Programming Languages and Compilers Commons^™

Msae: An R Package Of Multivariate Fay-Herriot Models For Small Area Estimation, Novia Permatasari, Azka Ubaidillah

The R Journal

The paper introduces an R Package of multivariate Fay-Herriot models for small area estimation named msae. This package implements four types of Fay-Herriot models, including univariate Fay-Herriot model (model 0), multivariate Fay-Herriot model (model 1), autoregressive multivariate Fay-Herriot model (model 2), and heteroskedastic autoregressive multivariate Fay-Herriot model (model 3). It also contains some datasets generated based on multivariate Fay-Herriot models. We describe and implement functions through various practical examples. Multivariate Fay-Herriot models produce a more efficient parameter estimation than direct estimation and univariate model.

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, 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, 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, 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.

Software Engineering And R Programming: A Call For Research, Melina Vidoni

The R Journal

Although R programming has been a part of research since its origins in the 1990s, few studies address scientific software development from a Software Engineering (SE) perspective. The past few years have seen unparalleled growth in the R community, and it is time to push the boundaries of SE research and R programming forwards. This paper discusses relevant studies that close this gap Additionally, it proposes a set of good practices derived from those findings aiming to act as a call-to-arms for both the R and RSE (Research SE) community to explore specific, interdisciplinary paths of research

Editorial, Dianne Cook

The R Journal

On behalf of the R Foundation and the Editorial board, I am pleased to present Volume 13 Issue 2 of the R Journal. This is the biggest issue ever!

First, some news from the Editorial board. A big thank you to Mike Kane, who has finished his term. As Editor-in-Chief in 2020, Mike expanded operations to include Associate Editors in the reviewing process. The R Journal now has a team of 20 Associate Editors. This has helped to manage the increasing number of submissions. We welcome new Associate Editors, Przemek Biecek, Chris Brunsdon, Mine Çetinkaya-Rundel, Kieran Healy, Adam Loy, Priyanga …

Elliptical Symmetry Tests In R, Slad̄Ana Babić, Christophe Ley, Marko Palangetić

The R Journal

The assumption of elliptical symmetry has an important role in many theoretical developments and applications. Hence, it is of primary importance to be able to test whether that assumption actually holds true or not. Various tests have been proposed in the literature for this problem. To the best of our knowledge, none of them has been implemented in R. This article describes the R package ellipticalsymmetry which implements several well-known tests for elliptical symmetry together with some recent tests. We demonstrate the testing procedures with a real data example.

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 …

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

Tramme: Mixed-Effects Transformation Models Using Template Model Builder, Bálint Tamási, Torsten Hothorn

The R Journal

Linear transformation models constitute a general family of parametric regression models for discrete and continuous responses. To accommodate correlated responses, the model is extended by incorporating mixed effects. This article presents the R package tramME, which builds on existing implementations of transformation models (mlt and tram packages) as well as Laplace approximation and automatic differentiation (using the TMB package), to calculate estimates and perform likelihood inference in mixed-effects transformation models. The resulting framework can be readily applied to a wide range of regression problems with grouped data structures.

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 6 months, 1077 new packages were added to the CRAN package repository. 113 packages were unarchived and 331 were archived. The following shows the growth of the number of active packages in the CRAN package repository:

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 …

Cat.Dt: An R Package For Fast Construction Of Accurate Computerized Adaptive Tests Using Decision Trees, Javier Rodríguez-Cuadrado, Juan C. Laria, David Delgado-Gómez

The R Journal

This article introduces the cat.dt package for the creation of Computerized Adaptive Tests (CATs). Unlike existing packages, the cat.dt package represents the CAT in a Decision Tree (DT) structure. This allows building the test before its administration, ensuring that the creation time of the test is independent of the number of participants. Moreover, to accelerate the construction of the tree, the package controls its growth by joining nodes with similar estimations or distributions of the ability level and uses techniques such as message passing and pre-calculations. The constructed tree, as well as the estimation procedure, can be visualized using the …

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, …

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 …

Spnetwork: A Package For Network Kernel Density Estimation, Jeremy Gelb

The R Journal

This paper introduces the new package spNetwork that provides functions to perform Network Kernel Density Estimate analysis (NKDE). This method is an extension of the classical Kernel Density Estimate (KDE), a non parametric approach to estimate the intensity of a spatial process. More specifically, it adapts the KDE for cases when the study area is a network, constraining the location of events (such as accidents on roads, leaks in pipes, fish in rivers, etc.). We present and discuss in this paper the three main versions of NKDE: simple, discontinuous, and continuous that are implemented in spNetwork. We illustrate how to …

Spfilter: An R Package For Semiparametric Spatial Filtering With Eigenvectors In (Generalized) Linear Models, Sebastian Juhl

The R Journal

Eigenvector-based Spatial filtering constitutes a highly flexible semiparametric approach to account for spatial autocorrelation in a regression framework. It combines judiciously selected eigenvectors from a transformed connectivity matrix to construct a synthetic spatial filter and remove spatial patterns from model residuals. This article introduces the spfilteR package that provides several useful and flexible tools to estimate spatially filtered linear and generalized linear models in R. While the package features functions to identify relevant eigenvectors based on different selection criteria in an unsupervised fashion, it also helps users to perform supervised spatial filtering and to select eigenvectors based on alternative user-defined …

Siqr: An R Package For Single-Index Quantile Regression, Tianhai Zu, Yan Yu

The R Journal

We develop an R package SIQR that implements the single-index quantile regression (SIQR) models via an efficient iterative local linear approach in Wu et al. (2010). Single-index quantile regression models are important tools in semiparametric regression to provide a comprehensive view of the conditional distributions of a response variable. It is especially useful when the data is heterogeneous or heavy-tailed. The package provides functions that allow users to fit SIQR models, predict, provide standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. We apply the R package SIQR to a well-known Boston Housing data.

Multiple Imputation And Synthetic Data Generation With Npbayesimputecat, Jingchen Hu, Olanrewaju Akande, Quanli Wang

The R Journal

In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular, at statistical agencies, the respondent-level data they collect from surveys and censuses can suffer from high rates of missingness. Furthermore, agencies are obliged to protect respondents’ privacy when publishing the collected data for public use. The NPBayesImputeCat R package, introduced in this paper, provides routines to i) create multiple imputations for missing data and ii) create synthetic data for statistical disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet process mixture of products of the multinomial distributions model used …