ks_cluster

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

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

ks_cluster function

<dl><dt>ncs</dt>
<dd>Vector of non-conformity scores</dd>
<dt>class_vec</dt>
<dd>Vector of class labels</dd>
<dt>m</dt>
<dd>Number of clusters to form</dd>
<dt>maxit</dt>
<dd>Maximum number of iterations for the clustering algorithm</dd>
<dt>nrep</dt>
<dd>Number of repetitions for the clustering algorithm</dd></dl>

Arguments

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

<dl>

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


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


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


<dt>maxit</dt>
<dd>Maximum number of iterations for the clustering algorithm</dd>


<dt>nrep</dt>
<dd>Number of repetitions for the clustering algorithm</dd>

</dl>

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

Description

Usage

Value

Arguments