Editorial

The ‘Editorial’ article from the 2018-2 issue.

On behalf of the editorial board, I am pleased to present Volume 10, Issue 2 of the R Journal.

This issue covers a wide range of topics through its \(37\) articles. As is typical, many of these are related to packages that provide tools for new statistical modeling in R. Examples in this issue include "clustMixType: User-Friendly Clustering of Mixed-Type Data in R" by Szepannek and "BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection" by Papageorgiou.

Several contributions highlight new packages that enhance and extend existing modeling areas. Examples of this are "Forecast Combinations in R using the ForecastComb Package" by Weiss, Raviv, and Roetzer; "testforDEP: An R Package for Modern Distribution-free Tests and Visualization Tools for Independence" by Miecznikowski, Hsu, Chen, and Vexler; "NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis in R" by Christensen; and "bnclassify: Learning Bayesian Network Classifiers" by Mihaljevic, Bielza, and Larrañaga".

Some of the contributions include easier interfaces to modeling, such as "SARIMA Analysis and Automated Model Reports with BETS, an R Package" by Speranza, Ferreira, and da Costa and "ShinyItemAnalysis for Teaching Psychometrics and to Enforce Routine Analysis of Educational Tests" by Martinková and Drabinová.

Other modeling topics are estimation, as in "SMM: An R Package for Estimation and Simulation of Discrete-time semi-Markov Models" by Vergne, Barbu, Bérard, Cellier, and Sautreuil; missing data, as in "Profile Likelihood Estimation of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data" by Li, Gillespie, Shedden, and Gillespie; and large data, as with "Basis-Adaptive Selection Algorithm in dr-package" by Yoo.

Included in this volume are two submissions extending R’s visualization capabilities: "ggplot2 Compatible Quantile-Quantile Plots in R" by Loy, Almeida, and Hofmann and "Geospatial Point Density" by Evangelista and Beskow.

Several new tools are described in the articles. New takes on some programming idioms are given in "Dot-Pipe: an S3 Extensible Pipe for R" by Mount and Zumel; some libraries from other languages have been ported over, such as described in "sdpt3r: Semidefinite Quadratic Linear Programming in R" by Rahman; and interfaces to other languages are detailed in "jsr223: A Java Platform Integration for R with Programming Languages Groovy, JavaScript, JRuby, Jython, and Kotlin" by Gilbert and Dahl and "RcppMsgPack: MsgPack Headers and Interface Functions for R" by Eddelbuettel and Ching.

As usual, we have a few application areas represented in this issue, for example "stplanr: A Package for Transport Planning" by Lovelace and Ellison.

Finally, as much as the R Journal helps put some sense of order to R’s ecosystem, it can’t possibly cover the tremendous growth (CRAN grew to 13,592 packages by year’s end; Bioconductor reports 1649 software packages). Silge, Nash, and Graves provide some insight in "Navigating the R Package Universe."

There are notes from several conferences included. These highlight R’s expanded reach into Latin America and Poland in addition to its reach into the pharmaceutical and medical professions. The are also the usual reports about changes to CRAN, Bioconductor, and R; and a report from the R Foundation.

This year, Di Cook joins the Editorial Board, as Roger Bivand rotates off. I extend my thanks to Roger for his many contributions. His efforts now allow us to add digital object identifiers (DOIs) for R Journal articles. I am very excited about all that Di will bring. She is already off and running, securing funding from the R Foundation that will allow the journal to have some paid assistance. I also wish to acknowledge the hard work of Norm Matloff and Olivia Lau. The R Journal’s popularity has grown–the two volumes of 2018 are over 1100 printed pages–and these two were instrumental in managing the growth.

Finally, I would like to thank the many reviewers that made this edition possible. In my view, one of the biggest contributions of the R Journal is to provide peer review for R’s sprawling package ecosystem and this only happens through the dedicated, informed, volunteer efforts of the reviewers.

Note

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