Numerical Analysis and Scientific Computing Commons^™

Testthat: Get Started With Testing, Hadley Wickham

The R Journal

Software testing is important, but many of us don’t do it because it is frustrating and boring. testthat is a new testing framework for R that is easy learn and use, and integrates with your existing workflow. This paper shows how, with illustrations from existing packages.

Rworldmap: A New R Package For Mapping Global Data, Andy South

The R Journal

rworldmap is a relatively new package available on CRAN for the mapping and visualisation of global data. The vision is to make the display of global data easier, to facilitate understanding and communication. The initial focus is on data referenced by country or grid due to the frequency of use of such data in global assessments. Tools to link data referenced by country (either name or code) to a map, and then to display the map are provided as are functions to map global gridded data. Country and gridded functions accept the same arguments to specify the nature of categories …

Differential Evolution With Deoptim, David Ardia, Kris Boudt, Peter Carl, Katharine M. Mullen, Brian G. Peterson

The R Journal

The R package DEoptim implements the Differential Evolution algorithm. This algorithm is an evolutionary technique similar to classic genetic algorithms that is useful for the solution of global optimization problems. In this note we provide an introduction to the package and demonstrate its utility for financial applications by solving a non-convex portfolio optimization problem.

Probabilistic Weather Forecasting In R, Chris Fraley, Adrian Raftery, Tilmann Gneiting, Mclean Sloughter, Veronica Berrocol

The R Journal

This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble post-processing via Bayesian model averaging (BMA), and Prob ForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). The model parameters are estimated from training data. The GOP technique uses geostatistical methods to produce probabilistic fore casts of entire weather fields for temperature or pressure, based on a single numerical forecast on …

Parallelizing Scale Invariant Feature Transform On A Distributed Memory Cluster, Stanislav Bobovych

Computer Science and Computer Engineering Undergraduate Honors Theses

Scale Invariant Feature Transform (SIFT) is a computer vision algorithm that is widely-used to extract features from images. We explored accelerating an existing implementation of this algorithm with message passing in order to analyze large data sets. We successfully tested two approaches to data decomposition in order to parallelize SIFT on a distributed memory cluster.

The R Journal (December 2010) 2(2): Complete Issue, The R Foundation

The R Journal

Contributed Research Articles

Solving Differential Equations in R, Karline Soetaert, Thomas Petzoldt and R. Woodrow Setzer

Source References, Duncan Murdoch

hglm: A Package for Fitting Hierarchical Generalized Linear Models, Lars Rönnegård, Xia Shen and Moudud Alam

dclone: Data Cloning in R, Péter Sólymos

stringr: Modern, Consistent String Processing, Hadley Wickham

Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations, David Ardia and Lennart F. Hoogerheide

cudaBayesreg: Bayesian Computation in CUDA, Adelino Ferreira da Silva

binGroup: A Package for Group Testing, Christopher R. Bilder, Boan Zhang, Frank Schaarschmidt, and Joshua M. Tebbs

The RecordLinkage Package: Detecting Errors in Data, Murat Sariyar …

The Recordlinkage Package: Detecting Errors In Data, Murat Sariyar, Andreas Borg

The R Journal

Record linkage deals with detecting homonyms and mainly synonyms in data. The package RecordLinkage provides means to per form and evaluate different record linkage methods. A stochastic framework is implemented which calculates weights through an EM algorithm. The determination of the necessary thresholds in this model can be achieved by tools of extreme value theory. Furthermore, machine learning methods are utilized, including decision trees (rpart), bootstrap aggregating (bagging), ada boost (ada), neural nets (nnet) and support vector machines (svm). The generation of record pairs and comparison patterns from single data …

Bingroup: A Package For Group Testing, Christopher R. Bilder, Boan Zhang, Frank Schaarschmidt, Joshua M. Tebbs

The R Journal

When the prevalence of a disease or of some other binary characteristic is small, group testing (also known as pooled testing) is frequently used to estimate the prevalence and/or to identify individuals as positive or negative. We have developed the binGroup package as the first package designed to address the estimation problem in group testing. We present functions to estimate an overall prevalence for a homogeneous population. Also, for this set ting, we have functions to aid in the very important choice of the group size. When individuals come from a heterogeneous population, our group testing regression functions can be …

Stringr: Modern, Consistent String Processing, Hadley Wickham

The R Journal

String processing is not glamorous, but it is frequently used in data cleaning and preparation. The existing string functions in R are powerful, but not friendly. To remedy this, the stringr package provides string functions that are simpler and more consistent, and also fixes some functionality that R is missing compared to other programming languages.

Source References, Duncan Murdoch

The R Journal

Since version 2.10.0, R includes expanded support for source references in R code and ‘.Rd’ files. This paper describes the origin and purposes of source references, and current and future support for them.

