Programming Languages and Compilers Commons^™

Mirecsurv Package: Prentice-Williams-Peterson Models With Multiple Imputation Of Unknown Number Of Previous Episodes, David Moriña, Gilma Hernández-Herrera, Albert Navarro

The R Journal

Left censoring can occur with relative frequency when analyzing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The miRecSurv package is based on the use of models with specific baseline hazard, with multiple imputation of the number of prior episodes when unknown by means of the COMPoisson distribution, a very flexible count distribution that can handle over, sub, and equidispersion, …

Bcmixed: A Package For Median Inference On Longitudinal Data With The Box–Cox Transformation, Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Masahiko Gosho

The R Journal

This article illustrates the use of the bcmixed package and focuses on the two main functions: bcmarg and bcmmrm. The bcmarg function provides inference results for a marginal model of a mixed effect model using the Box–Cox transformation. The bcmmrm function provides model median inferences based on the mixed effect models for repeated measures analysis using the Box–Cox transformation for longitudinal randomized clinical trials. Using the bcmmrm function, analysis results with high power and high interpretability for treatment effects can be obtained for longitudinal randomized clinical trials with skewed outcomes. Further, the bcmixed package provides summarizing and visualization tools, which …

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2021-07-06 and 2021-12-22.

Donations

Jordan Aharoni (Canada) b-data GmbH (Switzerland) Mark Cachia (Canada) Shalese Fitzgerald (United States) Knut Helge Jensen (Norway) Roger Koenker (United Kingdom) Merck Research Laboratories, Kenilwort (United States) Statistik Aargau, Aarau (Switzerland)

Mgee2: An R Package For Marginal Analysis Of Longitudinal Ordinal Data With Misclassified Responses And Covariates, Yuliang Xu, Shuo Shuo Liu, Grace Y. Yi

The R Journal

Marginal methods have been widely used for analyzing longitudinal ordinal data due to their simplicity in model assumptions, robustness in inference results, and easiness in the implementation. However, they are often inapplicable in the presence of measurement errors in the variables. Under the setup of longitudinal studies with ordinal responses and covariates subject to misclassification, Chen et al. (2014) developed marginal methods for misclassification adjustments using the second-order estimating equations and proposed a two-stage estimation approach when the validation subsample is available. Parameter estimation is conducted through the Newton-Raphson algorithm, and the asymptotic distribution of the estimators is established. While …

Survidm: An R Package For Inference And Prediction In An Illness-Death Model, Gustavo Soutinho, Marta Sestelo, Luís Meira-Machado

The R Journal

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate "diseased" state before entering into a terminal absorbing state. In these models, one important goal is the modeling of transition rates which is usually done by studying the relationship between covariates and disease evolution. However, biomedical researchers are also interested in reporting other interpretable results in a simple and …

Lg: An R Package For Local Gaussian Approximations, Håkon Otneim

The R Journal

The package lg for the R programming language provides implementations of recent methodological advances on applications of the local Gaussian correlation. This includes the estimation of the local Gaussian correlation itself, multivariate density estimation, conditional density estimation, various tests for independence and conditional independence, as well as a graphical module for creating dependence maps. This paper describes the lg package, its principles, and its practical use.

Analysis Of Corneal Data In R With The Rpaci Package, Darío Ramos-López, Ana D. Maldonado

The R Journal

In ophthalmology, the early detection of keratoconus is still a crucial problem. Placido disk corneal topographers are essential in clinical practice, and many indices for diagnosing corneal irregularities exist. The main goal of this work is to present the R package rPACI, providing several functions to handle and analyze corneal data. This package implements primary indices of corneal irregularity (based on geometrical properties) and compound indices built from the primary ones, either using a generalized linear model or as a Bayesian classifier using a hybrid Bayesian network and performing approximate inference. rPACI aims to make the analysis of corneal …

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 second half of 2022.

Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network, Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes

The R Journal

One of the most common applications of spatial data analysis is detecting zones, at a certain scale, where a point-referenced event under study is especially concentrated. The detection of such zones, which are usually referred to as hotspots, is essential in certain fields such as criminology, epidemiology, or traffic safety. Traditionally, hotspot detection procedures have been developed over areal units of analysis. Although working at this spatial scale can be suitable enough for many research or practical purposes, detecting hotspots at a more accurate level (for instance, at the road segment level) may be more convenient sometimes. Furthermore, it is …

We Need Trustworthy R Packages, William Michael Landau

The R Journal

There is a need for rigorous software engineering in R packages, and there is a need for new research to bridge scientific computing with more traditional computing. Automated tools, interdisciplinary graduate courses, code reviews, and a welcoming developer community will continue to democratize best practices. Democratized software engineering will improve the quality, correctness, and integrity of scientific software, and by extension, the disciplines that rely on it

A Guided Tour Of Bayesian Regression, Andrés Ramírez–Hassan, Mateo Graciano-Londoño

The R Journal

This paper presents a Graphical User Interface (GUI) to carry out a Bayesian regression analysis in a very friendly environment without any programming skills (drag and drop). This paper is designed for teaching and applied purposes at an introductory level. Our GUI is based on an interactive web application using shiny and libraries from R. We carry out some applications to highlight the potential of our GUI for applied researchers and practitioners. In addition, the Help option in the main tap panel has an extended version of this paper, where we present the basic theory underlying all regression models that …

