Computer Sciences Commons^™

Clustcurv: An R Package For Determining Groups In Multiple Curves, Nora M. Villanueva, Marta Sestelo, Luis Meira-Machado, Javier Roca-Pardiñas

The R Journal

In many situations, it could be interesting to ascertain whether groups of curves can be performed, especially when confronted with a considerable number of curves. This paper introduces an R package, known as clustcurv, for determining clusters of curves with an automatic selection of their number. The package can be used for determining groups in multiple survival curves as well as for multiple regression curves. Moreover, it can be used with large numbers of curves. An illustration of the use of clustcurv is provided, using both real data examples and artificial data

Towards A Grammar For Processing Clinical Trial Data, Michael J. Kane

The R Journal

The goal of this paper is to help define a path toward a grammar for processing clinical trials by a) defining a format in which we would like to represent data from standardized clinical trial data b) describing a standard set of operations to transform clinical trial data into this format, and c) to identify a set of verbs and other functionality to facilitate data processing and encourage reproducibility in the processing of these data. It provides a background on standard clinical trial data and goes through a simple preprocessing example illustrating the value of the proposed approach through the …

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2021-01-29 and 2021-07-05.

Package Wsbackfit For Smooth Backfitting Estimation Of Generalized Structured Models, Javier Roca-Pardiñas, María Xosé Rodríguez-Álvarez, Stefan Sperlich

The R Journal

A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, generalized varying coefficient model, generalized additive models, mixtures, potentially including parametric parts. The kernel-based weighted smooth backfitting belongs to the statistically most efficient procedures for this model class. Its asymptotic properties are well-understood thanks to the large body of literature about this estimator. The introduced weights allow for the inclusion of sampling weights, trimming, and efficient estimation under heteroscedasticity. Further options facilitate easy handling of aggregated data, prediction, and …

Dchaos: An R Package For Chaotic Time Series Analysis, Julio E. Sandubete, Lorenzo Escot

The R Journal

Chaos theory has been hailed as a revolution of thoughts and attracting ever-increasing attention of many scientists from diverse disciplines. Chaotic systems are non-linear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory is the detection of chaotic behavior from empirical time-series data. One of the main features of chaos is the well-known initial-value sensitivity property. Methods and techniques related to testing the hypothesis of chaos try to quantify the initial-value sensitive property estimating the so-called Lyapunov exponents. This paper describes the main estimation methods of the Lyapunov exponent from …

Rlumcarlo: Simulating Cold Light Using Monte Carlo Methods, Sebastian Kreutzer, Johannes Friedrich, Vasilis Pagonis, Christian Laag, Ena Rajovic, Christoph Schmidt

The R Journal

The luminescence phenomena of insulators and semiconductors (e.g., natural minerals such as quartz) have various application domains. For instance, Earth Sciences and archaeology exploit luminescence as a dating method. Herein, we present the R package RLumCarlo implementing sets of luminescence models to be simulated with Monte Carlo (MC) methods. MC methods make a powerful ally to all kinds of simulation attempts involving stochastic processes. Luminescence production is such a stochastic process in the form of charge (electron-hole pairs) interaction within insulators and semiconductors. To simulate luminescence-signal curves, we distribute single and independent MC processes to virtual MC clusters. RLumCarlo comes …

Reproducible Summary Tables With The Gtsummary Package, Daniel D. Sjoberg, Karissa Whiting, Michael Curry, Jessica A. Lavery, Joseph Larmarange

The R Journal

The gtsummary package provides an elegant and flexible way to create publication-ready summary tables in R. A critical part of the work of statisticians, data scientists, and analysts is summarizing data sets and regression models in R and publishing or sharing polished summary tables. The gtsummary package was created to streamline these everyday analysis tasks by allowing users to easily create reproducible summaries of data sets, regression models, survey data, and survival data with a simple interface and very little code. The package follows a tidy framework, making it easy to integrate with standard data workflows, and offers many table …

Rocnreg: An R Package For Receiver Operating Characteristic Curve Inference With And Without Covariates, María Xosé Rodríguez-Álvarez, Vanda Inácio

The R Journal

This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden index criterion and the criterion that sets a target value for the false positive …

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.13 was released on 20 May, 2021. It is compatible with R 4.1.0 and consists of 2042 software packages, 406 experiment data packages, 965 up-to-date annotation packages, and 29 workflows.

