Maintained by Ryan Tibshirani Uncertainties in Predictive Inference: Out-of-Sample Fitting and Cross-Validation 2018

This project contains software tools for conformal inference. The current emphasis is on conformal prediction in regression. Soon, we will add tools for density estimation and classification.

The folder "conformalInference" can be installed as an R package, providing access to the software tools, and the file "conformalInference.pdf" contains documentation.

The folder "examples" contains R code to reproduces all examples in the paper "Distribution-Free Predictive Inference for Regression" (see reference below). This R code relies on the "conformalInference" R package.

Some key references (in reverse chronological order):

• "Distribution-Free Predictive Inference for Regression" by Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan Tibshirani, and Larry Wasserman, http:https://arxiv.org/pdf/xxxx.pdf, 2016.
• "Classification with Confidence" by Jing Lei, Biometrika, 101(4), 755-769, 2014.
• "Distribution-Free Prediction Bands for Non-parametric Regression" by Jing Lei and Larry Wasserman, Journal of the Royal Statistical Society: Series B, 76(1), 71-96, 2014.
• "A Conformal Prediction Approach to Explore Functional Data" by Jing Lei, Alessandro Rinaldo, and Larry Wasserman, Annals of Mathematics and Artificial Intelligence, 74(4), 29-43, 2013.
• "Distribution Free Prediction Sets" by Jing Lei, James Robins, and Larry Wasserman, Journal of the American Statistical Association, 108(501), 278-287, 2013.
• "On-line Predictive Linear Regression" by Vladimir Vovk, Ilia Nouretdinov, and Alex Gammerman, Annals of Statistics, 37(3), 1566-1590, 2009.
• "Algorithmic Learning in a Random World" by Vladimir Vovk, Alex Gammerman, and Glenn Shafer, Springer, 2005.