John M. Chambers Statistical Software Award

In 1998 the Association for Computing Machinery (ACM) presented its Software System Award to John Chambers for the design and development of S. The award included a prize of $10,000, which Dr. Chambers generously donated to the Statistical Computing Section to endow an annual prize for student statistical software. The prize carries with it a cash award of $2000. The award is given for the development and implementation of computational tools for the statistical profession by a graduate or undergraduate student.

2024 Competition

The submission form is available here.

The Statistical Computing Section of the American Statistical Association announces the competition for the John M. Chambers Statistical Software Award. In 1998 the Association for Computing Machinery (ACM) presented the ACM Software System Award to John Chambers for the design and development of S. Dr. Chambers generously donated his award to the Statistical Computing Section to endow an annual prize for statistical software written by, or in collaboration with, an undergraduate or graduate student. The prize carries with it a cash award of $2,000.

Both individuals and teams are eligible to participate in the competition. To be eligible, at least one individual within the team must have begun the development while a student and must either currently be a student, or have completed all requirements for her/his last degree after January 1, 2023. The award will be given to the student, or split between student team members if the team consists of multiple students, up to a maximum of three students. If the software was created by a team, the contribution of the student(s) must be substantial.

To apply for the award, teams must provide the following materials:

  • Current CVs of all team members.
  • A letter from a faculty mentor at the academic institution of one of the students. The letter should confirm that the student had substantial participation in the development of the software, certify her/his student status when the software began to be developed, confirm that he/she is still a student (or provide a date of degree completion), and briefly discuss the importance of the software to statistical practice.
  • A brief, one to two page description of the software, summarizing what it does, how it does it, and why it is an important contribution. If any student team member has continued developing the software after finishing her/his studies, the description should indicate what was developed when the individual was a student and what has been added since.
  • An installable software package with its source code for use by the award committee. It should be accompanied by enough information to allow the judges to effectively use and evaluate the software (including its design considerations). This information can be provided in a variety of ways, including but not limited to: a user manual, a manuscript, a URL, and online help to the system.

All materials must be in English. We prefer that electronic text be submitted as PDF files. The entries will be judged on a variety of dimensions, including the importance and relevance for statistical practice of the tasks performed by the software, ease of use, clarity of description, elegance and availability for use by the statistical community. Preference will be given to those entries that are grounded in software design rather than calculation. The decision of the award committee is final.

All application materials MUST BE RECEIVED by 5:00 PM EST, Thursday, December 15, 2023. The submission window will be open at on December 1, 2023. Award announcements will be made by January 15, 2024. Questions are to be emailed to Philip Waggoner.


On Saturday May 15, the Association for Computing Machinery (ACM) presented its prestigious Software System Award to researcher John Chambers of Bell Labs. The award was presented for the design of the S System for statistical computing, which the ACM said has "forever altered how people analyze, visualize, and manipulate data. S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers." And in more good news for statistical computing, Chambers announced plans to turn over his $10,000 award to the American Statistical Association to endow a new prize that will recognize outstanding student work in software for statistics.

This is the first time in its 17-year history that the Software System Award has been given for data analysis software, and the first time it has been given to a statistician. Beginning with the UNIX* System - created in 1969 by Bell Labs researchers Dennis Ritchie and Ken Thompson - the Software System Award has recognized ideas and developments that have had a major, lasting impact on computing, such as TCP/IP and the World Wide Web.

The first versions of S in the 1970s pioneered the use of data visualization and interactive statistical computing. Subsequent versions provided richly enhanced modeling capability, and user extensibility, based on its functional object-based approach.

Still more recent versions provide a powerful class/method structure, new techniques to deal with large objects, extended interfaces to other languages and files, object-based documentation compatible with HTML, and powerful interactive programming techniques. The commercial version, S-Plus, is used across many disciplines where analysts must struggle with creative ways to manage and extract useful information from data. More information about S is available at the Bell Lab's web site .

John Chambers is one of the researchers pursuing a new joint project, Omega, aimed at the next generation of statistical software. In this project, emphasis is on Java and distributed computing, with the goal of a wide range of new software in open source, benefiting and involving the whole statistical computing community. More about Omega and its activities can be found on the project web site.

Looking toward the future in another way, Chambers donated the prize money from his Software System Award - all $10,000 of it - to the ASA, to endow a prize for the best student software written to support the computing used in statistics.

The purpose of the prize, Chambers said, "is to recognize contributions in the design and implementation of software that has value for the statistical community, and to raise awareness in that community of the importance of good software to those involved in statistical applications and research." In particular, Chambers hopes the existence of the prize will foster greater recognition that software design is a key component of statistical research, deserving recognition on the same level as mathematical theory and other essential elements.

Statistical software created by undergraduate or graduate students in any field will be eligible for the prize. The prize will be administered by the Statistical Computing section of the ASA. Details of the prize will appear in future issues of the Newsletter and Amstat News.

John Chambers (right) hands over the ACM prize to 1999 Computing Section Chair Jim Rosenberger (left). The money will be used to establish an award supporting student research in statistical computing.



