Numerical Analysis and Scientific Computing Commons^™

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.

Ftsa: An R Package For Analyzing Functional Time Series, Han Lin Shang

The R Journal

Recent advances in computer recording and storing technology have tremendously increased the presence of functional data, whose graphical representation can be infinite-dimensional curve, image, or shape. When the same functional object is observed over a period of time, such data are known as functional time series. This article makes first attempt to describe several techniques (centered around functional principal component analysis) for modeling and forecasting functional time series from a computational aspect, using a readily-available R addon package. These methods are demonstrated using age-specific Australian fertility rate data from 1921 to 2006, and monthly sea surface temperature data from January …

An Introduction To The Ecotroph R Package: Analyzing Aquatic Ecosystem Trophic Networks, Mathieu Colléter, Jérôme Guitton, Didier Gascuel

The R Journal

Recent advances in aquatic ecosystem modelling have particularly focused on trophic network analysis through trophodynamic models. We present here a R package devoted to a recently developed model, EcoTroph. This model enables the analysis of aquatic ecological networks and the related impacts of fisheries. It was available through a plug-in in the well-known Ecopath with Ecosim software or through implementations in Excel sheets. The R package we developed simplifies the access to the EcoTroph model and offers a new interfacing between two widely used software, Ecopath and R.

Fast Pure R Implementation Of Gee: Application Of The Matrix Package, Lee S. Mcdaniel, Nicholas C. Henderson, Paul J. Rathouz

The R Journal

Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C …

Rcmdrplugin.Temis, A Graphical Integrated Text Mining Solution In R, Milan Bouchet-Valat, Gilles Bastin

The R Journal

We present the package RcmdrPlugin.temis, a graphical user interface for user-friendly text mining in R. Built as a plug-in to the R Commander provided by the Rcmdr package, it brings together several existing packages and provides new features streamlining the process of importing, managing and analyzing a corpus, in addition to saving results and plots to a report file. Beyond common-file formats, automated import of corpora from the Dow Jones Factiva content provider and Twitter is supported. Featured analyses include vocabulary and dissimilarity tables, terms frequencies, terms specific of levels of a variable, term co-occurrences, time series, correspondence analysis …

Conference Report: R/Finance 2013, Joshua Ulrich

The R Journal

The fifth annual R/Finance conference for applied finance using R was held in Chicago, IL, USAonFriday May17andSaturday May 18, 2013.

The conference provided a venue to discuss how R can be used for portfolio management, time series analysis, advanced risk analysis, high-performance computing, market microstructure, and econometrics. As in prior years, the conference had 300 attendees from several countries (including several European countries, South Africa, Australia, and Russia). The program included seminars, keynotes, full-length talks, and lightning talks. The conference also provided exceptional networking opportunities.

The R Journal (June 2013) 5(1): Complete Issue, The R Foundation

The R Journal

Editorial, Hadley Wickham

Contributed Research Articles

RTextTools: A Supervised Learning Package for Text Classification, Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, and Wouter van Atteveldt

Generalized Simulated Annealing for Global Optimization: The GenSA Package, Yang Xiang, Sylvain Gubian, Brian Suomela, and Julia Hoeng

Multiple Factor Analysis for Contingency Tables in the FactoMineR Package, Belchin Kostov, Mónica Bécue-Bertaut, and François Husson

Hypothesis Tests for Multivariate Linear Models Using the car Package, John Fox, Michael Friendly, and Sanford Weisberg

osmar: OpenStreetMap and R, Manuel J. A. Eugster and Thomas Schlesinger

ftsa: An R Package for Analyzing Functional Time Series, …

Changes On Cran, Kurt Hornik, Achim Zeileis

The R Journal

New CRAN task views

New packages in CRAN task views

New contributed packages

Other changes

Dynamics Of The Fitzhugh-Nagumo Neuron Model, Zechariah Thurman

Physics

In this paper, the dynamical behavior of the Fitzhugh-Nagumo model is examined. The relationship between neuron input current and the firing frequency of the neuron is characterized. Various coupling schemes are also examined, and their effects on the dynamics of the system is discussed. The phenomenon of stochastic resonance is studied for a single uncoupled Fitzhugh-Nagumo neuron.

