topK

Identify top K elements of a vector.

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

topK function

<dl><dt>x</dt>
<dd>The vector.</dd>
<dt>K</dt>
<dd>The number of indices to return.</dd>
<dt>ignoreties</dt>
<dd>If <code>FALSE</code>, will return a vector of the
indices whose elements are greater than or equal to the Kth
largest element, resulting in a vector possibly of length
greater than <code>K</code> in the case of ties.</dd></dl>

Arguments

Author

Top K elements of a vector. — topK

<dl>

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


<dt>K</dt>
<dd>The number of indices to return.</dd>


<dt>ignoreties</dt>
<dd>If <code>FALSE</code>, will return a vector of the
indices whose elements are greater than or equal to the Kth
largest element, resulting in a vector possibly of length
greater than <code>K</code> in the case of ties.</dd>

</dl>

topK: Top K elements of a vector.

Description

Usage

Arguments

Author

Details

Examples