Dimred And Coranking - Unifying Dimensionality Reduction In R,
2018
MaxPlanck Institute for Biogeochemisty
Dimred And Coranking - Unifying Dimensionality Reduction In R, Guido Kraemer, Markus Reichstein, Miguel D. Mahecha
The R Journal
“Dimensionality reduction” (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. DR ca nbe realized by a plethora of methods with different properties, objectives, and, hence, (dis)advantages. The resulting low-dimensional data embeddings are often difficult to compare with objective criteria. Here, we introduce the dimRed and coRanking packages for the R language. These open source software packages enable users to easily access multiple classical and advanced DR methods using a common interface. The packages also provide quality indicators for the embeddings and easy visualization of high dimensional …
Collections In R: Review And Proposal,
2018
University of Maryland
Collections In R: Review And Proposal, Timothy Barry
The R Journal
R is a powerful tool for data processing, visualization, and modeling. However, R is slower than other languages used for similar purposes, such as Python. One reason for this is that R lacks base support for collections, abstract data types that store, manipulate, and return data (e.g., sets, maps, stacks). An exciting recent trend in the R extension ecosystem is the development of collection packages, packages that provide classes that implement common collections. At least 12 collection packages are available across the two major R extension repositories, the Comprehensive R Archive Network (CRAN) and Bioconductor. In this article, we compare …
Pstat: An R Package To Assess Population Differentiation In Phenotypic Traits,
2018
Université de Limoges
Pstat: An R Package To Assess Population Differentiation In Phenotypic Traits, Stéphane Blondeau Da Silva, Anne Da Silva
The R Journal
The package Pstat calculates PST values to assess differentiation among populations from a set of quantitative traits and provides bootstrapped distributions and confidence intervals for PST. Variations of PST as a function of the parameter c/h2 are studied as well. The package implements different transformations of the measured phenotypic traits to eliminate variation resulting from allometric growth, including calculation of residuals from linear regression, Reist standardization, and the Aitchison transformation.
Residuals And Diagnostics For Binary And Ordinal Regression Models: An Introduction To The Sure Package,
2018
Wright State University
Residuals And Diagnostics For Binary And Ordinal Regression Models: An Introduction To The Sure Package, Brandon M. Greenwell, Andrew J. Mccarthy, Bradley C. Boehmke, Dungang Liu
The R Journal
Residual diagnostics is an important topic in the classroom, but it is less often used in practice when the response is binary or ordinal. Part of the reason for this is that generalized models for discrete data, like cumulative link models and logistic regression, do not produce standard residuals that are easily interpreted as those in ordinary linear regression. In this paper, we introduce the R package sure, which implements a recently developed idea of SUrrogate REsiduals. We demonstrate the utility of the package in detection of cumulative link model misspecification with respect to mean structures, link functions, …
Hrm: An R Package For Analysing High-Dimensional Multi-Factor Repeated Measures
Authors: Martin Happ, Solomon W. Harrar And Arne C. Bathke,
2018
University of Salzburg
Hrm: An R Package For Analysing High-Dimensional Multi-Factor Repeated Measures Authors: Martin Happ, Solomon W. Harrar And Arne C. Bathke, Martin Happ, Solomon W. Harrar, Arne C. Bathke
The R Journal
High-dimensional longitudinal data pose a serious challenge for statistical inference as many test statistics cannot be computed for high-dimensional data, or they do not maintain the nominal type-I error rate, or have very low power. Therefore, it is necessary to derive new inference methods capable of dealing with high dimensionality, and to make them available to statistics practitioners. One such method is implemented in the package HRM described in this article. This new method uses a similar approach as the Welch-Satterthwaite t-test approximation and works very well for high-dimensional data as long as the data distribution is not too skewed …
Inventorymodel: An R Package For Centralized Inventory Problems,
2018
Universidade de Vigo
Inventorymodel: An R Package For Centralized Inventory Problems, Alejandro Saavedra-Nieves
The R Journal
