Numerical Analysis and Scientific Computing Commons^™

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 …

Mirecsurv Package: Prentice-Williams-Peterson Models With Multiple Imputation Of Unknown Number Of Previous Episodes, David Moriña, Gilma Hernández-Herrera, Albert Navarro

The R Journal

Left censoring can occur with relative frequency when analyzing recurrent events in epidemiological studies, especially observational ones. Concretely, the inclusion of individuals that were already at risk before the effective initiation in a cohort study may cause the unawareness of prior episodes that have already been experienced, and this will easily lead to biased and inefficient estimates. The miRecSurv package is based on the use of models with specific baseline hazard, with multiple imputation of the number of prior episodes when unknown by means of the COMPoisson distribution, a very flexible count distribution that can handle over, sub, and equidispersion, …

Bcmixed: A Package For Median Inference On Longitudinal Data With The Box–Cox Transformation, Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Masahiko Gosho

The R Journal

This article illustrates the use of the bcmixed package and focuses on the two main functions: bcmarg and bcmmrm. The bcmarg function provides inference results for a marginal model of a mixed effect model using the Box–Cox transformation. The bcmmrm function provides model median inferences based on the mixed effect models for repeated measures analysis using the Box–Cox transformation for longitudinal randomized clinical trials. Using the bcmmrm function, analysis results with high power and high interpretability for treatment effects can be obtained for longitudinal randomized clinical trials with skewed outcomes. Further, the bcmixed package provides summarizing and visualization tools, which …

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2021-07-06 and 2021-12-22.

Donations

Jordan Aharoni (Canada) b-data GmbH (Switzerland) Mark Cachia (Canada) Shalese Fitzgerald (United States) Knut Helge Jensen (Norway) Roger Koenker (United Kingdom) Merck Research Laboratories, Kenilwort (United States) Statistik Aargau, Aarau (Switzerland)

Mgee2: An R Package For Marginal Analysis Of Longitudinal Ordinal Data With Misclassified Responses And Covariates, Yuliang Xu, Shuo Shuo Liu, Grace Y. Yi

The R Journal

Marginal methods have been widely used for analyzing longitudinal ordinal data due to their simplicity in model assumptions, robustness in inference results, and easiness in the implementation. However, they are often inapplicable in the presence of measurement errors in the variables. Under the setup of longitudinal studies with ordinal responses and covariates subject to misclassification, Chen et al. (2014) developed marginal methods for misclassification adjustments using the second-order estimating equations and proposed a two-stage estimation approach when the validation subsample is available. Parameter estimation is conducted through the Newton-Raphson algorithm, and the asymptotic distribution of the estimators is established. While …

Survidm: An R Package For Inference And Prediction In An Illness-Death Model, Gustavo Soutinho, Marta Sestelo, Luís Meira-Machado

The R Journal

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate "diseased" state before entering into a terminal absorbing state. In these models, one important goal is the modeling of transition rates which is usually done by studying the relationship between covariates and disease evolution. However, biomedical researchers are also interested in reporting other interpretable results in a simple and …

Lg: An R Package For Local Gaussian Approximations, Håkon Otneim

The R Journal

The package lg for the R programming language provides implementations of recent methodological advances on applications of the local Gaussian correlation. This includes the estimation of the local Gaussian correlation itself, multivariate density estimation, conditional density estimation, various tests for independence and conditional independence, as well as a graphical module for creating dependence maps. This paper describes the lg package, its principles, and its practical use.

Analysis Of Corneal Data In R With The Rpaci Package, Darío Ramos-López, Ana D. Maldonado

The R Journal

In ophthalmology, the early detection of keratoconus is still a crucial problem. Placido disk corneal topographers are essential in clinical practice, and many indices for diagnosing corneal irregularities exist. The main goal of this work is to present the R package rPACI, providing several functions to handle and analyze corneal data. This package implements primary indices of corneal irregularity (based on geometrical properties) and compound indices built from the primary ones, either using a generalized linear model or as a Bayesian classifier using a hybrid Bayesian network and performing approximate inference. rPACI aims to make the analysis of corneal …

News From The Forwards Taskforce, Heather Turner

The R Journal

Forwards is an R Foundation taskforce working to widen the participation of underrepresented groups in the R project and in related activities, such as the useR! conference. This report rounds up activities of the taskforce during the second half of 2022.

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 …