Numerical Analysis and Scientific Computing Commons^™

Analyzing Basket Trials Under Multisource Exchangeability Assumptions, Michael J. Kane, Nan Chen, Alexander M. Kaizer, Xun Jiang, H Amy Xia, Brian P. Hobbs

The R Journal

Basket designs are prospective clinical trials that are devised with the hypothesis that the presence of selected molecular features determine a patient’s subsequent response to a particular “targeted” treatment strategy. Basket trials are designed to enroll multiple clinical subpopulations to which it is assumed that the therapy in question offers beneficial efficacy in the presence of the targeted molecular profile. The treatment, however, may not offer acceptable efficacy to all subpopulations enrolled. Moreover, for rare disease settings, such as oncology wherein these trials have become popular, marginal measures of statistical evidence are difficult to interpret for sparsely enrolled subpopulations. Consequently, …

Motbfs: An R Package For Learning Hybrid Bayesian Networks Using Mixtures Of Truncated Basis Functions, Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón, Thomas D. Nielsen

The R Journal

This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions. This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixtures of polynomials as special cases. The package implements functions for learning the parameters of univariate, multivariate, and conditional distributions, and provides support for parameter learning in Bayesian networks with both discrete and continuous variables. Probabilistic inference using forward sampling is also implemented. Part of the functionality of the MoTBFs package relies on the bnlearn package, which includes functions for learning the …

A Graphical Eda Tool With Ggplot2: Brinton, Pere Millán-Martínez, Ramon Oller

The R Journal

We present brinton package, which we developed for graphical exploratory data analysis in R. Based on ggplot2, gridExtra and rmarkdown, brinton package introduces wideplot() graphics for exploring the structure of a dataset through a grid of variables and graphic types. It also introduces longplot() graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. Finally, it introduces the plotup() function, which complements the previous two functions in that it presents a particular graphic for a specific variable of a dataset. This set of functions is …

Nts: An R Package For Nonlinear Time Series Analysis, Xialu Liu, Rong Chen, Ruey Tsay

The R Journal

Linear time series models are commonly used in analyzing dependent data and in forecasting. On the other hand, real phenomena often exhibit nonlinear behavior and the observed data show nonlinear dynamics. This paper introduces the R package NTS that offers various computational tools and nonlinear models for analyzing nonlinear dependent data. The package fills the gaps of several outstanding R packages for nonlinear time series analysis. Specifically, the NTS package covers the implementation of threshold autoregressive (TAR) models, autoregressive conditional mean models with exogenous variables (ACMx), functional autoregressive models, and state-space models. Users can also evaluate and compare the performance …

Aquadtree: An R Package For Quadtree Anonymization Of Point Data, Raymond Lagonigro, Ramon Oller, Joan Carles Martori

The R Journal

The demand for precise data for analytical purposes grows rapidly among the research community and decision makers as more geographic information is being collected. Laws protecting data privacy are being enforced to prevent data disclosure. Statistical institutes and agencies need methods to preserve confidentiality while maintaining accuracy when disclosing geographic data. In this paper we present the AQuadtree package, a software intended to produce and deal with official spatial data making data privacy and accuracy compatible. The lack of specific methods in R to anonymize spatial data motivated the development of this package, providing an automatic aggregation tool to anonymize …

Kspm: A Package For Kernel Semi-Parametric Models, Catherine Schramm, Sébastien Jacquemont, Karim Oualkacha, Aurélie Labbe, Celia M. T. Greenwood

The R Journal

Kernel semi-parametric models and their equivalence with linear mixed models provide analysts with the flexibility of machine learning methods and a foundation for inference and tests of hypothesis. These models are not impacted by the number of predictor variables, since the kernel trick transforms them to a kernel matrix whose size only depends on the number of subjects. Hence, methods based on this model are appealing and numerous, however only a few R programs are available and none includes a complete set of features. Here, we present the KSPM package to fit the kernel semi-parametric model and its extensions in …

Ordinalclust: An R Package To Analyze Ordinal Data, Margot Selosse, Julien Jacques, Christophe Biernacki

The R Journal

Ordinal data are used in many domains, especially when measurements are collected from people through observations, tests, or questionnaires. ordinalClust is an innovative R package dedicated to ordinal data that provides tools for modeling, clustering, co-clustering and classifying such data. Ordinal data are modeled using the BOS distribution, which is a model with two meaningful parameters referred to as "position" and "precision". The former indicates the mode of the distribution and the latter describes how scattered the data are around the mode: the user is able to easily interpret the distribution of their data when given these two parameters. The …

A Fast And Scalable Implementation Method For Competing Risks Data With The R Package Fastcmprsk, Eric S. Kawaguchi, Jenny I. Shen, Gang Li, Marc A. Suchard

The R Journal

Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine and Gray (1999) proportional subdistribution hazards model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan …

Six Years Of Shiny In Research: Collaborative Development Of Web Tools In R, Peter Kasprzak, Lachlan Mitchell, Olena Kravchuk, Andy Timmins

The R Journal

The use of Shiny in research publications is investigated over the six and a half years since the appearance of this popular web application framework for R, which has been utilised in many varied research areas. While it is demonstrated that the complexity of Shiny applications is limited by the background architecture, and real security concerns exist for novice app developers, the collaborative benefits are worth attention from the wider research community. Shiny simplifies the use of complex methodologies for people of different specialities, at the level of proficiency appropriate for the end user. This enables a diverse community of …

Assembling Pharmacometric Datasets In R: The Puzzle Package, Mario González-Sales, Olivier Barrière, Pierre Olivier Tremblay, Guillaume Bonnefois, Julie Desrochers, Fahima Nekka

The R Journal

Pharmacometric analyses are integral components of the drug development process. The core of each pharmacometric analysis is a dataset. The time required to construct a pharmacometrics dataset can sometimes be higher than the effort required for the modeling per se. To simplify the process, the puzzle R package has been developed aimed at simplifying and facilitating the time consuming and error prone task of assembling pharmacometrics datasets.