Inventory management of goods is an integral part of logistics systems; common to various economic sectors such as industry, agriculture and trade; and independent of production volume. In general, as companies seek to minimize economic losses, studies on problems of multi-agent inventory have increased in recent years. A multi-agent inventory problem is a situation in which several agents face individual inventory problems and agree to coordinate their orders with the objective of reducing their costs. The R package Inventorymodel allows the determination of both the optimal policy for some inventory situations with deterministic demands and the allocation of costs from …
Rpostgis: Linking R With A Postgis Spatial Database,
2018
University of Florida
Rpostgis: Linking R With A Postgis Spatial Database, David Bucklin, Mathieu Basille
The R Journal
With the proliferation of sensors and the ease of data collection from online sources, large datasets have become the norm in many scientific disciplines, and efficient data storage, management, and retrival is imperative for large research projects. Relational databases provide a solution, but in order to be useful, must be able to be linked to analysis and visualization tools, such as R. Here, we present a package intended to facilitate integration of R with the open-source database software PostgreSQL, with a focus on its spatial extension, PostGIS. The package rpostgis (version 1.4.1) provides methods for spatial data handling (vector and …
R Package Imputetestbench To Compare Imputation Methods For Univariate Time Series,
2018
USEPA National Health and Environmental Effects Research Laboratory
R Package Imputetestbench To Compare Imputation Methods For Univariate Time Series, Marcus W. Beck, Neeraj Bokde, Gualberto Asencio-Cortés, Kishore Kulat
The R Journal
Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are …
Advanced Bayesian Multilevel Modeling With The R Package Brms,
2018
University of Münster
Advanced Bayesian Multilevel Modeling With The R Package Brms, Paul-Christian Bürkner
The R Journal
The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models which are fit with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. Multivariate models can be fit as well. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of …
Welfare, Inequality And Poverty Analysis With Rtip: An Approach Based On Stochastic Dominance,
2018
Universidad de Cádiz
Welfare, Inequality And Poverty Analysis With Rtip: An Approach Based On Stochastic Dominance, Angel Berihuete, Carmen D. Ramos, Miguel A. Sordo
The R Journal
Disparities in economic welfare, inequality and poverty across and within countries are of great interest to sociologists, economists, researchers, social organizations and political scientists. Information about these topics is commonly based on surveys. We present a package called rtip that implements techniques based on stochastic dominance to make unambiguous comparisons, in terms of welfare, poverty and inequality, among income distributions. Besides providing point estimates and confidence intervals for the most commonly used indicators of these characteristics, the package rtip estimates the usual Lorenz curve, the generalized Lorenz curve, the TIP (Three I’s of Poverty) curve and allows to test statistically …
Support Vector Machines For Survival Analysis With R,
2018
Universität zu Lübeck
Support Vector Machines For Survival Analysis With R, Césaire J.K. Fouodo, Inke R. König, Claus Weihs, Andreas Ziegler, Marvin N. Wright
The R Journal
This article introduces the R package survivalsvm, implementing support vector machines for survival analysis. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in a single model. We describe survival support vector machines and their implementation, provide examples and compare the prediction performance with the Cox proportional hazards model, random survival forests and gradient boosting using …
Mglm: An R Package For Multivariate Categorical Data Analysis,
2018
University of California, Los Angeles
Mglm: An R Package For Multivariate Categorical Data Analysis, Juhyun Kim, Yiwen Zhang, Joshua Day, Hua Zhou
The R Journal
Data with multiple responses is ubiquitous in modern applications. However, few tools are available for regression analysis of multivariate counts. The most popular multinomial-logit model has a very restrictive mean-variance structure, limiting its applicability to many data sets. This article introduces an R package MGLM, short for multivariate response generalized linear models, that expands the current tools for regression analysis of polytomous data. Distribution fitting, random number generation, regression, and sparse regression are treated in a unifying framework. The algorithm, usage, and implementation details are discussed.
