kmeans_cluster_qecdf

Function to Perform K-Means Clustering on Quantile Empirical Cumulative Distribution Functions (qECDFs) of Non-Conformity Scores

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

kmeans_cluster_qecdf function

<dl><dt>ncs</dt>
<dd>Vector of non-conformity scores</dd>
<dt>class_vec</dt>
<dd>Vector of class labels</dd>
<dt>q</dt>
<dd>Quantiles to use for the qECDFs, default is a sequence from 0.1 to 0.9 in steps of 0.1</dd>
<dt>m</dt>
<dd>Number of clusters to form</dd></dl>

Arguments

Function to Perform K-Means Clustering on Quantile Empirical Cumulative Distribution Functions (qECDFs) of Non-Conformity Scores — kmeans_cluster_qecdf

<dl>

<dt>ncs</dt>
<dd>Vector of non-conformity scores</dd>


<dt>class_vec</dt>
<dd>Vector of class labels</dd>


<dt>q</dt>
<dd>Quantiles to use for the qECDFs, default is a sequence from 0.1 to 0.9 in steps of 0.1</dd>


<dt>m</dt>
<dd>Number of clusters to form</dd>

</dl>

kmeans_cluster_qecdf: Function to Perform K-Means Clustering on Quantile Empirical Cumulative Distribution Functions (qECDFs) of Non-Conformity Scores

Description

Usage

Value

Arguments