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Full-Text Articles in Numerical Analysis and Scientific Computing

Conference Report Of Why R? Turkey 2021, Mustafa Cavus, Olgun Aydin, Ozan Evkaya, Ozancan Ozdemir, Deniz Bezer, Ugur Dar Jun 2021

Conference Report Of Why R? Turkey 2021, Mustafa Cavus, Olgun Aydin, Ozan Evkaya, Ozancan Ozdemir, Deniz Bezer, Ugur Dar

The R Journal

The Why R? Turkey 2021 as a three-day online conference was organized to bring together researchers and professionals from Turkey on April 16-17-18, 2021. We hereby aimed to promote the R community in Turkey by bringing R users with different backgrounds such as genetics, sociology, finance, economy, bio-statistics. There were 8 thematic sessions and 18 invited speakers. In this article, it is aimed to describe the preparation phase, technical details, and the impact of the conference on audience.

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News From The Forwards Taskforce, Heather Turner Jun 2021

News From The Forwards Taskforce, Heather Turner

The R Journal

Forwards is an R Foundation taskforce working to widen the participation of underrepresented groups in the R project and in related activities, such as the useR! conference. This report rounds up activities of the taskforce during the first half of 2021.

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Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis Jun 2021

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 6 months, 1290 new packages were added to the CRAN package repository. 116 packages were unarchived and 467 were archived. The following shows the growth of the number of active packages in the CRAN package repository

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Regularized Transformation Models: The Tramnet Package, Lucas Kook, Torsten Hothorn Jun 2021

Regularized Transformation Models: The Tramnet Package, Lucas Kook, Torsten Hothorn

The R Journal

The tramnet package implements regularized linear transformation models by combining the flexible class of transformation models from tram with constrained convex optimization implemented in CVXR. Regularized transformation models unify many existing and novel regularized regression models under one theoretical and computational framework. Regularization strategies implemented for transformation models in tramnet include the Lasso, ridge regression, and the elastic net and follow the parameterization in glmnet. Several functionalities for optimizing the hyperparameters, including model-based optimization based on the mlrMBO package, are implemented. A multitude of S3 methods is deployed for visualization, handling, and simulation purposes. This work aims at illustrating all …

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The Hbv.Ianigla Hydrological Model, Ezequiel Toum, Mariano H. Masiokas, Ricardo Villalba, Pierre Pitte, Lucas Ruiz Jun 2021

The Hbv.Ianigla Hydrological Model, Ezequiel Toum, Mariano H. Masiokas, Ricardo Villalba, Pierre Pitte, Lucas Ruiz

The R Journal

Over the past 40 years, the HBV (Hydrologiska Byråns Vattenbalansavdelning) hydrological model has been one of the most used worldwide due to its robustness, simplicity, and reliable results. Despite these advantages, the available versions impose some limitations for research studies in mountain watersheds dominated by ice-snow melt runoff (i.e., no glacier module, a limited number of elevation bands, among other constraints). Here we present HBV.IANIGLA, a tool for hydroclimatic studies in regions with steep topography and/or cryospheric processes which provides a modular and extended implementation of the HBV model as an R package. To our knowledge, this is the first …

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Clustcurv: An R Package For Determining Groups In Multiple Curves, Nora M. Villanueva, Marta Sestelo, Luis Meira-Machado, Javier Roca-Pardiñas Jun 2021

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

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Towards A Grammar For Processing Clinical Trial Data, Michael J. Kane Jun 2021

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 …

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R Foundation News, Torsten Hothorn Jun 2021

R Foundation News, Torsten Hothorn

The R Journal

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

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Package Wsbackfit For Smooth Backfitting Estimation Of Generalized Structured Models, Javier Roca-Pardiñas, María Xosé Rodríguez-Álvarez, Stefan Sperlich Jun 2021

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 …

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Dchaos: An R Package For Chaotic Time Series Analysis, Julio E. Sandubete, Lorenzo Escot Jun 2021

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 …

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Rlumcarlo: Simulating Cold Light Using Monte Carlo Methods, Sebastian Kreutzer, Johannes Friedrich, Vasilis Pagonis, Christian Laag, Ena Rajovic, Christoph Schmidt Jun 2021

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 …

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Reproducible Summary Tables With The Gtsummary Package, Daniel D. Sjoberg, Karissa Whiting, Michael Curry, Jessica A. Lavery, Joseph Larmarange Jun 2021

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 …

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Rocnreg: An R Package For Receiver Operating Characteristic Curve Inference With And Without Covariates, María Xosé Rodríguez-Álvarez, Vanda Inácio Jun 2021

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 …

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News From The Bioconductor Project, Bioconductor Core Team Jun 2021

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.

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Statistical Quality Control With The Qcr Package, Miguel Flores, Rubén Fernández-Casal, Salvador Naya, Javier Tarrío-Saavedra Jun 2021

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 …

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Jmcmprsk: An R Package For Joint Modelling Of Longitudinal And Survival Data With Competing Risks, Hong Wang, Ning Li, Shanpeng Li, Gang Li Jun 2021

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 …

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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 Jun 2021

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.

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Changes In R 4.0–4.1, Tomas Kalibera, Sebastian Meyer, Kurt Hornik Jun 2021

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.

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Bayesspsurv: An R Package To Estimate Bayesian (Spatial) Split-Population Survival Models, Brandon Bolte, Nicolás Schmidt, Sergio Béjar, Nguyen Huynh, Bumba Mukherjee Jun 2021

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 …

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Gofcopula: Goodness-Of-Fit Tests For Copulae, Ostap Okhrin, Simon Trimborn, Martin Waltz Jun 2021

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, …

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Working With Crsp/Compustat In R: Reproducible Empirical Asset Pricing, Majeed Simaan Jun 2021

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 …

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Garchx: Flexible And Robust Garch-X Modeling, Genaro Sucarrat Jun 2021

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 …

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Onestep: Le Cam's One-Step Estimation Procedure, Alexandre Brouste, Christophe Dutang, Darel Noutsa Mieniedou Jun 2021

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.

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Wide-To-Tall Data Reshaping Using Regular Expressions And The Nc Package, Toby Dylan Hocking Jun 2021

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).

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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 Jun 2021

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, …

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Conversations In Time: Interactive Visualization To Explore Structured Temporal Data, Earo Wang, Dianne Cook Jun 2021

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 …

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Automating Reproducible, Collaborative Clinical Trial Document Generation With The Listdown Package, Michael Kane, Xun Jiang, Simon Urbanek Jun 2021

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 …

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Editorial, Dianne Cook Jun 2021

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.

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Penphcure: Variable Selection In Proportional Hazards Cure Model With Time-Varying Covariates, Alessandro Beretta, Cédric Heuchenne Jun 2021

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 …

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Corn Co-Product Logistics: An Application Of Linear Programming, Dmitry Kalashnikov Adams May 2021

Corn Co-Product Logistics: An Application Of Linear Programming, Dmitry Kalashnikov Adams

Department of Agricultural Economics: Dissertations, Theses, and Student Research

The purpose of this thesis is not to explore new ways to apply or to study the general field of linear programming. Rather the emphasis is on applying a particular type of linear programming to a specific problem. In this thesis the classic case of linear programing - the transportation problem – is used to optimize corn co-product logistics between six ethanol producing facilities. At the core, the problem of corn germ logistics lies in transporting products from areas of excess supply to areas with excess demand. The challenge of optimizing corn germ logistics lies in managing transportation between producing …

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