Programming Languages and Compilers Commons^™

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 …

Rpostgis: Linking R With A Postgis Spatial Database, David Bucklin, Mathieu Basille

The R Journal

With the proliferation of sensors and the ease of data collection from online sources, large datasets have become the norm in many scientific disciplines, and efficient data storage, management, and retrival is imperative for large research projects. Relational databases provide a solution, but in order to be useful, must be able to be linked to analysis and visualization tools, such as R. Here, we present a package intended to facilitate integration of R with the open-source database software PostgreSQL, with a focus on its spatial extension, PostGIS. The package rpostgis (version 1.4.1) provides methods for spatial data handling (vector and …

R Package Imputetestbench To Compare Imputation Methods For Univariate Time Series, Marcus W. Beck, Neeraj Bokde, Gualberto Asencio-Cortés, Kishore Kulat

The R Journal

Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are …

Advanced Bayesian Multilevel Modeling With The R Package Brms, Paul-Christian Bürkner

The R Journal

The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models which are fit with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. Multivariate models can be fit as well. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of …

Welfare, Inequality And Poverty Analysis With Rtip: An Approach Based On Stochastic Dominance, Angel Berihuete, Carmen D. Ramos, Miguel A. Sordo

The R Journal

Disparities in economic welfare, inequality and poverty across and within countries are of great interest to sociologists, economists, researchers, social organizations and political scientists. Information about these topics is commonly based on surveys. We present a package called rtip that implements techniques based on stochastic dominance to make unambiguous comparisons, in terms of welfare, poverty and inequality, among income distributions. Besides providing point estimates and confidence intervals for the most commonly used indicators of these characteristics, the package rtip estimates the usual Lorenz curve, the generalized Lorenz curve, the TIP (Three I’s of Poverty) curve and allows to test statistically …