Numerical Analysis and Scientific Computing Commons^™

Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. Mcculloch

LSU New Orleans Theses and Dissertations

This thesis introduces a new methodology for robust form parameter design of complex hull form geometry via constraint programming, automatic differentiation, interval arithmetic, and truncated hierarchical B- splines. To date, there has been no clearly stated methodology for assuring consistency of general (equality and inequality) constraints across an entire geometric form parameter ship hull design space. In contrast, the method to be given here can be used to produce guaranteed narrowing of the design space, such that infeasible portions are eliminated. Furthermore, we can guarantee that any set of form parameters generated by our method will be self consistent. It …

Pasnet: Pathway-Associated Sparse Deepneural Network For Prognosis Prediction From High-Throughput Data, Jie Hao, Youngsoon Kim, Tae-Kyung Kim, Mingon Kang

Faculty Articles

Background: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis maybe caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. Results: In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes …

A Snowball's Chance: Debt Snowball Vs. Debt Avalanche, Evan Mcallister

Senior Honors Projects, 2010-2019

Traditional mathematical analysis states that the most efficient way to pay off interest-bearing consumer debt is to pay the individual debts in order from largest to smallest interest rate. In doing this, the debtor will eliminate the largest sources of interest first, thus shortening the overall time-to-pay. This method is known as the “Debt Avalanche.” The “Debt Snowball” method, popularized in large part by investor-author David Ramsey, recommends that consumers pay debts in order from smallest to largest, regardless of interest rate. In this paper, I conduct an empirical analysis of the Federal Reserve’s Survey of Consumer Finance (SCF), calculating …

Computational Modeling Of Radiation Interactions With Molecular Nitrogen, Tyler Reese

Dissertations

The ability to detect radiation through identifying secondary effects it has on its surrounding medium would extend the range at which detections could be made and would be a valuable asset to many industries. The development of such a detection instrument requires an accurate prediction of these secondary effects. This research aims to improve on existing modeling techniques and help provide a method for predicting results for an affected medium in the presence of radioactive materials. A review of radioactivity and the interactions mechanisms for emitted particles as well as a brief history of the Monte Carlo Method and its …

The R Journal (December 2018) 10(2): Complete Issue, The R Foundation

The R Journal

Editorial, John Verzani

Contributed Research Articles

stplanr: A Package for Transport Planning, Robin Lovelace and Richard Ellison

The utiml Package: Multi-label Classification in R, Adriano Rivolli and Andre C. P. L. F. de Carvalho

rcss: R Package for Optimal Convex Stochastic Switching, Juri Hinz and Jeremy Yee

nsROC: An R package for Non-Standard ROC Curve Analysis, Sonia Pérez-Fernández, Pablo Martínez-Camblor, Peter Filzmoser, and Norberto Corral

addhaz: Contribution of Chronic Diseases to the Disability Burden Using R, Renata Tiene de Carvalho Yokota, Caspar WN Looman, Wilma Johanna Nusselder, Herman Van Oyen, and Geert Molenberghs

Snowboot: Bootstrap Methods for Network Inference, Yuzhou …

Empathetic Computing For Inclusive Application Design, Kenny Choo Tsu Wei

Dissertations and Theses Collection (Open Access)

The explosive growth of the ecosystem of personal and ambient computing de- vices coupled with the proliferation of high-speed connectivity has enabled ex- tremely powerful and varied mobile computing applications that are used every- where. While such applications have tremendous potential to improve the lives of impaired users, most mobile applications have impoverished designs to be inclusive– lacking support for users with specific disabilities. Mobile app designers today haveinadequate support to design existing classes of apps to support users with specific disabilities, and more so, lack the support to design apps that specifically target these users. One way to resolve …

Testfordep: An R Package For Modern Distribution-Free Tests And Visualization Tools For Independence, Jeffrey C. Miecznikowski, En-Shuo Hsu, Yanhua Chen, Albert Vexler

The R Journal

This article introduces testforDEP, a portmanteau R package implementing for the first time several modern tests and visualization tools for independence between two variables. While classical tests for independence are in the base R packages, there have been several recently developed tests for independence that are not available in R. This new package combines the classical tests including Pearson’s product moment correlation coefficient method, Kendall’s τ rank correlation coefficient method and Spearman’s ρ rank correlation coefficient method with modern tests consisting of an empirical likelihood based test, a density-based empirical likelihood ratio test, Kallenberg data-driven test, maximal information coefficient …

