Numerical Analysis and Scientific Computing Commons^™

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.

An Introduction To Principal Surrogate Evaluation With The Pseval Package, Michael C. Sachs, Erin E. Gabriel

The R Journal

We describe a new package called pseval that implements the core methods for the evaluation of principal surrogates in a single clinical trial. It provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation methods are provided, including print, summary, plot, and testing. We summarize the main statistical methods that are implemented in the package and illustrate its use from the perspective …

Quantreg.Nonpar: An R Package For Performing Nonparametric Series Quantile Regression, Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov, Iván Fernández-Val

The R Journal

The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides point-wise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.

Two-Tier Latent Class Irt Models In R, Silvia Bacci, Francesco Bartolucci

The R Journal

In analyzing data deriving from the administration of a questionnaire to a group of individuals, Item Response Theory (IRT) models provide a flexible framework to account for several aspects involved in the response process, such as the existence of multiple latent traits. In this paper, we focus on a class of semi-parametric multidimensional IRT models, in which these traits are represented through one or more discrete latent variables; these models allow us to cluster individuals into homogeneous latent classes and, at the same time, to properly study item characteristics. In particular, we follow a within-item multidimensional formulation similar to that …

Qtools: A Collection Of Models And Tools For Quantile Inference, Marco Geraci

The R Journal

Quantiles play a fundamental role in statistics. The quantile function defines the distribution of a random variable and, thus, provides a way to describe the data that is specular but equivalent to that given by the corresponding cumulative distribution function. There are many advantages in working with quantiles, starting from their properties. The renewed interest in their usage seen in the last years is due to the theoretical, methodological, and software contributions that have broadened their applicability. This paper presents the R package Qtools, a collection of utilities for unconditional and conditional quantiles.

R Foundation News, Torsten Hothorn

The R Journal

New benefectors

Donations

New supporting institutions

New supporting members

Ggfortify: Unified Interface To Visualize Statistical Results Of Popular R Packages, Yuan Yuan, Masaaki Horikoshi, Wenxuan Li

The R Journal

The ggfortify package provides a unified interface that enables users to use one line of code to visualize statistical results of many R packages using ggplot2 idioms. With the help of ggfortify, statisticians, data scientists, and researchers can avoid the sometimes repetitive work of using the ggplot2 syntax to achieve what they need.

Ake: An R Package For Discrete And Continuous Associated Kernel Estimations, Wanbitching E. Wansouwé, Sobom M. Somé, Célestin C. Kokonendji

The R Journal

Kernel estimation is an important technique in exploratory data analysis. Its utility relies on its ease of interpretation, especially based on graphical means. The Ake package is introduced for univariate density or probability mass function estimation and also for continuous and discrete regression functions using associated kernel estimators. These associated kernels have been proposed due to their specific features of variables of interest. The package focuses on associated kernel methods appropriate for continuous (bounded, positive) or discrete (count, categorical) data often found in applied settings. Furthermore, optimal bandwidths are selected by cross-validation for any associated kernel and by Bayesian methods …

Eicompare: Comparing Ecological Inference Estimates Across Ei And Ei:Rc, Loren Collingwood, Kassra Oskooii, Sergio Garcia-Rios, Matt Barreto

The R Journal

Social scientists and statisticians often use aggregate data to predict individual-level behavior because the latter are not always available. Various statistical techniques have been developed to make inferences from one level (e.g., precinct) to another level (e.g., individual voter) that minimize errors associated with ecological inference. While ecological inference has been shown to be highly problematic in a wide array of scientific fields, many political scientists and analysis employ the techniques when studying voting patterns. Indeed, federal voting rights lawsuits now require such an analysis, yet expert reports are not consistent in which type of ecological inference is used. This …

Calculating Biological Module Enrichment Or Depletion And Visualizing Data On Large-Scale Molecular Maps With Acsnminer And Rnavicell Packages, Paul Deveau, Emmanuel Barillot, Valentina Boeva, Andrei Zinovyev, Eric Bonnet

The R Journal

Biological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, the Atlas of Cancer Signaling Network (ACSN) being an example. All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe ACSNMineR for calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSNmolecular pathways gene sets, but can use any gene set encoded as a GMT file, …

Escape From Boxland, Barret Schloerke, Hadley Wickham, Dianne Cook, Heike Hofmann

The R Journal

A library of common geometric shapes can be used to train our brains for understanding data structure in high-dimensional Euclidean space. This article describes the methods for producing cubes, spheres, simplexes, and tori in multiple dimensions. It also describes new ways to define and generate high-dimensional tori. The algorithms are described, critical code chunks are given, and a large collection of generated data are provided. These are available in the R package geozoo, and selected movies and images, are available on the GeoZoo web site (http://schloerke.github.io/geozoo/)