kendalltau

This function provides a Monte Carlo approximation to Kendall's tau
measure of association.

A collection of functions for Kronecker structured covariance
estimation and testing under the array normal model. For estimation,
maximum likelihood and Bayesian equivariant estimation procedures are
implemented. For testing, a likelihood ratio testing procedure is
available. This package also contains additional functions for manipulating
and decomposing tensor data sets. This work was partially supported by NSF
grant DMS-1505136. Details of the methods are described in
Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.

David Gerard

tensr

Covariance Inference and Decompositions for Tensor Datasets

Peter Hoff

kendalltau function

<dl><dt>x</dt>
<dd>a vector.</dd>
<dt>y</dt>
<dd>a vector.</dd>
<dt>nmc</dt>
<dd>an integer number of Monte Carlo simulations.</dd></dl>

Arguments

Author

Kendall's tau measure of association. — kendalltau

<dl>

<dt>x</dt>
<dd>a vector.</dd>


<dt>y</dt>
<dd>a vector.</dd>


<dt>nmc</dt>
<dd>an integer number of Monte Carlo simulations.</dd>

</dl>

kendalltau: Kendall's tau measure of association.

Description

Usage

Value

Arguments

Author

Examples