Numerical Analysis and Scientific Computing Commons^™

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.

Discovery And Characterization Of Small Molecule Rac1 Inhibitors, Jamie L. Arnst, Ashley L. Hein, Margaret A. Taylor, Nick Y. Palermo, Jacob I. Contreras, Yogesh A. Sonawane, Andrw O. Wahl, Michel M. Ouellette, Amarnath Natarajan, Ying Yan

Holland Computing Center: Faculty Publications

Aberrant activation of Rho GTPase Rac1 has been observed in various tumor types, including pancreatic cancer. Rac1 activates multiple signaling pathways that lead to uncontrolled proliferation, invasion and metastasis. Thus, inhibition of Rac1 activity is a viable therapeutic strategy for proliferative disorders such as cancer. Here we identified small molecule inhibitors that target the nucleotide-binding site of Rac1 through in silico screening. Follow up in vitro studies demonstrated that two compounds blocked active Rac1 from binding to its effector PAK1. Fluorescence polarization studies indicate that these compounds target the nucleotide-binding site of Rac1. In cells, both compounds blocked Rac1 binding …

End-To-End Molecular Communication Channels In Cell Metabolism: An Information Theoretic Study, Zahmeeth Sayed Sakkaff, Jennie L. Catlett, Mikaela Cashman, Massimiliano Pierobon, Nicole R. Buan, Myra B. Cohen, Christine A. Kelley

Department of Biochemistry: Faculty Publications

The opportunity to control and fine-tune the behavior of biological cells is a fascinating possibility for many diverse disciplines, ranging from medicine and ecology, to chemical industry and space exploration. While synthetic biology is providing novel tools to reprogram cell behavior from their genetic code, many challenges need to be solved before it can become a true engineering discipline, such as reliability, safety assurance, reproducibility and stability. This paper aims to understand the limits in the controllability of the behavior of a natural (non-engineered) biological cell. In particular, the focus is on cell metabolism, and its natural regulation mechanisms, and …

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 …

Diverse: An R Package To Analyze Diversity In Complex Systems, Miguel R. Guevara, Dominik Hartmann, Marcelo Mendoza

The R Journal

The package diverse provides an easy-to-use interface to calculate and visualize different aspects of diversity in complex systems. In recent years, an increasing number of research projects in social and interdisciplinary sciences, including fields like innovation studies, scientometrics, economics, and network science have emphasized the role of diversification and sophistication of socioeconomic systems. However, so far no dedicated package exists that covers the needs of these emerging fields and interdisciplinary teams. Most packages about diversity tend to be created according to the demands and terminology of particular areas of natural and biological sciences. The package diverse uses interdisciplinary concepts of …

Design Of The Tronco Bioconductor Package For Translational Oncology, Marco Antoniotti, Giulio Caravagna, Luca De Sano, Alex Graudenzi, Giancarlo Mauri, Bud Mishra, Daniele Ramazzotti

The R Journal

Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patients (cross-sectional data) have been defined in the literature since late the 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations.

TRONCO is an R package for TRanslational ONcology which provides a series of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements …

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

The R Journal

QPot (pronounced ky oo + p¨ at) is an R package for analyzing two-dimensional systems of stochastic differential equations. It provides users with a wide range of tools to simulate, analyze, and visualize the dynamics of these systems. One of QPot’s key features is the computation of the quasi-potential, an important tool for studying stochastic systems. Quasi-potentials are particularly useful for comparing the relative stabilities of equilibria in systems with alternative stable states. This paper describes QPot’s primary functions, and explains how quasi-potentials can yield insights about the dynamics of stochastic systems. Three worked examples guide users through the application …

Multiplencc: Inverse Probability Weighting Of Nested Case-Control Data, Nathalie C. Støer, Sven Ove Samuelsen

The R Journal

Reuse of controls from nested case-control designs can increase efficiency in many situations, for instance with competing risks or in other multiple endpoints situations. The matching between cases and controls must be broken when controls are to be used for other endpoints. A weighted analysis can then be performed to take care of the biased sampling from the cohort. We present the R package multipleNCC for reuse of controls in nested case-control studies by inverse probability weighting of the partial likelihood. The package handles right-censored, left-truncated and additionally matched data, and varying numbers of sampled controls and the whole analysis …

