Programming Languages and Compilers Commons^™

Conference Report Polish Academic R User Meeting, Maciej Beręsewicz, Alicja Szabelska, Joanna Zyprych-Walczak, Łukasz Wawrowski

The R Journal

The first national conference “Polish Academic R User Meeting” (PAZUR) was held at the Poznan University of Economics from October 15–17, 2014. The organizers of the conference were the Department of Statistics at the Poznan University of Economics (PUE), the Department of Mathematical and Statistical Methods at the Poznan University of Life Sciences (PULS) and SKN Estymator, the students scientific association that resides at the Department of Statistics at the Poznan University of Economics. The honorary patronage of the conference took Professor Emil Panek, Dean of the Faculty of Informatics and Electronic Economy PUE and Professor Wiesław Koziara, Dean of …

Conference Report: R In Insurance 2014, Markus Gesmann, Andreas Tsanakas

The R Journal

The 2nd Rin Insurance conference took place at Cass Business School London on 14 July 2014. This one-day conference focused once more on the wide range of applications of Rin insurance, actuarial science and beyond. The conference programme covered topics including reserving, pricing, loss modelling, the use of R in a production environment and muchmore.

Qmethod: A Package To Explore Human Perspectives Using Q Methodology, Aiora Zabala

The R Journal

Q is a methodology to explore the distinct subjective perspectives that exist within a group. It is used increasingly across disciplines. The methodology is semi-qualitative and the data are analysed using data reduction methods to discern the existing patterns of thought. This package is the first to perform Q analysis in R, and it provides many advantages to the existing software: namely, it is fully cross-platform, the algorithms can be transparently examined, it provides results in a clearly structured and tabulated form ready for further exploration and modelling, it produces a graphical summary of the results, and it generates a …

Farewell's Linear Increments Model For Missing Data: The Flim Package, Rune Hoff, Jon Michael Gran, Daniel Farewell

The R Journal

Missing data is common in longitudinal studies. We present a package for Farewell’s Linear Increments Model for Missing Data (the FLIM package), which can be used to fit linear models for observed increments of longitudinal processes and impute missing data. The method is valid for data with regular observation patterns. The end result is a list of fitted models and a hypothetical complete dataset corresponding to the data we might have observed had individuals not been missing. The FLIM package may also be applied to longitudinal studies for causal analysis, by considering counterfactual data as missing data- for instance to …

Smr: An R Package For Computing The Externally Studentized Normal Midrange Distribution, Ben Dêivide, Oliveira Batista, Daniel Furtado Ferreira

The R Journal

The main purpose of this paper is to present the main algorithms underlining the con struction and implementation of the SMR package, whose aim is to compute studentized normal midrange distribution. Details on the externally studentized normal midrange and standardized normal midrange distributions are also given. The package follows the same structure as the prob ability functions implemented in R. That is: the probability density function (dSMR), the cumulative distribution function (pSMR), the quantile function (qSMR) and the random number generating function (rSMR). Pseudocode and illustrative examples of how to use the package are presented.

Sgof: An R Package For Multiple Testing Problems, Irene Castro-Conde, Jacob De Uña-Álvarez

The R Journal

In this paper we present a new R package called sgof for multiple hypothesis testing. The principal aim of this package is to implement SGoF-type multiple testing methods, known to be more powerful than the classical false discovery rate (FDR) and family-wise error rate (FWER) based methods in certain situations, particularly when the number of tests is large. This package includes Bi nomial and Conservative SGoF and the Bayesian and Beta-Binomial SGoF multiple testing procedures, which are adaptations of the original SGoF method to the Bayesian setting and to possibly correlated tests, respectively. The sgof package also implements the Benjamini-Hochberg …

Flexible R Functions For Processing Accelerometer Data, With Emphasis On Nhanes 2003–2006, Dane R. Van Domelen, W. Stephen Pittard

