Numerical Analysis and Scientific Computing Commons^™

Aquadtree: An R Package For Quadtree Anonymization Of Point Data, Raymond Lagonigro, Ramon Oller, Joan Carles Martori

The R Journal

The demand for precise data for analytical purposes grows rapidly among the research community and decision makers as more geographic information is being collected. Laws protecting data privacy are being enforced to prevent data disclosure. Statistical institutes and agencies need methods to preserve confidentiality while maintaining accuracy when disclosing geographic data. In this paper we present the AQuadtree package, a software intended to produce and deal with official spatial data making data privacy and accuracy compatible. The lack of specific methods in R to anonymize spatial data motivated the development of this package, providing an automatic aggregation tool to anonymize …

Kspm: A Package For Kernel Semi-Parametric Models, Catherine Schramm, Sébastien Jacquemont, Karim Oualkacha, Aurélie Labbe, Celia M. T. Greenwood

The R Journal

Kernel semi-parametric models and their equivalence with linear mixed models provide analysts with the flexibility of machine learning methods and a foundation for inference and tests of hypothesis. These models are not impacted by the number of predictor variables, since the kernel trick transforms them to a kernel matrix whose size only depends on the number of subjects. Hence, methods based on this model are appealing and numerous, however only a few R programs are available and none includes a complete set of features. Here, we present the KSPM package to fit the kernel semi-parametric model and its extensions in …

Ordinalclust: An R Package To Analyze Ordinal Data, Margot Selosse, Julien Jacques, Christophe Biernacki

The R Journal

Ordinal data are used in many domains, especially when measurements are collected from people through observations, tests, or questionnaires. ordinalClust is an innovative R package dedicated to ordinal data that provides tools for modeling, clustering, co-clustering and classifying such data. Ordinal data are modeled using the BOS distribution, which is a model with two meaningful parameters referred to as "position" and "precision". The former indicates the mode of the distribution and the latter describes how scattered the data are around the mode: the user is able to easily interpret the distribution of their data when given these two parameters. The …

A Fast And Scalable Implementation Method For Competing Risks Data With The R Package Fastcmprsk, Eric S. Kawaguchi, Jenny I. Shen, Gang Li, Marc A. Suchard

The R Journal

Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine and Gray (1999) proportional subdistribution hazards model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan …

Six Years Of Shiny In Research: Collaborative Development Of Web Tools In R, Peter Kasprzak, Lachlan Mitchell, Olena Kravchuk, Andy Timmins

The R Journal

The use of Shiny in research publications is investigated over the six and a half years since the appearance of this popular web application framework for R, which has been utilised in many varied research areas. While it is demonstrated that the complexity of Shiny applications is limited by the background architecture, and real security concerns exist for novice app developers, the collaborative benefits are worth attention from the wider research community. Shiny simplifies the use of complex methodologies for people of different specialities, at the level of proficiency appropriate for the end user. This enables a diverse community of …

Assembling Pharmacometric Datasets In R: The Puzzle Package, Mario González-Sales, Olivier Barrière, Pierre Olivier Tremblay, Guillaume Bonnefois, Julie Desrochers, Fahima Nekka

The R Journal

Pharmacometric analyses are integral components of the drug development process. The core of each pharmacometric analysis is a dataset. The time required to construct a pharmacometrics dataset can sometimes be higher than the effort required for the modeling per se. To simplify the process, the puzzle R package has been developed aimed at simplifying and facilitating the time consuming and error prone task of assembling pharmacometrics datasets.