Semiparametric Generalized Linear Models With The Gldrm Package,
2018
University of Wisconsin–Madison
Semiparametric Generalized Linear Models With The Gldrm Package, Michael J. Wurm, Paul J. Rathouz
The R Journal
This paper introduces a new algorithm to estimate and perform inferences on a recently proposed and developed semiparametric generalized linear model (glm). Rather than selecting a particular parametric exponential family model, such as the Poisson distribution, this semiparametric glm assumes that the response is drawn from the more general exponential tilt family. The regression coefficients and unspecified reference distribution are estimated by maximizing a semiparametric likelihood. The new algorithm incorporates several computational stability and efficiency improvements over the algorithm originally proposed. In particular, the new algorithm performs well for either small or large support for the nonparametric response distribution. The …
Ratingscalereduction Package: Stepwise Rating Scale Item Reduction Without Predictability Loss,
2018
Laurentian University
Ratingscalereduction Package: Stepwise Rating Scale Item Reduction Without Predictability Loss, Waldemar W. Koczkodaj, Feng Li, Alicja Wolny–Dominiak
The R Journal
This study presents an innovative method for reducing the number of rating scale items without predictability loss. The “area under the receiver operator curve” method (AUC ROC) is used for the stepwise method of reducing items of a rating scale. RatingScaleReduction R package contains the presented implementation. Differential evolution (a metaheuristic for optimization) was applied to one of the analyzed datasets to illustrate that the presented stepwise method can be used with other classifiers to reduce the number of rating scale items (variables). The targeted areas of application are decision making, data mining, machine learning, and psychometrics.
News From The Bioconductor Project,
2018
University of Nebraska - Lincoln
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 3.7 was released on 1 May, 2018. It is compatible with R 3.5.1 and consists of 1560 software packages, 342 experiment data packages, and 919 up-to-date annotation packages. The release announcement includes descriptions of 98 new software packages and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands
A System For An Accountable Data Analysis Process In R,
2018
UT Health San Antonio
A System For An Accountable Data Analysis Process In R, Jonathan Gelfond, Martin Goros, Brian Hernandez, Alex Bokov
The R Journal
Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the ’verifiable …
Generalized Additive Model Multiple Imputation By Chained Equations With Package Imputerobust,
2018
Universität Hamburg
Generalized Additive Model Multiple Imputation By Chained Equations With Package Imputerobust, Daniel Salfran, Martin Spiess
The R Journal
Data analysis, common to all empirical sciences, often requires complete data sets. Unfortunately, real world data collection will usually result in data values not being observed. We present a package for robust multiple imputation (the ImputeRobust package) that allows the use of generalized additive models for location, scale, and shape in the context of chained equations. The paper describes the basics of the imputation technique which builds on a semi-parametric regression model (GAMLSS) and the algorithms and functions provided with the corresponding package. Furthermore, some illustrative examples are provided.
Fhdi: An R Package For Fractional Hot Deck Imputation,
2018
Yonsei University
Fhdi: An R Package For Fractional Hot Deck Imputation, Jongho Im, In Ho Cho, Jae Kwang Kim
The R Journal
Fractional hot deck imputation (FHDI), proposed by Kalton and Kish (1984) and investigated by Kim and Fuller (2004), is a tool for handling item nonresponse in survey sampling. In FHDI, each missing item is filled with multiple observed values yielding a single completed data set for subsequent analyses. An R package FHDI is developed to perform FHDI and also the fully efficient fractional imputation (FEFI) method of (Fuller and Kim, 2005) to impute multivariate missing data with arbitrary missing patterns. FHDI substitutes missing items with a few observed values jointly obtained from a set of donors whereas the FEFI uses …
Stackelberg Security Games: Looking Beyond A Decade Of Success,
2018
Singapore Management University
Stackelberg Security Games: Looking Beyond A Decade Of Success, Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, Milind Tambe
Research Collection School Of Computing and Information Systems
The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.
Changes On Cran,
2018
WU Wirtschaftsuniversität Wien
Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis
The R Journal
In the past 7 months, 1178 new packages were added to the CRAN package repository. 18 packages were unarchived, 493 archived and none removed. The following shows the growth of the number of active packages in the CRAN package repository:
