Variable Importance Plots: An Introduction To The Vip Package,
2020
University of Cincinnati
Variable Importance Plots: An Introduction To The Vip Package, Brandon M. Bartoszuk, Marek Gagolewski
The R Journal
In the era of “big data”, it is becoming more of a challenge to not only build state-of-the-art predictive models, but also gain an understanding of what’s really going on in the data. For example, it is often of interest to know which, if any, of the predictors in a fitted model are relatively influential on the predicted outcome. Some modern algorithms—like random forests (RFs) and gradient boosted decision trees (GBMs)—have a natural way of quantifying the importance or relative influence of each feature. Other algorithms—like naive Bayes classifiers and support vector machines—are not capable of doing so and model-agnostic …
Npordtests: An R Package Of Nonparametric Tests For Equality Of Location Against Ordered Alternatives,
2020
Gazi University Faculty of Science
Npordtests: An R Package Of Nonparametric Tests For Equality Of Location Against Ordered Alternatives, Bulent Altunkaynak, Hamza Gamgam
The R Journal
Ordered alternatives are an important statistical problem in many situation such as increased risk of congenital malformation caused by excessive alcohol consumption during pregnancy life test experiments, drug-screening studies, dose-finding studies, the dose-response studies, age-related response. There are numerous other examples of this nature. In this paper, we present the npordtests package to test the equality of locations for ordered alternatives. The package includes the Jonckheere Terpstra, Beier and Buning’s Adaptive, Modified Jonckheere-Terpstra, Terpstra-Magel, Ferdhiana Terpstra-Magel, KTP, S and Gaur’s Gc tests. A simulation study is conducted to determine which test is the most appropriate test for which scenario and …
Mistr: A Computational Framework For Mixture And Composite Distributions,
2020
Vienna University of Economics and Business
Mistr: A Computational Framework For Mixture And Composite Distributions, Lukas Sablica, Kurt Hornik
The R Journal
No abstract provided.
Changes On Cran,
2020
WU Wirtschaftsuniversität Wien
Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis
The R Journal
In the past 8 months, 1554 new packages were added to the CRAN package repository. 96 packages were unarchived and 843 were archived. The following shows the growth of the number of active packages in the CRAN package repository:
Skew-T Expected Information Matrix Evaluation And Use For Standard Error Calculations,
2020
University of Washington
Skew-T Expected Information Matrix Evaluation And Use For Standard Error Calculations, R. Douglas Martin, Chindhanai Uthaisaad, Daniel Z. Xia
The R Journal
Skew-t distributions derived from skew-normal distributions, as developed by Azzalini and several co-workers, are popular because of their theoretical foundation and the availability of computational methods in the R package sn. One difficulty with this skew-t family is that the elements of the expected information matrix do not have closed form analytic formulas. Thus, we developed a numerical integration method of computing the expected information matrix in the R package skewtInfo. The accuracy of our expected information matrix calculation method was confirmed by comparing the result with that obtained using an observed information matrix for a very large sample …
Individual-Level Modelling Of Infectious Disease Data: Epiilm,
2020
University of Calgary
Individual-Level Modelling Of Infectious Disease Data: Epiilm, Vineetha Warriyar, Waleed Almutiry, Rob Deardon
The R Journal
In this article we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. Use of the package is demonstrated through examples involving both simulated and real data.
