, Stamen Maps or Cloud Made Maps with the layered grammar of graphics implementation of ggplot2. In addition, several new utility functions are introduced which allow the user to access the Google Geocoding, Distance Matrix, and Directions APIs. The result is an easy, consistent and modular frame work for spatial graphics with several convenient tools …

Let Graphics Tell The Story: Datasets In R, Antony Unwin, Heike Hofmann, Dianne Cook

The R Journal

Graphics are good for showing the information in datasets and for complementing modelling. Sometimes graphics show information models miss, sometimes graphics help to make model results more understandable, and sometimes models show whether information from graphics has statistical support or not. It is the interplay of the two approaches that is valuable. Graphics could be used a lot more in R examples and we explore this idea with some datasets available in R packages.

Stellar: A Package To Manage Stellar Evolution Tracks And Isochrones, Matteo Dell’Omodarme, Giada Valle

The R Journal

We present the R package stellaR, which is designed to access and manipulate publicly available stellar evolutionary tracks and isochrones from the Pisa low-mass database. The procedures for extracting important stages in the evolution of a star from the database, for constructing isochrones from stellar tracks and for interpolating among tracks are discussed and demonstrated.

Due to the advance in the instrumentation, nowadays astronomers can deal with a huge amount of high-quality observational data. In the last decade impressive improvements of spectroscopic and photometric observational capabilities made available data which stimulated the research in the globular clusters field. The theoretical …

Osmar: Openstreetmap And R, Manuel J. Eugster, Thomas Schlesinger

The R Journal

OpenStreetMap provides freely accessible and editable geographic data. The osmar package smoothly integrates the OpenStreetMap project into the R ecosystem. The osmar package provides infrastructure to access OpenStreetMap data from different sources, to enable working with the OSM data in the familiar R idiom, and to convert the data into objects based on classes provided by existing Rpackages. This paper explains the package’s concept and shows how to use it. As an application we present a simple navigation device

Hypothesis Tests For Multivariate Linear Models Using The Car Package, John Fox, Michael Friendly, Sanford Weisberg

The R Journal

The multivariate linear model can be fit with the lm function in R, where the left-hand side of the model comprises a matrix of response variables, and the right-hand side is specified exactly as for a univariate linear model (i.e., with a single response variable). This paper explains how to use the Anova and linearHypothesis functions in the car package to perform convenient hypothesis tests for parameters in multivariate linear models, including models for repeated-measures data.

Translating Probability Density Functions: From R To Bugs And Back Again, David S. Lebauer, Michael C. Dietze, Benjamin M. Bolker

The R Journal

The ability to implement statistical models in the BUGS language facilitates Bayesian inference by automating MCMC algorithms. Software packages that interpret the BUGS language include OpenBUGS, WinBUGS, and JAGS. R packages that link BUGS software to the R environment, including rjags and R2WinBUGS, are widely used in Bayesian analysis. Indeed, many packages in the Bayesian task view on CRAN (http://cran.r-project.org/web/views/Bayesian.html) depend on this integration. However, the R and BUGS languages use different representations of common probability density functions, creating a potential for errors to occur in the implementation or interpretation of analyses that use both languages. Here we review different …

Estimating Spatial Probit Models In R, Stefan Wilhelm, Miguel Godinho De Matos

The R Journal

In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance gains through parallelization of the Markov Chain Monte Carlo approach.

Rtexttools: A Supervised Learning Package For Text Classification, Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, Wouter Van Atteveldt

The R Journal

Social scientists have long hand-labeled texts to create datasets useful for studying topics from congressional policymaking to media reporting. Many social scientists have begun to incorporate machine learning into their toolkits. RTextTools was designed to make machine learning accessible by providing a start-to-finish product in less than 10 steps. After installing RTextTools, the initial step is to generate a document term matrix. Second, a container object is created, which holds all the objects needed for further analysis. Third, users can use up to nine algorithms to train their data. Fourth, the data are classified. Fifth, the classification is summarized. …

Editorial, Hadley Wickham

The R Journal

I’m very pleased to published my first issue of the R Journal as editor. As well as this visible indication of my working, I’ve also been hard at work behind the scenes to modernise some of the code that powers the R Journal.

Changes In R, The R Core Team

The R Journal

CHANGES IN R 3.0.1

CHANGES IN R 3.0.0

CHANGES IN R VERSION 2.15.3

Qca: A Package For Qualitative Comparative Analysis, Alrik Thiem, Adrian DuşA

The R Journal

We present QCA, a package for performing Qualitative Comparative Analysis (QCA). QCA is becoming increasingly popular with social scientists, but none of the existing software alternatives covers the full range of core procedures. This gap is now filled by QCA. After a mapping of the method’s diffusion, we introduce some of the package’s main capabilities, including the calibration of crisp and fuzzy sets, the analysis of necessity relations, the construction of truth tables and the derivation of complex,parsimonious and intermediate solutions.

Mpoly: Multivariate Polynomials In R, David Kahle

The R Journal

The mpoly package is a general purpose collection of tools for symbolic computing with multivariate polynomials in R. In addition to basic arithmetic, mpoly can take derivatives of polynomials, compute Gröbner bases of collections of polynomials, and convert polynomials into a functional form to be evaluated. Among other things, it is hoped that mpoly will provide an R-based foundation for the computational needs of algebraic statisticians.

The R User Conference 2013, User 2013 Organising Committee

The R Journal

The ninth R user conference will take place at the University of Castilla-La Mancha, Albacete, Spain from Wednesday 10 July 2013 to Friday 12 July 2013. Following previous useR! conferences, this meeting of the R user community will

• focus on R as the ‘lingua franca’ of data analysis and statistical computing;
• provide a platform for R users to discuss and exchange ideas on how R can be used for statistical computation, data analysis, visualization and exciting applications in various fields;
• give an overview of the new features of the ever evolving R project.