Numerical Analysis and Scientific Computing Commons^™

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

The Rwiener Package: An R Package Providing Distribution Functions For The Wiener Diffusion Model, Dominik Wabersich, Joachim Vandekerckhove

The R Journal

We present the RWiener package that provides R functions for the Wiener diffusion model. The core of the package are the four distribution functions dwiener, pwiener, qwiener and rwiener, which use up-to-date methods, implemented in C, and provide fast and accurate computation of the density, distribution, and quantile function, as well as a random number generator for the Wiener diffusion model. We used the typical Wiener diffusion model with four parameters: boundary separation, non-decision time, initial bias and drift rate parameter. Beyond the distribution functions, weprovide extended likelihood-based functions that can be used for parameter estimation and model selection. The …

The R Journal (December 2013) 5(2): Complete Issue, The R Foundation

The R Journal

Editorial, Hadley Wickham

Contributed Research Articles

factorplot: Improving Presentation of Simple Contrasts in Generalized Linear Models, David A. Armstrong II

spMC: Modelling Spatial Random Fields with Continuous Lag Markov Chains, Luca Sartore

RNetCDF: A Package for Reading and Writing NetCDF Datasets, Pavel Michna and Milton Woods

Surface Melting Curve Analysis with R, Stefan Rödiger, Alexander Böhm and Ingolf Schimke

Performance Attribution for Equity Portfolios, Yang Lu and David Kane

ExactCIdiff: An R Package for Computing Exact Confidence Intervals for the Difference of Two Proportions, Guogen Shan and Weizhen Wang

rlme: An R Package for Rank-Based Estimation and Prediction in Random …

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

Conference Report: Deuxièmes Rencontres R, Aurelie Siberchicot, Stephane Dray

The R Journal

Following the success of the first "Rencontres R" (Bordeaux, 2-3 July 2012, http://r2012. bordeaux.inria.fr/), the second meeting was held in Lyon on 27-28 June 2013. This French speaking conference was a great success with 216 participants (110 were present at the first conference). The aim of the meeting was to provide a national forum for the exchange and sharing of ideas on the use of R in different disciplines. The number of participants and the list of sponsors (http://r2013-lyon.sciencesconf.org/resource/sponsors) demonstrate the increasing impact of R in both industry and academia in France. The program, detailed below, consisted of plenary sessions, …

News From The Bioconductor Project, Bioconductor Team

The R Journal

Bioconductor 2.13 was released on 15 October2013. It is compatible with R3.0.2, and consists of 749 software packages, 179 experiment data packages, and more than 690 up-to-date annotation packages. The release includes 84 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_13_release/

Complognormal: An R Package For Composite Lognormal Distributions, S. Nadarajah, S. A.A. Bakar

The R Journal

In recent years, composite models based on the lognormal distribution have become popular in actuarial sciences and related areas. In this short note, we present a new R package for computing the probability density function, cumulative density function, and quantile function, and for generating random numbersof anycomposite model based on the lognormal distribution. The use of the package is illustrated using a real data set.

Dynamic Parallelization Of R Functions, Stefan Böhringer

The R Journal

R offers several extension packages that allow it to perform parallel computations. These operate on fixed points in the program flow and make it difficult to deal with nested parallelism and to organize parallelism in complex computations in general. In this article we discuss, first, of how to detect parallelism in functions, and second, how to minimize user intervention in that process. We present a solution that requires minimal code changes and enables to flexibly and dynamically choose the degree of parallelization in the resulting computation. An implementation is provided by the R package parallelize.dynamic and practical issues are discussed …

Surface Melting Curve Analysis With R, Stefan Rödiger, Alexander Böhm, Ingolf Schimke

The R Journal

Nucleic acid Melting Curve Analysis is a powerful method to investigate the interaction of double stranded nucleic acids. Many researchers rely on closed source software which is not ubiquitously available, and gives only little control over the computation and data presentation. R in contrast, is open source, highly adaptable and provides numerous utilities for data import, sophisticated statistical analysis and presentation in publication quality. This article covers methods, implemented in the MBmca package, for DNA Melting Curve Analysis on microbead surfaces. Particularly, the use of the second derivative melting peaks is suggested as an additional parameter to characterize the melting …