Smm: An R Package For Estimation And Simulation Of Discrete-Time Semi-Markov Models, Vlad Stefan Barbu, Caroline Bérard, Dominique Cellier, Mathilde Sautreuil, Nicolas Vergne

The R Journal

Semi-Markov models, independently introduced by Lévy (1954), Smith (1955) and Takacs (1954), are a generalization of the well-known Markov models. For semi-Markov models, sojourn times can be arbitrarily distributed, while sojourn times of Markov models are constrained to be exponentially distributed (in continuous time) or geometrically distributed (in discrete time). The aim of this paper is to present the R package SMM, devoted to the simulation and estimation of discrete-time multi-state semi-Markov and Markov models. For the semi-Markov case we have considered: parametric and non-parametric estimation; with and without censoring at the beginning and/or at the end of sample …

Fica: Fastica Algorithms And Their Improved Variants, Jari Miettinen, Klaus Nordhausen, Sara Taskinen

The R Journal

In independent component analysis (ICA) one searches for mutually independent nongaussian latent variables when the components of the multivariate data are assumed to be linear combinations of them. Arguably, the most popular method to perform ICA is FastICA. There are two classical versions, the deflation-based FastICA where the components are found one by one, and the symmetric FastICA where the components are found simultaneously. These methods have been implemented previously in two R packages, fastICA and ica. We present the R package fICA and compare it to the other packages. Additional features in fICA include optimization of the extraction order …

Snowboot: Bootstrap Methods For Network Inference, Yuzhou Chen, Yulia R. Gel, Vyacheslav Lyubchich, Kusha Nezafati

The R Journal

Complex networks are used to describe a broad range of disparate social systems and natural phenomena, from power grids to customer segmentation to human brain connectome. Challenges of parametric model specification and validation inspire a search for more data-driven and flexible nonparametric approaches for inference of complex networks. In this paper we discuss methodology and R implementation of two bootstrap procedures on random networks, that is, patchwork bootstrap of Thompson et al. (2016) and Gel et al. (2017) and vertex bootstrap of Snijders and Borgatti (1999). To our knowledge, the new R package snowboot is the first implementation of the …

Addhaz: Contribution Of Chronic Diseases To The Disability Burden Using R, Renata Tiene De Carvalho Yokota, Caspar Wn Looman, Wilma Johanna Nusselder, Herman Van Oyen, Geert Molenberghs

The R Journal

The increase in life expectancy followed by the burden of chronic diseases contributes to disability at older ages. The estimation of how much chronic conditions contribute to disability can be useful to develop public health strategies to reduce the burden. This paper introduces the R package addhaz, which is based on the attribution method (Nusselder and Looman, 2004) to partition disability into the additive contributions of diseases using cross-sectional data. The R package includes tools to fit the additive hazard model, the core of the attribution method, to binary and multinomial outcomes. The models are fitted by maximizing the …

Stplanr: A Package For Transport Planning, Robin Lovelace, Richard Ellison

The R Journal

Tools for transport planning should be flexible, scalable, and transparent. The stplanr package demonstrates and provides a home for such tools, with an emphasis on spatial transport data and non-motorized modes. The stplanr package facilitates common transport planning tasks including: downloading and cleaning transport datasets; creating geographic “desire lines” from origin-destination (OD) data; route assignment, locally and interfaces to routing services such as CycleStreets.net; calculation of route segment attributes such as bearing and aggregate flow; and ‘travel watershed’ analysis. This paper demonstrates this functionality using reproducible examples on real transport datasets. More broadly, the experience of developing and using R …

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.5.2

CHANGES IN R 3.5.1

Conference Report: Why R? 2018, Michał Burdukiewicz, Marta Karas, Leon Eyrich Jessen, Marcin KosińSki, Bernd Bischl, Stefan Rödiger

The R Journal

The primary purpose of the Why R? 2018 conference was to provide R programming language enthusiasts with an opportunity to meet and discuss experiences in R software development and analysis applications, for both academia and industry professionals. The event was held 2-5 August, 2018 in a city of Wroclaw, a strong academic and business center of Poland. The total of approximately 250 people from 6 countries attended the main conference event. Additionally, approximately 540 R users attended the pre-meetings in eleven cities across Europe (Figure 2).

