Computer Sciences Commons^™

Jomo: A Flexible Package For Two-Level Joint Modelling Multiple Imputation, Matteo Quartagno, Simon Grund, James Carpenter

The R Journal

Multiple imputation is a tool for parameter estimation and inference with partially observed data, which is used increasingly widely in medical and social research. When the data to be imputed are correlated or have a multilevel structure — repeated observations on patients, school children nested in classes within schools within educational districts — the imputation model needs to include this structure. Here we introduce our joint modelling package for multiple imputation of multilevel data, jomo, which uses a multivariate normal model fitted by Markov Chain Monte Carlo (MCMC). Compared to previous packages for multilevel imputation, e.g. pan, jomo adds the …

Cvcrand: A Package For Covariate-Constrained Randomization And The Clustered Permutation Test For Cluster Randomized Trials, Hengshi Yu, Fan Li, John A. Gallis, Elizabeth L. Turner

The R Journal

The cluster randomized trial (CRT) is a randomized controlled trial in which randomization is conducted at the cluster level (e.g., school or hospital) and outcomes are measured for each individual within a cluster. Often, the number of clusters available to randomize is small (≤ 20), which increases the chance of baseline covariate imbalance between comparison arms. Such imbalance is particularly problematic when the covariates are predictive of the outcome because it can threaten the internal validity of the CRT. Pair-matching and stratification are two restricted randomization approaches that are frequently used to ensure balance at the design stage. An alternative, …

Biclustermd: An R Package For Biclustering With Missing Values, John Reisner, Hieu Pham, Sigurdur Olafsson, Stephen Vardeman, Jing Li

The R Journal

Biclustering is a statistical learning technique that attempts to find homogeneous partitions of rows and columns of a data matrix. For example, movie ratings might be biclustered to group both raters and movies. biclust is a current R package allowing users to implement a variety of biclustering algorithms. However, its algorithms do not allow the data matrix to have missing values. We provide a new R package, biclustermd, which allows users to perform biclustering on numeric data even in the presence of missing values.

Modeling Regimes With Extremes: The Bayesdfa Package For Identifying And Forecasting Common Trends And Anomalies In Multivariate Time-Series Data, Eric J. Ward, Sean C. Anderson, Luis A. Damiano, Mary E. Hunsicker, Michael A. Litzow

The R Journal

The bayesdfa package provides a flexible Bayesian modeling framework for applying dynamic factor analysis (DFA) to multivariate time-series data as a dimension reduction tool. The core estimation is done with the Stan probabilistic programming language. In addition to being one of the few Bayesian implementations of DFA, novel features of this model include (1) optionally modeling latent process deviations as drawn from a Student-t distribution to better model extremes, and (2) optionally including autoregressive and moving-average components in the latent trends. Besides estimation, we provide a series of plotting functions to visualize trends, loadings, and model predicted values. A secondary …

Ppci: An R Package For Cluster Identification Using Projection Pursuit, David P. Hofmeyr, Nicos G. Pavlidis

The R Journal

This paper presents the R package PPCI which implements three recently proposed projection pursuit methods for clustering. The methods are unified by the approach of defining an optimal hyperplane to separate clusters, and deriving a projection index whose optimiser is the vector normal to this separating hyperplane. Divisive hierarchical clustering algorithms that can detect clusters defined in different subspaces are readily obtained by recursively bi-partitioning the data through such hyperplanes. Projecting onto the vector normal to the optimal hyperplane enables visualisations of the data that can be used to validate the partition at each level of the cluster hierarchy. Clustering …

Coxed: An R Package For Computing Duration-Based Quantities From The Cox Proportional Hazards Model, Jonathan Kropko, Jeffrey J. Harden

The R Journal

The Cox proportional hazards model is one of the most frequently used estimators in duration (survival) analysis. Because it is estimated using only the observed durations’ rank ordering, typical quantities of interest used to communicate results of the Cox model come from the hazard function (e.g., hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague and difficult for many audiences of research to understand. We introduce a suite of methods in the R package coxed to address these problems. The package allows researchers to calculate duration-based quantities from Cox model results, such as the expected …

Orthodr: Semiparametric Dimension Reduction Via Orthogonality Constrained, Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao, Peng Xu, Wenzhuo Zhou, Xin Zhang

The R Journal

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by Wen and Yin (2012) for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform …

Using Web Services To Work With Geodata In R, Jan-Philipp Kolb

The R Journal

Through collaborative mapping, a massive amount of data is accessible. Many individuals contribute information each day. The growing amount of geodata is gathered by volunteers or obtained via crowd-sourcing. One outstanding example of this is the OpenStreetMap (OSM) Project which provides access to big data in geography. Another online mapping service that enables the integration of geodata into the analysis is Google Maps. The expanding content and the availability of geographic information radically changes the perspective on geodata (Chilton 2009). Recently many application programming interfaces (APIs) have been built on OSM and Google Maps. That leads to a point where …

