All of the presentations posted from useR! 2016 are made available to you under the Creative Commons License (http://creativecommons.org/licenses/by-nc-nd/3.0/legalcode). Microsoft makes no claims of ownership of any of this content. We are providing the live stream and the recordings as a service to the R community and the greater data science community around the world.
About the Conference
- The invited keynote lectures cover a broad spectrum of topics ranging from technical and R-related computing issues to general statistical topics of current interest.
- The user-contributed presentations and posters are submitted as abstracts prior to the conference and may be related to (virtually) any R-related topic. The presentations are typically organized in sessions of either broad or special interest, which also comprise a "free" discussion format. Such a discussion format not only provides a forum for software demonstrations and detailed discussions but also supports the self-organization of the respective communities. The duration is between 15 or 20 minutes for the focus and kaleidoscope sessions, and 5 minutes for lightning talks.
This year the conference will be held at the campus of Stanford University in Stanford, CA. The conference is being organized with support from the Department of Statistics, Stanford University and the Stanford Libraries.
The program will cover topics such as
- History of R and computing with data
- Bayesian Statistics
- Economics, Finance and Insurance
- High Performance Computing
- Reproducible Research
- Industrial Applications
- Statistical Learning with Big Data
- Spatial Statistics
- Visualization & Graphics
- and many more.
The year 2016 will be special in at least two ways: it marks the 40th anniversary of the S language (the language underlying R), and is also the year that John Chambers, an inventor of S, turns 75. Holding the conference at Stanford, the epicenter of Silicon Valley, is especially timely given the prominent position of R in the rapidly expanding fields of big data and data science.