Conference Report: Latinr 2018, Laura Acion, Natalia Da Silva, Riva Quiroga

The R Journal

LatinR <- Latin American Conference about the Use of R in Research + Development (LatinR) was an international conference whose goal was bringing together the Latin Ameri can R community. The inagural LatinR took place at the Universidad de Palermo in Buenos Aires, Argentina, on September 3 to 5, 2018. About 100 participants from more than 10 differ ent countries (e.g., Argentina, Uruguay, Chile, Peru, Ecuador, Brazil, Costa Rica, Venezuela, Spain, United States, Canada) attended LatinR.

LatinR will be an annual meeting that will rotate among different countries in Latin America. LatinR 2019 will be hosted by the Universidad Católica de Chile in Santiago de Chile on September 25 to 27

Shinyitemanalysis For Teaching Psychometrics And To Enforce Routine Analysis Of Educational Tests, Patrícia Martinková, Adéla Drabinová

The R Journal

This work introduces ShinyItemAnalysis, an R package and an online shiny application for psychometric analysis of educational tests and items. ShinyItemAnalysis covers a broad range of psychometric methods and offers data examples, model equations, parameter estimates, interpretation of results, together with a selected R code, and is therefore suitable for teaching psychometric concepts with R. Furthermore, the application aspires to be an easy-to-use tool for analysis of educational tests by allowing the users to upload and analyze their own data and to automatically generate analysis reports in PDF or HTML. We argue that psychometric analysis should be a routine …

Bnclassify: Learning Bayesian Network Classifiers, Bojan Mihaljević, Concha Bielza, Pedro Larrañaga

The R Journal

The bnclassify package provides state-of-the art algorithms for learning Bayesian network classifiers from data. For structure learning it provides variants of the greedy hill-climbing search, a well-known adaptation of the Chow-Liu algorithm and averaged one-dependence estimators. It provides Bayesian and maximum likelihood parameter estimation, as well as three naive-Bayes specific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on medium sized data sets. The bnclassify package provides utilities for model evaluation, such as cross-validated accuracy and penalized log-likelihood scores, and analysis of the underlying networks, including network …

Networktoolbox: Methods And Measures For Brain, Cognitive, And Psychometric Network Analysis In R, Alexander P. Christensen

The R Journal

This article introduces the NetworkToolbox package for R. Network analysis offers an intuitive perspective on complex phenomena via models depicted by nodes (variables) and edges (correlations). The ability of networks to model complexity has made them the standard approach for modeling the intricate interactions in the brain. Similarly, networks have become an increasingly attractive model for studying the complexity of psychological and psychopathological phenomena. NetworkToolbox aims to provide researchers with state-of-the-art methods and measures for estimating and analyzing brain, cognitive, and psychometric networks. In this article, I introduce NetworkToolbox and provide a tutorial for applying some the package’s functions to …

Dynamic Simulation And Testing For Single-Equation Cointegrating And Stationary Autoregressive Distributed Lag Models, Soren Jordan, Andrew Q. Philips

The R Journal

While autoregressive distributed lag models allow for extremely flexible dynamics, interpreting the substantive significance of complex lag structures remains difficult. In this paper we discuss dynamac (dynamic autoregressive and cointegrating models), an R package designed to assist users in estimating, dynamically simulating, and plotting the results of a variety of autoregressive distributed lag models. It also contains a number of post-estimation diagnostics, including a test for cointegration for when researchers are estimating the error-correction variant of the autoregressive distributed lag model.

Bnsp: An R Package For Fitting Bayesian Semiparametric Regression Models And Variable Selection, Georgios Papageorgiou

The R Journal

The R package BNSP provides a unified framework for semiparametric location-scale regression and stochastic search variable selection. The statistical methodology that the package is built upon utilizes basis function expansions to represent semiparametric covariate effects in the mean and variance functions, and spike-slab priors to perform selection and regularization of the estimated effects. In addition to the main function that performs posterior sampling, the package includes functions for assessing convergence of the sampler, summarizing model fits, visualizing covariate effects and obtaining predictions for new responses or their means given feature/covariate vectors.

