lp_distance

Takes in a double matrix \(\mathbf{X}\), whose transpose \(\mathbf{X}^T\)
has \(N\) rows, and computes a vector recording all
\({N \choose 2}\) pairwise \(l_p^p\) distances of \(\mathbf{X}^T\),
ordered lexicographically.

Given a multivariate dataset and some knowledge about the dependencies
between its features, it is customary to fit a statistical model to the features
to infer parameters of interest. Such a procedure implicitly assumes that the
sample is exchangeable. This package provides a flexible non-parametric test
of this exchangeability assumption, allowing the user to specify the feature
dependencies by hand as long as features can be grouped into disjoint independent sets.
This package also allows users to test a dual hypothesis, which is, given that the
sample is exchangeable, does a proposed grouping of the features into disjoint sets
also produce statistically independent sets of features? See Aw, Spence and Song (2023)
for the accompanying paper.

Alan Aw

flintyR

Simple and Flexible Tests of Sample Exchangeability

Jeffrey Spence

lp_distance function

<dl><dt>X</dt>
<dd>double matrix (arma::mat class)</dd>
<dt>p</dt>
<dd>numeric Minkowski power (double class)</dd></dl>

Arguments

Fast \(l_p^p\) Distance Vector Computation — lp_distance

<dl>

<dt>X</dt>
<dd>double matrix (arma::mat class)</dd>


<dt>p</dt>
<dd>numeric Minkowski power (double class)</dd>

</dl>

lp_distance: Fast \(l_p^p\) Distance Vector Computation

Description

Usage

Value

Arguments

Examples