The review panel of the John M. Chambers Statistical Software Award consisted of Stephen Berg (Section on Statistical Computing), Yixuan Qiu (Section on Statistical Computing) and Samantha Tyler (Section on Statistical Computing; chair of the review panel). The 2023 John M. Chambers Statistical Software Award goes to:

Haiyang Huang and Yingfan Wang (for python package PaCMAP,, Duke University.

The review panel also selected two honorable mentions:

Junhao Huang and Jin Zhu (for package abess,, National University of Singapore and Sun Yat-Sen University;

Yelie Yuan (for R package wdnet,, University of Connecticut.


The review panel of the John M. Chambers Statistical Software Award consisted of Yixuan Qiu (Section on Statistical Computing), Samantha Tyler (Section on Statistical Computing; chair of the review panel) and Philip Waggoner (Section on Statistical Computing). The 2022 John M. Chambers Statistical Software Award goes to:

Hubert Baniecki (for python package dalex,, Warsaw University of Technology.

An honorable mention goes to

Vittorio Orlandi (for R package FLAME,, Duke University.


The review panel of the Chambers Statistical Software Award consisted of Brennan Bean (Section on Statistical Computing), Philip Waggoner (Section on Statistical Computing), and Samantha Tyler (Section on Statistical Computing; chair of the review panel). The 2021 John M. Chambers Statistical Software Award goes to: 


The review panel of the Chambers Statistical Software Award consisted of Joyce Robbins, Deepayan Sarker, and Yihui Xie (chair). We did not grant the Chambers Statistical Software Award this year. 

An honorable mention goes to:

Jialin Ma (for R package symengine,, Georgia Institute of Technology.


The review panel of the Chambers Statistical Software Award consisted of Amelia McNamara, Deepayan Sarker, and Yihui Xie (chair). The 2019 John M. Chambers Statistical Software Award goes to:

An honorable mention goes to:


This year we had 12 entries. The award is shared by two winners.

  • Dustin Tran (Department of Computer Science, Columbia University) for edward, a Python package for probabilistic modeling, inference, and criticism.
  • Nan Xiao (School of Mathematics and Statistics, Central South University, China) for liftr, an R package for persistent reproducible reporting.

Thanks to the Chambers Award review committee consisting of Patrick Breheny (chair), Deepayan Sarkar, and Yihui Xie.


Carson Sievert
(Iowa State University).

plotly for R


Winner: Tong He (Simon Fraser University) and Tianqi Chen (University of Washington) XGBoost [additional contributors]

Honorable Mention: Spectra, by Yixuan Qiu (Purdue University)


Kyle Dwayne Bemis
Purdue University



Maria Oliveira
University of Santiago de Compostela

Nonparametric circular methods for density and regression (NPCirc)


R. Philip Chalmers
York University, Toronto

Unidimensional and Multidimensional Item Response Modeling with the mirt Package


Jennifer Bobb and Ravi Varadhan
Johns Hopkins University

turboEM: A Suite of Convergence Acceleration Schemes for EM and MM algorithms


Ian Fellows

Deducer's Plot Builder: A General GUI for Statistical Graphics


Michael Kane

The Bigmemory Project


Yihui Xie
Renmin University of China



Alejandro Jara Vallejos
Catholic University of Leuven



Heather Turner and David Firth
University of Warwick



Hadley Wickham
Iowa State University

Reshape and ggplot


Markus Helbig
University of Augsburg

JGR - a Java GUI for R


Daniel Adler
University of Gottingen

RGL - A library linking R to Open GL
An extension to the R system allowing for 3D visualization

Lee Wilkinson, Chair of the Section (left), and Daniel Adler(right). Taken at the Joint Computing and Graphics Sections Mixer/Business Meeting in San Francisco (8/03).


Deepayan Sarkar
University of Wisconsin -- Madison


A package implementing Trellis Graphics in R


Simon Urbanek
University of Augsburg

Klassification - Interactive Methods for Trees
Interactive software for the graphical analysis of trees.

Simon Urbanek (left) and John Chambers (right). Taken at the Joint Computing and Graphics Sections Mixer/Business Meeting in New York City (8/02).


Dr. Alessandra Brazzale
Institute for Systems Science and Biomedical Engineering of the Italian National Research Council

HOA: Higher-Order Asymptotics
A library of S-PLUS functions (and data) for higher-order asymptotic inference


From left to right: Brian Smith, John Chambers and Sylvia Winkler. Taken at the Joint Computing and Graphics Sections Mixer/Business Meeting in Indianapolis (8/00).

  • Sylvia Winkler
    University of Augsburg

    CASSATT: Coordinate Analysing Statistical Software Applying Tandem Transformations
    The interactive software CASSATT has been developed to visualise high-dimensional, multivariate data using parallel coordinates. CASSATT is platform independent and widens the range of methods for analysing parallel coordinate data. CASSATT uses interactive tools to extend the basic graphic displays.

  • Brian Smith
    University of Iowa

    BOA: Bayesian Output Analysis Program
    BOA is an S-PLUS/R program for carrying out convergence diagnostics and statistical and graphical analysis of Monte Carlo sampling output. It can be used as an output processor for the BUGS software or for any other program which produces sampling output.