QR decomposition, constraining the R matrix to have non-negative diagonal
entries.

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

qr2 function

<dl><dt>X</dt>
<dd>A matrix of dimension \(n\) by \(p\) where \(n \ge p\)</dd></dl>

Arguments

Author

QR Decomposition. — qr2

<dl>

<dt>X</dt>
<dd>A matrix of dimension \(n\) by \(p\) where \(n \ge p\)</dd>

</dl>

qr2: QR Decomposition.

Description

Usage

Value

Arguments

Author

Details

See Also