Numerical Analysis and Scientific Computing Commons^™

Forecast Combinations In R Using The Forecastcomb Package, Christoph E. Weiss, Eran Raviv, Gernot Roetzer

The R Journal

This paper introduces the R package ForecastComb. The aim is to provide researchers and practitioners with a comprehensive implementation of the most common ways in which forecasts can be combined. The package in its current version covers 15 popular estimation methods for creating a combined forecasts – including simple methods, regression-based methods, and eigenvector-based methods. It also includes useful tools to deal with common challenges of forecast combination (e.g., missing values in component forecasts, or multicollinearity), and to rationalize and visualize the combination results.

The Politeness Package: Detecting Politeness In Natural Language, Michael Yeomans, Alejandro Kantor, Dustin Tingley

The R Journal

This package provides tools to extract politeness markers in English natural language. It also allows researchers to easily visualize and quantify politeness between groups of documents. This package combines and extends prior research on the linguistic markers of politeness (Brown and Levinson, 1987; Danescu-Niculescu-Mizil et al., 2013; Voigt et al., 2017). We demonstrate two applications for detecting politeness in natural language during consequential social interactions— distributive negotiations, and speed dating.

Jsr223: A Java Platform Integration For R With Programming Languages Groovy, Javascript, Jruby, Jython, And Kotlin, Floid R. Gilbert, David B. Dahl

The R Journal

The R package jsr223 is a high-level integration for five programming languages in the Java platform: Groovy, JavaScript, JRuby, Jython, and Kotlin. Each of these languages can use Java objects in their own syntax. Hence, jsr223 is also an integration for R and the Java platform. It enables developers to leverage Java solutions from within R by embedding code snippets or evaluating script f iles. This approach is generally easier than rJava’s low-level approach that employs the Java Native Interface. jsr223’s multi-language support is dependent on the Java Scripting API: an implementation of “JSR-223: Scripting for the Java …

Rcppmsgpack: Messagepack Headers And Interface Functions For R, Travers Ching, Dirk Eddelbuettel

The R Journal

MessagePack, or MsgPack for short, or when referring to the implementation, is an efficient binary serialization format for exchanging data between different programming languages. The RcppMsgPack package provides R with both the MessagePack C++ header files, and the ability to access, create and alter MessagePack objects directly from R. The main driver functions of the R interface are two functions msgpack_pack and msgpack_unpack. The function msgpack_pack serializes Robjects to a raw MessagePack message. The function msgpack_unpack de-serializes MessagePack messages back into R objects. Several helper functions are available to aid in processing and formatting data including msgpack_simplify, msgpack_format and msgpack_map

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

The R Journal

In the past 6 months, 1029 new packages were added to the CRAN package repository. 68 packages were unarchived, 122 archived, and one removed. The following shows the growth of the number of active packages in the CRAN package repository

Rfsa: An R Package For Finding Best Subsets And Interactions, Joshua Lambert, Liyu Gong, Corrine F. Elliott, Katherine Thompson, Arnold Stromberg

The R Journal

Herein we present the R package rFSA, which implements an algorithm for improved variable selection. The algorithm searches a data space for models of a user-specified form that are statistically optimal under a measure of model quality. Many iterations afford a set of feasible solutions (or candidate models) that the researcher can evaluate for relevance to his or her questions of interest. The algorithm can be used to formulate new or to improve upon existing models in bioinformatics, health care, and myriad other fields in which the volume of available data has outstripped researchers’ practical and computational ability to explore …

Revengc: An R Package To Reverse Engineer Summarized Data, Samantha Duchscherer, Robert Stewart, Marie Urban

The R Journal

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations, such as the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and World Bank. The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers censored and/or decoupled data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Ggplot2 Compatible Quantile-Quantile Plots In, Alexandre Almeida, Adam Loy, Heike Hofmann

The R Journal

Q-Q plots allow us to assess univariate distributional assumptions by comparing a set of quantiles from the empirical and the theoretical distributions in the form of a scatterplot. To aid in the interpretation of Q-Q plots, reference lines and confidence bands are often added. We can also detrend the Q-Q plot so the vertical comparisons of interest come into focus. Various implementations of Q-Q plots exist in R, but none implements all of these features. qqplotr extends ggplot2 to provide a complete implementation of Q-Q plots. This paper introduces the plotting framework provided by qqplotr and provides multiple examples of …

Measurement Errors In R, Iñaki Ucar, Edzer Pebesma, Arturo Azcorra

The R Journal

This paper presents an R package to handle and represent measurements with errors in a very simple way. We briefly introduce the main concepts of metrology and propagation of uncertainty, and discuss related R packages. Building upon this, we introduce the errors package, which provides a class for associating uncertainty metadata, automated propagation and reporting. Working with errors enables transparent, lightweight, less error-prone handling and convenient representation of measurements with errors. Finally, we discuss the advantages, limitations and future work of computing with errors.

