We Need Trustworthy R Packages,
2021
Eli Lilly and Company
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,
2021
Universidad EAFIT
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 …
Visual Diagnostics For Constrained Optimisation With Application To Guided Tours,
2021
Monash University
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 …
A Unifying Framework For Parallel And Distributed Processing In R Using Futures,
2021
University of California, San Francisco
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,
2021
Genentech, Inc.
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,
2021
University of Twente
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. …
Passo: An R Package For Assessing Partial Association Between Ordinal Variables,
2021
University of Kansas
Passo: An R Package For Assessing Partial Association Between Ordinal Variables, Shaobo Li, Xiaorui Zhu, Yuejie Chen, Dungang Liu
The R Journal
Partial association, the dependency between variables after adjusting for a set of covariates, is an important statistical notion for scientific research. However, if the variables of interest are ordered categorical data, the development of statistical methods and software for assessing their partial association is limited. Following the framework established by Liu et al. (2021), we develop an R package PAsso for assessing Partial Associations between ordinal variables. The package provides various functions that allow users to perform a wide spectrum of assessments, including quantification, visualization, and hypothesis testing. In this paper, we discuss the implementation of PAsso in …
An R Package For Non-Normal Multivariate Distributions: Simulation And Probability Calculations From Multivariate Lomax (Pareto Type Ii) And Other Related Distributions,
2021
University of California, San Francisco
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, …
Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games,
2021
Nanyang Technological University
Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong
Research Collection School Of Computing and Information Systems
Coordinated control of multi-agent teams is an important task in many real-time strategy (RTS) games. In most prior work, micromanagement is the commonly used strategy whereby individual agents operate independently and make their own combat decisions. On the other extreme, some employ a macromanagement strategy whereby all agents are controlled by a single decision model. In this paper, we propose a hierarchical command and control architecture, consisting of a single high-level and multiple low-level reinforcement learning agents operating in a dynamic environment. This hierarchical model enables the low-level unit agents to make individual decisions while taking commands from the high-level …
Risk-Based Machine Learning Approaches For Probabilistic Transient Stability,
2021
University of Nebraska-Lincoln
Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive …
Intelligent Resource Prediction For Hpc And Scientific Workflows,
2021
Clemson University
Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy
All Dissertations
Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …
The R Developer Community Does Have A Strong Software Engineering Culture,
2021
The rOpenSci Project
The R Developer Community Does Have A Strong Software Engineering Culture, Maëlle Salmon, Karthik Ram
The R Journal
There is a strong software engineering culture in the R developer community. We recommend creating, updating and vetting packages as well as keeping up with community standards. We invite contributions to the rOpenSci project, where participants can gain experience that will shape their work and that of their peers.
Component Damage Source Identification For Critical Infrastructure Systems,
2021
University of Arkansas, Fayetteville
Component Damage Source Identification For Critical Infrastructure Systems, Nathan Davis
Graduate Theses and Dissertations
Cyber-Physical Systems (CPS) are becoming increasingly prevalent for both Critical Infrastructure and the Industry 4.0 initiative. Bad values within components of the software portion of CPS, or the computer systems, have the potential to cause major damage if left unchecked, and so detection and locating of where these occur is vital. We further define features of these computer systems and create a use-based system topology. We then introduce a function to monitor system integrity and the presence of bad values as well as an algorithm to locate them. We then show an improved version, taking advantage of several system properties …
Analysis Of Titan's Fluvial Features Using Numerical Modeling,
2021
University of Arkansas, Fayetteville
Analysis Of Titan's Fluvial Features Using Numerical Modeling, Jeshurun Horton
Mechanical Engineering Undergraduate Honors Theses
River channels have been observed near the Huygens probe landing site on the surface of Titan, along with evidence of rounded water ice boulders transported through fluid flow. Evidence near the landing site suggests active flow of liquid methane, which has motivated the study of the effects of sediment load and channel sizes on Titan’s fluvial features. A numerical model is used to determine the viscosity, flow velocity, and critical boulder transport diameter based on channel size, slope, and a range of sediment concentrations. This model achieves two ends: first, observed boulder diameters are used to determine the ideal channel …
Rapid Method For Consistency And Concentration Reporting Of Cannabidiol Using 1H-Nmr And Computer-Assisted Chemical Software,
2021
Portland State University
Rapid Method For Consistency And Concentration Reporting Of Cannabidiol Using 1H-Nmr And Computer-Assisted Chemical Software, Michael A. Fernando
University Honors Theses
An integrated computational method was demonstrated with hemp-derived Cannabidiol for an assessment of its purity and concentration. The sample was structurally verified, high purity, and 2.98 mmol/L in dissolved DMSO. The method presented is a general approach to assessing purity and concentration for any small organic molecule in CMC-Assist.
Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning,
2021
Singapore Management University
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 …
Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach,
2021
Singapore Management University
Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ …
Decentralized Deterministic Multi-Agent Reinforcement Learning,
2021
Singapore Management University
Decentralized Deterministic Multi-Agent Reinforcement Learning, Antoine Grosnit, Desmond Cai, Laura Wynter
Research Collection School Of Computing and Information Systems
Recent work in multi-agent reinforcement learning (MARL) by [Zhang, ICML12018] provided the first decentralized actor-critic algorithm to offer convergence guarantees. In that work, policies are stochastic and are defined on finite action spaces. We extend those results to develop a provably-convergent decentralized actor-critic algorithm for learning deterministic policies on continuous action spaces. Deterministic policies are important in many real-world settings. To handle the lack of exploration inherent in deterministic policies we provide results for the off-policy setting as well as the on-policy setting. We provide the main ingredients needed for this problem: the expression of a local deterministic policy gradient, …
Comparison Of Multiple Imputation Algorithms And Verification Using Whole-Genome Sequencing In The Cmuh Genetic Biobank,
2021
China Medical University Hospital, Taiwan
Comparison Of Multiple Imputation Algorithms And Verification Using Whole-Genome Sequencing In The Cmuh Genetic Biobank, Ting-Yuan Liu, Chih-Fan Lin, Hsing-Tsung Wu, Ya-Lun Wu, Yu-Chia Chen, Chi-Chou Liao, Yu-Pao Chou, Dysan Chao, Hsing-Fang Lu, Ya-Sian Chang, Jan-Gowth Chang, Kai-Cheng Hsu, Fuu‑Jen Tsai
BioMedicine
A genome-wide association study (GWAS) can be conducted to systematically analyze the contributions of genetic factors to a wide variety of complex diseases. Nevertheless, existing GWASs have provided highly ethnic specific data. Accordingly, to provide data specific to Taiwan, we established a large-scale genetic database in a single medical institution at the China Medical University Hospital. With current technological limitations, microarray analysis can detect only a limited number of single-nucleotide polymorphisms (SNPs) with a minor allele frequency of >1%. Nevertheless, imputation represents a useful alternative means of expanding data. In this study, we compared four imputation algorithms in terms of …
Molecular Dynamics Simulations Of Vibrational Infrared And Raman Spectra Of H5o2+,
2021
Kennesaw.State University
Molecular Dynamics Simulations Of Vibrational Infrared And Raman Spectra Of H5o2+, Oluwaseun Omodemi, Ivonne Meares, Gabriella Garofalo, Martina Kaledin
Symposium of Student Scholars
We report infrared (IR) and Raman vibrational spectra of H5O2+ protonated water dimer using computational chemistry methods, the normal mode analysis (NMA), and molecular dynamics (MD) simulations. Various computational methods and basis sets were used. We also located the H5O2+ stationary points on the potential energy surface using the Gaussian 16 program. The H5O2+ Zundel complex serves as a benchmark system to study the proton transfer process. We also investigated IR and Raman intensities of other deuterated analogs, such as D5O2+, D4 …
