Conformal Inference Project. 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

References in zbMATH (referenced in 23 articles )

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  1. Cella, Leonardo; Martin, Ryan: Validity, consonant plausibility measures, and Conformal prediction (2022)
  2. Diquigiovanni, Jacopo; Fontana, Matteo; Vantini, Simone: Conformal prediction bands for multivariate functional data (2022)
  3. Solari, Aldo; Djordjilović, Vera: Multi split conformal prediction (2022)
  4. Xie, Min-ge; Zheng, Zheshi: Homeostasis phenomenon in conformal prediction and predictive distribution functions (2022)
  5. Barber, Rina Foygel; Candès, Emmanuel J.; Ramdas, Aaditya; Tibshirani, Ryan J.: Predictive inference with the jackknife+ (2021)
  6. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  7. Cansu Alakus, Denis Larocque, Aurelie Labbe: RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests (2021) arXiv
  8. Hoeltgebaum, Henrique; Adams, Niall; Lau, F. Din-Houn: Unsupervised streaming anomaly detection for instrumented infrastructure (2021)
  9. Hooker, Giles; Mentch, Lucas; Zhou, Siyu: Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance (2021)
  10. Jung, Sungkyu; Park, Kiho; Kim, Byungwon: Clustering on the torus by conformal prediction (2021)
  11. Lu, Benjamin; Hardin, Johanna: A unified framework for random forest prediction error estimation (2021)
  12. Medarametla, Dhruv; Candès, Emmanuel: Distribution-free conditional median inference (2021)
  13. Vansteelandt, Stijn; Dukes, Oliver: Discussion of: “More efficient policy learning via optimal retargeting” and “Learning optimal distributionally robust individualized treatment rules”: new objectives for policy learning (2021)
  14. Watson, David S.; Wright, Marvin N.: Testing conditional independence in supervised learning algorithms (2021)
  15. Candès, Emmanuel; Sabatti, Chiara: Discussion of the paper “Prediction, estimation, and attribution” by B. Efron (2020)
  16. Foygel Barber, Rina: Is distribution-free inference possible for binary regression? (2020)
  17. Lei, Jing: Cross-validation with confidence (2020)
  18. Xie, Min-ge; Zheng, Zheshi: Discussion of Professor Bradley Efron’s article on “Prediction, estimation, and attribution” (2020)
  19. Buja, Andreas; Kuchibhotla, Arun Kumar; Berk, Richard; George, Edward; Tchetgen Tchetgen, Eric; Zhao, Linda: Rejoinder: Models as approximations (2019)
  20. Ota, Hirofumi; Kato, Kengo; Hara, Satoshi: Quantile regression approach to conditional mode estimation (2019)

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