Puzzle consist of a series of functions written in R. These functions create, from tabulated files, datasets that are compatible with the formatting requirements of the gold standard non-linear mixed effects modeling …

Fitzroy: An R Package To Encourage Reproducible Sports Analysis, Robert Nguyen, James Day, David Warton, Oscar Lane

The R Journal

The importance of reproducibility, and the related issue of open access to data, has received a lot of recent attention. Momentum on these issues is gathering in the sports analytics community. While Australian Rules football (AFL) is the leading commercial sport in Australia, unlike popular international sports, there has been no mechanism for the public to access comprehensive statistics on players and teams. Expert commentary currently relies heavily on data that isn’t made readily accessible and this produces an unnecessary barrier for the development of an inclusive sports analytics community. We present the R package fitzRoy to provide easy access …

Comparing Multiple Survival Functions With Crossing Hazards In R, Hsin-Wen Chang, Pei-Yuan Tsai, Jen-Tse Kao, Guo-You Lan

The R Journal

It is frequently of interest in time-to-event analysis to compare multiple survival functions nonparametrically. However, when the hazard functions cross, tests in existing R packages do not perform well. To address the issue, we introduce the package survELtest, which provides tests for comparing multiple survival functions with possibly crossing hazards. Due to its powerful likelihood ratio formulation, this is the only R package to date that works when the hazard functions cross. We illustrate the use of the procedures in survELtest by applying them to data from randomized clinical trials and simulated datasets. We show that these methods lead …

The Biglasso Package: A Memory- And Computation-Efficient Solver For Lasso Model Fitting With Big Data In R, Yaohui Zeng, Patrick Breheny

The R Journal

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation …

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

The R Journal

In the past 4 months, 818 new packages were added to the CRAN package repository. 100 packages were archived and 248 were archived. The following shows the growth of the number of active packages in the CRAN package repository

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2020-09-09 and 2021-01-28.

Rngforgpd: An R Package For Generation Of Univariate And Multivariate Generalized Poisson Data, Hesen Li, Hakan Demirtas, Ruizhe Chen

The R Journal

This article describes the R package RNGforGPD, which is designed for the generation of univariate and multivariate generalized Poisson data. Some illustrative examples are given, the utility and functionality of the package are demonstrated; and its performance is assessed via simulations that are devised around both artificial and real data.

Species Distribution Modeling Using Spatial Point Processes: A Case Study Of Sloth Occurrence In Costa Rica, Paula Moraga

The R Journal

Species distribution models are widely used in ecology for conservation management of species and their environments. This paper demonstrates how to fit a log-Gaussian Cox process model to predict the intensity of sloth occurrence in Costa Rica, and assess the effect of climatic factors on spatial patterns using the R-INLA package. Species occurrence data are retrieved using spocc, and spatial climatic variables are obtained with raster. Spatial data and results are manipulated and visualized by means of several packages such as raster and tmap. This paper provides an accessible illustration of spatial point process modeling that can …

Tulip: A Toolbox For Linear Discriminant Analysis With Penalties, Yuqing Pan, Qing Mai, Xin Zhang

The R Journal

Linear discriminant analysis (LDA) is a powerful tool in building classifiers with easy computation and interpretation. Recent advancements in science technology have led to the popularity of datasets with high dimensions, high orders and complicated structure. Such datasetes motivate the generalization of LDA in various research directions. The R package TULIP integrates several popular high-dimensional LDA-based methods and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification. Functions are included for model fitting, cross validation and prediction. In addition, motivated by datasets with diverse sources of predictors, we further include functions for covariate adjustment. Our package is …

Testing The Equality Of Normal Distributed And Independent Groups’ Means Under Unequal Variances By Doex Package, Mustafa Cavus, Berna Yazıcı

The R Journal

In this paper, we present the doex package contains the tests for equality of normal distributed and independent group means under unequal variances such as Cochran F, Welch-Aspin, Welch, Box, Scott-Smith, Brown-Forsythe, Johansen F, Approximate F, Alexander-Govern, Generalized F, Modified Brown-Forsythe, Permutation F, Adjusted Welch, B2, Parametric Bootstrap, Fiducial Approach, and Alvandi Generalized F-test. Most of these tests are not available in any package. Thus, doex is easy to use for researchers in multidisciplinary studies. In this study, an extensive Monte-Carlo simulation study is conducted to investigate the performance of the the tests for equality of normal distributed group means …

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.12 was released on 28 October, 2020. It is compatible with R 4.0.3 and consists of 1974 software packages, 398 experiment data packages, 968 up-to-date annotation packages, and 28 workflows. Books are a new addition, built regularly from source and therefore fully reproducible; an example is the community-developed Orchestrating Single-Cell Analysis with Bioconductor.