Numerical Analysis and Scientific Computing Commons^™

Clustering.Sc.Dp: Optimal Clustering With Sequential Constraint By Using Dynamic Programming, Tibor Szkaliczki

The R Journal

The general clustering algorithms do not guarantee optimality because of the hardness of the problem. Polynomial-time methods can find the clustering corresponding to the exact optimum only in special cases. For example, the dynamic programming algorithm can solve the one-dimensional clustering problem, i.e., when the items to be clustered can be characterised by only one scalar number. Optimal one-dimensional clustering is provided by package Ckmeans.1d.dp in R. The paper shows a possible generalisation of the method implemented in this package to multidimensional data: the dynamic programming method can be applied to find the optimum clustering of vectors when only subsequent …

Fwdselect: An R Package For Variable Selection In Regression Models, Marta Sestelo, Nora M. Villanueva, Luis Meira-Machado, Javier Roca-Pardiñas

The R Journal

In multiple regression models, when there are a large number (p) of explanatory variables which may o rmay not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q p) predictors which will establish the model with the best prediction capacity. FWDselect package introduces a new forward stepwise based selection procedure to select the best model in different regression frameworks (parametric or nonparametric). The developed methodology, which can be equally applied to linear models, generalized linear models or generalized additive …

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 1211 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 3.3 by installing R 3.3.1 and evaluating the commands

R Packages To Aid In Handling Web Access Logs, Oliver Keyes, Bob Rudis, Jay Jacobs

The R Journal

Web access logs contain information on HTTP(S) requests and form a key part of both industry and academic explorations of human behaviour on the internet. But the preparation (reading, parsing and manipulation) of that data is just unique enough to make generalized tools unfit for the task, both in programming time and processing time which are compounded when dealing with large data sets common with web access logs. In this paper we explain and demonstrate a series of packages designed to efficiently read in, parse and munge access log data, allowing researchers to handle URLs and IP addresses easily. These …

An Interactive Survey Application For Validating Social Network Analysis Techniques, Mitchell Joblin, Wolfgang Mauerer

The R Journal

Social network analysis is extremely well supported by the R community and is routinely used for studying the relationships between people engaged in collaborative activities. While there has been rapid development of new approaches and metrics in this field, the challenging question of validity (how well insights derived from social networks agree with reality) is often difficult to address. We propose the use of several R packages to generate interactive surveys that are specifically well suited for validating social network analyses. Using our web-based survey application, we were able to validate the results of applying community-detection algorithms to infer the …

Conference Report: User! 2016, Joe Rickert

The R Journal

The 12th international R user conference, useR! 2016, took place at Stanford University, Stanford CA fromJune27throughJune30th. Hosted by the Stanford University Department of Statistics and the Stanford Libraries, the conference took place at the Frances Arrillaga Alumni Center, on the surrounding lawns and in several adjacent buildings. The floor to ceiling windows of the larger conference rooms, the garden locations for coffee and meals and the beautiful weather contributed to making the event a classic California experience.

Schemaonread: A Package For Schema-On-Read In R, Michael J. North

The R Journal

SchemaOnRead is a CRAN package that provides an extensible mechanism for importing a wide range of file types into R as well as support for the emerging schema-on-read paradigm in R. The schema-on-read tools within the package include a single function call that recursively reads folders with text, comma separated value, raster image, R data, HDF5, NetCDF, spreadsheet, Weka, Epi Info, Pajek network, R network, HTML, SPSS, Systat, and Stata files. It also recursively reads folders (e.g., schemaOnRead("folder")), returning a nested list of the contained elements. The provided tools can be used as-is or easily customized to implement tool chains …

Keyplayer: An R Package For Locating Key Players In Social Networks, Weihua An, Yu-Hsin Liu

The R Journal

Interest in social network analysis has exploded in the past few years, partly thanks to the advancements in statistical methods and computing for network analysis. A wide range of the methods for network analysis is already covered by existent R packages. However, no comprehensive packages are available to calculate group centrality scores and to identify key players (i.e., those players who constitute the most central group) in a network. These functionalities are important because, for example, many social and health interventions rely on key players to facilitate the intervention. Identifying key players is challenging because players who are individually the …

