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". This R code relies on the "conformalInference" R package.
Main reference:
- "Distribution-Free Predictive Inference for Regression" by Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan Tibshirani, and Larry Wasserman, https://arxiv.org/abs/1604.04173, 2016.
Related references (in reverse chronological order):
- "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.
To install the conformalInference R package directly from github, run the following in R:
library(devtools)
install_github(repo="ryantibs/conformal", subdir="conformalInference")