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 …
Bssm: Bayesian Inference Of Non-Linear And Non-Gaussian State Space Models In R,
2021
University of Jyväskylä
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 …
Passed: Calculate Power And Sample Size For Two Sample Tests,
2021
University of Missouri
Passed: Calculate Power And Sample Size For Two Sample Tests, Jinpu Li, Ryan .. Knigge, Kaiyi Chen, Emily V. Leary
The R Journal
Power and sample size estimation are critical aspects of study design to demonstrate minimized risk for subjects and justify the allocation of time, money, and other resources. Researchers often work with response variables that take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.
Automatic Time Series Forecasting With Ata Method In R: Ataforecasting Package,
2021
Dokuz Eylul University
Automatic Time Series Forecasting With Ata Method In R: Ataforecasting Package, Ali Sabri Taylan, Güçkan Yapar, Hanife Taylan Selamlar
The R Journal
Ata method is a new univariate time series forecasting method that provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. The Ata method’s forecasting performance is superior to existing methods in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or deseasonalized time series, where the deseasonalization can be performed via any preferred decomposition method. The R package ATAforecasting was developed as a comprehensive toolkit for automatic time series forecasting. It focuses on modeling all types of time series components with any preferred Ata methods and handling seasonality patterns by …
Robustbf: An R Package For Robust Solution To The Behrens-Fisher Problem,
2021
Eskisehir Osmangazi University
Robustbf: An R Package For Robust Solution To The Behrens-Fisher Problem, Gamze Güven, ŞÜkrü Acıtaş, Hatice ŞAmkar, Birdal ŞEnoğLu
The R Journal
Welch’s two-sample t-test based on least squares (LS) estimators is generally used to test the equality of two normal means when the variances are not equal. However, this test loses its power when the underlying distribution is not normal. In this paper, two different tests are proposed to test the equality of two long-tailed symmetric (LTS) means under heterogeneous variances. Adaptive modified maximum likelihood (AMML) estimators are used in developing the proposed tests since they are highly efficient under LTS distribution. An R package called RobustBF is given to show the implementation of these tests. Simulated Type I error rates …
Volesti: Volume Approximation And Sampling For Convex Polytopes In R,
2021
National & Kapodistrian University of Athens
Volesti: Volume Approximation And Sampling For Convex Polytopes In R, Apostolos Chalkis, Vissarion Fisikopoulos
The R Journal
Sampling from high-dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering, artificial intelligence, and machine learning. In this paper, we present volesti, an R package that provides efficient, scalable algorithms for volume estimation, uniform, and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and provides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language.
Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network,
2021
University of València
Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network, Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes
The R Journal
One of the most common applications of spatial data analysis is detecting zones, at a certain scale, where a point-referenced event under study is especially concentrated. The detection of such zones, which are usually referred to as hotspots, is essential in certain fields such as criminology, epidemiology, or traffic safety. Traditionally, hotspot detection procedures have been developed over areal units of analysis. Although working at this spatial scale can be suitable enough for many research or practical purposes, detecting hotspots at a more accurate level (for instance, at the road segment level) may be more convenient sometimes. Furthermore, it is …
Maint.Data: Modelling And Analysing Interval Data In R,
2021
Universidade Católica Portuguesa
Maint.Data: Modelling And Analysing Interval Data In R, A Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias
The R Journal
We present the CRAN R package MAINT.Data for the modelling and analysis of multivariate interval data, i.e., where units are described by variables whose values are intervals of R, representing intrinsic variability. Parametric inference methodologies based on probabilistic models for interval variables have been developed, where each interval is represented by its midpoint and log-range, for which multivariate Normal and Skew-Normal distributions are assumed. The intrinsic nature of the interval variables leads to special structures of the variance-covariance matrix, which are represented by four different possible configurations. MAINT.Data implements the proposed methodologies in the S4 object system, introducing a …
Matchthem: Matching And Weighting After Multiple Imputation,
2021
Johns Hopkins University School of Medicine
Matchthem: Matching And Weighting After Multiple Imputation, Farhad Pishgar, Noah Greifer, Clémence Leyrat, Elizabeth Stuart
The R Journal
Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and reducing the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, …
Diproperm: An R Package For The Diproperm Test,
2021
University of North Carolina at Chapel Hil
Diproperm: An R Package For The Diproperm Test, Andrew G. Allmon, J.S. Marron, Michael G. Hudgens
The R Journal
High-dimensional low sample size (HDLSS) data sets frequently emerge in many biomedical applications. The direction-projection-permutation (DiProPerm) test is a two-sample hypothesis test for comparing two high-dimensional distributions. The DiProPerm test is exact, i.e., the type I error is guaranteed to be controlled at the nominal level for any sample size, and thus is applicable in the HDLSS setting. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set
Bayessenmc: An R Package For Bayesian Sensitivity Analysis Of Misclassification,
2021
University of Minnesota Twin Cities
Bayessenmc: An R Package For Bayesian Sensitivity Analysis Of Misclassification, Jinhui Yang, Lifeng Lin, Haitao Chu
The R Journal
