Lp Algorithms For Portfolio Optimization: The Portfoliooptim Package,
2018
University of Warsaw
Lp Algorithms For Portfolio Optimization: The Portfoliooptim Package, Andrzej Palczewski
The R Journal
The paper describes two algorithms for financial portfolio optimization with the following risk measures: CVaR, MAD, LSAD and dispersion CVaR. These algorithms can be applied to discrete distributions of asset returns since then the optimization problems can be reduced to linear programs. The first algorithm solves a simple recourse problem as described by Haneveld using Benders de composition method. The second algorithm finds an optimal portfolio with the smallest distance to a given benchmark portfolio and is an adaptation of the least norm solution (called also normal solution) of linear programs due to Zhao and Li. The algorithms are implemented …
R Day Report,
2018
Laboratório de Estatística e Geoinformação
R Day Report, Fernando P. Mayer, Walmes M. Zeviani, Wagner H. Bonat, Elias T. Krainski, Paulo J. Ribeiro Jr.
The R Journal
R Day1- National Meeting of R Users, took place on May, 22, 2018 at Federal University of Paraná (UFPR), Curitiba, Brazil. It was the first event in Brazil endorsed by The R Foundation.
Setmethods: An Add-On R Package For Advanced Qca,
2018
Central European University
Setmethods: An Add-On R Package For Advanced Qca, Ioana-Elena Oana, Carsten Q. Scheider
The R Journal
This article presents the functionalities of the R package SetMethods, aimed at performing advanced set-theoretic analyses. This includes functions for performing set-theoretic multi-method research, set-theoretic theory evaluation, Enhanced Standard Analysis, diagnosing the impact of temporal, spatial, or substantive clusterings of the data on the results obtained via Qualitative Comparative Analysis (QCA), indirect calibration, and visualising QCA results via XY plots or radar charts. Each functionality is presented in turn, the conceptual idea and the logic behind the procedure being first summarized, and afterwards illustrated with data from Schneider et al. (2010).
Simple Features For R: Standardized Support For Spatial Vector Data,
2018
Heißenbergstraße 2
Simple Features For R: Standardized Support For Spatial Vector Data, Edzer Pebesma
The R Journal
Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos, and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to connect R to other computer systems. We illustrate this with examples of its use.
Epistemic Game Theory: Putting Algorithms To Work,
2018
Mimar Sinan Fine Arts University
Epistemic Game Theory: Putting Algorithms To Work, Bilge BaşEr, Nalan Cinemre
The R Journal
The aim of this study is to construct an epistemic model in which each rational choice under common belief in rationality is supplemented by a type which expresses such a belief. In practice, the finding of type depends on manual solution approach with some mathematical operations in scope of the theory. This approach becomes less convenient with the growth of the size of the game. To solve this difficulty, a linear programming model is constructed for two-player, static and non-cooperative games to find the type that is supporting that player’s rational choice is optimal under common belief in rationality and …
Icsoutlier: Unsupervised Outlier Detection For Low-Dimensional Contamination Authors: Structure,
2018
University Toulouse 1 Capitole
Icsoutlier: Unsupervised Outlier Detection For Low-Dimensional Contamination Authors: Structure, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen
The R Journal
Detecting outliers in a multivariate and unsupervised context is an important and ongoing problem notably for quality control. Many statistical methods are already implemented in R and are briefly surveyed in the present paper. But only a few lead to the accurate identification of potential outliers in the case of a small level of contamination. In this particular context, the Invariant Coordinate Selection (ICS) method shows remarkable properties for identifying outliers that lie on a low-dimensional subspace in its first invariant components. It is implemented in the ICSOutlier package. The main function of the package, ics.outlier, offers the possibility of …
Onewaytests: An R Package For One-Way Tests In Independent Groups Designs,
2018
Hacettepe University Faculty of Medicine
Onewaytests: An R Package For One-Way Tests In Independent Groups Designs, Osman Dag, Anil Dolgun, Naime Meric Konar
The R Journal
One-way tests in independent groups designs are the most commonly utilized statistical methods with applications on the experiments in medical sciences, pharmaceutical research, agriculture, biology, engineering, social sciences and so on. In this paper, we present the one-way tests package to investigate treatment effects on the dependent variable. The package offers the one-way tests in independent groups designs, which include ANOVA, Welch’s heteroscedastic F test, Welch’s heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander Govern test, James second order test and Kruskal-Wallis test. The package also provides pairwise comparisons, graphical approaches, and assesses variance homogeneity and …
Bayesian Testing, Variable Selection And Model Averaging In Linear Models Using R With Bayesvarsel,
2018
Universidad de Castilla-La Mancha
Bayesian Testing, Variable Selection And Model Averaging In Linear Models Using R With Bayesvarsel, Gonzalo Garcia-Donato, Anabel Forte
The R Journal
In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples.
Arco: An R Package To Estimate Artificial Counterfactuals,
2018
Military Institute of Engineering
Arco: An R Package To Estimate Artificial Counterfactuals, Yuri R. Fonseca, Ricardo P. Masini, Marcelo C. Medeiros, Gabriel F.R. Vasconcelos
The R Journal
In this paper we introduce the ArCo package for R which consists of a set of functions to implement the the Artificial Counterfactual (ArCo) methodology to estimate causal effects of an intervention (treatment) on aggregated data and when a control group is not necessarily available. The ArCo method is a two-step procedure, where in the first stage a counterfactual is estimated from a large panel of time series from a pool of untreated peers. In the second-stage, the average treatment effect over the post-intervention sample is computed. Standard inferential procedures are available. The package is illustrated with both simulated and …
Realvams: An R Package For Fitting A Multivariate Value-Added Model (Vam),
2018
Arizona State University
Realvams: An R Package For Fitting A Multivariate Value-Added Model (Vam), Jennifer Broatch, Jennifer Green, Andrew Karl
The R Journal
We present RealVAMS, an R package for fitting a generalized linear mixed model to multimembership data with partially crossed and partially nested random effects. RealVAMS utilizes a multivariate generalized linear mixed model with pseudo-likelihood approximation for fitting normally distributed continuous response(s) jointly with a binary outcome. In an educational context, the model is referred to as a multidimensional value-added model, which extends previous theory to estimate the relationships between potential teacher contributions toward different student outcomes and to allow the consideration of a binary, real-world outcome such as graduation. The simultaneous joint modeling of continuous and binary outcomes was not …
Editorial,
2018
R Project
Editorial, John Verzani
The R Journal
On behalf of the Editorial Board, I am pleased to present Volume 10, Issue 1 of the R Journal. This issue contains 36 contributed articles. The majority of which cover new or newly enhanced packages on CRAN.
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 …
