Computer Sciences Commons^™

Changes On Cran, Kurt Hornik, Achim Zeileis

The R Journal

New packages in CRAN task views

New contributed packages

Other changes

Network And Energy-Aware Resource Selection Model For Opportunistic Grids, Izaias Faria, Mario Dantas, Miriam A M Capretz, Wilson Higashino

Electrical and Computer Engineering Publications

Due to increasing hardware capacity, computing grids have been handling and processing more data. This has led to higher amount of energy being consumed by grids; hence the necessity for strategies to reduce their energy consumption. Scheduling is a process carried out to define in which node tasks will be executed in the grid. This process can significantly impact the global system performance, including energy consumption. This paper focuses on a scheduling model for opportunistic grids that considers network traffic, distance between input files and execution node as well as the execution node status. The model was tested in a …

Query Analyzer And Manager For Complex Event Processing As A Service, Wilson Higashino, Cedric Eichler, Miriam A M Capretz, Thierry Monteil, M. Beatriz F. Toledo, Patricia Stolf

Electrical and Computer Engineering Publications

Complex Event Processing (CEP) is a set of tools and techniques that can be used to obtain insights from high-volume, high-velocity continuous streams of events. CEP-based systems have been adopted in many situations that require prompt establishment of system diagnostics and execution of reaction plans, such as in monitoring of complex systems. This article describes the Query Analyzer and Manager (QAM) module, a first effort toward the development of a CEP as a Service (CEPaaS) system. This module is responsible for analyzing user-defined CEP queries and for managing their execution in distributed cloud-based environments. Using a language-agnostic internal query representation, …

Evaluation Of Particle Swarm Optimization Applied To Grid Scheduling, Wilson Higashino, Miriam A M Capretz, M. Beatriz F. Toledo

Electrical and Computer Engineering Publications

The problem of scheduling independent users’ jobs to resources in Grid Computing systems is of paramount importance. This problem is known to be NP-hard, and many techniques have been proposed to solve it, such as heuristics, genetic algorithms (GA), and, more recently, particle swarm optimization (PSO). This article aims to use PSO to solve grid scheduling problems, and compare it with other techniques. It is shown that many often-overlooked implementation details can have a huge impact on the performance of the method. In addition, experiments also show that the PSO has a tendency to stagnate around local minima in high-dimensional …

Service Evolution Patterns, Shuying Wang, Wilson Higashino, Michael Hayes, Miriam A M Capretz

Electrical and Computer Engineering Publications

Service evolution is the process of maintaining and evolving existing Web services to cater for new requirements and technological changes. In this paper, a service evolution model is proposed to analyze service dependencies, identify changes on services and estimate impact on consumers that will use new versions of these services. Based on the proposed service evolution model, four service evolution patterns are described: compatibility, transition, split-map, and merge-map. These proposed patterns provide reusable templates to encourage well-defined service evolution while minimizing issues that arise otherwise. They can be applied in the service evolution scenario where a single service is used …

Contextual Anomaly Detection In Big Sensor Data, Michael Hayes, Miriam A M Capretz

Electrical and Computer Engineering Publications

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection …

Graph-Regularized Dual Lasso For Robust Eqtl Mapping, Wei Cheng, Xiang Zhang, Zhishan Guo, Yu Shi, Wei Wang

Computer Science Faculty Research & Creative Works

Motivation: As a promising tool for dissecting the genetic basis of complex traits, expression quantitative trait loci (eQTL) mapping has attracted increasing research interest. An important issue in eQTL mapping is how to effectively integrate networks representing interactions among genetic markers and genes. Recently, several Lasso-based methods have been proposed to leverage such network information. Despite their success, existing methods have three common limitations: (i) a preprocessing step is usually needed to cluster the networks; (ii) the incompleteness of the networks and the noise in them are not considered; (iii) other available information, such as location of genetic markers and …

Recommendation Support For Multi-Attribute Databases, Jilian Zhang

Dissertations and Theses Collection (Open Access)

This dissertation studies the subject of providing recommendation support for multi-attribute databases. Recommendation is an important and very useful information evaluation mechanism that explores a database of huge volume, and retrieves from it the interesting data items (tuples) for users based on their preferences.

The R Journal (June 2014) 6(1): Complete Issue, The R Foundation

The R Journal

Editorial, Deepayan Sarkar

Contributed Research Articles

Taming PITCHf/x Data with XML2R and pitchRx, Carson Sievert

A Multiscale Test of Spatial Stationarity for Textured Images in R, Matthew A. Nunes, Sarah L. Taylor, and Idris A. Eckley

Stratified Weibull Regression Model for Interval-Censored Data, Xiangdong Gu, David Shapiro, Michael D. Hughes, and Raji Balasubramanian

brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data, John Muschelli, Elizabeth Sweeney, and Ciprian Crainiceanu

The RWiener Package: an R Package Providing Distribution Functions for the Wiener Diffusion Model, Dominik Wabersich, and Joachim Vandekerckhove

PivotalR: A Package for Machine Learning on Big Data, Hai …

R Foundation News, Kurt Hornik

The R Journal

Donations and new members

• Donations
• New benefactors
• New supporting institutions
• New supporting members

Web Technologies Task View, Patrick Mair, Scott Chamberlain

The R Journal

This article presents the CRAN Task View on Web Technologies. We describe the most important aspects of Web Technologies and Web Scraping and list some of the packages that are currently available on CRAN. Finally, we plot the network of Web Technology related package dependencies.