The R Journal

Accelerometers are a valuable tool for measuring physical activity (PA) in epidemiological studies. However, considerable processing is needed to convert time-series accelerometer data into meaningful variables for statistical analysis. This article describes two recently developed R packages for processing accelerometer data. The package accelerometry contains functions for performing various data processing procedures, such as identifying periods of non-wear time and bouts of activity. The functions are flexible, computationally efficient, and compatible with uniaxial or triaxial data. The package nhanesaccel is specifically for processing data from the National Health and Nutrition Examination Survey (NHANES), years 2003–2006. Its primary function generates measures …

Automatic Conversion Of Tables To Longform Dataframes, Jimmy Oh

The R Journal

TableToLongForm automatically converts hierarchical Tables intended for a human reader into a simple LongForm dataframe that is machine readable, making it easier to access and use the data for analysis. It does this by recognising positional cues present in the hierarchical Table (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into a LongForm dataframe. The article motivates the benefit of such a conversion with an example Table, followed by a short user manual, which includes a comparison between the simple one argument call to TableToLongForm, with code for an equivalent manual conversion. …

Gset: An R Package For Exact Sequential Test Of Equivalence Hypothesis Based On Bivariate Non-Central T-Statistics, Fang Liu

The R Journal

The R package gset calculates equivalence and futility boundaries based on the exact bivariate non-central t test statistics. It is the first R package that targets specifically at the group sequential test of equivalence hypotheses. The exact test approach adopted by gset neither assumes the large-sample normality of the test statistics nor ignores the contribution to the overall Type I error rate from rejecting one out of the two one-sided hypotheses under a null value. The features of gset include: error spending functions, computation of equivalence boundaries and futility boundaries, either binding or nonbinding, depiction of stagewise boundary plots, and …

Editorial, Deepayan Sarkar

The R Journal

On behalf of the editorial board, I am pleased to publish Volume 6, Issue 2 of the R Journal.

Mvn: An R Package For Assessing Multivariate Normality, Selcuk Korkmaz, Dincer Goksuluk, Gokmen Zararsiz

The R Journal

Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, canonical correlation, etc. It is important to assess multivariate normality in order to proceed with such statistical methods. There are many analytical methods proposed for checking multivariate normality. However, deciding which method to use is a challenging process, since each method may give different results under certain conditions. Hence, we may say that there is no best method, which is valid under any condition, for normality checking. In addition to numerical results, it is very useful to …

Ngspatial: A Package For Fitting The Centered Autologistic And Sparse Spatial Generalized Linear Mixed Models For Areal Data, John Hughes

The R Journal

Two important recent advances in areal modeling are the centered autologistic model and the sparse spatial generalized linear mixed model (SGLMM), both of which are reparameterizations of traditional models. The reparameterizations improve regression inference by alleviating spatial confounding, and the sparse SGLMM also greatly speeds computing by reducing the dimension of the spatial random effects. Package ngspatial (’ng’ = non-Gaussian) provides routines for fitting these new models. The package supports composite likelihood and Bayesian inference for the centered autologistic model, and Bayesian inference for the sparse SGLMM.

Coordinate-Based Meta-Analysis Of Fmri Studies With R, Andrea Stocco

The R Journal

This paper outlines how to conduct a simple meta-analysis of neuroimaging foci of activation in R. In particular, the first part of this paper reviews the nature of fMRI data, and presents a brief overview of the existing packages that can be used to analyze fMRI data in R. The second part illustrates how to handle fMRI data by showing how to visualize the results of different neuroimaging studies in a so-called orthographic view, where the spatial distribution of the foci of activation from different fMRI studies can be inspected visually.

