Programming Languages and Compilers Commons^™

Aliner: An R Package For Optimizing Feature-Weighted Alignments And Linguistic Distances, Sean S. Downey, Guowei Sun, Peter Norquest

The R Journal

Linguistic distance measurements are commonly used in anthropology and biology when quantitative and statistical comparisons between words are needed. This is common, for example, when analyzing linguistic and genetic data. Such comparisons can provide insight into historical population patterns and evolutionary processes. However, the most commonly used linguistic distances are derived from edit distances, which do not weight phonetic features that may, for example, represent smaller-scale patterns in linguistic evolution. Thus, computational methods for calculating feature-weighted linguistic distances are needed for linguistic, biological, and evolutionary applications; additionally, the linguistic distances presented here are generic and may have broader applications in …

Multilabel Classification With R Package Mlr, Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl

The R Journal

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of random ForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.

Weighted Effect Coding For Observational Data With Wec, Rense Nieuwenhuis, Manfred Te Grotenhuis, Ben Pelzer

The R Journal

Weighted effect coding refers to a specific coding matrix to include factor variables in generalised linear regression models. With weighted effect coding, the effect for each category represents the deviation of that category from the weighted mean (which corresponds to the sample mean). This technique has particularly attractive properties when analysing observational data, that commonly are unbalanced. The wec package is introduced, that provides functions to apply weighted effect coding to factor variables, and to interactions between (a.) a factor variable and a continuous variable and between (b.) two factor variables.

Pdp: An R Package For Constructing Partial Dependence Plots, Brandon M. Greenwell

The R Journal

Complex nonparametric models—like neural networks, random forests, and support vector machines—are more common than ever in predictive analytics, especially when dealing with large observational databases that don’t adhere to the strict assumptions imposed by traditional statistical techniques (e.g., multiple linear regression which assumes linearity, homoscedasticity, and normality). Unfortunately, it can be challenging to understand the results of such models and explain them to management. Partial dependence plots offer a simple solution. Partial dependence plots are low dimensional graphical renderings of the prediction function so that the relationship between the outcome and predictors of interest can be more easily understood. These …

R Foundation News, Torsten Hothorn

The R Journal

Donations and members

Donations

Supporting benefactors

Supporting institutions

Supporting members

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.4.1

CHANGES IN R 3.4.0

CHANGES IN R 3.3.3

Checkmate: Fast Argument Checks For Defensive R Programming, Michel Lang

The R Journal

Dynamically typed programming languages like R allow programmers to write generic, flexible and concise code and to interact with the language using an interactive Readeval-print-loop (REPL). However, this flexibility has its price: As the R interpreter has no information about the expected variable type, many base functions automatically convert the input instead of raising an exception. Unfortunately, this frequently leads to runtime errors deeper down the call stack which obfuscates the original problem and renders debugging challenging. Even worse, unwanted conversions can remain undetected and skew or invalidate the results of a statistical analysis. As a resort, assertions can be …

Pgee: An R Package For Analysis Of Longitudinal Data With High-Dimensional Covariates, Gul Inan, Lan Wang

The R Journal

We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) procedure proposed by Wang et al. (2012) to analyze longitudinal data with a large number of covariates. The PGEE package includes three main functions: CVfit, PGEE, and MGEE. The CVfit function computes the cross-validated tuning parameter for penalized generalized estimating equations. The function PGEE performs simultaneous estimation and variable selection for longitudinal data with high-dimensional covariates; whereas the function MGEE fits unpenalized GEE to the data for comparison. The R package PGEE is illustrated using a yeast cell-cycle gene expression data set.

Conference Report: European R Users Meeting 2016, Maciej Beręsewicz, Adolfo Alvarez, Przemysław Biecek, Marcin K. Dyderski, Marcin Kosinski, Jakub Nowosad, Kamil Rotter, Alicja Szabelska-Beręsewicz, Marcin Szymkowiak, Łukasz Wawrowski, Joanna Zyprych-Walczak

The R Journal

The European R Users Meeting (eRum) 2016 was an international conference aimed at integrating users of the R language. eRum 2016 was held between October 12 and 14, 2016, in Pozna´ n, Poland at Pozna´ n University of Economics and Business (http://erum.ue. poznan.pl/).