Mapping And Measuring Country Shapes, Nils B. Weidmann, Kristian Skrede Gleditsch

The R Journal

The article introduces the cshapes R package, which includes our CShapes dataset of contemporary and historical country boundaries, as well as computational tools for computing geographical measures from these maps. We provide an overview of the need for considering spatial dependence in comparative re search, how this requires appropriate historical maps, and detail how the cshapes associated R package cshapes can contribute to these ends. We illustrate the use of the package for drawing maps, computing spatial variables for countries, and generating weights matrices for spatial statistics.

Dclone: Data Cloning In R, Péter Sólymos

The R Journal

The dclone R package contains low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov Chain Monte Carlo methods with support for JAGS, WinBUGS and OpenBUGS.

Solving Differential Equations In R, Karline Soetaert, Thomas Petzoldt, R. Woodrow Setzer

The R Journal

Although R is still predominantly applied for statistical analysis and graphical representation, it is rapidly becoming more suitable for mathematical computing. One of the fields where considerable progress has been made recently is the solution of differential equations. Here we give a brief overview of differential equations that can now be solved by R.

Spikeslab: Prediction And Variable Selection Using Spike And Slab Regression, Hemant Ishwaran, Udaya B. Kogalur, J. Sunil Rao

The R Journal

Weighted generalized ridge regression offers unique advantages in correlated high dimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used in tandem with these Bayesian estimates. In this article, we de scribe the R-software package spikeslab for implementing this new spike and slab prediction and variable selection methodology.

Hglm: A Package For Fitting Hierarchical Generalized Linear Models, Lars Rönnegård, Xia Shen, Moudud Alam

The R Journal

We present the hglm package for fit ting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Bayesian Estimation Of The Garch(1,1) Model With Student-T Innovations, David Ardia, Lennart F. Hoogerheide

The R Journal

This note presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning an MCMC sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns

Online Reproducible Research: An Application To Multivariate Analysis Of Bacterial Dna Fingerprint Data, Jean Thioulouse, Claire Valiente-Moro, Lionel Zenner

The R Journal

This paper presents an example of online reproducible multivariate data analysis. This example is based on a web page providing an online computing facility on a server. HTML forms contain editable R code snippets that can be executed in any web browser thanks to the Rweb software. The example is based on the multivariate analysis of DNA fingerprints of the internal bacterial flora of the poultry red mite Dermanyssus gallinae. Several multivariate data analysis methods from the ade4 package are used to compare the fingerprints of mite pools coming from various poultry farms. All the computations and graphical displays …

Cudabayesreg: Bayesian Computation In Cuda, Adelino Ferreira Da Silva

The R Journal

Graphical processing units are rapidly gaining maturity as powerful general parallel computing devices. The package cudaBayesreg uses GPU–oriented procedures to improve the performance of Bayesian computations. The paper motivates the need for devising high performance computing strategies in the con text of fMRI data analysis. Some features of the package for Bayesian analysis of brain fMRI data are illustrated. Comparative computing performance figures between sequential and parallel implementations are presented as well.

The R Journal (June 2010) 2(1): Complete Issue, The R Foundation

The R Journal

Contributed Research Articles

IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments, Setia Pramana, Dan Lin, Philippe Haldermans, Ziv Shkedy, Tobias Verbeke, Hinrich Göhlmann, An De Bondt, Willem Talloen, and Luc Bijnens

MCMC for Generalized Linear Mixed Models with glmmBUGS, Patrick Brown and Lutong Zhou

Mapping and Measuring Country Shapes, Nils B. Weidmann and Kristian Skrede Gleditsch

tmvtnorm: A Package for the Truncated Multivariate Normal Distribution, Stefan Wilhelm and B. G. Manjunath

neuralnet: Training of Neural Networks, Frauke Günther and Stefan Fritsch

glmperm: A Permutation of Regressor Residuals Test for Inference in Generalized Linear Models, Wiebke Werft and …

Isogene: An R Package For Analyzing Dose-Response Studies In Microarray Experiments, Setia Pramana, Dan Lin, Philippe Haldermans, Ziv Shkedy, Tobias Verbeke, Hinrich Göhlmann, An De Bondt, Williem Talloen, Luc Bijnens

The R Journal

IsoGene is an R package for the analysis of dose-response microarray experiments to identify gene or subsets of genes with a mono tone relationship between the gene expression and the doses. Several testing procedures (i.e., the likelihood ratio test, Williams, Marcus, the M, and Modified M), that take into account the order restriction of the means with respect to the increasing doses are implemented in the package. The inference is based on resampling methods, both permutations and the Significance Analysis of Microarrays (SAM).

