Digital Commons Network^™

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, …

Charting A New Frontier Integrating Mathematical Modeling In Complex Biological Systems From Molecules To Ecosystems, Katharine A. White, Kira D. Mcentire, Nicole R. Buan, Lecia Robinson, Elisar Barbar

Department of Biochemistry: Faculty Publications

Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers, and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize …

Biodiversity Scale-Dependence And Opposing Multi-Level Correlations Underlie Differences Among Taxonomic, Phylogenetic And Functional Diversity, Nadejda A. Mirochnitchenko, Erica F. Stuber, Joseph J. Fontaine

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

Aim: Biodiversity is a multidimensional property of biological communities that represents different information depending on how it is measured, but how dimensions relate to one another and under what conditions is not well understood. We explore how taxonomic, phylogenetic, and functional diversity can differ in scale-of-effect dependence and habitat-biodiversity relationships, and subsequently how spatial differences among biodiversity dimensions may arise. Location: Nebraska, United States. Taxon: Birds. Methods: Across 2016 and 2017, we conducted 2,641 point counts at 781 sites. We modeled the occupancy of 141 species using Bayesian Bernoulli-Bernoulli hierarchical logistic regressions. We calculated species richness (SR), phylogenetic diversity (PD), …

Continuous Turbidity Data Used To Compute Constituent Concentrations In The South Loup River, Nebraska, 2017–18, David L. Rus, Brenda K. Densmore

United States Geological Survey: Water Reports and Publications

The South Loup River in central Nebraska has been impaired by bacteria since at least 2004, which has resulted in the river not meeting its intended use as a recreational waterway. As part of a strategy for reducing the bacterial load in the river, the United States Geological Survey (USGS), in cooperation with the Lower Loup Natural Resources District, made continuous estimates of Escherichia coli (E. coli) and nutrient concentrations during seasonal monitoring at the South Loup River at Saint Michael, Nebraska, during 2017–18. Continuous turbidity data were collected from mid-April through October in 2017 and 2018 …

Students' Definitions Of Academic Success, Brent Kelderman

Department of Teaching, Learning, and Teacher Education: Theses and Other Student Research

Academic success has not been well defined consistently, and when definitions have been proposed (e.g. Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006 ; York, Gibson, & Rankin, 2015), they are not always measured consistently. Is it good grades (Parker, Summerfeldt, Hogan, & Majeski, 2004), and if so, what constitutes a good grade and what should be graded? Is it understanding the material, and if so, is understanding shown by simply completing the academic tasks (Choi, 2005)? Is it perseverance and grit (Tang, Wang, Guo, & Salmela-Aro, 2019), and if so, why do many other definitions and views focus so much …

The Role Of The Next Generation Science Standards In Teachers' Curricular Choices In The Life Science Classroom, Amy Tankersley

Department of Teaching, Learning, and Teacher Education: Theses and Other Student Research

In 2013 a group of experts used A Framework for K-12 Science Education (NRC, 2012) to draft the Next Generation Science Standards (NGSS), which aims to increase science literacy through integrating the concepts and practices of science into comprehensive standards for science (NGSS Lead States, 2013). As students learn to explore the content and practices of scientists, ideally, they should increase their ability to think and reason in ways that will benefit their ability to participate in society. Because of the complex nature of systematic education reform, teachers need to put significant time into implementing NGSS to fulfill the goals …

Statistical Mediation Analysis In Regression Discontinuity Design For Causal Inference, Donna Chen

College of Education and Human Sciences: Dissertations, Theses, and Student Research

Regression discontinuity designs (RDDs) are the most robust quasi-experimental design, but current statistical models are limited to estimates for the simple causal relationship between only two variables: the independent and dependent variables. In practice, intervening variables (or mediators) are often observed as part of the causal chain. Mediators explain the why and how a treatment or intervention works. Therefore, mediation and RDD analysis combined can be a useful tool in identifying key components or processes that make intervention programs effective while making causal inferences for improving student achievement, despite natural constraints, limitations, and ethical considerations. Without an integrated framework of …