Statistical Quality Control With The Qcr Package, Miguel Flores, Rubén Fernández-Casal, Salvador Naya, Javier Tarrío-Saavedra

The R Journal

The R package qcr for Statistical Quality Control (SQC) is introduced and described. It includes a comprehensive set of univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC (qcc, MSQC), qcr provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, [], S, R, p, np, c, u, EWMA, and CUSUM. In addition, it includes functions to perform multivariate control charts such as Hotelling T² , MEWMA and MCUSUM. As …

Jmcmprsk: An R Package For Joint Modelling Of Longitudinal And Survival Data With Competing Risks, Hong Wang, Ning Li, Shanpeng Li, Gang Li

The R Journal

In this paper, we describe an R package named JMcmprsk, for joint modelling of longitudinal and survival data with competing risks. The package in its current version implements two joint models of longitudinal and survival data proposed to handle competing risks survival data together with continuous and ordinal longitudinal outcomes respectively (Elashoff et al., 2008; Li et al., 2010). The corresponding R implementations are further illustrated with real examples. The package also provides simulation functions to simulate datasets for joint modelling with continuous or ordinal outcomes under the competing risks scenario, which provide useful tools to validate and evaluate …

R Medicine 2020: The Power Of Going Virtual, Elizabeth J. Atkinson, Peter D. Higgins, Denise Esserman, Michael J. Kane, Steven J. Schwager, Joseph B. Rickert, Daniella Mark, Mara Alexeev, Stephan Kadauke

The R Journal

The third annual R/Medicine conference was planned as a physical event to be held in Philadelphia at the end of August 2020. However, a nationwide lockdown induced by the COVID-19 pandemic required a swift transition to a virtual conference. This article describes the challenges and benefits we encountered with this transition and provides an overview of the conference content.

Changes In R 4.0–4.1, Tomas Kalibera, Sebastian Meyer, Kurt Hornik

The R Journal

We give a selection of the most important changes in R 4.1.0. Some statistics on source code commits and bug tracking activities are also provided.

Bayesspsurv: An R Package To Estimate Bayesian (Spatial) Split-Population Survival Models, Brandon Bolte, Nicolás Schmidt, Sergio Béjar, Nguyen Huynh, Bumba Mukherjee

The R Journal

Survival data often include a fraction of units that are susceptible to an event of interest as well as a fraction of “immune” units. In many applications, spatial clustering in unobserved risk factors across nearby units can also affect their survival rates and odds of becoming immune. To address these methodological challenges, this article introduces our BayesSPsurv R-package, which fits parametric Bayesian Spatial split-population survival (cure) models that can account for spatial autocorrelation in both subpopulations of the user’s time-to-event data. Spatial autocorrelation is modeled with spatially weighted frailties, which are estimated using a conditionally autoregressive prior. The user can …

Gofcopula: Goodness-Of-Fit Tests For Copulae, Ostap Okhrin, Simon Trimborn, Martin Waltz

The R Journal

The last decades show an increased interest in modeling various types of data through copulae. Different copula models have been developed, which lead to the challenge of finding the best fitting model for a particular dataset. From the other side, a strand of literature developed a list of different Goodness-of-Fit (GoF) tests with different powers under different conditions. The usual practice is the selection of the best copula via the p-value of the GoF test. Although this method is not purely correct due to the fact that non-rejection does not imply acception, this strategy is favored by practitioners. Unfortunately, …

Working With Crsp/Compustat In R: Reproducible Empirical Asset Pricing, Majeed Simaan

The R Journal

It is common to come across SAS or Stata manuals while working on academic empirical finance research. Nonetheless, given the popularity of open-source programming languages such as R, there are fewer resources in R covering popular databases such as CRSP and COMPUSTAT. The aim of this article is to bridge the gap and illustrate how to leverage R in working with both datasets. As an application, we illustrate how to form size-value portfolios with respect to Fama and French (1993) and study the sensitivity of the results with respect to different inputs. Ultimately, the purpose of the article is to …

