Numerical Analysis and Scientific Computing Commons^™

Splitting It Up: The Spduration Split-Population Duration Regression Package For Time-Varying Covariates, Andreas Beger, Daniel W. Hill Jr, Nils W. Metternich, Shahryar Minhas, Michael D. Ward

The R Journal

We present an implementation of split-population duration regression in the spduration (Beger et al., 2017) package for R that allows for time-varying covariates. The statistical model accounts for units that are immune to a certain outcome and are not part of the duration process the researcher is primarily interested in. We provide insights for when immune units exist, that can significantly increase the predictive performance compared to standard duration models. The package includes estimation and several post-estimation methods for split-population Weibull and log-logistic models. Weprovide an empirical application to data on military coups.

Partial Rank Data With The Hyper2 Package: Likelihood Functions For Generalized Bradley-Terry Models, Robin K. S Hankin

The R Journal

Here I present the hyper2 package for generalized Bradley-Terry models and give examples from two competitive situations: single scull rowing, and the competitive cooking game show Master Chef Australia. A number of natural statistical hypotheses may be tested straightforwardly using the software.

Riskregression: Predicting The Risk Of An Event Using Cox Regression Models, Brice Ozenne, Anne Lyngholm Sørensen, Thomas Scheike, Christian Torp-Pedersen, Thomas Alexander Gerds

The R Journal

In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++functions with an R inter face for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by-product we obtain fast access …

Anomalydetection: Implementation Of Augmented Network Log Anomaly Detection Procedures, Robert J. Gutierrez, Bradley C. Boehmke, Air Force Institute Of Technology, Cade M. Saie, Trevor J. Bihl

The R Journal

As the number of cyber-attacks continues to grow on a daily basis, so does the delay in threat detection. For instance, in 2015, the Office of Personnel Management discovered that approximately 21.5 million individual records of Federal employees and contractors had been stolen. On average, the time between an attack and its discovery is more than 200 days. In the case of the OPM breach, the attack had been going on for almost a year. Currently, cyber analysts inspect numerous potential incidents on a daily basis, but have neither the time nor the resources available to perform such a task. …

Liureg: A Comprehensive R Package For The Liu Estimation Of Linear Regression Model With Collinear Regressors, Muhammad Imdadullah, Muhammad Aslam, Saima Altaf

The R Journal

The Liu regression estimator is now a commonly used alternative to the conventional ordinary least squares estimator that avoids the adverse effects in the situations when there exists a considerable degree of multicollinearity among the regressors. There are only a few software packages available for estimation of the Liu regression coefficients, though with limited methods to estimate the Liu biasing parameter without addressing testing procedures. Our liureg package can be used to estimate the Liu regression coefficients utilizing a range of different existing biasing parameters, to test these coefficients with more than 15 Liu related statistics, and to present different …

Ider: Intrinsic Dimension Estimation With R, Hideitsu Hino

The R Journal

In many data analyses, the dimensionality of the observed data is high while its intrinsic dimension remains quite low. Estimating the intrinsic dimension of an observed dataset is an essential preliminary step for dimensionality reduction, manifold learning, and visualization. This paper introduces an R package, named ider, that implements eight intrinsic dimension estimation methods, including a recently proposed method based on a second-order expansion of a probability mass function and a generalized linear model. The usage of each function in the package is explained with datasets generated using a function that is also included in the package

Furniture For Quantitative Scientists, Tyson S. Barrett, Emily Brignone

The R Journal

A basic understanding of the distributions of study variables and the relationships among them is essential to inform statistical modeling. This understanding is achieved through the computation of summary statistics and exploratory data analysis. Unfortunately, this step tends to be under-emphasized in the research process, in part because of the often tedious nature of thorough exploratory data analysis. The table1() function in the furniture package streamlines much of the exploratory data analysis process, making the computation and communication of summary statistics simple and beautiful while offering significant time-savings to the researcher

Crtgeedr: An R Package For Doubly Robust Generalized Estimating Equations Estimations In Cluster Randomized Trials With Missing Data, Melanie Prague, Rui Wang, Victor De Gruttola

The R Journal

Semi-parametric approaches based on generalized estimating equations (GEE) are widely used to analyze correlated outcomes in longitudinal settings. In this paper, we present a package CRTgeeDR developed for cluster randomized trials with missing data (CRTs). For use of inverse probability weighting to adjust for missing data in cluster randomized trials, we show that other software lead to biased estimation for non-independence working correlation structure. CRTgeeDR solves this problem. We also extend the ability of existing packages to allow augmented Doubly Robust GEEestimation (DR). Simulation studies demonstrate the consistency of estimators implemented in CRTgeeDR compared to packages such as geepack and …

Queueing: A Package For Analysis Of Queueing Networks And Models In R, Pedro Cañadilla Jiménez, Yolanda Román Montoya

The R Journal

queueing is a package that solves and provides the main performance measures for both basic Markovian queueing models and single and multiclass product-form queueing networks. It can be used both in education and for professional purposes. It provides an intuitive, straightforward wayto build queueing models using S3 methods. The package solves Markovian models of the form M/M/c/K/M/FCFS, open and closed single class Jackson networks, open and closed multiclass networks and mixed networks. Markovian models are used when both the customer inter-arrival time and the server processing time are exponentially distributed. Queueing network solvers are useful for modelling situations in which …

Allele Imputation And Haplotype Determination From Databases Composed Of Nuclear Families, Nathan Medina-Rodríguez, Ángelo Santana