Retrieval And Analysis Of Eurostat Open Data With The Eurostat Package, Leo Lahti, Janne Huovari, Markus Kainu, Przemysław Biecek

The R Journal

The increasing availability of open statistical data resources is providing novel opportunities for research and citizen science. Efficient algorithmic tools are needed to realize the full potential of the new information resources. We introduce the eurostat R package that provides a collection of custom tools for the Eurostat open data service, including functions to query, download, manipulate, and visualize these data sets in a smooth, automated and reproducible manner. The online documentation provides detailed examples on the analysis of these spatio-temporal data collections. This work provides substantial improvements over the previously available tools, and has been extensively tested by an …

Emsaov: An R Package For The Analysis Of Variance With The Expected Mean Squares And Its Shiny Application, Hye-Min Choe, Mijeong Kim, Eun-Kyung Lee

The R Journal

EMSaov is a new R package that we developed to provide users with an analysis of variance table including the expected mean squares (EMS) for various types of experimental design. It is not easy to find the appropriate test, particularly the denominator for the F statistic that depends on the EMS, when some variables exhibit random effects or when we use a special experimental design such as nested design, repeated measures design, or split-plot design. With EMSaov, a user can easily f ind the F statistic denominator and can determine how to analyze the data when using a special …

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

The R Journal

In the past 4 months, 794 new packages were added to the CRAN package repository. 16 packages were unarchived, 98 archived and 1 removed. The following shows the growth of the number of active packages in the CRAN package repository:

Smoof: Single- And Multi-Objective Optimization Test Functions, Jakob Bossek

The R Journal

Benchmarking algorithms for optimization problems usually is carried out by running the algorithms under consideration on a diverse set of benchmark or test functions. A vast variety of test functions was proposed by researchers and is being used for investigations in the literature. The smoof package implements a large set of test functions and test function generators for both the single and multi-objective case in continuous optimization and provides functions to easily create own test functions. Moreover, the package offers some additional helper methods, which can be used in the context of optimization.

Counterfactual: An R Package For Counterfactual Analysis, Mingli Chen, Victor Chernozhukov, Iván Fernández-Val, Blaise Melly

The R Journal

The Counterfactual package implements the estimation and inference methods of Cher nozhukov et al. (2013) for counterfactual analysis. The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions. This paper serves as an introduction to the package and displays basic functionality of the commands contained within.

Implementing A Metapopulation Bass Diffusion Model Using The R Package Desolve, Jim Duggan

The R Journal

Diffusion is a fundamental process in physical, biological, social and economic settings. Consumer products often go viral, with sales driven by the word of mouth effect, as their adoption spreads through a population. The classic diffusion model used for product adoption is the Bass diffusion model, and this divides a population into two groups of people: potential adopters who are likely to adopt a product, and adopters who have purchased the product, and influence others to adopt. The Bass diffusion model is normally captured in an aggregate form, where no significant consumer differences are modeled. This paper extends the Bass …

Spcadjust: An R Package For Adjusting For Estimation Error In Control Charts, Axel Gandy, Jan Terje Kvaløy

The R Journal

In practical applications of control charts the in-control state and the corresponding chart parameters are usually estimated based on some past in-control data. The estimation error then needs to be accounted for. In this paper we present an R package, spcadjust, which implements a bootstrap based method for adjusting monitoring schemes to take into account the estimation error. By bootstrapping the past data this method guarantees, with a certain probability, a conditional performance of the chart. In spcadjust the method is implement for various types of Shewhart, CUSUM and EWMA charts,various performance criteria, and both parametric and non-parametric bootstrap …

Coxphmic: An R Package For Sparse Estimation Of Cox Proportional Hazards Models Via Approximated Information Criteria, Razieh Nabi, Xiaogang Su

