hausdorff

Computes the Hausdorff distance between two sets represented as vectors <code>v1</code> and <code>v2</code>. If <code>v1 == NULL</code> and <code>v2 != NULL</code>, then the largest distance between an element of <code>v1</code> and the set \(\{1,n\}\) is returned, and vice versa. If both vectors are <code>NULL</code>, <code>0</code> is returned.

Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) <arXiv:2306.04702> , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) <doi:10.1111/rssb.12243>, and the method of Pilliat et al (2023) <doi:10.1214/23-EJS2126>.

Moen Per August Jarval

HDCD

High-Dimensional Changepoint Detection

Per August Jarval Moen

hausdorff function

<dl><dt>v1</dt>
<dd>Vector representing set 1</dd>
<dt>v2</dt>
<dd>Vector representing set 2</dd>
<dt>n</dt>
<dd>Sample size (only relevant when either <code>v1</code> or <code>v2</code> is <code>NULL</code>)</dd></dl>

Arguments

Hausdorff distance between two sets — hausdorff

<dl>

<dt>v1</dt>
<dd>Vector representing set 1</dd>


<dt>v2</dt>
<dd>Vector representing set 2</dd>


<dt>n</dt>
<dd>Sample size (only relevant when either <code>v1</code> or <code>v2</code> is <code>NULL</code>)</dd>

</dl>

hausdorff: Hausdorff distance between two sets

Description

Usage

Value

Arguments

Examples