Numerical Analysis and Scientific Computing Commons^™

Lba: An R Package For Latent Budget Analysis, Enio G. Jelihovschi, Ivan Bezerra Allaman

The R Journal

The latent budget model is a mixture model for compositional data sets in which the entries, a contingency table, may be either realizations from a product multinomial distribution or distribution free. Based on this model, the latent budget analysis considers the interactions of two variables; the explanatory (row) and the response (column) variables. The package lba uses expectation-maximization and active constraints method (ACM) to carry out, respectively, the maximum likelihood and the least squares estimation of the model parameters. It contains three main functions, lba which performs the analysis, goodnessfit for model selection and goodness of fit and the plotting …

Icsoutlier: Unsupervised Outlier Detection For Low-Dimensional Contamination Authors: Structure, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen

The R Journal

Detecting outliers in a multivariate and unsupervised context is an important and ongoing problem notably for quality control. Many statistical methods are already implemented in R and are briefly surveyed in the present paper. But only a few lead to the accurate identification of potential outliers in the case of a small level of contamination. In this particular context, the Invariant Coordinate Selection (ICS) method shows remarkable properties for identifying outliers that lie on a low-dimensional subspace in its first invariant components. It is implemented in the ICSOutlier package. The main function of the package, ics.outlier, offers the possibility of …

Onewaytests: An R Package For One-Way Tests In Independent Groups Designs, Osman Dag, Anil Dolgun, Naime Meric Konar

The R Journal

One-way tests in independent groups designs are the most commonly utilized statistical methods with applications on the experiments in medical sciences, pharmaceutical research, agriculture, biology, engineering, social sciences and so on. In this paper, we present the one-way tests package to investigate treatment effects on the dependent variable. The package offers the one-way tests in independent groups designs, which include ANOVA, Welch’s heteroscedastic F test, Welch’s heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander Govern test, James second order test and Kruskal-Wallis test. The package also provides pairwise comparisons, graphical approaches, and assesses variance homogeneity and …

Bayesian Testing, Variable Selection And Model Averaging In Linear Models Using R With Bayesvarsel, Gonzalo Garcia-Donato, Anabel Forte

The R Journal

In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples.

Tackling Uncertainties Of Species Distribution Model Projections With Package Mopa, M. Iturbide, J. Bedia, J.M. Gutiérrez

The R Journal

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning in the context of climate change. Nevertheless, SDM projections are affected by a wide range of uncertainty factors (related to training data, climate projections and SDM techniques), which limit their potential value and credibility. The new package mopa provides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R …

Panjen: An R Package For Ranking Transformations In A Linear Regression, Cathrine Ulla Jensen, Toke Emil Panduro

The R Journal

PanJen is an R-package for ranking transformations in linear regressions. It provides users with the ability to explore the relationship between a dependent variable and its independent variables. The package offers an easy and data-driven way to choose a functional form in multiple linear regression models by comparing a range of parametric transformations. The parametric functional forms are benchmarked against each other and a non-parametric transformation. The package allows users to generate plots that show the relation between a covariate and the dependent variable. Furthermore, PanJen will enable users to specify specific functional transformations, driven by a priori and theory-based …

Arco: An R Package To Estimate Artificial Counterfactuals, Yuri R. Fonseca, Ricardo P. Masini, Marcelo C. Medeiros, Gabriel F.R. Vasconcelos

The R Journal

In this paper we introduce the ArCo package for R which consists of a set of functions to implement the the Artificial Counterfactual (ArCo) methodology to estimate causal effects of an intervention (treatment) on aggregated data and when a control group is not necessarily available. The ArCo method is a two-step procedure, where in the first stage a counterfactual is estimated from a large panel of time series from a pool of untreated peers. In the second-stage, the average treatment effect over the post-intervention sample is computed. Standard inferential procedures are available. The package is illustrated with both simulated and …

Infotrad: An R Package For Estimating The Probability Of Informed Trading, Duygu Çelik, Murat Tiniç

The R Journal

The purpose of this paper is to introduce the R package InfoTrad for estimating the probability of informed trading (PIN) initially proposed by Easley et al. (1996). PIN is a popular information asymmetry measure that proxies the proportion of informed traders in the market. This study provides a short survey on alternative estimation techniques for the PIN. There are many problems documented in the existing literature in estimating PIN. InfoTrad package aims to address two problems. First, the sequential trading structure proposed by Easley et al. (1996) and later extended by Easley et al. (2002) is prone to sample selection …

Conference Report: Erum 2018, Gergely Daróczi

The R Journal

The European R Users Meeting (eRum) is an international conference that aims at bringing together users of the R language living in Europe– in the years when the useR! conference is hosted outside of the continent.

The first eRum conference was held in 2016 in Poznan, Poland with around 250 attendees and 20 sessions spanning over 3 days, including more than 80 speakers. Around that time, we also held a smaller conference in Budapest: the first satRday event happened with 25 speakers and almost 200 attendees from 19 countries in 2016.