Visual Diagnostics For Constrained Optimisation With Application To Guided Tours, H Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez

The R Journal

A guided tour helps to visualise high-dimensional data by showing low-dimensional projections along a projection pursuit optimisation path. Projection pursuit is a generalisation of principal component analysis in the sense that different indexes are used to define the interestingness of the projected data. While much work has been done in developing new indexes in the literature, less has been done on understanding the optimisation. Index functions can be noisy, might have multiple local maxima as well as an optimal maximum, and are constrained to generate orthonormal projection frames, which complicates the optimization. In addition, projection pursuit is primarily used for …

A Unifying Framework For Parallel And Distributed Processing In R Using Futures, Henrik Bengtsson

The R Journal

A future is a programming construct designed for concurrent and asynchronous evaluation of code, making it particularly useful for parallel processing. The future package implements the Future API for programming with futures in R. This minimal API provides sufficient constructs for implementing parallel versions of well-established, high-level map-reduce APIs. The future ecosystem supports exception handling, output and condition relaying, parallel random number generation, and automatic identification of globals lowering the threshold to parallelize code. The Future API bridges parallel frontends with parallel backends, following the philosophy that end-users are the ones who choose the parallel backend while the developer focuses …

Compmodels: A Suite Of Computer Model Test Functions For Bayesian Optimization, Tony Pourmohamad

The R Journal

The CompModels package for R provides a suite of computer model test functions that can be used for computer model prediction/emulation, uncertainty quantification, and calibration. Moreover, the CompModels package is especially well suited for the sequential optimization of computer models. The package is a mix of real-world physics problems, known mathematical functions, and black-box functions that have been converted into computer models with the goal of Bayesian (i.e., sequential) optimization in mind. Likewise, the package contains computer models that represent either the constrained or unconstrained optimization case, each with varying levels of difficulty. In this paper, we illustrate the use …

Generalized Linear Randomized Response Modeling Using Glmmrr, Jean-Paul Fox, Konrad Klotzke, Duco Veen

The R Journal

Randomized response (RR) designs are used to collect response data about sensitive behaviors (e.g., criminal behavior, sexual desires). The modeling of RR data is more complex since it requires a description of the RR process. For the class of generalized linear mixed models (GLMMs), the RR process can be represented by an adjusted link function, which relates the expected RR to the linear predictor for most common RR designs. The package GLMMRR includes modified link functions for four different cumulative distributions (i.e., logistic, cumulative normal, Gumbel, Cauchy) for GLMs and GLMMs, where the package lme4 facilitates ML and REML estimation. …

Passo: An R Package For Assessing Partial Association Between Ordinal Variables, Shaobo Li, Xiaorui Zhu, Yuejie Chen, Dungang Liu

The R Journal

Partial association, the dependency between variables after adjusting for a set of covariates, is an important statistical notion for scientific research. However, if the variables of interest are ordered categorical data, the development of statistical methods and software for assessing their partial association is limited. Following the framework established by Liu et al. (2021), we develop an R package PAsso for assessing Partial Associations between ordinal variables. The package provides various functions that allow users to perform a wide spectrum of assessments, including quantification, visualization, and hypothesis testing. In this paper, we discuss the implementation of PAsso in …

An R Package For Non-Normal Multivariate Distributions: Simulation And Probability Calculations From Multivariate Lomax (Pareto Type Ii) And Other Related Distributions, Zhixin Lun, Ravindra Khattree

The R Journal

Convenient and easy-to-use programs are readily available in R to simulate data from and probability calculations for several common multivariate distributions such as normal and t. However, functions for doing so from other less common multivariate distributions, especially those which are asymmetric, are not as readily available, either in R or otherwise. We introduce the R package NonNorMvtDist to generate random numbers from multivariate Lomax distribution, which constitutes a very flexible family of skewed multivariate distributions. Further, by applying certain useful properties of multivariate Lomax distribution, multivariate cases of generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, …

The R Developer Community Does Have A Strong Software Engineering Culture, Maëlle Salmon, Karthik Ram

The R Journal

There is a strong software engineering culture in the R developer community. We recommend creating, updating and vetting packages as well as keeping up with community standards. We invite contributions to the rOpenSci project, where participants can gain experience that will shape their work and that of their peers.

Jited: A Framework For Jit Education In The Classroom, Caleb Watts

Master's Theses

The study of programming languages is a rich field within computer science, incorporating both the abstract theoretical portions of computer science and the platform specific details. Topics studied in programming languages, chiefly compilers or interpreters, are permanent fixtures in programming that students will interact with throughout their career. These systems are, however, considerably complicated, as they must cover a wide range of functionality in order to enable languages to be created and run. The process of educating students thus requires that the demanding workload of creating one of the systems be balanced against the time and resources present in a …

Web Service Quality-Dased Profiling And Selection, Ahmed Magdi Hamza

Archived Theses and Dissertations

Guaranteeing quality of service has been recently labeled as one of multiple major research challenges in the service oriented architecture. In effect, Web service selection from a set of matched services offering the same functional requirements, and ultimately claiming certain quality of service guarantees about themselves is not enough. A need emerges for the existence of a trusted third party that monitors Web service quality indicators, yet in a way that does not interfere with the normal operation of the Web service itself. The third party will eventually provide consumers with guarantees about Web service quality. In this research we …