Basis-Adaptive Selection Algorithm In Dr-Package, Jae Keun Yoo

The R Journal

Sufficient dimension reduction (SDR) turns out to be a useful dimension reduction tool in high-dimensional regression analysis. Weisberg (2002) developed the dr-package to implement the four most popular SDR methods. However, the package does not provide any clear guidelines as to which method should be used given a data. Since the four methods may provide dramatically different dimension reduction results, the selection in the dr-package is problematic for statistical practitioners. In this paper, a basis-adaptive selection algorithm is developed in order to relieve this issue. The basic idea is to select an SDR method that provides the highest …

Rcss: R Package For Optimal Convex Stochastic Switching, Juri Hinz, Jeremy Yee

The R Journal

The R package rcss provides users with a tool to approximate the value functions in the Bellman recursion under certain assumptions that guarantee desirable convergence properties. This R package represents the first software implementation of these methods using matrices and nearest neighbors. This package also employs a pathwise dynamic method to gauge the quality of these value function approximations. Statistical analysis can be performed on the results to obtain other useful practical insights. This paper describes rcss version 1.6.

Idmtpreg: Regression Model For Progressive Illness Death Data, Leyla Azarang, Manuel Oviedo De La Fuente

The R Journal

The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the …

Conference Report: R / Medicine Report, Joseph Rickert, Naras Balasubramanian, Michael Kane

The R Journal

R has found widespread use and is flourishing in bioinformatics, the pharmaceutical industry, clinical trials, and basic science labs. While R is being adopted in clinical informatics, its potential has not yet been realized. We believe R will fundamentally transform the space because of its strengths in making new methods available, the availability of tools for reproducible research, its interfaces to other languages, and it’s ability to disseminate new approaches through web interfaces and packaging for rapid prototyping of ideas and implementations.

Moreover, while large number of clinicians, scientists, and statisticians contribute to data driven medical science, communication between individuals …

Conference Report: Ser Iii, Ariel Levy, Luciane F. Alcoforado, Orlando Celso Longo

The R Journal

SER is a multidisciplinary event, which integrates professionals, students, and practioners from most diversified knowledge areas who make use of data analysis. The first edition took place on May 2016 as the initiative of a group of professors from the Fluminense Federal University, partners of other Institutions, and was supported by CAPES (Coordination for higher Education Staff Development). SER event was recognized by the R foundation (2018)1 for its pioneering in Latin America in bringing together an expressive number of R users.

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 3.8 was released on 31 October, 2018. It is compatible with R 3.5.2 and consists of 1649 software packages, 360 experiment data packages, and 941 up-to-date annotation packages. The release announcement includes descriptions of 95 new software packages and updated NEWS files for many additional packages.

Navigating The R Package Universe, Julia Silge, John C. Nash, Spencer Graves

The R Journal

Today, the enormous number of contributed packages available to R users outstrips any given user’s ability to understand how these packages work, their relative merits, or how they are related to each other. We organized a plenary session at useR!2017 in Brussels for the R community to think through these issues and ways forward. This session considered three key points of discussion. Users can navigate the universe of R packages with (1) capabilities for directly searching for R packages, (2) guidance for which packages to use, e.g., from CRAN Task Views and other sources, and (3) access to commoninterfaces for …

Consistency Cubes: A Fast, Efficient Method For Exact Boolean Minimization, Adrian Dusa

The R Journal

A lot of effort has been spent over the past few decades in the QCA methodology field, to develop efficient Boolean minimization algorithms to derive an exact,and more importantly complete list of minimal prime implicants that explain the initial, observed positive configurations.

As the complexity grows exponentially with every new condition, the required computer memory goes past the current computer resources and the polynomial time required to solve this problem quickly grows towards infinity.

This paper introduces a new alternative to the existing non-polynomial attempts. It completely solves the memory problem, and preliminary tests show it is exponentially hundreds of …

Conference Report: R / Pharma 2018, Joseph Rickert

The R Journal

The R / Pharma conference began as grass-roots initiative led by data scientists working in the pharmaceutical industry to promote the use of R in Pharma, and to establish and share best practices. The founding members organized the project as an R Consortium working group, and undertook the ambitious task of launching an annual conference envisioned as a relatively small, collegial, industry-oriented event with a strong scientific program.

Forecast Combinations In R Using The Forecastcomb Package, Christoph E. Weiss, Eran Raviv, Gernot Roetzer

The R Journal

This paper introduces the R package ForecastComb. The aim is to provide researchers and practitioners with a comprehensive implementation of the most common ways in which forecasts can be combined. The package in its current version covers 15 popular estimation methods for creating a combined forecasts – including simple methods, regression-based methods, and eigenvector-based methods. It also includes useful tools to deal with common challenges of forecast combination (e.g., missing values in component forecasts, or multicollinearity), and to rationalize and visualize the combination results.