In case–control studies, the odds ratio is commonly used to summarize the association between a binary exposure and a dichotomous outcome. However, exposure misclassification frequently appears in case–control studies due to inaccurate data reporting, which can produce bias in measures of association. In this article, we implement a Bayesian sensitivity analysis of misclassification to provide a full posterior inference on the corrected odds ratio under both non-differential and differential misclassification. We present an R (R Core Team, 2018) package BayesSenMC, which provides user-friendly functions for its implementation. The usage is illustrated by a real data analysis on the association between …
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 …
Ngsseml: Non-Gaussian State Space With Exact Marginal Likelihood,
2021
Universidade Federal de Minas Gerais
Ngsseml: Non-Gaussian State Space With Exact Marginal Likelihood, Thiago R. Santos, Glaura C. Franco, Dani Gamerman
The R Journal
The number of packages/software for Gaussian State Space models has increased over recent decades. However, there are very few codes available for non-Gaussian State Space (NGSS) models due to analytical intractability that prevents exact calculations. One of the few tractable exceptions is the family of NGSS with exact marginal likelihood, named NGSSEML. In this work, we present the wide range of data formats and distributions handled by NGSSEML and a package in the R language to perform classical and Bayesian inference for them. Special functions for filtering, forecasting, and smoothing procedures and the exact calculation of the marginal likelihood function …
Dad: An R Package For Visualisation, Classification And Discrimination Of Multivariate Groups Modelled By Their Densities,
2021
Université d’Angers
Dad: An R Package For Visualisation, Classification And Discrimination Of Multivariate Groups Modelled By Their Densities, Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard
The R Journal
Multidimensional scaling (MDS), hierarchical cluster analysis (HCA), and discriminant analysis (DA) are classical techniques which deal with data made of n individuals and p variables. When the individuals are divided into T groups, the R package dad associates with each group a multivariate probability density function and then carries out these techniques on the densities, which are estimated by the data under consideration. These techniques are based on distance measures between densities: chi-square, Hellinger, Jeffreys, Jensen-Shannon, and Lp for discrete densities, Hellinger , Jeffreys, L2 , and 2-Wasserstein for Gaussian densities, and L2 for numeric non-Gaussian densities …
Estimating Social Influence Effects In Networks Using A Latent Space Adjusted Approach In R,
2021
University of Connecticut - Storrs
Estimating Social Influence Effects In Networks Using A Latent Space Adjusted Approach In R, Ran Xu
The R Journal
Social influence effects have been extensively studied in various empirical network research. However, many challenges remain in estimating social influence effects in networks, as influence effects are often entangled with other factors, such as homophily in the selection process and the common social-environmental factors that individuals are embedded in. Methods currently available either do not solve these problems or require stringent assumptions. Recent works by Xu (2018) and others have shown that a latent space adjusted approach based on the latent space model has the potential to disentangle the influence effects from other processes, and the simulation evidence has shown …
G2f As A Novel Tool To Find And Fill Gaps In Metabolic Networks,
2021
Universidad Nacional de Colombia
G2f As A Novel Tool To Find And Fill Gaps In Metabolic Networks, Daniel Osorio, Kelly Botero, Andrés Pinzón Velasco, Nicolás Mendoza-Mejía, Felipe Rojas-Rodríguez, George Barreto, Janneth González
The R Journal
During the building of a genome-scale metabolic model, there are several dead-end metabolites and substrates which cannot be imported, produced, nor used by any reaction incorporated in the network. The presence of these dead-end metabolites can block out the net flux of the objective function when it is evaluated through Flux Balance Analysis (FBA), and when it is not blocked, bias in the biological conclusions increase. In this aspect, the refinement to restore the connectivity of the network can be carried out manually or using computational algorithms. The g2f package was designed as a tool to find the gaps from …
Rejoinder: Software Engineering And R Programming,
2021
Australian National University
Rejoinder: Software Engineering And R Programming, Melina Vidoni
The R Journal
It is a pleasure to take part in such fruitful discussion about the relationship between Software Engineering and R programming, and what could be gain by allowing each to look more closely at the other. Several discussants make valuable arguments that ought to be further discussed
The R Quest: From Users To Developers,
2021
University of Auckland
The R Quest: From Users To Developers, Simon Urbanek
The R Journal
R is not a programming language, and this produces the inherent dichotomy between analytics and software engineering. With the emergence of data science, the opportunity exists to bridge this gap, especially through teaching practices.
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.
Web Service Quality-Dased Profiling And Selection,
2021
The American University in Cairo AUC
Web Service Quality-Dased Profiling And Selection, Ahmed Magdi Hamza
Archived Theses and Dissertations
Guaranteeing quality of service has been recently labeled as one of multiple major research challenges in the service oriented architecture. In effect, Web service selection from a set of matched services offering the same functional requirements, and ultimately claiming certain quality of service guarantees about themselves is not enough. A need emerges for the existence of a trusted third party that monitors Web service quality indicators, yet in a way that does not interfere with the normal operation of the Web service itself. The third party will eventually provide consumers with guarantees about Web service quality. In this research we …
