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predictset  

Conformal Prediction and Uncertainty Quantification
View on CRAN: Click here


Download and install predictset package within the R console
Install from CRAN:
install.packages("predictset")

Install from Github:
library("remotes")
install_github("cran/predictset")

Install by package version:
library("remotes")
install_version("predictset", "0.3.0")



Attach the package and use:
library("predictset")
Maintained by
Charles Coverdale
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-19
Latest Update: 2026-03-19
Description:
Implements conformal prediction methods for constructing prediction intervals (regression) and prediction sets (classification) with finite-sample coverage guarantees. Methods include split conformal, 'CV+' and 'Jackknife+' (Barber et al. 2021) <doi:10.1214/20-AOS1965>, 'Conformalized Quantile Regression' (Romano et al. 2019) <doi:10.48550/arXiv.1905.03222>, 'Adaptive Prediction Sets' (Romano, Sesia, Candes 2020) <doi:10.48550/arXiv.2006.02544>, 'Regularized Adaptive Prediction Sets' (Angelopoulos et al. 2021) <doi:10.48550/arXiv.2009.14193>, Mondrian conformal prediction for group-conditional coverage (Vovk et al. 2005), weighted conformal prediction for covariate shift (Tibshirani et al. 2019), and adaptive conformal inference for sequential prediction (Gibbs and Candes 2021). All methods are distribution-free and provide calibrated uncertainty quantification without parametric assumptions. Works with any model that can produce predictions from new data, including 'lm', 'glm', 'ranger', 'xgboost', and custom user-defined models.
How to cite:
Charles Coverdale (2026). predictset: Conformal Prediction and Uncertainty Quantification. R package version 0.3.0, https://cran.r-project.org/web/packages/predictset. Accessed 17 Jul. 2026.
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