validate_distance_inputs

Internal helper to validate distance-related arguments used across
conformal, bootstrap, mondrian, ccp, and bccp functions.

internal

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) <DOI:10.1017/pan.2025.10010>.

David Randahl

pintervals

Model Agnostic Prediction Intervals

Jonathan P. Williams

Anders Hjort

validate_distance_inputs function

<dl><dt>distance_features_calib</dt>
<dd>Features for calibration set</dd>
<dt>distance_features_pred</dt>
<dd>Features for prediction set</dd>
<dt>calib_length</dt>
<dd>Expected number of calibration observations</dd>
<dt>pred_length</dt>
<dd>Expected number of prediction observations</dd>
<dt>fn_name</dt>
<dd>Name of the calling function, used in error messages</dd></dl>

Arguments

Validate distance weighting inputs — validate_distance_inputs

<dl>

<dt>distance_features_calib</dt>
<dd>Features for calibration set</dd>


<dt>distance_features_pred</dt>
<dd>Features for prediction set</dd>


<dt>calib_length</dt>
<dd>Expected number of calibration observations</dd>


<dt>pred_length</dt>
<dd>Expected number of prediction observations</dd>


<dt>fn_name</dt>
<dd>Name of the calling function, used in error messages</dd>

</dl>

validate_distance_inputs: Validate distance weighting inputs

Description

Usage

Value

Arguments