Editorial, Michael Lawrence

The R Journal

On behalf of the editorial board, I am pleased to publish Volume 8, Issue 2 of the R Journal. This issue contains 33 contributed research articles. Each of them either presents an R package, a specific extension of an R package or applications using R packages available from the Comprehensive R Archive Network (CRAN, http:://CRAN.R-project.org). This issue highlights the breadth and depth of the R package ecosystem, covering advances in statistical computing and visualization, as well as novel applications of R in specific domains. The authors have described a small but representative sample of the now more than 11000 packages …

Normal Tolerance Interval Procedures In The Tolerance Package, Derek S. Young

The R Journal

Statistical tolerance intervals are used for a broad range of applications, such as quality control, engineering design tests, environmental monitoring, and bioequivalence testing. tolerance is the only R package devoted to procedures for tolerance intervals and regions. Perhaps the most commonly-employed functions of the package involve normal tolerance intervals. A number of new procedures for this setting have been included in recent versions of tolerance. In this paper, we discuss and illustrate the functions that implement these normal tolerance interval procedures, one of which is a new, novel type of operating characteristic curve.

Micompr: An R Package For Multivariate Independent Comparison Of Observations, Nuno Fachada, João Rodrigues, Vitor V. Lopes, Rui C. Martins, Agostinho C. Rosa

The R Journal

The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations

Rnrfa: An R Package To Retrieve, Filter And Visualize Data From The Uk National River Flow Archive, Claudia Vitolo, Matthew Fry, Wouter Buytaert

The R Journal

The UK National River Flow Archive (NRFA) stores several types of hydrological data and metadata: daily river flow and catchment rainfall time series, gauging station and catchment information. Data are served through the NRFA webservices via experimental RESTful APIs. Obtaining NRFA data can be unwieldy due to complexities in handling HTTP GET requests and parsing responses in JSON and XML formats. The rnrfa package provides a set of functions to programmatically access, filter, and visualize NRFA data using simple R syntax. This paper describes the structure of the rnrfa package, including examples using the main functions gdf() and cmr() for …

Simulating Correlated Binary And Multinomial Responses Under Marginal Model Specification: The Simcormultres Package, Anestis Touloumis

The R Journal

We developed the R package SimCorMultRes to facilitate simulation of correlated categorical (binary and multinomial) responses under a desired marginal model specification. The simulated correlated categorical responses are obtained by applying threshold approaches to correlated continuous responses of underlying regression models and the dependence structure is parametrized in terms of the correlation matrix of the latent continuous responses. This article provides an elaborate introduction to the SimCorMultRes package demonstrating its design and usage via three examples. The package can be obtained via CRAN.

Nmfgpu4r: Gpu-Accelerated Computation Of The Non-Negative Matrix Factorization (Nmf) Using Cuda Capable Hardware, Sven Koitka, Christoph M. Friedrich

The R Journal

In this work, a novel package called nmfgpu4R is presented, which offers the computation of Non-negative Matrix Factorization (NMF) on Compute Unified Device Architecture (CUDA) platforms within the R environment. Benchmarks show a remarkable speed-up in terms of time per iteration by utilizing the parallelization capabilities of modern graphics cards. Therefore the application of NMFgets more attractive for real-world sized problems because the time to compute a factorization is reduced by an order of magnitude.

Comf: An R Package For Thermal Comfort Studies, Marcel Schweiker

The R Journal

The field of thermal comfort generated a number of thermal comfort indices. Their code implementation needs to be done by individual researchers. This paper presents the R package, comf, which includes functions for common and new thermal comfort indices. Additional functions allow comparisons between the predictive performance of these indices. This paper reviews existing thermal comfort indices and available code implementations. This is followed by the description of the R package and an example how to use the R package for the comparison of different thermal comfort indices on data from a thermal comfort study.