Numerical Analysis and Scientific Computing Commons^™

Cat.Dt: An R Package For Fast Construction Of Accurate Computerized Adaptive Tests Using Decision Trees, Javier Rodríguez-Cuadrado, Juan C. Laria, David Delgado-Gómez

The R Journal

This article introduces the cat.dt package for the creation of Computerized Adaptive Tests (CATs). Unlike existing packages, the cat.dt package represents the CAT in a Decision Tree (DT) structure. This allows building the test before its administration, ensuring that the creation time of the test is independent of the number of participants. Moreover, to accelerate the construction of the tree, the package controls its growth by joining nodes with similar estimations or distributions of the ability level and uses techniques such as message passing and pre-calculations. The constructed tree, as well as the estimation procedure, can be visualized using the …

Spnetwork: A Package For Network Kernel Density Estimation, Jeremy Gelb

The R Journal

This paper introduces the new package spNetwork that provides functions to perform Network Kernel Density Estimate analysis (NKDE). This method is an extension of the classical Kernel Density Estimate (KDE), a non parametric approach to estimate the intensity of a spatial process. More specifically, it adapts the KDE for cases when the study area is a network, constraining the location of events (such as accidents on roads, leaks in pipes, fish in rivers, etc.). We present and discuss in this paper the three main versions of NKDE: simple, discontinuous, and continuous that are implemented in spNetwork. We illustrate how to …

Spfilter: An R Package For Semiparametric Spatial Filtering With Eigenvectors In (Generalized) Linear Models, Sebastian Juhl

The R Journal

Eigenvector-based Spatial filtering constitutes a highly flexible semiparametric approach to account for spatial autocorrelation in a regression framework. It combines judiciously selected eigenvectors from a transformed connectivity matrix to construct a synthetic spatial filter and remove spatial patterns from model residuals. This article introduces the spfilteR package that provides several useful and flexible tools to estimate spatially filtered linear and generalized linear models in R. While the package features functions to identify relevant eigenvectors based on different selection criteria in an unsupervised fashion, it also helps users to perform supervised spatial filtering and to select eigenvectors based on alternative user-defined …

Siqr: An R Package For Single-Index Quantile Regression, Tianhai Zu, Yan Yu

The R Journal

We develop an R package SIQR that implements the single-index quantile regression (SIQR) models via an efficient iterative local linear approach in Wu et al. (2010). Single-index quantile regression models are important tools in semiparametric regression to provide a comprehensive view of the conditional distributions of a response variable. It is especially useful when the data is heterogeneous or heavy-tailed. The package provides functions that allow users to fit SIQR models, predict, provide standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. We apply the R package SIQR to a well-known Boston Housing data.

Multiple Imputation And Synthetic Data Generation With Npbayesimputecat, Jingchen Hu, Olanrewaju Akande, Quanli Wang

The R Journal

In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular, at statistical agencies, the respondent-level data they collect from surveys and censuses can suffer from high rates of missingness. Furthermore, agencies are obliged to protect respondents’ privacy when publishing the collected data for public use. The NPBayesImputeCat R package, introduced in this paper, provides routines to i) create multiple imputations for missing data and ii) create synthetic data for statistical disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet process mixture of products of the multinomial distributions model used …

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.

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 …

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. …

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, …

Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this …