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tensr: Covariance Inference and Decompositions for Tensor Datasets
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>.
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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[aut, cre],
Peter Hoff [aut]