Physical Sciences and Mathematics Commons^™

The Vote Package: Single Transferable Vote And Other Electoral Systems In R, Adrian E. Raftery, Hana ŠevčÍková, Bernard W. Silverman

The R Journal

We describe the vote package in R, which implements the plurality (or first-past-the-post), two-round runoff, score, approval, and Single Transferable Vote (STV) electoral systems, as well as methods for selecting the Condorcet winner and loser. We emphasize the STV system, which we have found to work well in practice for multi-winner elections with small electorates, such as committee and council elections, and the selection of multiple job candidates. For single-winner elections, STV is also called Instant Runoff Voting (IRV), Ranked Choice Voting (RCV), or the alternative vote (AV) system. The package also implements the STV system with equal preferences, for …

Volume Approximation And Sampling For Convex Polytopes In R, Apostolos Chalkis, Vissarion Fisikopoulos

The R Journal

Sampling from high-dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering, artificial intelligence, and machine learning. In this paper, we present volesti, an R package that provides efficient, scalable algorithms for volume estimation, uniform, and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and provides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language

Openskies - Integration Of Aviation Data Into The R Ecosystem, Rafael Ayala, Daniel Ayala, Lara Sellés Vidal, David Ruiz

The R Journal

Aviation data has become increasingly more accessible to the public thanks to the adoption of technologies such as Automatic Dependent Surveillance-Broadcast (ADS-B) and Mode S, which provide aircraft information over publicly accessible radio channels. Furthermore, the OpenSky Network provides multiple public resources to access such air traffic data from a large network of ADS-B receivers. Here, we present openSkies, the first R package for processing public air traffic data. The package provides an interface to the OpenSky Network resources, standardized data structures to represent the different entities involved in air traffic data, and functionalities to analyze and visualize such …

A New Versatile Discrete Distribution, Rolf Turner

The R Journal

This paper introduces a new flexible distribution for discrete data. Approximate moment estimators of the parameters of the distribution, to be used as starting values for numerical optimization procedures, are discussed. “Exact” moment estimation, effected via a numerical procedure, and maximum likelihood estimation, are considered. The quality of the results produced by these estimators is assessed via simulation experiments. Several examples are given of fitting instances of the new distribution to real and simulated data. It is noted that the new distribution is a member of the exponential family. Expressions for the gradient and Hessian of the log-likelihood of the …

Passed: Calculate Power And Sample Size For Two Sample Tests, University Of Missouri Li, Ryan P. Knigge, Kaiyi Chen, Emily V. Leary

The R Journal

Power and sample size estimation are critical aspects of study design to demonstrate minimized risk for subjects and justify the allocation of time, money, and other resources. Researchers often work with response variables that take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.

Robust And Efficient Optimization Using A Marquardt-Levenberg Algorithm With R Package Marqlevalg, Viviane Philipps, Boris P. Hejblum, Mélanie Prague, Daniel Commenges, Cécile Proust-Lima

The R Journal

Implementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problems: too loose convergence criteria and too long calculation time. By relying on a Marquardt-Levenberg algorithm (MLA), a Newton-like method particularly robust for solving local optimization problems, we provide with marqLevAlg package an efficient and general-purpose local optimizer which (i) prevents convergence to saddle points by using a stringent convergence criterion based on the relative distance to minimum/maximum in addition to the stability of the parameters and of the objective function; and (ii) reduces the computation time in …

Emss: New Em-Type Algorithms For The Heckman Selection Model In R, Kexuan Yang, Sang Kyu Lee, Jun Zhao, Hyoung-Moon Kim

The R Journal

When investigators observe non-random samples from populations, sample selectivity problems may occur. The Heckman selection model is widely used to deal with selectivity problems. Based on the EM algorithm, Zhao et al. (2020) developed three algorithms, namely, ECM, ECM(NR), and ECME(NR), which also have the EM algorithm’s main advantages: stability and ease of implementation. This paper provides the implementation of these three new EM-type algorithms in the package EMSS and illustrates the usage of the package on several simulated and real data examples. The comparison between the maximum likelihood estimation method (MLE) and three new EM-type algorithms in robustness issues …