Survboost: An R Package For High-Dimensional Variable Selection In The Stratified Proportional Hazards Model Via Gradient Boosting,
2020
University of Michigan
Survboost: An R Package For High-Dimensional Variable Selection In The Stratified Proportional Hazards Model Via Gradient Boosting, Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang
The R Journal
High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package, SurvBoost, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables. Simulation studies …
Copulacenr: Copula Based Regression Models For Bivariate Censored Data In R,
2020
Renmin University of China, University of Pittsburgh
Copulacenr: Copula Based Regression Models For Bivariate Censored Data In R, Tao Sun, Ying Ding
The R Journal
Bivariate time-to-event data frequently arise in research areas such as clinical trials and epidemiological studies, where the occurrence of two events are correlated. In many cases, the exact event times are unknown due to censoring. The copula model is a popular approach for modeling correlated bivariate censored data, in which the two marginal distributions and the between margin dependence are modeled separately. This article presents the R package CopulaCenR, which is designed for modeling and testing bivariate data under right or (general) interval censoring in a regression setting. It provides a variety of Archimedean copula functions including a flexible two-parameter …
Sortedeffects: Sorted Causal Effects In R,
2020
Boston University
Sortedeffects: Sorted Causal Effects In R, Schuowen Chen, Victor Chernozhukov, Iván Fernández-Val, Ye Luo
The R Journal
Chernozhukov et al. (2018) proposed the sorted effect method for nonlinear regression models. This method consists of reporting percentiles of the partial effects, the sorted effects, in addition to the average effect commonly used to summarize the heterogeneity in the partial effects. They also propose to use the sorted effects to carry out classification analysis where the observational units are classified as most and least affected if their partial effect are above or below some tail sorted effects. The R package SortedEffects implements the estimation and inference methods therein and provides tools to visualize the results. This vignette serves as …
A Virtualization Based System Infrastructure For Dynamic Program Analysis,
2020
Singapore Management University
A Virtualization Based System Infrastructure For Dynamic Program Analysis, Jiaqi Hong
Dissertations and Theses Collection (Open Access)
Dynamic malware analysis schemes either run the target program as is in an isolated environment assisted by additional hardware facilities or modify it with instrumentation code statically or dynamically. The hardware-assisted schemes usually trap the target during its execution to a more privileged environment based on the available hardware events. The more privileged environment is not accessible by the untrusted kernel, thus this approach is often applied for transparent and secure kernel analysis. Nevertheless, the isolated environment induces a virtual address gap between the analyzer and the target, which hinders effective and efficient memory introspection and undermines the correctness of …
R Foundation News,
2020
Universität Zürich
R Foundation News, Torsten Hothorn
The R Journal
Membership fees and donations received between 2020-02-24 and 2020-09-08.
Conference Report: Why R? 2019,
2020
Warsaw University of Technology
Conference Report: Why R? 2019, Michał Burdukiewicz, Filip Pietluch, Jarosław Chilimoniuk, Katarzyna Sidorczuk, Dominik Rafacz, Leon Eyrich Jessen, Stefan Rödiger, Marcin Kosiński, Piotr Wójcik
The R Journal
WhyR?conferences have been the hallmark of the Why R? Foundation (whyr.pl). Our goal has been to establish a series of international R-related events in Poland. After three years, weare happy to announce that our main event, the Why R? conference, has become one of the largest annual R conferences in Central Europe.
Provenance Of R’S Gradient Optimizers,
2020
University of Ottawa
Provenance Of R’S Gradient Optimizers, John C. Nash
The R Journal
Gradient optimization methods (function minimizers) are well-represented in both the base and package universe of R (R Core Team, 2019). However, some of the methods and the codes developed from them were published before standards for hardware and software were established, in particular the IEEE arithmetic (IEEE, 1985). There have been cases of unexpected behaviour or outright errors, and these are the focus of the histoRicalg project. A summary history of some of the tools in R for gradient optimization methods is presented to give perspective on such methods and the occasions where they could be used effectively.
The Rockerverse: Packages And Applications For Containerisation With R,
2020
University of Münster
The Rockerverse: Packages And Applications For Containerisation With R, Daniel Nüst, Dirk Eddelbuettel, Dom Bennett, Robrecht Cannoodt, Dav Clark, Gergely Daróczi, Mark Edmondson, Colin Fay, Ellis Hughes, Lars Kjeldgaard, Sean Lopp, Ben Marwick, Heather Nolis, Jacqueline Nolis, Hong Ooi, Kathik Ram, Noam Ross, Lori Shepard, Péter Sólymos, Tyson Lee Swetnam, Nitesh Turaga, Charlotte Van Petegem, Jason Williams, Craig Willis, Nan Xiao
The R Journal
The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We …
Projectmanagement: An R Package For Managing Projects,
2020
Universidade da Coruña
Projectmanagement: An R Package For Managing Projects, Juan Carlos Gonçalves-Dosantos, Ignacio García-Jurado, Julián Costa
The R Journal
Project management is an important body of knowledge and practices that comprises the planning, organisation and control of resources to achieve one or more pre-determined objectives. In this paper, we introduce ProjectManagement, a new R package that provides the necessary tools to manage projects in a broad sense, and illustrate its use by examples.