Garchx: Flexible And Robust Garch-X Modeling, Genaro Sucarrat

The R Journal

The garchx package provides a user-friendly, fast, flexible, and robust framework for the estimation and inference of GARCH(p, q,r)-X models, where p is the ARCH order, q is the GARCH order, r is the asymmetry or leverage order, and ’X’ indicates that covariates can be included. Quasi Maximum Likelihood (QML) methods ensure estimates are consistent and standard errors valid, even when the standardized innovations are non-normal or dependent, or both. Zero-coefficient restrictions by omission enable parsimonious specifications, and functions to facilitate the non-standard inference associated with zero-restrictions in the null-hypothesis are provided. Finally, in the formal comparisons of …

Onestep: Le Cam's One-Step Estimation Procedure, Alexandre Brouste, Christophe Dutang, Darel Noutsa Mieniedou

The R Journal

The OneStep package proposes principally an eponymic function that numerically computes Le Cam’s one-step estimator, which is asymptotically efficient and can be computed faster than the maximum likelihood estimator for large datasets. Monte Carlo simulations are carried out for several examples (discrete and continuous probability distributions) in order to exhibit the performance of Le Cam’s one-step estimation procedure in terms of efficiency and computational cost on observation samples of finite size.

Wide-To-Tall Data Reshaping Using Regular Expressions And The Nc Package, Toby Dylan Hocking

The R Journal

Regular expressions are powerful tools for extracting tables from non-tabular text data. Capturing regular expressions that describe the information to extract from column names can be especially useful when reshaping a data table from wide (few rows with many regularly named columns) to tall (fewer columns with more rows). We present the R package nc (short for named capture), which provides functions for wide-to-tall data reshaping using regular expressions. We describe the main new ideas of nc, and provide detailed comparisons with related R packages (stats, utils, data.table, tidyr, tidyfast, tidyfst, reshape2, cdata).

Stratamatch: Prognostic Score Stratification Using A Pilot Design, Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Andrew B. Goldstone, Peter Chiu, Y Joseph Woo, Jonathan H. Chen

The R Journal

Optimal propensity score matching has emerged as one of the most ubiquitous approaches for causal inference studies on observational data. However, outstanding critiques of the statistical properties of propensity score matching have cast doubt on the statistical efficiency of this technique, and the poor scalability of optimal matching to large data sets makes this approach inconvenient if not infeasible for sample sizes that are increasingly commonplace in modern observational data. The stratamatch package provides implementation support and diagnostics for ‘stratified matching designs,’ an approach that addresses both of these issues with optimal propensity score matching for large-sample observational studies. First, …

Conversations In Time: Interactive Visualization To Explore Structured Temporal Data, Earo Wang, Dianne Cook

The R Journal

Temporal data often has a hierarchical structure, defined by categorical variables describing different levels, such as political regions or sales products. The nesting of categorical variables produces a hierarchical structure. The tsibbletalk package is developed to allow a user to interactively explore temporal data, relative to the nested or crossed structures. It can help to discover differences between category levels, and uncover interesting periodic or aperiodic slices. The package implements a shared tsibble object that allows for linked brushing between coordinated views, and a shiny module that aids in wrapping timelines for seasonal patterns. The tools are demonstrated using two …

Automating Reproducible, Collaborative Clinical Trial Document Generation With The Listdown Package, Michael Kane, Xun Jiang, Simon Urbanek

The R Journal

the conveyance of clinical trial explorations and analysis results from a statistician to a clinical investigator is a critical component of the drug development and clinical research cycle. Automating the process of generating documents for data descriptions, summaries, exploration, and analysis allows the statistician to provide a more comprehensive view of the information captured by a clinical trial, and efficient generation of these documents allows the statistican to focus more on the conceptual development of a trial or trial analysis and less on the implementation of the summaries and results on which decisions are made. This paper explores the use …

Editorial, Dianne Cook

The R Journal

First, some news about the journal board. Welcome to Gavin Simpson, who joins as a new Executive Editor! In addition, welcome to our new Associate Editors Nicholas Tierney, Isabella Gollini, Rasmus Bååth, Mark van der Loo, Elizabeth Sweeney, Louis Aslett and Katarina Domijan. With the large volume of submissions, the Associate Editors now play a vital role in processing articles.