Spatial Uncertainty Propagation Analysis With The Spup R Package, Kasia Sawicka, Gerard B.M. Heuvelink, Dennis J.J. Walvoort

The R Journal

Many environmental and geographical models, such as those used in land degradation, agroecological and climate studies, make use of spatially distributed inputs that are known imperfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs via the environmental model. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. The package also accommodates spatial auto-correlation within a variable and cross-correlation between variables. The MC realizations may be …

The Utiml Package: Multi-Label Classification In R, Adriano Rivolli, Andre C.P.L.F. De Carvalho

The R Journal

Learning classification tasks in which each instance is associated with one or more labels are known as multi-label learning. The implementation of multi-label algorithms, performed by different researchers, have several specificities, like input/output format, different internal functions, distinct programming language, to mention just some of them. As a result, current machine learning tools include only a small subset of multi-label decomposition strategies. The utiml package is a framework for the application of classification algorithms to multi-label data. Like the well known MULAN used with Weka, it provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, …

Nsroc: An R Package For Non-Standard Roc Curve Analysis, Sonia Pérez-Fernández, Pablo Martínez-Camblor, Peter Filzmoser, Norberto Corral

The R Journal

The receiver operating characteristic (ROC) curve is a graphical method which has become standard in the analysis of diagnostic markers, that is, in the study of the classification ability of a numerical variable. Most of the commercial statistical software provide routines for the standard ROC curve analysis. Of course, there are also many R packages dealing with the ROC estimation as well as other related problems. In this work we introduce the nsROC package which incorporates some new ROC curve procedures. Particularly: ROC curve comparison based on general distances among functions for both paired and unpaired designs; efficient confidence bands …

Stilt: Easy Emulation Of Time Series Ar(1) Computer Model Output In Multidimensional Parameter Space, Roman Olson, Kelsey L. Ruckert, Won Chang, Klaus Keller, Murali Haran, Soon-Il An

The R Journal

Statistically approximating or “emulating” time series model output in parameter space is a common problem in climate science and other fields. There are many packages for spatio-temporal modeling. However, they often lack focus on time series, and exhibit statistical complexity. Here, we present the R package stilt designed for simplified AR(1) time series Gaussian process emulation, and provide examples relevant to climate modelling. Notably absent is Markov chain Monte Carlo estimation – a challenging concept to many scientists. We keep the number of user choices to a minimum. Hence, the package can be useful pedagogically, while still applicable to real …

R Foundation News, Torsten Hothorn

The R Journal

Donations and members

Donations

Supporting benefactors

Supporting institutions

Supporting members

Sarima Analysis And Automated Model Reports With Bets, An R Package, Talitha F. Speranza, Pedro C. Ferreira, Jonatha A. Da Costa

The R Journal

This article aims to demonstrate how the powerful features of the R package BETS can be applied to SARIMA time series analysis. BETS provides not only thousands of Brazilian economic time series from different institutions, but also a range of analytical tools, and educational resources. In particular, BETS is capable of generating automated model reports for any given time series. These reports rely on a single function call and are able to build three types of models (SARIMA being one of them). The functions need few inputs and output rich content. The output varies according to the inputs and usually …

Profile Likelihood Estimation Of The Correlation Coefficient In The Presence Of Left, Right Or Interval Censoring And Missing Data, Yanming Li, Brenda W. Gillespie, Kerby Shedden, John A. Gillespie

The R Journal

We discuss implementation of a profile likelihood method for estimating a Pearson correlation coefficient from bivariate data with censoring and/or missing values. The method is implemented in an R package clikcorr which calculates maximum likelihood estimates of the correlation coefficient when the data are modeled with either a Gaussian or a Student t-distribution, in the presence of left, right, or interval censored and/or missing data. The R package includes functions for conducting inference and also provides graphical functions for visualizing the censored data scatter plot and profile log likelihood function. The performance of clikcorr in a variety of circumstances is …

Dot-Pipe: An S3 Extensible Pipe For R, John Mount, Nina Zumel

The R Journal

Pipe notation is popular with a large league of R users, with magrittr being the dominant realization. However, this should not be enough to consider piping in R as a settled topic that is not subject to further discussion, experimentation, or possibility for improvement. To promote innovation opportunities, we describe the wrapr R package and “dot-pipe” notation, a well behaved sequencing operator with S3 extensibility. We include a number of examples of using this pipe to interact with and extend other R packages.

Clustmixtype: User-Friendly Clustering Of Mixed-Type Data In R, Gero Szepannek

The R Journal

Clustering algorithms are designed to identify groups in data where the traditional emphasis has been on numeric data. In consequence, many existing algorithms are devoted to this kind of data even though a combination of numeric and categorical data is more common in most business applications. Recently, new algorithms for clustering mixed-type data have been proposed based on Huang’s k-prototypes algorithm. This paper describes the R package clustMixType which provides an implementation of k-prototypes in R.

Editorial, John Verzani

The R Journal

On behalf of the editorial board, I am pleased to present Volume 10, Issue 2 of the R Journal.

This issue covers a wide range of topics through its 37 articles. As is typical, many of these are related to packages that provide tools for new statistical modeling in R. Examples in this issue include "clustMixType: User-Friendly Clustering of Mixed-Type Data in R" by Szepannek and "BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection" by Papageorgiou.