The eRum 2018 conference is heritage of these two …

Realvams: An R Package For Fitting A Multivariate Value-Added Model (Vam), Jennifer Broatch, Jennifer Green, Andrew Karl

The R Journal

We present RealVAMS, an R package for fitting a generalized linear mixed model to multimembership data with partially crossed and partially nested random effects. RealVAMS utilizes a multivariate generalized linear mixed model with pseudo-likelihood approximation for fitting normally distributed continuous response(s) jointly with a binary outcome. In an educational context, the model is referred to as a multidimensional value-added model, which extends previous theory to estimate the relationships between potential teacher contributions toward different student outcomes and to allow the consideration of a binary, real-world outcome such as graduation. The simultaneous joint modeling of continuous and binary outcomes was not …

Approximating The Sum Of Independent Non-Identical Binomial Random Variables, Boxiang Liu, Thomas Quertermous

The R Journal

The distribution of the sum of independent non-identical binomial random variables is frequently encountered in areas such as genomics, healthcare, and operations research. Analytical solutions for the density and distribution are usually cumbersome to find and difficult to compute. Several methods have been developed to approximate the distribution, among which is the saddlepoint approximation. However, implementation of the saddlepoint approximation is non-trivial. In this paper, we implement the saddlepoint approximation in the sinib package and provide two examples to illustrate its usage. One example uses simulated data while the other uses real-world healthcare data. The sinib package addresses the gap …

Editorial, John Verzani

The R Journal

On behalf of the Editorial Board, I am pleased to present Volume 10, Issue 1 of the R Journal. This issue contains 36 contributed articles. The majority of which cover new or newly enhanced packages on CRAN.

Nonparametric Independence Tests And K-Sample Tests For Large Sample Sizes Using Package Hhg, Barak Brill, Yair Heller, Ruth Heller

The R Journal

Nonparametric tests of independence and k-sample tests are ubiquitous in modern applications, but they are typically computationally expensive. We present a family of nonparametric tests that are computationally efficient and powerful for detecting any type of dependence between a pair of univariate random variables. The computational complexity of the suggested tests is sub-quadratic in sample size, allowing calculation of test statistics for millions of observations. We survey both algorithms and the HHG package in which they are implemented, with usage examples showing the implementation of the proposed tests for both the independence case and the k-sample problem. The tests are …

Dimred And Coranking - Unifying Dimensionality Reduction In R, Guido Kraemer, Markus Reichstein, Miguel D. Mahecha

The R Journal

“Dimensionality reduction” (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. DR ca nbe realized by a plethora of methods with different properties, objectives, and, hence, (dis)advantages. The resulting low-dimensional data embeddings are often difficult to compare with objective criteria. Here, we introduce the dimRed and coRanking packages for the R language. These open source software packages enable users to easily access multiple classical and advanced DR methods using a common interface. The packages also provide quality indicators for the embeddings and easy visualization of high dimensional …

Collections In R: Review And Proposal, Timothy Barry

The R Journal

R is a powerful tool for data processing, visualization, and modeling. However, R is slower than other languages used for similar purposes, such as Python. One reason for this is that R lacks base support for collections, abstract data types that store, manipulate, and return data (e.g., sets, maps, stacks). An exciting recent trend in the R extension ecosystem is the development of collection packages, packages that provide classes that implement common collections. At least 12 collection packages are available across the two major R extension repositories, the Comprehensive R Archive Network (CRAN) and Bioconductor. In this article, we compare …

Pstat: An R Package To Assess Population Differentiation In Phenotypic Traits, Stéphane Blondeau Da Silva, Anne Da Silva

The R Journal

The package Pstat calculates P_ST values to assess differentiation among populations from a set of quantitative traits and provides bootstrapped distributions and confidence intervals for P_ST. Variations of P_ST as a function of the parameter c/h² are studied as well. The package implements different transformations of the measured phenotypic traits to eliminate variation resulting from allometric growth, including calculation of residuals from linear regression, Reist standardization, and the Aitchison transformation.

Residuals And Diagnostics For Binary And Ordinal Regression Models: An Introduction To The Sure Package, Brandon M. Greenwell, Andrew J. Mccarthy, Bradley C. Boehmke, Dungang Liu

The R Journal

Residual diagnostics is an important topic in the classroom, but it is less often used in practice when the response is binary or ordinal. Part of the reason for this is that generalized models for discrete data, like cumulative link models and logistic regression, do not produce standard residuals that are easily interpreted as those in ordinary linear regression. In this paper, we introduce the R package sure, which implements a recently developed idea of SUrrogate REsiduals. We demonstrate the utility of the package in detection of cumulative link model misspecification with respect to mean structures, link functions, …

Hrm: An R Package For Analysing High-Dimensional Multi-Factor Repeated Measures Authors: Martin Happ, Solomon W. Harrar And Arne C. Bathke, Martin Happ, Solomon W. Harrar, Arne C. Bathke

The R Journal

High-dimensional longitudinal data pose a serious challenge for statistical inference as many test statistics cannot be computed for high-dimensional data, or they do not maintain the nominal type-I error rate, or have very low power. Therefore, it is necessary to derive new inference methods capable of dealing with high dimensionality, and to make them available to statistics practitioners. One such method is implemented in the package HRM described in this article. This new method uses a similar approach as the Welch-Satterthwaite t-test approximation and works very well for high-dimensional data as long as the data distribution is not too skewed …

R Foundation News, Torsten Hothorn

The R Journal

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Inventorymodel: An R Package For Centralized Inventory Problems, Alejandro Saavedra-Nieves

The R Journal

Inventory management of goods is an integral part of logistics systems; common to various economic sectors such as industry, agriculture and trade; and independent of production volume. In general, as companies seek to minimize economic losses, studies on problems of multi-agent inventory have increased in recent years. A multi-agent inventory problem is a situation in which several agents face individual inventory problems and agree to coordinate their orders with the objective of reducing their costs. The R package Inventorymodel allows the determination of both the optimal policy for some inventory situations with deterministic demands and the allocation of costs from …