The R Journal

In this paper, we describe an R package named coxphMIC, which implements the sparse estimation method for Cox proportional hazards models via approximated information criterion (Su et al., 2016). The developed methodology is named MIC which stands for “Minimizing approximated Information Criteria". A reparameterization step is introduced to enforce sparsity while at the same time keeping the objective function smooth. As a result, MIC is computationally fast with a superior performance in sparse estimation. Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing post-selection inference (Leeb and Pötscher, 2005). The MIC method and its R implementation …

Isogenegui: Multiple Approaches For Dose-Response Analysis Of Microarray Data Using R, Martin Otava, Rudradev Sengupta, Ziv Shkedy, Dan Lin, Setia Pramana, Tobias Verbeke, Philippe Haldermans, Ludwig A. Hothorn, Daniel Gerhard, Rebecca M. Kuiper, Florian Klinglmueller, Adetayo Kasim

The R Journal

The analysis of transcriptomic experiments with ordered covariates, such as dose-response data, has become a central topic in bioinformatics, in particular in omics studies. Consequently, multiple R packages on CRAN and Bioconductor are designed to analyse microarray data from various perspectives under the assumption of order restriction. We introduce the new R package IsoGene Graphical User Interface (IsoGeneGUI), an extension of the original IsoGene package that includes methods from most of available R packages designed for the analysis of order restricted microarray data, namely orQA, ORIClust, goric and ORCME. The methods included in the new …

Gsympoint: An R Package To Estimate The Generalized Symmetry Point, An Optimal Cut-Off Point For Binary Classification In Continuous Diagnostic Tests, Mónica López-Ratón, Elisa M. Molanes-López, Emilio Letón, Carmen Cadarso-Suárez

The R Journal

In clinical practice, it is very useful to select an optimal cutpoint in the scale of a continuous biomarker or diagnostic test for classifying individuals as healthy or diseased. Several methods for choosing optimal cutpoints have been presented in the literature, depending on the ultimate goal. One of these methods, the generalized symmetry point, recently introduced, generalizes the symmetry point by incorporating the misclassification costs. Two statistical approaches have been proposed in the literature for estimating this optimal cutpoint and its associated sensitivity and specificity measures, a parametric method based on the generalized pivotal quantity and a nonparametric method based …

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high throughput genomic data. Bioconductor 3.5 was released on 25 April, 2017. It is compatible with R 3.4 and consists of 1383 software packages, 316 experiment data packages, and 911 up-to-date annotation packages. The release announcement includes descriptions of 88 new packages, and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands

Bayesbinmix: An R Package For Model Based Clustering Of Multivariate Binary Data, Panagiotis Papastamoulis, Magnus Rattray

The R Journal

The BayesBinMix package offers a Bayesian framework for clustering binary data with or without missing values by fitting mixtures of multivariate Bernoulli distributions with an unknown number of components. It allows the joint estimation of the number of clusters and model parameters using Markov chain Monte Carlo sampling. Heated chains are run in parallel and accelerate the convergence to the target posterior distribution. Identifiability issues are addressed by implementing label switching algorithms. The package is demonstrated and benchmarked against the Expectation Maximization algorithm using a simulation study as well as a real dataset.

The R Journal (December 2016) 8(2): Complete Issue, The R Foundation

The R Journal

Editorial, Michael Lawrence

Contributed Research Articles

multipleNCC: Inverse Probability Weighting of Nested Case-Control Data, Nathalie C. Støer and Sven Ove Samuelsen

QPot: An R Package for Stochastic Differential Equation Quasi-Potential Analysis, Christopher M. Moore, Christopher R. Stieha, Ben C. Nolting, Maria K. Cameron, and Karen C. Abbott

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy, Marco Antoniotti, Giulio Caravagna, Luca De Sano, Alex Graudenzi, Giancarlo Mauri, Bud Mishra, and Daniele Ramazzotti

diverse: An R Package to Analyze Diversity in Complex Systems, Miguel R. Guevara, Dominik Hartmann, and Marcelo Mendoza

Simulating Correlated Binary and Multinomial Responses under Marginal Model Specification: …

