getCov

Computes covariance matrix entries and associated alpha, beta
and gamma quantities defined in Aw, Spence and Song (2023),
assuming the \(P\) features of the dataset \(\mathbf{X}\) are independent.

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

getCov function

<dl><dt>X</dt>
<dd>The binary or real matrix</dd>
<dt>p</dt>
<dd>The power \(p\) of \(l_p^p\), i.e., \(||x||_p^p = (x_1^p+...x_n^p)\)</dd></dl>

Arguments

Covariance Computations Between Pairs of Distances (Independent Case) — getCov

<dl>

<dt>X</dt>
<dd>The binary or real matrix</dd>


<dt>p</dt>
<dd>The power \(p\) of \(l_p^p\), i.e., \(||x||_p^p = (x_1^p+...x_n^p)\)</dd>

</dl>

getCov: Covariance Computations Between Pairs of Distances (Independent Case)

Description

Usage

Value

Arguments

Details