Numerical Analysis and Scientific Computing Commons^™

The R Journal (December 2015) 7(2): Complete Issue, The R Foundation

The R Journal

Editorial, Bettina Grün

Contributed Research Articles

Fitting Conditional and Simultaneous Autoregressive Spatial Models in hglm, Moudud Alam, Lars Rönnegård, and Xia Shen

VSURF: An R Package for Variable Selection Using Random Forests, Robin Genuer, Jean-Michel Poggi, and Christine Tuleau-Malot

zoib: An R Package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression, Fang Liu, and Yunchuan Kong

apc: An R Package for Age-Period-Cohort Analysis, Bent Nielsen

QuantifQuantile: An R Package for Performing Quantile Regression Through Optimal Quantization, Isabelle Charlier, Davy Paindaveine, and Jérôme Saracco

Numerical Evaluation of the Gauss Hypergeometric Function with the hypergeo Package, Robin K. S. …

An R Package For The Panel Approach Method For Program Evaluation: Pampe, Ainhoa Vega-Bayo

The R Journal

The pampe package for R implements the panel data approach method for program evaluation designed to estimate the causal effects of political interventions or treatments. This procedure exploits the dependence among cross-sectional units to construct a counterfactual of the treated unit(s), and it is an appropriate method for research events that occur at an aggregate level like countries or regions and that affect only one or a small number of units. The implementation of the pampe package is illustrated using data from Hong Kong and 24 other units, by examining the economic impact of the political and economic integration of …

Energy Forecasting For Event Venues: Big Data And Prediction Accuracy, Katarina Grolinger, Alexandra L'Heureux, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy …

Changes On Cran, Kurt Hornik, Achim Zeileis

The R Journal

New packages in CRAN task views

New contributed packages

Other changes

Conference Report: User! 2015, Torben Tvedebrink

The R Journal

The11thinternational R user conference, useR! 2015, took place in Aalborg, Denmark, 1–3 July 2015. The Department of Mathematical Sciences, Aalborg University, hosted the conference, which took place in Aalborg Congress and Culture Centre.

Open-Channel Computation With R, Michael C. Koohafkan, Bassam A. Younis

The R Journal

The rivr package provides a computational toolset for simulating steady and unsteady one dimensional flows in open channels. It is designed primarily for use by instructors of undergraduate and graduate-level open-channel hydrodynamics courses in such diverse fields as river engineering, physical geography and geophysics. The governing equations used to describe open-channel flows are briefly presented, followed by example applications. These include the computation of gradually varied flows and two examples of unsteady flows in channels—namely, the tracking of the evolution of a flood wave in a channel and the prediction of extreme variation in the water-surface profile that results when …

Mmpp: A Package For Calculating Similarity And Distance Metrics For Simple And Marked Temporal Point Processes, Hideitsu Hino, Ken Takano, Noboru Murata

The R Journal

A simple temporal point process (SPP) is an important class of time series, where the sample realization of the process is solely composed of the times at which events occur. Particular examples of point process data are neuronal spike patterns or spike trains, and a large number of distance and similarity metrics for those data have been proposed. A marked point process (MPP) is an extension of a simple temporal point process, in which a certain vector valued mark is associated with each of the temporal points in the SPP. Analyses of MPPs are of practical importance because instances of …

Mtk: A General-Purpose And Extensible R Environment For Uncertainty And Sensitivity Analyses Of Numerical Experiments, Juhui Wang, Robert Faivre, Hervé Richard, Hervé Monod

The R Journal

Along with increased complexity of the models used for scientific activities and engineering come diverse and greater uncertainties. Today, effectively quantifying the uncertainties contained in a model appears to be more important than ever. Scientific fellows know how serious it is to calibrate their model in a robust way, and decision-makers describe how critical it is to keep the best effort to reduce the uncertainties about the model. Effectively accessing the uncertainties about the model requires mastering all the tasks involved in the numerical experiments, from optimizing the experimental design to managing the very time consuming aspect of model simulation …

Abctools: An R Package For Tuning Approximate Bayesian Computation Analyses, Matthew A. Nunes, Dennis Prangle

The R Journal

Approximate Bayesian computation (ABC) is a popular family of algorithms which perform approximate parameter inference when numerical evaluation of the likelihood function is not possible but data can be simulated from the model. They return a sample of parameter values which produce simulations close to the observed dataset. A standard approach is to reduce the simulated and observed datasets to vectors of summary statistics and accept when the difference between these is below a specified threshold. ABC can also be adapted to perform model choice.

