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pintervals (version 1.0.1)

Model Agnostic Prediction Intervals

Description

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 (2024) .

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Version

Install

install.packages('pintervals')

Monthly Downloads

412

Version

1.0.1

License

GPL (>= 3)

Maintainer

David Randahl

Last Published

January 11th, 2026

Functions in pintervals (1.0.1)

interval_coverage

Empirical coverage of prediction intervals

interval_miscoverage

Empirical miscoverage of prediction intervals

Mean interval score (MIS) for prediction intervals

heterogeneous_error

Heterogeneous Error Function for Non-Conformity Scores

Mean width of prediction intervals

U.S. county-level turnout and demographic context (MIT Election Lab 2018 Election Analysis Dataset + additions)

Helper for minimum quantile to alpha function

Logistic Kernel Function

Function to perform Kolmogorov-Smirnov clustering on non-conformity scores

pinterval_parametric

#' Parametric prediction intervals for continuous predictions

kmeans_cluster_qecdf

Function to perform K-means clustering on quantile empirical cumulative distribution functions (qECDFs) of non-conformity scores

pinterval_mondrian

Mondrian conformal prediction intervals for continuous predictions

pinterval_conformal

Conformal Prediction Intervals of Continuous Values

ks_cluster_assignment_step

Function to assign classes to clusters based on Kolmogorov-Smirnov clustering

ks_cluster_init_step

Function to initialize clusters for Kolmogorov-Smirnov clustering

Clustered conformal prediction intervals for continuous predictions

pinterval_bootstrap

Bootstrap prediction intervals

Bin-conditional conformal prediction intervals for continuous predictions

Function to compute the within-cluster sum of squares (WCSS) for a set of clusters

Zero-adjusted Relative Error Function for Non-Conformity Scores by predicted Values with a small adjustment

pintervals-package

pintervals: Model Agnostic Prediction Intervals

Raw Error Function for Non-Conformity Scores

reciprocal_linear_kern

Reciprocal Linear Kernel Function

Relative Error Function for Non-Conformity Scores by predicted Values

Non-Conformity Score Computation Function

optimize_clusters

Function to optimize clusters based on the Calinski-Harabasz index

Function to compute the Calinski-Harabasz index for a set of clusters

Function to compute the between-cluster sum of squares (BCSS) for a set of clusters

Bin chopper function for binned bootstrapping

Cauchy Kernel Function

class_to_clusters

Function to convert class vector to cluster vector based on calibrated clusters

Grid search for lower and upper bounds of continuous conformal prediction intervals

Gaussian Kernel Function

Function to cluster non-conformity scores using either Kolmogorov-Smirnov or K-means clustering

Function to find the minimum distance between a class and a set of clusters

bindividual_alpha

Bin-individual alpha function for conformal prediction

Absolute Error Function for Non-Conformity Scores

coverage_gap_finder

Function to find the coverage gap for a set of clusters

Inner function for grid search

contiguize_intervals

Contiguize non-contiguous intervals

bootstrap_inner

Bootstrap function for bootstrapping the prediction intervals

flatten_cp_bin_intervals

Flatten binned conformal prediction intervals to contiguous intervals

Election-year democracy indicators from V-Dem (1946–2024)

Function to convert distance measure to probability