Model Agnostic Prediction Intervals

Provides tools for estimating model-agnostic prediction intervals using conformal prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2026) .

pintervals

pintervals is an R package for computing model-agnostic prediction intervals. It provides a unified, easy-to-use interface for generating such intervals using inductive conformal prediction, bootstrapping, and parametric methods. The model-agnostic nature of the functions allows the user to quantify the uncertainty around predictions from any model that produces point predictions — linear models, machine learning algorithms, or anything else.

Why pintervals?

Point predictions on their own don't tell you how much to trust them. pintervals lets you attach calibrated uncertainty to predictions without being tied to a specific modelling framework. Prediction intervals are generated with the pinterval_* functions, where each function maps to a specific framework for generating the predictions. pintervals generally require an out-of-sample calibration data set on which errors can be computed.

• Conformal prediction (pinterval_conformal()) — distribution-free intervals with finite-sample marginal coverage guarantees under the assumption of exchangeability. pintervals implements the split/inductive conformal prediction algorithm.
• Mondrian conformal prediction (pinterval_mondrian()) — group-conditional coverage for heterogeneous data with finite-sample marginal coverage guarantees within each group, under the assumption of exchangeability.
• Clustered conformal prediction (pinterval_ccp()) — pools similar groups together for more efficient group-conditional intervals when groups are numerous or small.
• Bin-conditional conformal prediction (pinterval_bccp()) — adaptive conformal prediction intervals conditional on the outcome, for skewed or imbalanced outcome distributions with finite-sample marginal coverage guarantees within binned ranges of the outcome.
• Distance-weighted conformal prediction — available for all conformal methods above, giving more weight to calibration points close to the new observation.
• Bootstrapped prediction intervals (pinterval_bootstrap()) — resampling-based intervals that make no distributional assumptions.
• Parametric prediction intervals (pinterval_parametric()) — fast, interpretable intervals under a specified (or custom) error distribution.

All pinterval_*() functions share a common interface: point predictions for a test set, plus predicted and true values from a calibration set, in — a tibble with the resulting lower and upper bounds out. Functions to evaluate interval performance, such as interval_coverage(), interval_width(), and interval_score(), are also included.

Installation

Install the released version from CRAN:

install.packages("pintervals")

Or install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("Doktorandahl/pintervals")

Quick example

library(pintervals)

# Split predicted/true outcome pairs into calibration and test sets
data("county_turnout", package = "pintervals")
set.seed(1)
calib_idx <- sample(nrow(county_turnout), size = 0.5 * nrow(county_turnout))
calib_data <- county_turnout[calib_idx, ]
test_data  <- county_turnout[-calib_idx, ]

# Conformal prediction intervals at 90% coverage
intervals <- pinterval_conformal(
  pred = test_data$predicted_turnout,
  calib = calib_data$predicted_turnout,
  calib_truth = calib_data$turnout,
  alpha = 0.1
)

# Check empirical coverage and average width
interval_coverage(
  truth = test_data$turnout,
  lower_bound = intervals$lower_bound,
  upper_bound = intervals$upper_bound
)

Learn more

For a full introduction to the package, the theory behind each method, and a worked example predicting U.S. county-level voter turnout, see the package vignette:

vignette("pintervals", package = "pintervals")

Bundled data

Citation

If you use pintervals in your research, please cite:

Randahl, D., Hjort, A., & Williams, J. P. (2026). pintervals: an R package for model-agnostic prediction intervals. arXiv preprint arXiv:2601.03994.