In this article, we present a new software package for R, abctools which provides methods …

Altopt: An R Package For Optimal Experimental Design Of Accelerated Life Testing, Kangwon Seo, Rong Pan

The R Journal

The R package ALTopt has been developed with the aim of creating and evaluating optimal experimental designs of censored accelerated life tests (ALTs). This package takes the generalized linear model approach to ALT planning, because this approach can easily handle censoring plans and derive information matrices for evaluating designs. Three types of optimality criteria are considered: D-optimality for model parameter estimation, U-optimality for reliability prediction at a single use condition, and I-optimality for reliability prediction over a region of use conditions. The Weibull distribution is assumed for failure time data and more than one stress factor can …

Practools: Computations For Design Of Finite Population Samples, Richard Valliant, Jill A. Dever, Frauke Kreuter

The R Journal

PracTools is an R package with functions that compute sample sizes for various types of finite population sampling designs when totals or means are estimated. One-, two-, and three-stage designs are covered as well as allocations for stratified sampling and probability proportional to size sampling. Sample allocations can be computed that minimize the variance of an estimator subject to a budget constraint or that minimize cost subject to a precision constraint. The package also contains some specialized functions for estimating variance components and design effects. Several finite populations are included that are useful for classroom instruction.

Clustvarlv: An R Package For The Clustering Of Variables Around Latent Variables, Evelyne Vigneau, Mingkun Chen, El Mostafa Qannari

The R Journal

The clustering of variables is a strategy for deciphering the underlying structure of a data set. Adopting an exploratory data analysis point of view, the Clustering of Variables around Latent Variables (CLV) approach has been proposed by Vigneau and Qannari (2003). Based on a family of optimization criteria, the CLV approach is adaptable to many situations. In particular, constraints may be introduced in order to take account of additional information about the observations and/or the variables. In this paper, the CLV method is depicted and the R package ClustVarLV including a set of functions developed so far within this framework …

Bsgs: Bayesian Sparse Group Selection, Kuo-Jung Lee, Ray-Bing Chen

The R Journal

An R package BSGS is provided for the integration of Bayesian variable and sparse group selection separately proposed by Chen et al. (2011) and Chen et al. (in press) for variable selection problems, even in the cases of large p and small n. This package is designed for variable selection problems including the identification of the important groups of variables and the active variables within the important groups. This article introduces the functions in the BSGS package that can be used to perform sparse group selection as well as variable selection through simulation studies and real data.

Srcs: Statistical Ranking Color Scheme For Visualizing Parameterized Multiple Pairwise Comparisons With R, Pablo J. Villacorta, José A. Sáez

The R Journal

The problem of comparing a new solution method against existing ones to find statistically significant differences arises very often in sciences and engineering. When the problem instance being solved is defined by several parameters, assessing a number of methods with respect to many problem configurations simultaneously becomes a hard task. Some visualization technique is required for presenting a large number of statistical significance results in an easily interpretable way. Here we review an existing color-based approach called Statistical Ranking Color Scheme (SRCS) for displaying the results of multiple pairwise statistical comparisons between several methods assessed separately on a number of …

Zoib: An R Package For Bayesian Inference For Beta Regression And Zero/One Inflated Beta Regression, Fang Liu, Yunchuan Kong

The R Journal

The beta distribution is a versatile function that accommodates a broad range of probability distribution shapes. Beta regression based on the beta distribution can be used to model a response variable y that takes values in open unit interval (0,1). Zero/one inflated beta (ZOIB) regression models can be applied when y takes values from closed unit interval [0,1]. The ZOIB model is based a piecewise distribution that accounts for the probability mass at 0 and 1, in addition to the probability density within (0,1). This paper introduces an R package– zoib that provides Bayesian inferences for a class of ZOIB …

Fitting Conditional And Simultaneous Autoregressive Spatial Models In Hglm, Moudud Alam, Lars Rönnegård, Xia Shen

The R Journal

We present a new version ( 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for con ditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SARrandomeffects into an independent, but heteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Vsurf: An R Package For Variable Selection Using Random Forests, Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot

The R Journal

This paper describes the R package VSURF. Based on random forests, and for both regression and classification problems, it returns two subsets of variables. The first is a subset of important variables including some redundancy which can be relevant for interpretation, and the second one is a smaller subset corresponding to a model trying to avoid redundancy focusing more closely on the prediction objective. The two-stage strategy is based on a preliminary ranking of the explanatory variables using the random forests permutation-based score of importance and proceeds using a stepwise forward strategy for variable introduction. The two proposals can …

Generalized Hermite Distribution Modelling With The R Package Hermite, David Moriña, Manuel Higueras, Pedro Puig, María Oliveira

The R Journal

The Generalized Hermite distribution (and the Hermite distribution as a particular case) is often used for fitting count data in the presence of over-dispersion or multimodality. Despite this, to our knowledge, no standard software packages have implemented specific functions to compute basic probabilities and make simple statistical inference based on these distributions. We present here a set of computational tools that allows the user to face these difficulties by modelling with the Generalized Hermite distribution using the R package hermite. The package can also be used to generate random deviates from a Generalized Hermite distribution and to use basic …

Working With Multilabel Datasets In R: The Mldr Package, Francisco Charte, David Charte