Comparing Machine Learning Techniques With State-Of-The-Art Parametric Prediction Models For Predicting Soybean Traits, Susweta Ray

Department of Statistics: Dissertations, Theses, and Student Research

Soybean is a significant source of protein and oil, and also widely used as animal feed. Thus, developing lines that are superior in terms of yield, protein and oil content is important to feed the ever-growing population. As opposed to the high-cost phenotyping, genotyping is both cost and time efficient for breeders while evaluating new lines in different environments (location-year combinations) can be costly. Several Genomic prediction (GP) methods have been developed to use the marker and environment data effectively to predict the yield or other relevant phenotypic traits of crops. Our study compares a conventional GP method (GBLUP), a …

Reintegrating Biology Through The Nexus Of Energy, Information, And Matter, Kim L. Hoke, Sara L. Zimmer, Adam B. Roddy, Mary Jo Ondrechen, Craig E. Williamson, Nicole R. Buan

Department of Biochemistry: Faculty Publications

Information, energy, and matter are fundamental properties of all levels of biological organization, and life emerges from the continuous flux of matter, energy, and information. This perspective piece defines and explains each of the three pillars of this nexus. We propose that a quantitative characterization of the complex interconversions between matter, energy, and information that compose this nexus will help us derive biological insights that connect phenomena across different levels of biological organization. We articulate examples from multiple biological scales that highlight how this nexus approach leads to a more complete understanding of the biological system. Metrics of energy, information, …

Preferential Flow In Riparian Buffers: Current Research And Future Needs., Derek M. Heeren, Lucie Guertault, Kyle Mankin

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Preferential flow in riparian buffers can substantially compromise their effectiveness in reducing contaminants from overland runoff. The objective of this article is to introduce a collection of five articles on current research into subsurface preferential flow measurement, visualization, modeling, and impacts on contaminant fate and transport at scales ranging from the subsurface pore scale to the plot scale to the watershed scale. This collection presents selected works from a broader invited session on “Preferential flow and piping in riparian buffers” at the 2020 ASABE Annual International Meeting. Major findings include: new methodologies, such as light transmission and geophysics, to characterize …

Complete Modeling Of The Chondrogenic Environment Under Continuous Low-Intensity Ultrasound, Heather Newell

Department of Chemical and Biomolecular Engineering: Dissertations, Theses, and Student Research

Articular cartilage is an avascular tissue that requires therapeutic intervention methods. This work answers the following: determine transducer operation to optimize the bioeffects; calculate the magnitude of pressure exerted on chondrocytes at an injury site; and confirm the theoretical findings by an animal model.

Earlier work has shown that cellular response to US is maximized at the resonance frequency of the cells. Resonance frequencies were calculated for chondrocytes in various layers. The latter configuration closest resembles in vivo conditions and the resonance occurred at 3.8±0.3 ������. The 3D model of US propagation in a rabbit knee was constructed from MRIs …

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 3.14 was released on 27 October, 2021. It is compatible with R 4.1.0 and consists of 2083 software packages, 408 experiment data packages, 904 up-to-date annotation packages, and 29 workflows.

Rpese: Risk And Performance Estimators Standard Errors With Serially Dependent Data, Anthony-Alexander Christidis, R Douglas Martin

The R Journal

The R package RPESE (Risk and Performance Estimators Standard Errors) implements a new method for computing accurate standard errors of risk and performance estimators when returns are serially dependent. The new method makes use of the representation of a risk or performance estimator as a summation of a time series of influence-function (IF) transformed returns, and computes estimator standard errors using a sophisticated method of estimating the spectral density at frequency zero of the time series of IF-transformed returns. Two additional packages used by RPESE are introduced, namely RPEIF which computes and provides graphical displays of the IF of risk …

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 …

Bssm: Bayesian Inference Of Non-Linear And Non-Gaussian State Space Models In R, Jouni Helske, Matti Vihola

The R Journal

We present an R package bssm for Bayesian non-linear/non-Gaussian state space modeling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package also accommodates discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package also implements a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers an easy-to-use interface to define models with linear-Gaussian state …