Spgarch: An R-Package For Spatial And Spatiotemporal Arch And Garch Models, Philipp Otto

The R Journal

In this paper, a general overview on spatial and spatiotemporal ARCH models is provided. In particular, we distinguish between three different spatial ARCH-type models. In addition to the original definition of Otto et al. (2016), we introduce an logarithmic spatial ARCH model in this paper. For this new model, maximum-likelihood estimators for the parameters are proposed. In addition, we consider a new complex-valued definition of the spatial ARCH process. Moreover, spatial GARCH models are briefly discussed. From a practical point of view, the use of the R-package spGARCH is demonstrated. To be precise, we show how the proposed spatial ARCH …

Hcmodelsets: An R Package For Specifying Sets Of Well-Fitting Models In High Dimensions, Henrique Hoeltgebaum, Heather Battey

The R Journal

In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify as many of these explanations as is feasible. The standard practice, by contrast, is to report a single effective model for prediction. This paper illustrates the R implementation of the new ideas in the package HCmodelSets, using simple reproducible examples and real data. Results of some simulation experiments are also reported.

The R Package Trafo For Transforming Linear Regression Models, Lily Medina, Ann-Kristin Kreutzmann, Natalia Rojas-Perilla, Piedad Castro

The R Journal

Researchers and data-analysts often use the linear regression model for descriptive, predictive, and inferential purposes. This model relies on a set of assumptions that, when not satisfied, yields biased results and noisy estimates. A common problem that can be solved in many ways – use of less restrictive methods (e.g. generalized linear regression models or non-parametric methods ), variance corrections or transformations of the response variable just to name a few. We focus on the latter option as it allows to keep using the simple and well-known linear regression model. The list of transformations proposed in the literature is long …

Comparing Namedcapture With Other R Packages For Regular Expressions, Toby Dylan Hocking

The R Journal

Regular expressions are powerful tools for manipulating non-tabular textual data. For many tasks (visualization, machine learning, etc), tables of numbers must be extracted from such data before processing by other R functions. We present the R package namedCapture, which facilitates such tasks by providing a new user-friendly syntax for defining regular expressions in R code. We begin by describing the history of regular expressions and their usage in R. We then describe the new features of the namedCapture package, and provide detailed comparisons with related R packages (rex, stringr, stringi, tidyr, rematch2, re2r).

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.10 was released on 30 October, 2019. It is compatible with R 3.6.1 and consists of 1823 software packages, 384 experiment data packages, 953 up-to-date annotation packages, and 27 workflows. The release announcement includes descriptions of 94 new software packages, and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands

Fitting Tails By The Empirical Residual Coefficient Of Variation: The Ercv Package, Joan Del Castillo, Isabel Serra, Maria Padilla, David Moriña

The R Journal

This article is a self-contained introduction to the R package ercv and to the methodology on which it is based through the analysis of nine examples. The methodology is simple and trustworthy for the analysis of extreme values and relates the two main existing methodologies. The package contains R functions for visualizing, fitting and validating the distribution of tails. It also provides multiple threshold tests for a generalized Pareto distribution, together with an automatic threshold selection algorithm.

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

The R Journal

In the past 4 months, 632 new packages were added to the CRAN package repository. 27 packages were unarchived and 182 were archived. The following shows the growth of the number of active packages in the CRAN package repository:

Rollmatch: An R Package For Rolling Entry Matching, Kasey Jones, Rob Chew, Allison Witman, Yiyan Liu

The R Journal

The gold standard of experimental research is the randomized control trial. However, interventions are often implemented without a randomized control group for practical or ethical reasons. Propensity score matching (PSM) is a popular method for minimizing the effects of a randomized experiment from observational data by matching members of a treatment group to similar candidates that did not receive the intervention. Traditional PSM is not designed for studies that enroll participants on a rolling basis and does not provide a solution for interventions in which the baseline and intervention period are undefined in the comparison group. Rolling Entry Matching (REM) …

Editorial, Michael J. Kane

The R Journal

On behalf of the editorial board, I am pleased to present Volume 11, Issue 2 of the R Journal and my first issue as the Editor in Chief. This year, both Colin Gillespie and Catherine Healey join the Editorial Board, and Norm Matloff will rotate out. The R Journal continues to see increases in impact and popularity and this year we plan on making advances to better serve the community and streamline the publishing process to meet the increase in submissions we have seen over the last few years.