Puzzle consist of a series of functions written in R. These functions create, from tabulated files, datasets that are compatible with the formatting requirements of the gold standard non-linear mixed effects modeling …

Fitzroy: An R Package To Encourage Reproducible Sports Analysis, Robert Nguyen, James Day, David Warton, Oscar Lane

The R Journal

The importance of reproducibility, and the related issue of open access to data, has received a lot of recent attention. Momentum on these issues is gathering in the sports analytics community. While Australian Rules football (AFL) is the leading commercial sport in Australia, unlike popular international sports, there has been no mechanism for the public to access comprehensive statistics on players and teams. Expert commentary currently relies heavily on data that isn’t made readily accessible and this produces an unnecessary barrier for the development of an inclusive sports analytics community. We present the R package fitzRoy to provide easy access …

Comparing Multiple Survival Functions With Crossing Hazards In R, Hsin-Wen Chang, Pei-Yuan Tsai, Jen-Tse Kao, Guo-You Lan

The R Journal

It is frequently of interest in time-to-event analysis to compare multiple survival functions nonparametrically. However, when the hazard functions cross, tests in existing R packages do not perform well. To address the issue, we introduce the package survELtest, which provides tests for comparing multiple survival functions with possibly crossing hazards. Due to its powerful likelihood ratio formulation, this is the only R package to date that works when the hazard functions cross. We illustrate the use of the procedures in survELtest by applying them to data from randomized clinical trials and simulated datasets. We show that these methods lead …

The Biglasso Package: A Memory- And Computation-Efficient Solver For Lasso Model Fitting With Big Data In R, Yaohui Zeng, Patrick Breheny

The R Journal

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation …

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

The R Journal

In the past 4 months, 818 new packages were added to the CRAN package repository. 100 packages were archived and 248 were archived. The following shows the growth of the number of active packages in the CRAN package repository

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2020-09-09 and 2021-01-28.

Rngforgpd: An R Package For Generation Of Univariate And Multivariate Generalized Poisson Data, Hesen Li, Hakan Demirtas, Ruizhe Chen

The R Journal

This article describes the R package RNGforGPD, which is designed for the generation of univariate and multivariate generalized Poisson data. Some illustrative examples are given, the utility and functionality of the package are demonstrated; and its performance is assessed via simulations that are devised around both artificial and real data.

Species Distribution Modeling Using Spatial Point Processes: A Case Study Of Sloth Occurrence In Costa Rica, Paula Moraga

The R Journal

Species distribution models are widely used in ecology for conservation management of species and their environments. This paper demonstrates how to fit a log-Gaussian Cox process model to predict the intensity of sloth occurrence in Costa Rica, and assess the effect of climatic factors on spatial patterns using the R-INLA package. Species occurrence data are retrieved using spocc, and spatial climatic variables are obtained with raster. Spatial data and results are manipulated and visualized by means of several packages such as raster and tmap. This paper provides an accessible illustration of spatial point process modeling that can …

Tulip: A Toolbox For Linear Discriminant Analysis With Penalties, Yuqing Pan, Qing Mai, Xin Zhang

The R Journal

Linear discriminant analysis (LDA) is a powerful tool in building classifiers with easy computation and interpretation. Recent advancements in science technology have led to the popularity of datasets with high dimensions, high orders and complicated structure. Such datasetes motivate the generalization of LDA in various research directions. The R package TULIP integrates several popular high-dimensional LDA-based methods and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification. Functions are included for model fitting, cross validation and prediction. In addition, motivated by datasets with diverse sources of predictors, we further include functions for covariate adjustment. Our package is …

Testing The Equality Of Normal Distributed And Independent Groups’ Means Under Unequal Variances By Doex Package, Mustafa Cavus, Berna Yazıcı

The R Journal

In this paper, we present the doex package contains the tests for equality of normal distributed and independent group means under unequal variances such as Cochran F, Welch-Aspin, Welch, Box, Scott-Smith, Brown-Forsythe, Johansen F, Approximate F, Alexander-Govern, Generalized F, Modified Brown-Forsythe, Permutation F, Adjusted Welch, B2, Parametric Bootstrap, Fiducial Approach, and Alvandi Generalized F-test. Most of these tests are not available in any package. Thus, doex is easy to use for researchers in multidisciplinary studies. In this study, an extensive Monte-Carlo simulation study is conducted to investigate the performance of the the tests for equality of normal distributed group means …

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 3.12 was released on 28 October, 2020. It is compatible with R 4.0.3 and consists of 1974 software packages, 398 experiment data packages, 968 up-to-date annotation packages, and 28 workflows. Books are a new addition, built regularly from source and therefore fully reproducible; an example is the community-developed Orchestrating Single-Cell Analysis with Bioconductor.