Stratigrapher: Concepts For Litholog Generation In R, Sébastien Wouters, Anne-Christine Da Silva, Frédéric Boulvain, Xavier Devleeschouwer

The R Journal

The StratigrapheR package proposes new concepts for the generation of lithological logs, or lithologs, in R. The generation of lithologs in a scripting environment opens new opportunities for the processing and analysis of stratified geological data. Among the new concepts presented: new plotting and data processing methodologies, new general R functions, and computer-oriented data conventions are provided. The package structure allows for these new concepts to be further improved, which can be done independently by any R user. The current limitations of the package are highlighted, along with the limitations in R for geological data processing, to help identify the …

Msae: An R Package Of Multivariate Fay-Herriot Models For Small Area Estimation, Novia Permatasari, Azka Ubaidillah

The R Journal

The paper introduces an R Package of multivariate Fay-Herriot models for small area estimation named msae. This package implements four types of Fay-Herriot models, including univariate Fay-Herriot model (model 0), multivariate Fay-Herriot model (model 1), autoregressive multivariate Fay-Herriot model (model 2), and heteroskedastic autoregressive multivariate Fay-Herriot model (model 3). It also contains some datasets generated based on multivariate Fay-Herriot models. We describe and implement functions through various practical examples. Multivariate Fay-Herriot models produce a more efficient parameter estimation than direct estimation and univariate model.

Software Engineering And R Programming: A Call For Research, Melina Vidoni

The R Journal

Although R programming has been a part of research since its origins in the 1990s, few studies address scientific software development from a Software Engineering (SE) perspective. The past few years have seen unparalleled growth in the R community, and it is time to push the boundaries of SE research and R programming forwards. This paper discusses relevant studies that close this gap Additionally, it proposes a set of good practices derived from those findings aiming to act as a call-to-arms for both the R and RSE (Research SE) community to explore specific, interdisciplinary paths of research

Editorial, Dianne Cook

The R Journal

On behalf of the R Foundation and the Editorial board, I am pleased to present Volume 13 Issue 2 of the R Journal. This is the biggest issue ever!

First, some news from the Editorial board. A big thank you to Mike Kane, who has finished his term. As Editor-in-Chief in 2020, Mike expanded operations to include Associate Editors in the reviewing process. The R Journal now has a team of 20 Associate Editors. This has helped to manage the increasing number of submissions. We welcome new Associate Editors, Przemek Biecek, Chris Brunsdon, Mine Çetinkaya-Rundel, Kieran Healy, Adam Loy, Priyanga …

Elliptical Symmetry Tests In R, Slad̄Ana Babić, Christophe Ley, Marko Palangetić

The R Journal

The assumption of elliptical symmetry has an important role in many theoretical developments and applications. Hence, it is of primary importance to be able to test whether that assumption actually holds true or not. Various tests have been proposed in the literature for this problem. To the best of our knowledge, none of them has been implemented in R. This article describes the R package ellipticalsymmetry which implements several well-known tests for elliptical symmetry together with some recent tests. We demonstrate the testing procedures with a real data example.

An R Package For Robust Solution To The Behrens-Fisher Problem, Gamze Güven, ŞÜkrü Acıtaş, Hatice ŞAmkar, Birdal ŞEnoğLu

The R Journal

Welch’s two-sample t-test based on least squares (LS) estimators is generally used to test the equality of two normal means when the variances are not equal. However, this test loses its power when the underlying distribution is not normal. In this paper, two different tests are proposed to test the equality of two long-tailed symmetric (LTS) means under heterogeneous variances. Adaptive modified maximum likelihood (AMML) estimators are used in developing the proposed tests since they are highly efficient under LTS distribution. An R package called RobustBF is given to show the implementation of these tests. Simulated Type I error rates …

Tramme: Mixed-Effects Transformation Models Using Template Model Builder, Bálint Tamási, Torsten Hothorn

The R Journal

Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME, which builds on existing implementations of transformation models (mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 6 months, 1077 new packages were added to the CRAN package repository. 113 packages were unarchived and 331 were archived. The following shows the growth of the number of active packages in the CRAN package repository:

Cat.Dt: An R Package For Fast Construction Of Accurate Computerized Adaptive Tests Using Decision Trees, Javier Rodríguez-Cuadrado, Juan C. Laria, David Delgado-Gómez

The R Journal

This article introduces the cat.dt package for the creation of Computerized Adaptive Tests (CATs). Unlike existing packages, the cat.dt package represents the CAT in a Decision Tree (DT) structure. This allows building the test before its administration, ensuring that the creation time of the test is independent of the number of participants. Moreover, to accelerate the construction of the tree, the package controls its growth by joining nodes with similar estimations or distributions of the ability level and uses techniques such as message passing and pre-calculations. The constructed tree, as well as the estimation procedure, can be visualized using the …

Spnetwork: A Package For Network Kernel Density Estimation, Jeremy Gelb

The R Journal

This paper introduces the new package spNetwork that provides functions to perform Network Kernel Density Estimate analysis (NKDE). This method is an extension of the classical Kernel Density Estimate (KDE), a non parametric approach to estimate the intensity of a spatial process. More specifically, it adapts the KDE for cases when the study area is a network, constraining the location of events (such as accidents on roads, leaks in pipes, fish in rivers, etc.). We present and discuss in this paper the three main versions of NKDE: simple, discontinuous, and continuous that are implemented in spNetwork. We illustrate how to …

Spfilter: An R Package For Semiparametric Spatial Filtering With Eigenvectors In (Generalized) Linear Models, Sebastian Juhl

The R Journal

Eigenvector-based Spatial filtering constitutes a highly flexible semiparametric approach to account for spatial autocorrelation in a regression framework. It combines judiciously selected eigenvectors from a transformed connectivity matrix to construct a synthetic spatial filter and remove spatial patterns from model residuals. This article introduces the spfilteR package that provides several useful and flexible tools to estimate spatially filtered linear and generalized linear models in R. While the package features functions to identify relevant eigenvectors based on different selection criteria in an unsupervised fashion, it also helps users to perform supervised spatial filtering and to select eigenvectors based on alternative user-defined …

Siqr: An R Package For Single-Index Quantile Regression, Tianhai Zu, Yan Yu

The R Journal

We develop an R package SIQR that implements the single-index quantile regression (SIQR) models via an efficient iterative local linear approach in Wu et al. (2010). Single-index quantile regression models are important tools in semiparametric regression to provide a comprehensive view of the conditional distributions of a response variable. It is especially useful when the data is heterogeneous or heavy-tailed. The package provides functions that allow users to fit SIQR models, predict, provide standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. We apply the R package SIQR to a well-known Boston Housing data.

Multiple Imputation And Synthetic Data Generation With Npbayesimputecat, Jingchen Hu, Olanrewaju Akande, Quanli Wang

The R Journal

In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular, at statistical agencies, the respondent-level data they collect from surveys and censuses can suffer from high rates of missingness. Furthermore, agencies are obliged to protect respondents’ privacy when publishing the collected data for public use. The NPBayesImputeCat R package, introduced in this paper, provides routines to i) create multiple imputations for missing data and ii) create synthetic data for statistical disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet process mixture of products of the multinomial distributions model used …

Mirecsurv Package: Prentice-Williams-Peterson Models With Multiple Imputation Of Unknown Number Of Previous Episodes, David Moriña, Gilma Hernández-Herrera, Albert Navarro

The R Journal

Left censoring can occur with relative frequency when analyzing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The miRecSurv package is based on the use of models with specific baseline hazard, with multiple imputation of the number of prior episodes when unknown by means of the COMPoisson distribution, a very flexible count distribution that can handle over, sub, and equidispersion, …

Bcmixed: A Package For Median Inference On Longitudinal Data With The Box–Cox Transformation, Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Masahiko Gosho

The R Journal

This article illustrates the use of the bcmixed package and focuses on the two main functions: bcmarg and bcmmrm. The bcmarg function provides inference results for a marginal model of a mixed effect model using the Box–Cox transformation. The bcmmrm function provides model median inferences based on the mixed effect models for repeated measures analysis using the Box–Cox transformation for longitudinal randomized clinical trials. Using the bcmmrm function, analysis results with high power and high interpretability for treatment effects can be obtained for longitudinal randomized clinical trials with skewed outcomes. Further, the bcmixed package provides summarizing and visualization tools, which …

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2021-07-06 and 2021-12-22.

