Programming Languages and Compilers Commons^™

Comparing Namedcapture With Other R Packages For Regular Expressions, Toby Dylan Hocking

The R Journal

Regular expressions are powerful tools for manipulating non-tabular textual data. For many tasks (visualization, machine learning, etc), tables of numbers must be extracted from such data before processing by other R functions. We present the R package namedCapture, which facilitates such tasks by providing a new user-friendly syntax for defining regular expressions in R code. We begin by describing the history of regular expressions and their usage in R. We then describe the new features of the namedCapture package, and provide detailed comparisons with related R packages (rex, stringr, stringi, tidyr, rematch2, re2r).

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 3.10 was released on 30 October, 2019. It is compatible with R 3.6.1 and consists of 1823 software packages, 384 experiment data packages, 953 up-to-date annotation packages, and 27 workflows. The release announcement includes descriptions of 94 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

Fitting Tails By The Empirical Residual Coefficient Of Variation: The Ercv Package, Joan Del Castillo, Isabel Serra, Maria Padilla, David Moriña

The R Journal

This article is a self-contained introduction to the R package ercv and to the methodology on which it is based through the analysis of nine examples. The methodology is simple and trustworthy for the analysis of extreme values and relates the two main existing methodologies. The package contains R functions for visualizing, fitting and validating the distribution of tails. It also provides multiple threshold tests for a generalized Pareto distribution, together with an automatic threshold selection algorithm.

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

The R Journal

In the past 4 months, 632 new packages were added to the CRAN package repository. 27 packages were unarchived and 182 were archived. The following shows the growth of the number of active packages in the CRAN package repository:

Rollmatch: An R Package For Rolling Entry Matching, Kasey Jones, Rob Chew, Allison Witman, Yiyan Liu

The R Journal

The gold standard of experimental research is the randomized control trial. However, interventions are often implemented without a randomized control group for practical or ethical reasons. Propensity score matching (PSM) is a popular method for minimizing the effects of a randomized experiment from observational data by matching members of a treatment group to similar candidates that did not receive the intervention. Traditional PSM is not designed for studies that enroll participants on a rolling basis and does not provide a solution for interventions in which the baseline and intervention period are undefined in the comparison group. Rolling Entry Matching (REM) …

Editorial, Michael J. Kane

The R Journal

On behalf of the editorial board, I am pleased to present Volume 11, Issue 2 of the R Journal and my first issue as the Editor in Chief. This year, both Colin Gillespie and Catherine Healey join the Editorial Board, and Norm Matloff will rotate out. The R Journal continues to see increases in impact and popularity and this year we plan on making advances to better serve the community and streamline the publishing process to meet the increase in submissions we have seen over the last few years.

Dr4pl: A Stable Convergence Algorithm For The 4 Parameter Logistic Model, Hyowon An, Justin T. Landis, Aubrey G. Bailey, James S. Marron, Dirk P. Dittmer

The R Journal

The 4 Parameter Logistic (4PL) model has been recognized as a major tool to analyze the relationship between doses and responses in pharmacological experiments. A main strength of this model is that each parameter contributes an intuitive meaning enhancing interpretability of a fitted model. However, implementing the 4PL model using conventional statistical software often encounters numerical errors. This paper highlights the issue of convergence failure and presents several causes with solutions. These causes include outliers and a non-logistic data shape, so useful remedies such as robust estimation, outlier diagnostics and constrained optimization are proposed. These features are implemented in a …

Associative Classification In R: Arc, Arulescba, And Rcba, Michael Hahsler, Ian Johnson, Tomáš Kliegr, Jaroslav Kuchař

The R Journal

Several methods for creating classifiers based on rules discovered via association rule mining have been proposed in the literature. These classifiers are called associative classifiers and the best-known algorithm is Classification Based on Associations (CBA). Interestingly, only very few implementations are available and, until recently, no implementation was available for R. Now, three packages provide CBA. This paper introduces associative classification, the CBA algorithm, and how it can be used in R. A comparison of the three packages is provided to give the potential user an idea about the advantages of each of the implementations. We also show how the …

Convergenceclubs: A Package For Performing The Phillips And Sul's Club Convergence Clustering Procedure, Roberto Sichera, Pietro Pizzuto

The R Journal

This paper introduces package ConvergenceClubs, which implements functions to perform the Phillips and Sul (2007, 2009) club convergence clustering procedure in a simple and reproducible manner. The approach proposed by Phillips and Sul to analyse the convergence patterns of groups of economies is formulated as a nonlinear time varying factor model that allows for different time paths as well as individual heterogeneity. Unlike other approaches in which economies are grouped a priori, it also allows the endogenous determination of convergence clubs. The algorithm, usage, and implementation details are discussed.

R News, R Core Team

The R Journal

CHANGES IN R 3.6.2

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Time Series Forecasting With Knn In R: The Tsfknn Package, Francisco Martínez, María P. Frías, Francisco Charte, Antonio J. Rivera

The R Journal

In this paper the tsfknn package for time series forecasting using k-nearest neighbor regression is described. This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies and among different functions to aggregate the targets of the nearest neighbors. It is also possible to assess the forecast accuracy of the KNN model.

