Editorial

The ‘Editorial’ article from the 2020-2 issue.

On behalf of the editorial board, I am pleased to present Volume 12 Issue 2 of the R Journal. This is my third and final issue as the Editor-in-Chief. In the last year, we have made some substantial changes to the journal that I believe will continue to increase our capacity to support the growing data science and computational statistics communities, and continue to raise the visibility of the journal. In the last few months we recruited 10 Associate Editors and we are continuing the recruitment process. I’d like to publicly welcome our new Associate Editors, and thank each of them for joining us, and for their contributions thus far to the journal.

We have also been making substantial improvements to the R Journal infrastructure, allowing us to more efficiently usher manuscripts through the review process. This effort has been made possible through an investment by the R Consortium. Thanks very much to Di Cook, Mitchell O’Hara-Wild, and Stephanie Kobakian for the new capabilities - they have made my job a lot easier.

I’d also like to welcome Di as the new Editor-in-Chief of the journal. She has been an instrumental member of the editorial team, she has provided me with insight and guidance with regard to the journal. I look forward to seeing how the journal progresses under her direction.

1 In this issue

News from the R Foundation and CRAN are included in this issue along an update on the e-Rum2020 conference that was held earlier. In addition, this issue features 23 contributed research articles that have been categorized below.

Papers focusing on health and clinical trial data

• A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk
• Assembling Pharmacometric Datasets in R - The puzzle Package
• Analyzing Basket Trials under Multisource Exchangeability Assumptions
• Comparing multiple survival functions with crossing hazards in R

Supervised and unsupervised model fitting

• The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R
• User-Specified General-to-Specific and Indicator Saturation Methods
• miWQS: Multiple Imputation Using Weighted Quantile Sum Regression
• NTS: An R Package for Nonlinear Time Series Analysis
• ordinalClust: An R Package to Analyse Ordinal Data
• TULIP: A Toolbox for Linear Discriminant Analysis with Penalties
• A Unified Algorithm for the Non-Convex Penalized Estimation: The ncpen Package
• KSPM: A Package For Kernel Semi-Parametric Models

Probability distributions and processes

• Testing the Equality of Normally Distributed Groups’ Means Under Unequal Variances by doex Package
• MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions
• Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Model Estimation and Simulation in R: The rmdcev Package
• Species Distribution Modeling using Spatial Point Processes: a Case Study of Sloth Occurrence in Costa Rica
• AQuadtree: an R Package for Quadtree Anonymization of Point Data
• RNGforGPD: An R Package for Generation of Univariate and Multivariate Generalized Poisson Data
• FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing

Visualization, reproducibilty, and collaboration

• A Graphical EDA Tool with ggplot2: brinton
• Six Years of Shiny in Research; Collaborative Development of Web Tools in R
• fitzRoy: An R Package to Encourage Reproducible Sports Analysis
• OpenLand: Software for Quantitative Analysis and Visualization of Land Use and Cover Change

Note

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