The R Journal

Most classification algorithms deal with datasets which have a set of input features, the variables to be used as predictors, and only one output class, the variable to be predicted. However, in late years many scenarios in which the classifier has to work with several outputs have come to life. Automatic labeling of text documents, image annotation or protein classification are among them. Multilabel datasets are the product of these new needs, and they have many specific traits. The mldr package allows the user to load datasets of this kind, obtain their characteristics, produce specialized plots, and manipulate them. The …

Numerical Evaluation Of The Gauss Hypergeometric Function With The Hypergeo Package, Robin K. S. Hankin

The R Journal

This paper introduces the hypergeo package of R routines for numerical calculation of hypergeometric functions. The package is focussed on efficient and accurate evaluation of the Gauss hypergeometric function over the whole of the complex plane within the constraints of fixed-precision arithmetic. The hypergeometric series is convergent only within the unit circle, so analytic continuation must be used to define the function outside the unit circle. This short document outlines the numerical and conceptual methods used in the package; and justifies the package philosophy, which is to maintain transparent and verifiable links between the software and Abramowitz and Stegun (1965). …

Treeclust: An R Package For Tree-Based Clustering Dissimilarities, Samuel E. Buttrey, Lyn R. Whitaker

The R Journal

This paper describes treeClust, an R package that produces dissimilarities useful for clustering. These dissimilarities arise from a set of classification or regression trees, one with each variable in the data acting in turn as a the response, and all others as predictors. This use of trees produces dissimilarities that are insensitive to scaling, benefit from automatic variable selection, and appear to perform well. The software allows a number of options to be set, affecting the set of objects returned in the call; the user can also specify a clustering algorithm and, optionally, return only the clustering vector. The …

Apc: An R Package For Age-Period-Cohort Analysis, Bent Nielsen

The R Journal

The apc package includes functions for age-period-cohort analysis based on the canonical parametrisation of Kuang et al. (2008a). The package includes functions for organizing the data, descriptive plots, a deviance table, estimation of (sub-models of) the age-period-cohort model, a plot for specification testing, plots of estimated parameters, and sub-sample analysis.

News From The Bioconductor Project, The Bioconductor Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high throughput genomic data. The 1104 software packages available in Bioconductor can be viewed at http://bioconductor.org/packages/. 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 version 3.2 by installing R 3.2.3 and evaluating the commands

Editorial, Bettina Grün

The R Journal

On behalf of the editorial board, I am pleased to publish Volume 7, Issue 2 of the R Journal. This issue contains 20 contributed research articles and several contributions to the News and Notes section.

Code Profiling In R: A Review Of Existing Methods And An Introduction To Package Guiprofiler, Angel Rubio, Fernando De Villar

The R Journal

Code analysis tools are crucial to understand program behavior. Profile tools use the results of time measurements in the execution of a program to gain this understanding and thus help in the optimization of the code. In this paper, we review the different available packages to profile R code and show the advantages and disadvantages of each of them. In additon, we present GUIProfiler, a package that fulfills some unmet needs

Package GUIProfiler generates an HTML report with the timing for each code line and the relationships between different functions. This package mimics the behavior of the MATLAB profiler. …

Quantifquantile: An R Package For Performing Quantile Regression Through Optimal Quantization, Isabelle Charlier, Davy Paindaveine, Jérôme Saracco

The R Journal

In quantile regression, various quantiles of a response variable Y are modelled as functions of covariates (rather than its mean). An important application is the construction of reference curves/surfaces and conditional prediction intervals for Y. Recently, a nonparametric quantile regression method based on the concept of optimal quantization was proposed. This method competes very well with k-nearest neighbor, kernel, and spline methods. In this paper, we describe an R package, called QuantifQuantile, that allows to perform quantization-based quantile regression. We describe the various functions of the package and provide examples.

Changes In R, The R Core Team

The R Journal

CHANGES IN R 3.2.3

CHANGES IN R 3.2.2

The R Consortium And The R Foundation, Martyn Plummer

The R Journal

The R Consortium was announced at the useR! 2015 conference in Aalborg, Denmark on 30 June. It is a non-profit organization set up to provide infrastructure for the R community. The purpose of this article is to explain some of the background to the setting up of the Consortium and how it interacts with the R Foundation.

An Immersive Telepresence System Using Rgb-D Sensors And Head-Mounted Display, Xinzhong Lu, Ju Shen, Saverio Perugini, Jianjun Yang

Computer Science Faculty Publications

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to effectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The …

Prediction Of Laser Ablation In Brain: Sensitivity, Calibration, And Validation, Samuel J. Fahrenholtz

Dissertations and Theses (Open Access)

The surgical planning of MR-guided laser induced thermal therapy (MRgLITT) stands to benefit from predictive computational modeling. The dearth of physical model parameter data leads to modeling uncertainty. This work implements a well-accepted framework with three key steps for model-building: model-parameter sensitivity analysis, model calibration, and model validation.

The sensitivity study is via generalized polynomial chaos (gPC) paired with a transient finite element (FEM) model. Uniform probability distribution functions (PDFs) capture the plausible range of values suggested by the literature for five model parameters. The five PDFs are input separately into the FEM model to gain a probabilistic sensitivity response …