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Travers Rhodes

Applied Predictive Technologies

9:00am PDT

Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 1) Econ 140

10:30am PDT

Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 2) Econ 140

1:00pm PDT

Regression Modeling Strategies and the rms package (Part 1) Lyons & Lodato

2:30pm PDT

Regression Modeling Strategies and the rms package (Part 2) Lyons & Lodato

4:15pm PDT

What's up with the R consortium? McCaw Hall

4:40pm PDT

Presentation of the Women in R task force McCaw Hall

5:00pm PDT

R-Ladies Presentation 1 McCaw Hall

5:10pm PDT

R-Ladies Presentation 2 McCaw Hall

5:20pm PDT

Discussion McCaw Hall

9:00am PDT

Forty years of S McCaw Hall

10:30am PDT

bamdit: An R Package for Bayesian meta-analysis of diagnostic test data Barnes & McDowell & Cranston R in machine learning competitions McCaw Hall

11:06am PDT

R: The last line of defense against bad debt Econ 140

1:54pm PDT

Zero-overhead integration of R, JS, Ruby and C/C++ Lane & Lyons & Lodato

2:30pm PDT

High-performance R with FastR Sponsor Pavilion Urban Mobility Modeling using R and Big Data from Mobile Phones Sponsor Pavilion Writing a dplyr backend to support out-of-memory data for Microsoft R Server Sponsor Pavilion

3:30pm PDT

Literate Programming McCaw Hall

4:45pm PDT

Covr: Bringing Code Coverage to R Econ 140 R at Microsoft McCaw Hall

9:00am PDT

Towards a grammar of interactive graphics McCaw Hall

10:30am PDT

Empowering Business Users with Shiny SIEPR 120

11:10am PDT

Scaling R for Business Analytics SIEPR 120

11:42am PDT

Exploring the R / SQL boundary SIEPR 130

1:00pm PDT

Reusable R for automation, small area estimation and legacy systems Barnes & McDowell & Cranston

1:18pm PDT

A Lap Around R Tools for Visual Studio McCaw Hall Tools for Robust R Packages SIEPR 130

1:30pm PDT

Statistical Computing with R Lane & Lyons & Lodato

1:54pm PDT

Checkmate: Fast and Versatile Argument Checks SIEPR 130 How to do one's taxes with R McCaw Hall

2:12pm PDT

brglm: Reduced-bias inference in generalized linear models Econ 140 Providing Digital Provenance: from Modeling through Production Barnes & McDowell & Cranston

2:30pm PDT

Energy prediction and load shaping for buildings Sponsor Pavilion

3:30pm PDT

Flexible and Interpretable Regression Using Convex Penalties McCaw Hall

4:30pm PDT

Cruise & Conference Dinner McCaw Hall

9:00am PDT

Statistical Thinking in a Data Science Course McCaw Hall

10:30am PDT

Gradient Boosted Trees Model: deploying R models into production environments* Lane & Lyons & Lodato

10:50am PDT

ranger: A fast implementation of random forests for high dimensional data Lane & Lyons & Lodato

11:26am PDT

Big data algorithms for rank-based estimation Lane & Lyons & Lodato

2:00pm PDT

RCloud - Collaborative Environment for Visualization and Big Data Analytics McCaw Hall