Factorplot: Improving Presentation Of Simple Contrasts In Generalized Linear Models, David A. Armstrong Ii

The R Journal

Recent statistical literature has paid attention to the presentation of pairwise comparisons either from the point of view of the reference category problem in generalized linear models (GLMs) or in terms of multiple comparisons. Both schools of thought are interested in the parsimonious presentation of sufficient information to enable readers to evaluate the significance of contrasts resulting from the inclusion of qualitative variables in GLMs. These comparisons also arise when trying to interpret multinomial models where one category of the dependent variable is omitted as a reference. While considerable advances have been made, opportunities remain to improve the presentation of …

Changes In R, The R Core Team

The R Journal

CHANGES IN R 3.0.2

R Foundation News, Kurt Hornik

The R Journal

Donations and new members

• Donations
• New supporting institutions
• New supporting members

The R In Robotics, André Dietrich, Sebastian Zug, Jörg Kaiser

The R Journal

The aim of this contribution is to connect two previously separated worlds: robotic application development with the Robot Operating System (ROS) and statistical programming with R. This fruitful combination becomes apparent especially in the analysis and visualization of sensory data. We therefore introduce a new language extension for ROS that allows to implement nodes in pure R. All relevant aspects are described in a step-by-step development of a common sensor data transformation node. This includes the reception of raw sensory data via the ROS network, message interpretation, bag-file analysis, transformation and visualization, as well as the transmission of newly generated …

Spmc: Modelling Spatial Random Fields With Continuous Lag Markov Chains, Luca Sartore

The R Journal

Currently, a part of the R statistical software is developed in order to deal with spatial models. More specifically, some available packages allow the user to analyse categorical spatial random patterns. However, only the spMC package considers a viewpoint based on transition probabilities between locations. Through the use of this package it is possible to analyse the spatial variability of data, make inference, predict and simulate the categorical classes in unobserved sites. An example is presented by analysing the well-known Swiss Jura data set.

Rlme: An R Package For Rank-Based Estimation And Prediction In Random Effects Nested Models, Yusuf K. Bilgic, Herbert Susmann

The R Journal

There is a lack of robust statistical analyses for random effects linear models. In practice, statistical analyses, including estimation, prediction and inference, are not reliable when data are unbalanced, of small size, contain outliers, or not normally distributed. It is fortunate that rank-based regression analysis is a robust nonparametric alternative to likelihood and least squares analysis. We propose an R package that calculates rank-based statistical analyses for two- and three-level random effects nested designs. In this package, a new algorithm which recursively obtains robust predictions for both scale and random effects is used, along with three rank-based fitting methods.

Performance Attribution For Equity Portfolios, Yang Lu, David Kane

The R Journal

The pa package provides tools for conducting performance attribution for long-only, single currency equity portfolios. The package uses two methods: the Brinson-Hood-Beebower model (hereafter referred to as the Brinson model) and a regression-based analysis. The Brinson model takes an ANOVA-type approach and decomposes the active return of any portfolio into asset allocation, stock selection, and interaction effect. The regression-based analysis utilizes estimated coefficients, based on a regression model, to attribute active return to different factors.

Temporal Disaggregation Of Time Series, Christoph Sax, Peter Steiner

The R Journal

Temporal disaggregation methods are used to disaggregate low frequency time series to higher frequency series, where either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. The package tempdisagg is a collection of several methods for temporal disaggregation.

On Sampling From The Multivariate T Distribution, Marius Hofert

The R Journal

The multivariate normal and the multivariate t distributions belong to the most widely used multivariate distributions in statistics, quantitative risk management, and insurance. In contrast to the multivariate normal distribution, the parameterization of the multivariate t distribution does not correspond to its moments. This, paired with a non-standard implementation in the R package mvtnorm, provides traps for working with the multivariate t distribution. In this paper, common traps are clarified and corresponding recent changes to mvtnorm are presented.

Editorial, Hadley Wickham

The R Journal

Welcome to volume 5, issue 2 of The R Journal. I’m very pleased to include 21 articles about R for your enjoyment.

