Five members of the R Core Team have been awarded the Rousseeuw Prize for Statistics for their decades of work building and maintaining the R Project. The 2026 laureates are the following:
- Brian Ripley, University of Oxford, United Kingdom
- Martin Maechler, ETH Zürich, Switzerland
- Kurt Hornik, Vienna University of Economics and Business, Austria
- Peter Dalgaard, Copenhagen Business School, Denmark
- Luke Tierney, University of Iowa
The international award, which recognizes major contributions to statistical research, honors their decades of work building R, the open-source language that has become the common foundation of modern statistical computing.
Half the prize money goes to the five laureates because they are deemed to have made the longest sustained contributions and half goes to the other members of the R Core Team.
Statistics: From Specialist Tool to Global Public Good
When R was first developed in the early 1990s, competent data analysis required access to commercial software that only well-funded institutions could afford. The R Core Team set out to change this. Driven by the conviction that everybody should have free access to state-of-the-art statistical methods, the five laureates dedicated a collective 30,000 hours to R.
By keeping R free and open source under the GNU General Public License, the team removed the financial barriers and, as a result, millions of users—researchers, students, hospitals, companies, and governments worldwide, including in developing countries—now have access to the same powerful tools, regardless of their resources.
R has also transformed how statistical knowledge is shared. A subset of the R Core Team created and maintains the Comprehensive R Archive Network, a repository of more than 23,000 freely available software packages extending R across virtually every field of research. Methodological innovations in modern statistics are typically developed using R and freely provided as extension packages, making them accessible to everyone. R is the basis of the Bioconductor software for research on genomic data. The extensive graphics capabilities of R make complex data visible and understandable to audiences well beyond the specialist community.
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