Numerical Analysis and Scientific Computing Commons^™

The Mosaic Package: Helping Students To Think With Data Using R, Randall Pruim, Daniel T. Kaplan, Nicholas J. Horton

The R Journal

The mosaic package provides a simplified and systematic introduction to the core functional ity related to descriptive statistics, visualization, modeling, and simulation-based inference required in first and second courses in statistics. This introduction to the package describes some of the guiding principles behind the design of the package and provides illustrative examples of several of the most important functions it implements. These can be combined to help students “think with data" using R in their early course work, starting with simple, yet powerful, declarative commands.

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

On Self-Selection Biases In Online Product Reviews, Nan Hu, Paul A. Pavlou, Jie Zhang

Research Collection School Of Computing and Information Systems

Online product reviews help consumers infer product quality, and the mean (average) rating is often used as a proxy for product quality. However, two self-selection biases, acquisition bias (mostly consumers with a favorable predisposition acquire a product and hence write a product review) and underreporting bias (consumers with extreme, either positive or negative, ratings are more likely to write reviews than consumers with moderate product ratings), render the mean rating a biased estimator of product quality, and they result in the well-known J-shaped (positively skewed, asymmetric, bimodal) distribution of online product reviews. To better understand the nature and consequences of …

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.

Compress: A Comprehensive Framework Of Trajectory Compression In Road Networks, Yunheng Han, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

More and more advanced technologies have become available to collect and integrate an unprecedented amount of data from multiple sources, including GPS trajectories about the traces of moving objects. Given the fact that GPS trajectories are vast in size while the information carried by the trajectories could be redundant, we focus on trajectory compression in this article. As a systematic solution, we propose a comprehensive framework, namely, COMPRESS (Comprehensive Paralleled Road-Network-Based Trajectory Compression), to compress GPS trajectory data in an urban road network. In the preprocessing step, COMPRESS decomposes trajectories into spatial paths and temporal sequences, with a thorough justification …

The Acquisition And Analysis Of Electroencephalogram Data For The Classification Of Benign Partial Epilepsy Of Childhood With Centrotemporal Spikes, Jessica A. Scarborough

Master's Theses

In this thesis, I will expand upon each step in the process of acquiring and analyzing electroencephalogram (EEG) for the classification of benign childhood epilepsy with centrotemporal spikes. Despite huge advancements in the field of health informatics—natural language processing, machine learning, predictive modeling—there are significant barriers to the access of clinical data. These barriers include information blocking, privacy policy concerns, and a lack of stakeholder support. We will see that these roadblocks are all responsible for stunting biomedical research in some way, including my own experiences in acquiring the data for the second chapter of this thesis.

This second chapter …

Comparing Tensorflow Deep Learning Performance Using Cpus, Gpus, Local Pcs And Cloud, John Lawrence, Jonas Malmsten, Andrey Rybka, Daniel A. Sabol, Ken Triplin

Publications and Research

Deep learning is a very computational intensive task. Traditionally GPUs have been used to speed-up computations by several orders of magnitude. TensorFlow is a deep learning framework designed to improve performance further by running on multiple nodes in a distributed system. While TensorFlow has only been available for a little over a year, it has quickly become the most popular open source machine learning project on GitHub. The open source version of TensorFlow was originally only capable of running on a single node while Google’s proprietary version only was capable of leveraging distributed systems. This has now changed. In this …

Optimized Forecasting Of Dominant U.S. Stock Market Equities Using Univariate And Multivariate Time Series Analysis Methods, Michael Schwartz

Computational and Data Sciences Theses

This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development …

Electrodynamical Modeling For Light Transport Simulation, Michael G. Saunders

Undergraduate Honors Theses

Modernity in the computer graphics community is characterized by a burgeoning interest in physically based rendering techniques. That is to say that mathematical reasoning from first principles is widely preferred to ad hoc, approximate reasoning in blind pursuit of photorealism. Thereby, the purpose of our research is to investigate the efficacy of explicit electrodynamical modeling by means of the generalized Jones vector given by Azzam [1] and the generalized Jones matrix given by Ortega-Quijano & Arce-Diego [2] in the context of stochastic light transport simulation for computer graphics. To augment the status quo path tracing framework with such a modeling …

Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …

Molecular Dynamics Simulations Of Dna-Functionalized Nanoparticle Building Blocks On Gpus, Tyler Landon Fochtman

Graduate Theses and Dissertations

This thesis discusses massively parallel molecular dynamics simulations of nBLOCKs using graphical processing units. nBLOCKs are nanoscale building blocks composed of gold nanoparticles functionalized with single-stranded DNA molecules. To explore greater simulation time scales we implement our nBLOCK computational model as an extension to the coarse grain molecular simulator oxDNA. oxDNA is parameterized to match the thermodynamics of DNA strand hybridization as well as the mechanics of single stranded DNA and double stranded DNA. In addition to an in-depth review of our implementation details we also provide results of the model validation and performance tests. These validation and performance tests …