Penphcure: Variable Selection In Proportional Hazards Cure Model With Time-Varying Covariates, Alessandro Beretta, Cédric Heuchenne

The R Journal

We describe the penPHcure R package, which implements the semiparametric proportional-hazards (PH) cure model of Sy and Taylor (2000) extended to time-varying covariates and the variable selection technique based on its SCAD-penalized likelihood proposed by Beretta and Heuchenne (2019a). In survival analysis, cure models are a useful tool when a fraction of the population is likely to be immune from the event of interest. They can separate the effects of certain factors on the probability of being susceptible and on the time until the occurrence of the event. Moreover, the penPHcure package allows the user to simulate data from a …

Towards A Machine Learning Based Generalizable Framework For Detecting Covid-19 Misinformation On Social Media, Yuanzhi Chen

School of Computing: Dissertations, Theses, and Student Research

Since the beginning of the COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, online social media has become a conduit for the rapid propagation of misinformation. The misinformation is a type of fake news that is created inadvertently without the intention of causing harm. Yet COVID-19 misinformation has caused serious social disruptions including accidental death and destruction of public property. Timely prevention of the propagation of online misinformation requires the development of automated detection tools. Machine learning (ML) based models have been used to automate techniques for identifying fake news. These techniques involve converting text data …

Using An Integrative Machine Learning Approach To Study Microrna Regulation Networks In Pancreatic Cancer Progression, Roland Madadjim

School of Computing: Dissertations, Theses, and Student Research

With advances in genomic discovery tools, recent biomedical research has produced a massive amount of genomic data on post-transcriptional regulations related to various transcript factors, microRNAs, lncRNAs, epigenetic modifications, and genetic variations. In this direction, the field of gene regulation network inference is created and aims to understand the interactome regulations between these molecules (e.g., gene-gene, miRNA-gene) that take place to build models able to capture behavioral changes in biological systems. A question of interest arises in integrating such molecules to build a network while treating each specie in its uniqueness. Given the dynamic changes of interactome in chaotic systems …

Separator Of Diametral Path Graphs, Cuong Than

School of Computing: Dissertations, Theses, and Student Research

Nowadays, graph algorithms have been applied to many practical issues such as networking, very large-scale integration (VLSI), and transport systems, etc. Constructing an algorithm to find a maximum independent set (MIS) is one of the first NP-Hard problems which have been studied for a long time. Most scientists believe that there is no polynomial-time algorithm for this problem in general graphs. However, many efficient algorithms and approximation schemes to resolve the MIS problem are found in some special graph classes. In this thesis, we are going to introduce a separator construction for a diametral path graph. By using a property …

Model Counting Meets F0 Estimation, A. Pavan, N. V. Vinodchandran, Arnab Bhattacharyya, Kuldeep S. Meel

School of Computing: Faculty Publications

Constraint satisfaction problems (CSP’s) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSP’s and computation of zeroth frequency moments (F₀) for data streams.

Our investigations lead us to observe striking similarity …

Modernizing Legacy Business Practices And Maintaining Backwards Compatibility When Replacing Legacy Software, Thomas Hillebrandt

Honors Program: Senior Projects (Public)

As technology advances and hardware as well as user expectations becomes more advanced, software systems must change alongside or go obsolete. When software is no longer developed, decisions must be made regarding its future. Through various methods, legacy software may continue to see usage far past its obsolescence, however legacy software will sooner or later face replacement by new applications, built for state-of-the-art machines, to comply with modern requirements. When writing new software to replace older programs, the added challenge for developers is to help the client also modernize their workflow. When a program has been in long time use …

Teachability And Interpretability In Reinforcement Learning, Jeevan Rajagopal

School of Computing: Dissertations, Theses, and Student Research

There have been many recent advancements in the field of reinforcement learning, starting from the Deep Q Network playing various Atari 2600 games all the way to Google Deempind's Alphastar playing competitively in the game StarCraft. However, as the field challenges more complex environments, the current methods of training models and understanding their decision making become less effective. Currently, the problem is partially dealt with by simply adding more resources, but the need for a better solution remains.

This thesis proposes a reinforcement learning framework where a teacher or entity with domain knowledge of the task to complete can assist …