Difnlr: Generalized Logistic Regression Models For Dif And Ddf Detection,
2020
Institute of Computer Science of the Czech Academy of Sciences, Charles University
Difnlr: Generalized Logistic Regression Models For Dif And Ddf Detection, Adéla Hladká, Patrícia Martinková
The R Journal
Differential item functioning (DIF) and differential distractor functioning (DDF) are impor tant topics in psychometrics, pointing to potential unfairness in items with respect to minorities or different social groups. Various methods have been proposed to detect these issues. The difNLR R package extends DIF methods currently provided in other packages by offering approaches based on generalized logistic regression models that account for possible guessing or inattention, and by pro viding methods to detect DIF and DDF among ordinal and nominal data. In the current paper, we describe implementation of the main functions of the difNLR package, from data generation, through …
Bayesmallows: An R Package For The Bayesian Mallows Model,
2020
University of Oslo
Bayesmallows: An R Package For The Bayesian Mallows Model, Øystein Sørensen, Marta Crispino, Qinghua Liu, Valeria Vitelli
The R Journal
BayesMallows is an R package for analyzing preference data in the form of rankings with the Mallows rank model, and its finite mixture extension, in a Bayesian framework. The model is grounded on the idea that the probability density of an observed ranking decreases exponentially with the distance to the location parameter. It is the first Bayesian implementation that allows wide choices of distances, and it works well with a large amount of items to be ranked. BayesMallows handles non-standard data: partial rankings and pairwise comparisons, even in cases including non-transitive preference patterns. The Bayesian paradigm allows coherent quantification of …
Tools For Analyzing R Code The Tidy Way,
2020
Wake Forest University
Tools For Analyzing R Code The Tidy Way, Lucy D'Agostino Mcgowan, Sean Kross, Jeffrey Leek
The R Journal
With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted. In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data analysis pipeline that contribute to the variability in results, we have created two R packages: matahari and tidycode. These packages build on methods created for natural language processing; rather than allowing for the processing of natural language, we focus on R code as the substrate of interest. The matahari package facilitates the logging of everything that is typed in the …
Spinifex: An R Package For Creating A Manual Tour Of Low-Dimensional Projections Of Multivariate Data,
2020
Monash University
Spinifex: An R Package For Creating A Manual Tour Of Low-Dimensional Projections Of Multivariate Data, Nicholas Spyrison, Dianne Cook
The R Journal
Dynamic low-dimensional linear projections of multivariate data collectively known as tours provide an important tool for exploring multivariate data and models. The R package tourr provides functions for several types of tours: grand, guided, little, local and frozen. Each of these can be viewed dynamically, or saved into a data object for animation. This paper describes a new package, spinifex, which provides a manual tour of multivariate data where the projection coefficient of a single variable is controlled. The variable is rotated fully into the projection, or completely out of the projection. The resulting sequence of projections can be …
Lspartition: Partitioning-Based Least Squares Regression,
2020
Princeton University
Lspartition: Partitioning-Based Least Squares Regression, Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
The R Journal
Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise polynomials. This article discusses the main methodological and numerical features of the R software package lspartition, which implements results for partitioning-based least squares (series) regression estimation and inference from Cattaneo and Farrell (2013) and Cattaneo, Farrell, and Feng (2020). These results cover the multivariate regression function as well as its derivatives. First, the package provides data-driven methods to choose the number of partition knots optimally, according to integrated mean squared error, yielding optimal point estimation. Second, robust …