Dr4pl: A Stable Convergence Algorithm For The 4 Parameter Logistic Model, Hyowon An, Justin T. Landis, Aubrey G. Bailey, James S. Marron, Dirk P. Dittmer

The R Journal

The 4 Parameter Logistic (4PL) model has been recognized as a major tool to analyze the relationship between doses and responses in pharmacological experiments. A main strength of this model is that each parameter contributes an intuitive meaning enhancing interpretability of a fitted model. However, implementing the 4PL model using conventional statistical software often encounters numerical errors. This paper highlights the issue of convergence failure and presents several causes with solutions. These causes include outliers and a non-logistic data shape, so useful remedies such as robust estimation, outlier diagnostics and constrained optimization are proposed. These features are implemented in a …

Associative Classification In R: Arc, Arulescba, And Rcba, Michael Hahsler, Ian Johnson, Tomáš Kliegr, Jaroslav Kuchař

The R Journal

Several methods for creating classifiers based on rules discovered via association rule mining have been proposed in the literature. These classifiers are called associative classifiers and the best-known algorithm is Classification Based on Associations (CBA). Interestingly, only very few implementations are available and, until recently, no implementation was available for R. Now, three packages provide CBA. This paper introduces associative classification, the CBA algorithm, and how it can be used in R. A comparison of the three packages is provided to give the potential user an idea about the advantages of each of the implementations. We also show how the …

Convergenceclubs: A Package For Performing The Phillips And Sul's Club Convergence Clustering Procedure, Roberto Sichera, Pietro Pizzuto

The R Journal

This paper introduces package ConvergenceClubs, which implements functions to perform the Phillips and Sul (2007, 2009) club convergence clustering procedure in a simple and reproducible manner. The approach proposed by Phillips and Sul to analyse the convergence patterns of groups of economies is formulated as a nonlinear time varying factor model that allows for different time paths as well as individual heterogeneity. Unlike other approaches in which economies are grouped a priori, it also allows the endogenous determination of convergence clubs. The algorithm, usage, and implementation details are discussed.

R News, R Core Team

The R Journal

CHANGES IN R 3.6.2

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The Idspatialstats R Package: Quantifying Spatial Dependence Of Infectious Disease Spread, John R. Giles, Henrik Salje, Justin Lessler

The R Journal

Spatial statistics for infectious diseases are important because the spatial and temporal scale over which transmission operates determine the dynamics of disease spread. Many methods for quantifying the distribution and clustering of spatial point patterns have been developed (e.g. K-function and pair correlation function) and are routinely applied to infectious disease case occurrence data. However, these methods do not explicitly account for overlapping chains of transmission and require knowledge of the underlying population distribution, which can be limiting when analyzing epidemic case occurrence data. Therefore, we developed two novel spatial statistics that account for these effects to estimate: 1) …

Time Series Forecasting With Knn In R: The Tsfknn Package, Francisco Martínez, María P. Frías, Francisco Charte, Antonio J. Rivera

The R Journal

In this paper the tsfknn package for time series forecasting using k-nearest neighbor regression is described. This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies and among different functions to aggregate the targets of the nearest neighbors. It is also possible to assess the forecast accuracy of the KNN model.

Bondvaluation: An R Package For Fixed Coupon Bond Analysis, Wadim Djatschenko

The R Journal

t The purpose of this paper is to introduce the R package BondValuation for the analysis of large datasets of fixed coupon bonds. The conceptual heterogeneity of fixed coupon bonds traded in the global markets imposes a high degree of complexity on their comparative analysis. Contrary to baseline fixed income theory, in practice, most bonds feature coupon period irregularities. In addition, there are a multitude of day count methods that determine the interest accrual, the cash flows and the discount factors used in bond valuation. Several R packages, e.g., fBonds, RQuantLib, and YieldCurve, provide tools for fixed income analysis. Nevertheless, …

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

School of Computing: Dissertations, Theses, and Student Research

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

Formal Modeling And Analysis Of A Family Of Surgical Robots, Niloofar Mansoor

School of Computing: Dissertations, Theses, and Student Research

Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counterexample showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality and building family-based techniques for both modeling and analysis. This thesis presents a novel approach for building an end to end dependability case for a software product line, where a property is formally modeled, a counterexample is found and then …

Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

School of Computing: Conference and Workshop Papers

Includes framing, overview, and discussion of the explorations pursued as part of the Digital Libraries, Intelligent Data Analytics, and Augmented Description demonstration project, pursued by members of the Aida digital libraries research team at the University of Nebraska-Lincoln through a research services contract with the Library of Congress. This presentation covered: Aida research team and background for the demonstration project; broad outlines of “Digital Libraries, Intelligent Data Analytics, and Augmented Description”; what changed for us as a research team over the collaboration and why; deliverables of our work; thoughts toward “What next”; and deep-dives into the explorations. The machine learning …

A Co-Optimal Coverage Path Planning Method For Aerial Scanning Of Complex Structures, Zhexiong Shang, Justin Bradley, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to …

Auditor: An R Package For Model-Agnostic Visual Validation And Diagnostics, Alicja Gosiewska, Przemysław Biecek

The R Journal

Machine learning models have successfully been applied to challenges in applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a complex model that fits the training data and results in high accuracy on test set. However, problems often arise when models are confronted with the real-world data. This paper describes methodology and tools for model-agnostic auditing. It provides functinos for assessing and comparing the goodness of fit and performance of models. In addition, the package may be used for analysis of the similarity of residuals and for identification of outliers and …