The end of the year also brings changes to the editorial board. Martyn Plummer is leaving the board after four years. Martyn was responsible for writing up the standard operating procedures for the journal, an act which has made my life as a new editor considerably easier! We welcome Michael Lawrence, who will join the editorial board in 2014. I am stepping down as Editor-in-Chief and will be leaving this task in the capable hands of Deepayan Sarkar.

Rnetcdf: A Package For Reading And Writing Netcdf Datasets, Pavel Michna, Milton Woods

The R Journal

This paper describes the RNetCDF package (version 1.6), an interface for reading and writing files in Unidata NetCDF format, and gives an introduction to the NetCDF file format. NetCDF is a machine independent binary file format which allows storage of different types of array based data, along with short metadata descriptions. The package presented here allows access to the most important functions of the NetCDF C-interface for reading, writing, and modifying NetCDF datasets. In this paper, we present a short overview on the NetCDF file format and show usage examples of the package.

Betategarch: Simulation, Estimation And Forecasting Of Beta-Skew-T-Egarch Models, Genaro Sucarrat

The R Journal

This paper illustrates the usage of the betategarch package, a package for the simulation, estimation and forecasting of Beta-Skew-t-EGARCH models. The Beta-Skew-t-EGARCH model is a dynamic model of the scale or volatility of financial returns. The model is characterised by its robustness to jumps or outliers, and by its exponential specification of volatility. The latter enables richer dynamics, since parameters need not be restricted to be positive to ensure positivity of volatility. In addition, the model also allows for heavy tails and skewness in the conditional return (i.e. scaled return), and for leverage and a time-varying long-term component in the …

Lfe: Linear Group Fixed Effects, Simen Gaure

The R Journal

Linear models with fixed effects and many dummy variables are common in some fields. Such models are straightforward to estimate unless the factors have too many levels. The R package lfe solves this problem by implementing a generalization of the within transformation to multiple factors, tailored for large problems.

Changes To Grid For R 3.0.0, Paul Murrell

The R Journal

From R 3.0.0, there is a new recommended way to develop new grob classes in grid. In a nutshell, two new “hook” functions, makeContext() and makeContent() have been added to grid to provide an alternative to the existing hook functions preDrawDetails(), drawDetails(), and postDrawDetails(). There is also a new function called grid.force(). This article discusses why these changes have been made, provides a simple demonstration of the use of the new functions, and discusses some of the implications for packages that build on grid.

Exactcidiff: An R Package For Computing Exact Confidence Intervals For The Difference Of Two Proportions, Guogen Shan, Weizhen Wang

The R Journal

Comparing two proportions through the difference is a basic problem in statistics and has applications in many fields. More than twenty confidence intervals (Newcombe, 1998a,b) have been proposed. Most of them are approximate intervals with an asymptotic infimum coverage probability much less than the nominal level. In addition, large sample may be costly in practice. So exact optimal confidence intervals become critical for drawing valid statistical inference with accuracy and precision. Recently, Wang (2010, 2012) derived the exact smallest (optimal) one-sided 1 confidence intervals for the difference of two paired or independent proportions. His intervals, however, are computer-intensive by nature. …

Pin: Measuring Asymmetric Information In Financial Markets With R, Paolo Zagaglia

The R Journal

The package PIN computes a measure of asymmetric information in financial markets, the so-called probability of informed trading. This is obtained from a sequential trade model and is used to study the determinants of an asset price. Since the probability of informed trading depends on the number of buy- and sell-initiated trades during a trading day, this paper discusses the entire modelling cycle, from data handling to the computation of the probability of informed trading and the estimation of parameters for the underlying theoretical model.

Statistical Software From A Blind Person's Perspective, A. Jonathan R. Godfrey

The R Journal

Blind people have experienced access issues to many software applications since the advent of the Windows operating system; statistical software has proven to follow the rule and not be an exception. The ability to use R within minutes of download with next to no adaptation has opened doors for accessible production of statistical analyses for this author (himself blind) and blind students around the world. This article shows how little is required to make R the most accessible statistical software available today. There is any number of ramifications that this opportunity creates for blind students, especially in terms of their …