Donations

Jordan Aharoni (Canada) b-data GmbH (Switzerland) Mark Cachia (Canada) Shalese Fitzgerald (United States) Knut Helge Jensen (Norway) Roger Koenker (United Kingdom) Merck Research Laboratories, Kenilwort (United States) Statistik Aargau, Aarau (Switzerland)

Mgee2: An R Package For Marginal Analysis Of Longitudinal Ordinal Data With Misclassified Responses And Covariates, Yuliang Xu, Shuo Shuo Liu, Grace Y. Yi

The R Journal

Marginal methods have been widely used for analyzing longitudinal ordinal data due to their simplicity in model assumptions, robustness in inference results, and easiness in the implementation. However, they are often inapplicable in the presence of measurement errors in the variables. Under the setup of longitudinal studies with ordinal responses and covariates subject to misclassification, Chen et al. (2014) developed marginal methods for misclassification adjustments using the second-order estimating equations and proposed a two-stage estimation approach when the validation subsample is available. Parameter estimation is conducted through the Newton-Raphson algorithm, and the asymptotic distribution of the estimators is established. While …

Survidm: An R Package For Inference And Prediction In An Illness-Death Model, Gustavo Soutinho, Marta Sestelo, Luís Meira-Machado

The R Journal

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate "diseased" state before entering into a terminal absorbing state. In these models, one important goal is the modeling of transition rates which is usually done by studying the relationship between covariates and disease evolution. However, biomedical researchers are also interested in reporting other interpretable results in a simple and …

Lg: An R Package For Local Gaussian Approximations, Håkon Otneim

The R Journal

The package lg for the R programming language provides implementations of recent methodological advances on applications of the local Gaussian correlation. This includes the estimation of the local Gaussian correlation itself, multivariate density estimation, conditional density estimation, various tests for independence and conditional independence, as well as a graphical module for creating dependence maps. This paper describes the lg package, its principles, and its practical use.

Analysis Of Corneal Data In R With The Rpaci Package, Darío Ramos-López, Ana D. Maldonado

The R Journal

In ophthalmology, the early detection of keratoconus is still a crucial problem. Placido disk corneal topographers are essential in clinical practice, and many indices for diagnosing corneal irregularities exist. The main goal of this work is to present the R package rPACI, providing several functions to handle and analyze corneal data. This package implements primary indices of corneal irregularity (based on geometrical properties) and compound indices built from the primary ones, either using a generalized linear model or as a Bayesian classifier using a hybrid Bayesian network and performing approximate inference. rPACI aims to make the analysis of corneal …

News From The Forwards Taskforce, Heather Turner

The R Journal

Forwards is an R Foundation taskforce working to widen the participation of underrepresented groups in the R project and in related activities, such as the useR! conference. This report rounds up activities of the taskforce during the second half of 2022.

We Need Trustworthy R Packages, William Michael Landau

The R Journal

There is a need for rigorous software engineering in R packages, and there is a need for new research to bridge scientific computing with more traditional computing. Automated tools, interdisciplinary graduate courses, code reviews, and a welcoming developer community will continue to democratize best practices. Democratized software engineering will improve the quality, correctness, and integrity of scientific software, and by extension, the disciplines that rely on it

A Guided Tour Of Bayesian Regression, Andrés Ramírez–Hassan, Mateo Graciano-Londoño

The R Journal

This paper presents a Graphical User Interface (GUI) to carry out a Bayesian regression analysis in a very friendly environment without any programming skills (drag and drop). This paper is designed for teaching and applied purposes at an introductory level. Our GUI is based on an interactive web application using shiny and libraries from R. We carry out some applications to highlight the potential of our GUI for applied researchers and practitioners. In addition, the Help option in the main tap panel has an extended version of this paper, where we present the basic theory underlying all regression models that …