ch_index

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

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) <DOI:10.48550/arXiv.2410.14507>.

David Randahl

pintervals

Model Agnostic Prediction Intervals

Jonathan P. Williams

Anders Hjort

ch_index function

<dl><dt>ncs</dt>
<dd>Vector of non-conformity scores</dd>
<dt>class_vec</dt>
<dd>Vector of class labels</dd>
<dt>clusters</dt>
<dd>List of clusters, where each element is a vector of class labels assigned to that cluster</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></dl>

Arguments

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

<dl>

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


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


<dt>clusters</dt>
<dd>List of clusters, where each element is a vector of class labels assigned to that cluster</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>

</dl>

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

Description

Usage

Value

Arguments