Oligomask: A Framework For Assessing And Removing The Effect Of Genetic Variants On Microarray Probes, Daniel Bottomly, Beth Wilmot, Shannon K. Mcweeney

The R Journal

As expression microarrays are typically designed relative to a reference genome, any individual genetic variant that overlaps a probe’s genomic position can possibly cause a reduction in hybridization due to the probe no longer being a perfect match to a given sample’s mRNA at that locus. If the samples or groups used in a microarray study differ in terms of genetic variants, the results of the microarray experiment can be negatively impacted. The oligoMask package is an R/SQLite framework which can utilize publicly available genetic variants and works in conjunction with the oligo package to read in the expression data …

Archiving Reproducible Research With R And Dataverse, Thomas J. Leeper

The R Journal

Reproducible research and data archiving are increasingly important issues in research involving statistical analyses of quantitative data. This article introduces the dvn package, which allows R users to publicly archive datasets, analysis files, codebooks, and associated metadata in Dataverse Network online repositories, an open-source data archiving project sponsored by Harvard University. In this article I review the importance of data archiving in the context of reproducible research, introduce the Dataverse Network, explain the implementation of the dvn package, and provide example code for archiving and releasing data using the package

Investr: An R Package For Inverse Estimation, Brandon M. Greenwell, Christine M. Schubert Kabban

The R Journal

Inverse estimation is a classical and well-known problem in regression. In simple terms, it involves the use of an observed value of the response to make inference on the corresponding unknown value of the explanatory variable. To our knowledge, however, statistical software is somewhat lacking the capabilities for analyzing these types of problems. In this paper, we introduce investr (which stands for inverse estimation in R), a package for solving inverse estimation problems in both linear and nonlinear regression models.1

Rotations: An R Package For So(3) Data, Bryan Stanfill, Heike Hofmann, Ulrike Genschel

The R Journal

In this article we introduce the rotations package which provides users with the ability to simulate, analyze and visualize three-dimensional rotation data. More specifically it includes four commonly used distributions from which to simulate data, four estimators of the central orientation, six confidence region estimation procedures and two approaches to visualizing rotation data. All of these features are available for two different parameterizations of rotations: three-by-three matrices and quaternions. In addition, two datasets are included that illustrate the use of rotation data in practice

Brainr: Interactive 3 And 4d Images Of High Resolution Neuroimage Data, John Muscelli, Elizabeth Sweeney, Ciprian Crainiceanu

The R Journal

We provide software tools for displaying and publishing interactive 3-dimensional (3D) and 4-dimensional (4D) figures to html webpages, with examples of high-resolution brain imaging. Our framework is based in the R statistical software using the rgl package, a 3D graphics library. We build on this package to allow manipulation of figures including rotation and translation, zooming, coloring of brain substructures, adjusting transparency levels, and addition/or removal of brain structures. The need for better visualization tools of ultra high dimensional data is ever present; we are providing a clean, simple, web-based option. We also provide a package (brainR) for …

The Stringdist Package For Approximate String Matching, Mark P.J. Van Der Loo

The R Journal

Comparing text strings in terms of distance functions is a common and fundamental task in many statistical text-processing applications. Thus far, string distance functionality has been somewhat scattered around R and its extension packages, leaving users with inconistent interfaces and encoding handling. The stringdist package was designed to offer a low-level interface to several popular string distance algorithms which have been re-implemented in C for this purpose. The package offers distances based on counting q-grams, edit-based distances, and some lesser known heuristic distance functions. Based on this functionality, the package also offers inexact matching equivalents of R’s native exact matching …

Sgr: A Package For Simulating Conditional Fake Ordinal Data, Luigi Lombardi, Massimiliano Pastore

The R Journal

Many self-report measures of attitudes, beliefs, personality, and pathology include items that can be easily manipulated by respondents. For example, an individual may deliberately attempt to manipulate or distort responses to simulate grossly exaggerated physical or psychological symptoms in order to reach specific goals such as, for example, obtaining financial compensation, avoiding being charged with a crime, avoiding military duty, or obtaining drugs. This article introduces the package sgr that can be used to perform fake data analysis according to the sample generation by replacement approach. The package includes functions for making simple inferences about discrete/ordinal fake data. The package …

The Gridsvg Package, Paul Murrell, Simon Potter

The R Journal

The gridSVG package can be used to generate a grid-based R plot in an SVG format, with the ability to add special effects to the plot. The special effects include animation, interactivity, and advanced graphical features, such as masks and filters. This article provides a basic introduction to important functions in the gridSVG package and discusses the advantages and disadvantages of gridSVG compared to similar R packages.