Rtableicc: An R Package For Random Generation Of 22k And Rc Contingency Tables, Haydar Demirhan

The R Journal

In this paper, we describe the R package rTableICC that provides an interface for random generation of 2 2 K and R Ccontingency tables constructed over either intraclass-correlated or uncorrelated individuals. Intraclass correlations arise in studies where sampling units include more than one individual and these individuals are correlated. The package implements random generation of contingency tables over individuals with or without intraclass correlations under various sampling plans. The package include two functions for the generation of K 2 2 tables over product-multinomial sampling schemes and that of 2 2 K tables under Poisson or multinomial sampling plans. It also …

Crowdsourced Data Preprocessing With R And Amazon Mechanical Turk, Thomas J. Leeper

The R Journal

This article introduces the use of the Amazon Mechanical Turk (MTurk) crowdsourcing platform as a resource for R users to leverage crowdsourced human intelligence for preprocessing “messy” data into a form easily analyzed within R. The article first describes MTurk and the MTurkR package, then outlines how to use MTurkR to gather and manage crowdsourced data with MTurk using some of the package’s core functionality. Potential applications of MTurkR include construction of manually coded training sets, human transcription and translation, manual data scraping from scanned documents, content analysis, image classification, and the completion of online survey questionnaires, among others. As …

Exploring Interaction Effects In Two-Factor Studies Using The Hiddenf Package In R., Christopher Franck, Jason A. Osborne

The R Journal

In crossed, two-factor studies with one observation per factor-level combination, interaction effects between factors can be hard to detect and can make the choice of a suitable statistical model difficult. This article describes hiddenf, an R package that enables users to quantify and characterize a certain form of interaction in two-factor layouts. When effects of one factor (a) fall into two groups depending on the level of another factor, and (b) are constant within these groups, the interaction pattern is deemed "hidden additivity" because within groups, the effects of the two factors are additive, while between groups the factors …

Gmdh: An R Package For Short Term Forecasting Via Gmdh-Type Neural Network Algorithms, Osman Dag, Ceylan Yozgatligil

The R Journal

Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without models, etc. In this study, the R package GMDH is presented to make short term forecasting through GMDH-type neural network algorithms. The GMDH package has options to use different transfer functions (sigmoid, radial basis, polynomial, and tangent functions) simultaneously or separately. Data on cancer death rate of Pennsylvania from …

The R Journal (August 2016) 8(1): Complete Issue, The R Foundation

The R Journal

Editorial, Michael Lawrence

Contributed Research Articles

metaplus: An R Package for the Analysis of Robust Meta-Analysis and Meta-Regression, Ken J. Beath

Gender Prediction Methods Based on First Names with genderizeR, Kamil Wais

Conditional Fractional Gaussian Fields with the Package FieldSim, Alexandre Brouste, Jacques Istas, and Sophie Lambert-Lacroix

rTableICC: An R Package for Random Generation of 22K and RC Contingency Tables, Haydar Demirhan

Maps, Coordinate Reference Systems and Visualising Geographic Data with mapmisc, Patrick E. Brown

Variable Clustering in High-Dimensional Linear Regression: The R Package clere, Loïc Yengo, Julien Jacques, Christophe Biernacki, and Mickael Canouil

Stylometry with R: A Package for …

Estimability Tools For Package Developers, Russell V. Lenth

The R Journal

When a linear model is rank-deficient, then predictions based on that model become questionable because not all predictions are uniquely estimable. However, some of them are, and the estimability package provides tools that package developers can use to tell which is which. With the use of these tools, a model object’s predict method could return estimable predictions as-is while f lagging non-estimable ones in some way, so that the user can know which predictions to believe. The estimability package also provides, as a demonstration, an estimability-enhanced epredict method to use in place of predict for models fitted using the stats …

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 …

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 …