A Modified Anonymisation Algorithm Towards Reducing Information Loss., Rose Tinabo

Doctoral

The growth of various technologies in the modern digital world results in the col- lection and storage of huge amounts of individual's data. In addition of providing direct services delivery, this data can be used for other non-direct activities known as secondary use. This includes activities such as doing research, analysis, quality and safety measurement, public health, and marketing.

Introducing Programmers To Pair Programming: A Controlled Experiment, A. S. M. Sajeev, Subhajit Datta

Research Collection School Of Computing and Information Systems

Pair programming is a key characteristic of the Extreme Programming (XP) method. Through a controlled experiment we investigate pair programming behaviour of programmers without prior experience in XP. The factors investigated are: (a) characteristics of pair programming that are less favored (b) perceptions of team effectiveness and how they relate to product quality, and (c) whether it is better to train a pair by giving routine tasks first or by giving complex tasks first. Our results show that: (a) the least liked aspects of pair programming were having to share the screen, keyboard and mouse, and having to switch between …

Real Time Event Detection In Twitter, Xun Wang, Feida Zhu, Jing Jiang, Sujian Li

Research Collection School Of Computing and Information Systems

Event detection has been an important task for a long time. When it comes to Twitter, new problems are presented. Twitter data is a huge temporal data flow with much noise and various kinds of topics. Traditional sophisticated methods with a high computational complexity aren’t designed to handle such data flow efficiently. In this paper, we propose a mixture Gaussian model for bursty word extraction in Twitter and then employ a novel time-dependent HDP model for new topic detection. Our model can grasp new events, the location and the time an event becomes bursty promptly and accurately. Experiments show the …

A Latent Variable Model For Viewpoint Discovery From Threaded Forum Posts, Minghui Qiu, Jing Jiang

Research Collection School Of Computing and Information Systems

Threaded discussion forums provide an important social media platform. Its rich user generated content has served as an important source of public feedback. To automatically discover the viewpoints or stances on hot issues from forum threads is an important and useful task. In this paper, we propose a novel latent variable model for viewpoint discovery from threaded forum posts. Our model is a principled generative latent variable model which captures three important factors: viewpoint specific topic preference, user identity and user interactions. Evaluation results show that our model clearly outperforms a number of baseline models in terms of both clustering …

Approximate Inference In Collective Graphical Models, Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich

Research Collection School Of Computing and Information Systems

We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical models, exact inference in CGMs is NP-hard even for tree-structured models. We then develop a tractable convex approximation to the NP-hard MAP inference problem in CGMs, and show how to use MAP inference for approximate marginal inference within the EM framework. We demonstrate empirically that these approximation techniques can reduce the computational cost of inference by two orders …

A Direct Mining Approach To Efficient Constrained Graph Pattern Discovery, Feida Zhu, Zequn Zhang, Qiang Qu

Research Collection School Of Computing and Information Systems

Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complete set of those with constraints still poses a huge challenge to the existing algorithms mainly due to the inherent bottleneck in the mining paradigm. In essence, mining requests with explicitly-specified constraints cannot be handled in a way that is direct and precise. In this paper, we propose a direct mining framework to solve the problem and illustrate our ideas in the context of a particular type of constrained frequent patterns — the “skinny” patterns, which are graph patterns with a long backbone from which …

Mining User Relations From Online Discussions Using Sentiment Analysis And Probabilistic Matrix Factorization, Minghui Qiu, Liu Yang, Jing Jiang

Research Collection School Of Computing and Information Systems

Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. Experiments with two tasks show that the learned latent factor representation can give good performance on a relation polarity prediction task and improve the performance of a subgroup detection task.