Functional MRI (fMRI) is one of the most important …

Bshazard: A Flexible Tool For Nonparametric Smoothing Of The Hazard Function, Paola Rebora, Agus Salim, Marie Reilly

The R Journal

The hazard function is a key component in the inferential process in survival analysis and relevant for describing the pattern of failures. However, it is rarely shown in research papers due to the difficulties in nonparametric estimation. We developed the bshazard package to facilitate the computation of a nonparametric estimate of the hazard function, with data-driven smoothing. The method accounts for left truncation, right censoring and possible covariates. B-splines are used to estimate the shape of the hazard within the generalized linear mixed models frame work. Smoothness is controlled by imposing an autoregressive structure on the baseline hazard coefficients. This …

Prinsimp, Jonathan Zhang, Nancy Heckman, Davor Cubranic, Joel G. Kingsolver, Travis Gaydos, J.S. Marron

The R Journal

Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, the space spanned by the low variation principal components may contain interesting structure, structure that PCA cannot find. Prinsimp is an R package that looks for interesting structure of low variability. “Interesting” is defined in terms of a simplicity …

Phaser: An R Package For Phase Plane Analysis Of Autonomous Ode Systems, Michael J. Grayling

The R Journal

When modelling physical systems, analysts will frequently be confronted by differential equations which cannot be solved analytically. In this instance, numerical integration will usually be the only way forward. However, for autonomous systems of ordinary differential equations (ODEs) in one or two dimensions, it is possible to employ an instructive qualitative analysis foregoing this requirement, using so-called phase plane methods. Moreover, this qualitative analysis can even prove to be highly useful for systems that can be solved analytically, or will be solved numerically anyway. The package phaseR allows the user to perform such phase plane analyses: determining the stability of …

Taming Pitchf/X Data With Xml2r And Pitchrx, Carson Sievert

The R Journal

XML2R is a framework that reduces the effort required to transform XML content into tables in a way that preserves parent to child relationships. pitchRx applies XML2R’s grammar for XML manipulation to Major League Baseball Advanced Media (MLBAM)’s Gameday data. With pitchRx, one can easily obtain and store Gameday data in a remote database. The Gameday website hosts a wealth of XML data, but perhaps most interesting is PITCHf/x. Among other things, PITCHf/x data can be used to recreate a baseball’s flight path from a pitcher’s hand to home plate. With pitchRx, one can easily create animations …

Applying Spartan To Understand Parameter Uncertainty In Simulations, Kieran Alden, Mark Read, Paul S. Andrews, Jon Timmis, Mark Coles

The R Journal

In attempts to further understand the dynamics of complex systems, the application of computer simulation is becoming increasingly prevalent. Whereas a great deal of focus has been placed in the development of software tools that aid researchers develop simulations, similar focus has not been applied in the creation of tools that perform a rigorous statistical analysis of results generated through simulation: vital in understanding how these results offer an insight into the captured system. This encouraged us to develop spartan, a package of statistical techniques designed to assist researchers in understanding the relationship between their simulation and the real system. …

The R Journal (December 2014) 6(2): Complete Issue, The R Foundation

The R Journal

Editorial, Deepayan Sarkar

Contributed Research Articles

Coordinate-Based Meta-Analysis of fMRI Studies with R, Andrea Stocco

Automatic Conversion of Tables to LongForm Dataframes, Jimmy Oh

Prinsimp, Jonathan Zhang, Nancy Heckman, Davor Cubranic, Joel G. Kingsolver, Travis Gaydos, and J. S. Marron

phaseR: An R Package for Phase Plane Analysis of Autonomous ODE Systems, Michael J. Grayling

Flexible R Functions for Processing Accelerometer Data, with Emphasis on NHANES 2003-2006, Dane R. Van Domelen and W. Stephen Pittard

Applying spartan to Understand Parameter Uncertainty in Simulations, Kieran Alden, Mark Read, Paul S. Andrews, Jon Timmis, and Mark Coles

ngspatial: A Package for Fitting …

Changes On Cran, Kurt Hornik, Achim Zeileis

The R Journal

New packages in CRAN task views

New contributed packages

Other changes

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 …