News From The Bioconductor Project, Bioconductor Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high throughput genomic data. The 824 software packages available in Bioconductor can be viewed at http://bioconductor.org/packages/release/. Navigate packages using ‘biocViews’ terms and title search. Each package has an html page with a description, links to vignettes, reference manuals, and usage statistics. Start using Bioconductor and R version 3.1.0 with

Rstorm: Developing And Testing Streaming Algorithms In R, Maurits Kaptein

The R Journal

Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This paper presents RStorm for the development and evaluation of streaming algorithms analogous to these production packages, but implemented fully in R. RStorm allows developers of streaming algorithms to quickly test, iterate, and evaluate various implementations of streaming algorithms. The paper provides both a canonical computer science example, …

A Multiscale Test Of Spatial Stationarity For Textured Images In R, Matthew A. Nunes, Sarah L. Taylor, Idris A. Eckley

The R Journal

The ability to automatically identify areas of homogeneous texture present within a greyscale image is an important feature of image processing algorithms. This article describes the R package LS2W stat which employs a recent wavelet-based test of stationarity for locally stationary random fields to assess such spatial homogeneity. By embedding this test within a quadtree image segmentation procedure we are also able to identify texture regions within an image.

Editorial, Deepayan Sarkar

The R Journal

Onbehalf of the editorial board, I am pleased to publish Volume 6, Issue 1 of the R Journal.

Rankcluster: An R Package For Clustering Multivariate Partial Rankings, Julien Jacques, Quentin Grimonprez, Christophe Biernacki

The R Journal

The Rankcluster package is the first R package proposing both modeling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modeled by the Insertion Sorting Rank (ISR) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows multivariate rankings to be taken into account, and clustering is performed by means of mixtures of multivariate ISR models. The parameters of the cluster (central rankings and dispersion parameters) help the practitioners to interpret the clustering. Moreover, the Rankcluster package provides an estimate of the missing ranking positions when …

Addendum To “Statistical Software From A Blind Person's Perspective'”, A. Johnathan, R. Godfrey, Robert Erhardt

The R Journal

This short note explains a solution to a problem for blind users when using the R terminal under Windows Vista or Windows 7, as identified in Godfrey (2013). We note the way the solution was discovered and subsequent confirmatory experiments.

Pivotalr: A Package For Machine Learning On Big Data, Hai Qian

The R Journal

PivotalR is an R package that provides a front-end to PostgreSQL and all PostgreSQL like databases such as Pivotal Inc.’s Greenplum Database (GPDB), HAWQ. When running on the products of Pivotal Inc., PivotalR utilizes the full power of parallel computation and distributive storage, and thus gives the normal R user access to big data. PivotalR also provides an R wrapper for MADlib. MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine-learning algorithms for structured and unstructured data. Thus PivotalR also enables the user to apply machine learning algorithms on big data.

Rose: A Package For Binary Imbalanced Learning, Nicola Lunardon, Giovanna Menardi, Nicola Torelli

The R Journal

The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies for the class imbalance and different metrics to evaluate accuracy are also provided. These are estimated by holdout, bootstrap, or cross-validation methods.

Stratified Weibull Regression Model For Interval-Censored Data, Xiangdong Gu, David Shapiro, Michael D. Hughes, Raji Balasubramanian

The R Journal

Interval censored outcomes arise when a silent event of interest is known to have occurred within a specific time period determined by the times of the last negative and first positive diagnostic tests. There is a rich literature on parametric and non-parametric approaches for the analysis of interval-censored outcomes. A commonly used strategy is to use a proportional hazards (PH) model with the baseline hazard function parameterized. The proportional hazards assumption can be relaxed in stratified models by allowing the baseline hazard function to vary across strata defined by a subset of explanatory variables. In this paper, we describe and …

Mrcv: A Package For Analyzing Categorical Variables With Multiple Response Options, Natalie A. Koziol, Christopher R. Bilder

The R Journal

Multiple response categorical variables (MRCVs), also known as “pick any” or “choose all that apply” variables, summarize survey questions for which respondents are allowed to select more than one category response option. Traditional methods for analyzing the association between categorical variables are not appropriate with MRCVs due to the within-subject dependence among responses. We have developed the MRCV package as the first R package available to correctly analyze MRCVdata. Statistical methods offered by our package include counterparts to traditional Pearson chi-square tests for independence and loglinear models, where bootstrap methods and Rao-Scott adjustments are relied on to obtain valid inferences. …

Changes In R, The R Core Team

The R Journal

CHANGES IN R 3.1.1

CHANGES IN R 3.1.0

CHANGES IN R 3.0.3