Kuhn-Tucker And Multiple Discrete-Continuous Extreme Value Model Estimation And Simulation In R: The Rmdcev Package, Patrick Lloyd-Smith

The R Journal

This paper introduces the package rmdcev in R for estimation and simulation of KuhnTucker demand models with individual heterogeneity. The models supported by rmdcev are the multiple-discrete continuous extreme value (MDCEV) model and Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. The rmdcev package also implements demand forecasting and welfare calculation for policy simulation. The purpose of this paper is to describe the model estimation and simulation framework and to demonstrate the functionalities of rmdcev using …

A Unified Algorithm For The Non-Convex Penalized Estimation: The Ncpen Package, Dongshin Kim, Sangin Lee, Sunghoon Kwon

The R Journal

Various R packages have been developed for the non-convex penalized estimation but they can only be applied to the smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP). We develop an R package, entitled ncpen, for the non-convex penalized estimation in order to make data analysts to experience other non-convex penalties. The package ncpen implements a unified algorithm based on the convex concave procedure and modified local quadratic approximation algorithm, which can be applied to a broader range of non-convex penalties, including the SCAD and MCP as special examples. Many user-friendly functionalities such as generalized information criteria, cross-validation …

User-Specified General-To-Specific And Indicator Saturation Methods, Genaro Sucarrat

The R Journal

General-to-Specific (GETS) modelling provides a comprehensive, systematic and cumulative approach to modelling that is ideally suited for conditional forecasting and counterfactual analysis, whereas Indicator Saturation (ISAT) is a powerful and flexible approach to the detection and estimation of structural breaks (e.g. changes in parameters), and to the detection of outliers. To these ends, multi path backwards elimination, single and multiple hypothesis tests on the coefficients, diagnostics tests andgoodness-of-fit measures are combined to produce a parsimonious final model. In many situations a specific model or estimator is needed, a specific set of diagnostics tests may be required, or a specific f …

Editorial, Michael J. Kane

The R Journal

On behalf of the editorial board, I am pleased to present Volume 12 Issue 2 of the R Journal. This is my third and final issue as the Editor-in-Chief. In the last year, we have made some substantial changes to the journal that I believe will continue to increase our capacity to support the growing data science and computational statistics communities, and continue to raise the visibility of the journal. In the last few months we recruited 10 Associate Editors and we are continuing the recruitment process. I’d like to publicly welcome our new Associate Editors, and thank each of …

Openland: Software For Quantitative Analysis And Visualization Of Land Use And Cover Change, Reginal Exavier, Peter Zeilhofer

The R Journal

There is an increasing availability of spatially explicit, freely available land use and cover (LUC) time series worldwide. Because of the enormous amount of data this represents, the continuous updates and improvements in spatial and temporal resolution and category differentiation, as well as increasingly dynamic and complex changes made, manual data extraction and analysis is highly time consuming, and making software tools available to automatize LUC data assessment is becoming imperative. This paper presents a software developed in R, which combines LUC raster time series data and their transitions, calculates state-of-the-art LUC change indicators, and creates spatio-temporal visualizations, all in …

E-Rum2020: How We Turned A Physical Conference Into A Successful Virtual Event, Mariachiara Fortuna, Francesca Vitalini, Mirko Signorelli, Emanuela Furfaro, Federico Marini, Gert Janssenswillen, Riccardo Porreca, Riccardo L. Rossi, Andrea Guzzo, Roberta Sirovich, Andrea Melloncelli, Lorenzo Salvi, Serena Signorelli, Filippo Chiarello