Downside Risk Evaluation With The R Package Gas, David Ardia, Kris Boudt, Leopoldo Catania

The R Journal

Financial risk managers routinely use non–linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so–called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions. This paper shows how the R package GAS can be used for both the dynamic prediction and the evaluation of downside risk. Emphasis is given to the two key financial downside risk measures: Value-at-Risk (VaR) and Expected Shortfall (ES). High-level functions for: (i) prediction, (ii) backtesting, and (iii) model comparison are discussed, and code examples are provided. …

Explanations Of Model Predictions With Live And Breakdown Packages, Mateusz Staniak, Przemysław Biecek

The R Journal

Complex models are commonly used in predictive modeling. In this paper we present R packages that can be used for explaining predictions from complex black box models and attributing parts of these predictions to input features. We introduce two new approaches and corresponding packages for such attribution, namely live and breakDown. We also compare their results with existing implementations of state-of-the-art solutions, namely, lime (Pedersen and Benesty, 2018) which implements Locally Interpretable Model-agnostic Explanations and iml (Molnar et al., 2018) which implements Shapley values.

Sdpt3r: Semidefinite Quadratic Linear Programming In R, Adam Rahman

The R Journal

We present the package sdpt3r, an R implementation of the Matlab package SDPT3 (Toh et al., 1999). The purpose of the software is to solve semidefinite quadratic linear programming (SQLP) problems, which encompasses problems such as D-optimal experimental design, the nearest correlation matrix problem, and distance weighted discrimination, as well as problems in graph theory such as finding the maximum cut or Lovasz number of a graph.

Current optimization packages in R include Rdsdp, Rcsdp, scs, cccp, and Rmosek. Of these, scs and Rmosek solve a similar suite of problems. In addition to these …

Geospatial Point Density, Paul F. Evangelista, David Beskow

The R Journal

This paper introduces a spatial point density algorithm designed to be explainable, meaning ful, and efficient. Originally designed for military applications, this technique applies to any spatial point process where there is a desire to clearly understand the measurement of density and maintain fidelity of the point locations. Typical spatial density plotting algorithms, such as kernel density estimation, implement some type of smoothing function that often results in a density value that is difficult to interpret. The purpose of the visualization method in this paper is to understand spatial point activity density with precision and meaning. The temporal tendency of …

Lmridge: A Comprehensive R Package For Ridge Regression, Muhammad Imdad Ullah, Bahauddin Zakariya University Aslam, Saima Atlaf

The R Journal

The ridge regression estimator, one of the commonly used alternatives to the conventional ordinary least squares estimator, avoids the adverse effects in the situations when there exists some considerable degree of multicollinearity among the regressors. There are many software packages available for estimation of ridge regression coefficients. However, most of them display limited methods to estimate the ridge biasing parameters without testing procedures. Our developed package, lmridge can be used to estimate ridge coefficients considering a range of different existing biasing parameters, to test these coefficients with more than 25 ridge related statistics, and to present different graphical displays of …

Lp Algorithms For Portfolio Optimization: The Portfoliooptim Package, Andrzej Palczewski

The R Journal

The paper describes two algorithms for financial portfolio optimization with the following risk measures: CVaR, MAD, LSAD and dispersion CVaR. These algorithms can be applied to discrete distributions of asset returns since then the optimization problems can be reduced to linear programs. The first algorithm solves a simple recourse problem as described by Haneveld using Benders de composition method. The second algorithm finds an optimal portfolio with the smallest distance to a given benchmark portfolio and is an adaptation of the least norm solution (called also normal solution) of linear programs due to Zhao and Li. The algorithms are implemented …

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.5.0 patched

CHANGES IN R 3.5.0

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

The R Journal

In the past 7 months, 1178 new packages were added to the CRAN package repository. 18 packages were unarchived, 493 archived and none removed. The following shows the growth of the number of active packages in the CRAN package repository:

R Day Report, Fernando P. Mayer, Walmes M. Zeviani, Wagner H. Bonat, Elias T. Krainski, Paulo J. Ribeiro Jr.

The R Journal

R Day¹- National Meeting of R Users, took place on May, 22, 2018 at Federal University of Paraná (UFPR), Curitiba, Brazil. It was the first event in Brazil endorsed by The R Foundation.

Setmethods: An Add-On R Package For Advanced Qca, Ioana-Elena Oana, Carsten Q. Scheider

The R Journal

This article presents the functionalities of the R package SetMethods, aimed at performing advanced set-theoretic analyses. This includes functions for performing set-theoretic multi-method research, set-theoretic theory evaluation, Enhanced Standard Analysis, diagnosing the impact of temporal, spatial, or substantive clusterings of the data on the results obtained via Qualitative Comparative Analysis (QCA), indirect calibration, and visualising QCA results via XY plots or radar charts. Each functionality is presented in turn, the conceptual idea and the logic behind the procedure being first summarized, and afterwards illustrated with data from Schneider et al. (2010).

Simple Features For R: Standardized Support For Spatial Vector Data, Edzer Pebesma

The R Journal

Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos, and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to connect R to other computer systems. We illustrate this with examples of its use.