The R Journal

The alleHap package is designed for imputing genetic missing data and reconstruct non recombinant haplotypes from pedigree databases in a deterministic way. When genotypes of related individuals are available in a number of linked genetic markers, the program starts by identifying haplotypes compatible with the observed genotypes in those markers without missing values. If haplotypes are identified in parents or offspring, missing alleles can be imputed in subjects containing missing values. Several scenarios are analyzed: family completely genotyped, children partially genotyped and parents completely genotyped, children fully genotyped and parents containing entirely or partially missing genotypes, and founders and their …

Fourierin: An R Package To Compute Fourier Integrals, Guillermo Basulto-Elias, Alicia Carriquiry, Kris De Brabanter, Daniel J. Nordman

The R Journal

We present the R package fourierin (Basulto-Elias, 2017) for evaluating functions defined as Fourier-type integrals over a collection of argument values. The integrals are finitely supported with integrands involving continuous functions of one or two variables. As an important application, such Fourier integrals arise in so-called “inversion formulas”, where one seeks to evaluate a probability density at a series of points from a given characteristic function (or vice versa) through Fourier transforms. This paper intends to fill a gap in current R software, where tools for repeated evaluation of functions as Fourier integrals are not directly available. We implement two …

Anchoreddistr: A Package For The Bayesian Inversion Of Geostatistical Parameters With Multi-Type And Multi-Scale Data, Heather Savoy, Falk Heße, Yoram Rubin

The R Journal

The Method of Anchored Distributions (MAD) is a method for Bayesian inversion designed for inferring both local (e.g. point values) and global properties (e.g. mean and variogram parameters) of spatially heterogenous fields using multi-type and multi-scale data. Software implementations of MAD exist in C++ and C# to import data, execute an ensemble of forward model simulations, and perform basic post-processing of calculating likelihood and posterior distributions for a given application. This article describes the R package anchoredDistr that has been built to provide an R based environment for this method. In particular, anchoredDistr provides a range of post-processing capabilities for …

Editorial, Roger Bivand

The R Journal

In my editorial for the 2017–1 issue, I concentrated on tabulating the status of this journal with respect to its authors and reviewers (updated tables below). This time, I was prompted by an interesting blog posting by Jan Wijffels of BNOSAC, describing the use of the udpipe package to apply natural language processing (NLP) to the CRAN package database available from tools::CRAN_package_db() since the release of R 3.4. The interactive NLP searcher is a dashboard permitting exploration of annotated CRAN package title and description NLP data.

Examining The Electrical Excitation, Calcium Signaling, And Mechanical Contraction Cycle In A Heart Cell, Kristen Deetz, Nygel Foster, Darius Leftwich, Chad Meyer, Shalin Patel, Carlos Barajas, Matthias K. Gobbert, Zana Coulibaly

Spora: A Journal of Biomathematics

As the leading cause of death in the United States, heart disease has become a principal concern in modern society. Cardiac arrhythmias can be caused by a dysregulation of calcium dynamics in cardiomyocytes. Calcium dysregulation, however, is not yet fully understood and is not easily predicted; this provides motivation for the subsequent research. Excitation-contraction coupling (ECC) is the process through which cardiomyocytes undergo contraction from an action potential. Calcium induced calcium release (CICR) is the mechanism through which electrical excitation is coupled with mechanical contraction through calcium signaling. The study of the interplay between electrical excitation, calcium signaling, and mechanical …

Perfect Ratings With Negative Comments: Learning From Contradictory Patient Survey Responses, Andrew S. Gallan, Marina Girju, Roxana Girju

Patient Experience Journal

This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores and comments from 56,900 patients, collected from January 2015 through October 2016. Of the total number of responses, 30,485 (54%) contained at least one comment. For our analysis, we use a two-step approach: a quantitative analysis on the domain scores augmented by a qualitative text analysis of patients’ comments. To focus the research, we start by building …

Teaching Stats For Data Science, Daniel Kaplan

Faculty Publications

“Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records. Such datasets call for techniques not often part of an introduction to statistics: modeling, consideration of covariates, sophisticated visualization, and causal reasoning. This article re-imagines introductory statistics as an introduction to data science and proposes a sequence of 10 blocks that together compose a suitable course for extracting information from contemporary data. Recent extensions to the mosaic packages for R together with tools from the “tidyverse” provide a concise and readable notation for wrangling, visualization, …

A High Quality, Eulerian 3d Fluid Solver In C++, Lejon Anthony Mcgowan

Computer Science and Software Engineering

Fluids are a part of everyday life, yet are one of the hardest elements to properly render in computer graphics. Water is the most obvious entity when thinking of what a fluid simulation can achieve (and it is indeed the focus of this project), but many other aspects of nature, like fog, clouds, and particle effects. Real-time graphics like video games employ many heuristics to approximate these effects, but large-scale renderers aim to simulate these effects as closely as possible.

In this project, I wish to achieve effects of the latter nature. Using the Eulerian technique of discrete grids, I …

A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific …

Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via …

Indexable Bayesian Personalized Ranking For Efficient Top-K Recommendation, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Top-k recommendation seeks to deliver a personalized recommendation list of k items to a user. The dual objectives are (1) accuracy in identifying the items a user is likely to prefer, and (2) efficiency in constructing the recommendation list in real time. One direction towards retrieval efficiency is to formulate retrieval as approximate k nearest neighbor (kNN) search aided by indexing schemes, such as locality-sensitive hashing, spatial trees, and inverted index. These schemes, applied on the output representations of recommendation algorithms, speed up the retrieval process by automatically discarding a large number of potentially irrelevant items when given a user …