The R Journal

The European R Users Meeting 2020 (e-Rum2020) was a conference that was held virtually in June 2020. Originally, e-Rum2020 had been planned as a physical event to be held in Milano. However, the spread of the COVID-19 pandemic and the declaration of a nationwide lockdown induced the Organizing Committee to fully rethink the event, and to turn it into a live virtual conference. In this article, we describe the challenges that we encountered during the organization of e-Rum2020, and how wereacted to them. In doing so, we aim to provide future conference organizers with useful information on how to organize …

Miwqs: Multiple Imputation Using Weighted Quantile Sum Regression, Paul M. Hargarten, David C. Wheeler

The R Journal

The miWQS package in the Comprehensive R Archive Network (CRAN) utilizes weighted quantile sum regression (WQS) in the multiple imputation (MI) framework. The data analyzed is a set/mixture of continuous and correlated components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, which may differ among the components. This type of data is found in areas such as chemical epidemiological studies, sociology, and genomics. The miWQS package can be run using complete or incomplete data, which may be placed in the first quantile, …

News From The Forwards Taskforce, Heather Turner

The R Journal

Forwards is an R Foundation taskforce working to widen the participation of under represented 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 2020.

Farmtest: An R Package For Factor-Adjusted Robust Multiple Testing, Koushiki Bose, Jianqing Fan, Yuan Ke, Xiaoou Pan, Wen-Xin Zhou

The R Journal

We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed by Fan et al. (2019) for large-scale simultaneous inference on mean effects. We use a multi-factor model to explicitly capture the dependence among a large pool of variables. Three types of factors are considered: observable, latent, and a mixture of observable and latent factors. The non-factor case, which corresponds to standard multiple mean testing under weak dependence, is also included. The library implements a series of adaptive Huber methods integrated with fast data-driven tuning schemes to estimate model …

Dataset And Evaluation Of Self-Supervised Learning For Panoramic Depth Estimation, Ryan Nett

Master's Theses

Depth detection is a very common computer vision problem. It shows up primarily in robotics, automation, or 3D visualization domains, as it is essential for converting images to point clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single camera). While this is possible, it is harder than LIDAR or stereo methods since depth can't be measured from monocular images, it has to …

Exploring Information For Quantum Machine Learning Models, Michael Telahun

Electronic Theses and Dissertations

Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state …

Static And Dynamical Properties Of Multiferroics, Sayed Omid Sayedaghaee

Graduate Theses and Dissertations

Since the silicon industrial revolution in the 1950s, a lot of effort was dedicated to the research and development activities focused on material and solid-state sciences. As a result, several cutting-edge technologies are emerging including the applications of functional materials in the design and enhancement of novel devices such as sensors, highly capable data storage media, actuators, transducers, and several other types of electronic tools. In the last two decades, a class of functional materials known as multiferroics has captured significant attention because of providing a huge potential for new designs due to possessing multiple ferroic order parameters at the …

Optimal Collaborative Path Planning For Unmanned Surface Vehicles Carried By A Parent Boat Along A Planned Route, Ari Carisza Graha Prasetia, I-Lin Wang, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In this paper, an effective mechanism using a fleet of unmanned surface vehicles (USVs) carried by a parent boat (PB) is proposed to complete search or scientific tasks over multiple target water areas within a shorter time . Specifically, multiple USVs can be launched from the PB to conduct such operations simultaneously, and each USV can return to the PB for battery recharging or swapping and data collection in order to continue missions in a more extended range. The PB itself follows a planned route with a flexible schedule taking into consideration locational constraints or collision avoidance in a real-world …

A Social Network Analysis Of Jobs And Skills, Derrick Ming Yang Lee, Dion Wei Xuan Ang, Grace Mei Ching Pua, Lee Ning Ng, Sharon Purbowo, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this study, we analyzed job roles and skills across industries in Singapore. Using social network analysis, we identified job roles with similar required skills, and we also identified relationships between job skills. Our analysis visualizes such relationships in an intuitive way. Insights derived from our analyses are expected to assist job seekers, employers as well as recruitment agencies wanting to understand trending and required job roles and skills in today’s fast changing world.