Two-Sided Exact Tests And Matching Confidence Intervals For Discrete Data, Michael P. Fay

The R Journal

There is an inherent relationship between two-sided hypothesis tests and confidence intervals. A series of two-sided hypothesis tests may be inverted to obtain the matching 100(1-)% confidence interval defined as the smallest interval that contains all point null parameter values that would not be rejected at the α level. Unfortunately, for discrete data there are several different ways of defining two-sided exact tests and the most commonly used two sided exact tests are defined one way, while the most commonly used exact confidence intervals are inversions of tests defined another way. This can lead to inconsistencies where the exact test …

Neuralnet: Training Of Neural Networks, Franke Günther, Stefan Fritsch

The R Journal

Artificial neural networks are applied in many situations. neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables. Thus, neural networks are used as extensions of generalized linear models. neuralnet is a very flexible package. The back propagation algorithm and three versions of resilient back-propagation are implemented and it provides a custom-choice of activation and error function. An arbitrary number of covariates and response variables as well as of hidden layers can theoretically be included. The paper gives a brief introduction to multi-layer perceptrons and resilient back-propagation …

Mcmc For Generalized Linear Mixed Models With Glmmbugs, Patrick Brown, Lutong Zhou

The R Journal

The glmmBUGS package is a bridging tool between Generalized Linear Mixed Models (GLMMs) in R and the BUGS language. It provides a simple way of performing Bayesian inference using Markov Chain Monte Carlo (MCMC) methods, taking a model formula and data frame in R and writing a BUGS model file, data file, and initial values files. Functions are provided to reformat and summarize the BUGS results. A key aim of the package is to provide files and objects that can be modified prior to calling BUGS, giving users a platform for customizing and extending the models to accommodate a wide …

Glmperm: A Permutation Of Regressor Residuals Test For Inference In Generalized Linear Models, Wiebke Werft, Axel Benner

The R Journal

We introduce a new R package called glmperm for inference in generalized linear models especially for small and moderate-sized data sets. The inference is based on the per mutation of regressor residuals test introduced by Potter (2005). The implementation of glmperm outperforms currently available permutation test software as glmperm can be applied in situations where more than one covariate is involved.

Tmvtnorm: A Package For The Truncated Multivariate Normal Distribution, Stefan Wilhelm, B. G. Manjunath

The R Journal

In this article we present tmvtnorm, an R package implementation for the truncated multivariate normal distribution. We consider random number generation with rejection and Gibbs sampling, computation of marginal densities as well as computation of the mean and co variance of the truncated variables. This contribution brings together latest research in this field and provides useful methods for both scholars and practitioners when working with truncated normal variables.

Sos: Searching Help Pages Of R Packages, Spencer Graves, Sundar Dorai-Raj, Romain François

The R Journal

The sos package provides a means to quickly and flexibly search the help pages of contributed packages, finding functions and datasets in seconds or minutes that could not be found in hours or days by any other means we know. Its findFn function accesses Jonathan Baron’s R Site Search database and returns the matches in a data frame of class "findFn", which can be further manipulated by other sos functions to produce, for example, an Excel file that starts with a summary sheet that makes it relatively easy to prioritize alternative packages for further study. As such, it provides a …

Rattle: A Data Mining Gui For R, Graham J. Williams

The R Journal

Data mining delivers insights, pat terns, and descriptive and predictive models from the large amounts of data available today in many organisations. The data miner draws heavily on methodologies, techniques and algorithms from statistics, machine learning, and computer science. R increasingly provides a powerful platform for data mining. However, scripting and programming is sometimes a challenge for data analysts moving into data mining. The Rattle package provides a graphical user interface specifically for data mining using R. It also provides a stepping stone toward using R as a programming language for data analysis.

Copas: An R Package For Fitting The Copas Selection Model, J. Carpenter, G. Rücker, G. Schhwarzer

The R Journal

This article describes the R package copas which is an add-on package to the R pack age meta. The R package copas can be used to f it the Copas selection model to adjust for bias in meta-analysis. A clinical example is used to illustrate fitting and interpreting the Copas selection model.

Party On!, Carolin Strobl, Torsten Hothorn, Achim Zeileis

The R Journal

Random forests are one of the most popular statistical learning algorithms, and a variety of methods for fitting random forests and related recursive partitioning approaches is available in R. This paper points out two important features of the random forest implementation cforest available in the party package: The resulting forests are unbiased and thus prefer able to the randomForest implementation avail able in randomForest if predictor variables are of different types. Moreover, a conditional per mutation importance measure has recently been added to the party package, which can help evaluate the importance of correlated predictor variables. The rationale of this …

Aspects Of The Social Organization And Trajectory Of The R Project, John Fox

The R Journal

Based partly on interviews with members of the R Core team, this paper considers the development of the R Project in the context of open-source software development and, more generally, voluntary activities. The paper de scribes aspects of the social organization of the R Project, including the organization of the R Core team; describes the trajectory of the R Project; seeks to identify factors crucial to the success of R; and speculates about the prospects for R.