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.3.2 patched

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high throughput genomic data. Bioconductor 3.4 was released on 18 October, 2016. It is com patible with R 3.3 and consists of 1296 software packages, 309 experiment data packages, and 933 up-to-date annotation packages. The release announcement includes descriptions of 101 new packages, and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands

Mctest: An R Package For Detection Of Collinearity Among Regressors, Muhammad Imdadullah, Muhammad Aslam, Saima Altaf

The R Journal

It is common for linear regression models to be plagued with the problem of multicollinearity when two or more regressors are highly correlated. This problem results in unstable estimates of regression coefficients and causes some serious problems in validation and interpretation of the model. Different diagnostic measures are used to detect multicollinearity among regressors. Many statistical software and R packages provide few diagnostic measures for the judgment of multicollinearity. Most widely used diagnostic measures in these software are: coefficient of determination (R2), variance inflation factor/tolerance limit (VIF/TOL), eigenvalues, condition number (CN) and condition index (CI) etc. In this manuscript, we …

Weighted Distance Based Discriminant Analysis: The R Package Wedibadis, Itziar Irigoien, Francesc Mestres, Concepcion Arenas

The R Journal

The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervised classification). WeDiBaDis is an easy to use package addressed to the biological and medical communities, and in general, to researchers interested in applied studies. It can be suitable when the user is interested in the problem of constructing a discriminant rule on the basis of distances between a relatively small number of instances or units of known unbalanced-class membership measured on many (possibly thousands) features of any type. This is a current situation when analyzing genetic biomedical data. This discriminant rule can then be used both, as …

Computing Pareto Frontiers And Database Preferences With The Rpref Package, Patrick Roocks

The R Journal

The concept of Pareto frontiers is well-known in economics. Within the database community there exist many different solutions for the specification and calculation of Pareto frontiers, also called Skyline queries in the database context. Slight generalizations like the combination of the Pareto operator with the lexicographical order have been established under the term database preferences. In this paper we present the rPref package which allows to efficiently deal with these concepts within R. With its help, database preferences can be specified in a very similar way as in a state-of-the-art database management system. Our package provides algorithms for an …

Dcovts: Distance Covariance/Correlation For Time Series, Maria Pitsillou, Konstantinos Fokianos

The R Journal

The distance covariance function is a new measure of dependence between random vectors. We drop the assumption of iid data to introduce distance covariance for time series. The R package dCovTS provides functions that compute and plot distance covariance and correlation functions for both univariate and multivariate time series. Additionally it includes functions for testing serial independence based on distance covariance. This paper describes the theoretical background of distance covariance methodology in time series and discusses in detail the implementation of these methods with the R package dCovTS.

Subgroup Discovery With Evolutionary Fuzzy Systems In R: The Sdefsr Package, Ángel M. García, Francisco Charte, Pedro González, Cristóbal J. Carmona, María J. Del Jesus

The R Journal

Subgroup discovery is a data mining task halfway between descriptive and predictive data mining. Nowadays it is very relevant for researchers due to the fact that the knowledge extracted is simple and interesting. For this task, evolutionary fuzzy systems are well suited algorithms because they can find a good trade-off between multiple objectives in large search spaces. In fact, this paper presents the SDEFSR package, which contains all the evolutionary fuzzy systems for subgroup discovery presented throughout the literature. It is a package without dependencies on other software, providing functions with recommended default parameters. In addition, it brings a graphical …

Variants Of Simple Correspondence Analysis, Rosaria Lombardo, Eric J. Beh

The R Journal

This paper presents the R package CAvariants (Lombardo and Beh, 2017). The package performs six variants of correspondence analysis on a two-way contingency table. The main function that shares the same name as the package– CAvariants– allows the user to choose (via a series of input parameters) from six different correspondence analysis procedures. These include the classical approach to (symmetrical) correspondence analysis, singly ordered correspondence analysis, doubly ordered correspondence analysis, non symmetrical correspondence analysis, singly ordered non symmetrical correspondence analysis and doubly ordered non symmetrical correspondence analysis. The code provides the flexibility for constructing either a classical correspondence plot or …