Bondvaluation: An R Package For Fixed Coupon Bond Analysis, Wadim Djatschenko

The R Journal

t The purpose of this paper is to introduce the R package BondValuation for the analysis of large datasets of fixed coupon bonds. The conceptual heterogeneity of fixed coupon bonds traded in the global markets imposes a high degree of complexity on their comparative analysis. Contrary to baseline fixed income theory, in practice, most bonds feature coupon period irregularities. In addition, there are a multitude of day count methods that determine the interest accrual, the cash flows and the discount factors used in bond valuation. Several R packages, e.g., fBonds, RQuantLib, and YieldCurve, provide tools for fixed income analysis. Nevertheless, …

Multimodal Mobile Sensing Systems For Physiological And Psychological Assessment, Nguyen Phan Sinh Huynh

Dissertations and Theses Collection (Open Access)

Sensing systems for monitoring physiological and psychological states have been studied extensively in both academic and industry research for different applications across various domains. However, most of the studies have been done in the lab environment with controlled and complicated sensor setup, which is only suitable for serious healthcare applications in which the obtrusiveness and immobility can be compromised in a trade-off for accurate clinical screening or diagnosing. The recent substantial development of mobile devices with embedded miniaturized sensors are now allowing new opportunities to adapt and develop such sensing systems in the mobile context. The ability to sense physiological …

Resampling-Based Analysis Of Multivariate Data And Repeated Measures Designs With The R Package Manova.Rm, Sarah Friedrich, Frank Konietschke, Markus Pauly

The R Journal

Nonparametric statistical inference methods for a modern and robust analysis of longitudinal and multivariate data in factorial experiments are essential for research. While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or equal covariance matrices) are implemented in statistical software packages, there is a need for user-friendly software that can be used for the analysis of data that do not fulfill the aforementioned assumptions and provide accurate p value and confidence interval estimates. Therefore, newly developed nonparametric statistical methods based on bootstrap- and permutation-approaches, which neither assume multivariate normality nor specific covariance matrices, have been …

The Landscape Of R Packages For Automated Exploratory Data Analysis, Mateusz Staniak, Przemysław Biecek

The R Journal

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering

There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). …

The Idspatialstats R Package: Quantifying Spatial Dependence Of Infectious Disease Spread, John R. Giles, Henrik Salje, Justin Lessler

The R Journal

Spatial statistics for infectious diseases are important because the spatial and temporal scale over which transmission operates determine the dynamics of disease spread. Many methods for quantifying the distribution and clustering of spatial point patterns have been developed (e.g. K-function and pair correlation function) and are routinely applied to infectious disease case occurrence data. However, these methods do not explicitly account for overlapping chains of transmission and require knowledge of the underlying population distribution, which can be limiting when analyzing epidemic case occurrence data. Therefore, we developed two novel spatial statistics that account for these effects to estimate: 1) …

Jomo: A Flexible Package For Two-Level Joint Modelling Multiple Imputation, Matteo Quartagno, Simon Grund, James Carpenter

The R Journal

Multiple imputation is a tool for parameter estimation and inference with partially observed data, which is used increasingly widely in medical and social research. When the data to be imputed are correlated or have a multilevel structure — repeated observations on patients, school children nested in classes within schools within educational districts — the imputation model needs to include this structure. Here we introduce our joint modelling package for multiple imputation of multilevel data, jomo, which uses a multivariate normal model fitted by Markov Chain Monte Carlo (MCMC). Compared to previous packages for multilevel imputation, e.g. pan, jomo adds the …

Biclustermd: An R Package For Biclustering With Missing Values, John Reisner, Hieu Pham, Sigurdur Olafsson, Stephen Vardeman, Jing Li

The R Journal

Biclustering is a statistical learning technique that attempts to find homogeneous partitions of rows and columns of a data matrix. For example, movie ratings might be biclustered to group both raters and movies. biclust is a current R package allowing users to implement a variety of biclustering algorithms. However, its algorithms do not allow the data matrix to have missing values. We provide a new R package, biclustermd, which allows users to perform biclustering on numeric data even in the presence of missing values.

Modeling Regimes With Extremes: The Bayesdfa Package For Identifying And Forecasting Common Trends And Anomalies In Multivariate Time-Series Data, Eric J. Ward, Sean C. Anderson, Luis A. Damiano, Mary E. Hunsicker, Michael A. Litzow

The R Journal

The bayesdfa package provides a flexible Bayesian modeling framework for applying dynamic factor analysis (DFA) to multivariate time-series data as a dimension reduction tool. The core estimation is done with the Stan probabilistic programming language. In addition to being one of the few Bayesian implementations of DFA, novel features of this model include (1) optionally modeling latent process deviations as drawn from a Student-t distribution to better model extremes, and (2) optionally including autoregressive and moving-average components in the latent trends. Besides estimation, we provide a series of plotting functions to visualize trends, loadings, and model predicted values. A secondary …

Coxed: An R Package For Computing Duration-Based Quantities From The Cox Proportional Hazards Model, Jonathan Kropko, Jeffrey J. Harden

The R Journal

The Cox proportional hazards model is one of the most frequently used estimators in duration (survival) analysis. Because it is estimated using only the observed durations’ rank ordering, typical quantities of interest used to communicate results of the Cox model come from the hazard function (e.g., hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague and difficult for many audiences of research to understand. We introduce a suite of methods in the R package coxed to address these problems. The package allows researchers to calculate duration-based quantities from Cox model results, such as the expected …