Editorial

“Editorial” published in The R Journal.

As part of our rotating editorial system, I am privileged to serve as Editor-in-Chief of the R Journal for 2026. We thank Rob Hyndman for his service as Editor-in-Chief in 2025 and for his leadership in guiding the journal. We are also pleased to welcome Vincent Arel-Bundock as our new Executive Editor. In addition, we welcome Karissa Whiting and H. Sherry Zhang as new Associate Editors. We extend our gratitude to the Associate Editors whose terms concluded in 2025: Adam Loy, Isabella Gollini, Lucy D’Agostino McGowan, Rasmus Bååth, Susan VanderPlas, and Yanfei Kang.

I am pleased to report that the R Journal continues to grow, both in the number of submissions received and the number of articles published. We are grateful to all authors who have entrusted us with their work, and to the reviewers whose time, expertise, and dedication help maintain the high standards of the journal.

We also thank Mitchell O’Hara-Wild, who served as the inaugural Technical Editor of the journal in 2025. Mitchell has provided invaluable technical support to the R Journal for several years, and we are grateful for his many contributions. We welcome Abhishek Ulayil as Technical Editor for 2026.

In this issue

On behalf of the editorial board, I am pleased to present Volume 18 Issue 1 of the R Journal. This issue features twenty research articles, plus news from the R Foundation.

The twenty articles reflect the steady expansion of statistical methods in R, with a clear push toward making modern approaches more usable in practice. Several of the contributions focus on deep learning and neural network models, bringing tools like GANs and survival deep learning into the R language. Another cluster of work develops Bayesian methods and there is also a strong set of papers on regression and flexible modelling techniques, including variable selection, distributional regression, kernel-based approaches, point processes and time series applications. Finally, a number of packages address applied problems in sampling, survey methods and specialised statistical workflows across fields such as ecology, health, and quantitative genetics.

All packages discussed are available on CRAN. Supplementary material with fully reproducible code is available for download from the R Journal website.