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.

Version: 1.0.0
Imports: stats
Suggests: knitr, rmarkdown
Published: 2016-02-03
Author: David Gerard [aut, cre], Peter Hoff [aut]
Maintainer: David Gerard <dcgerard at uchicago.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: tensr results

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Reference manual: tensr.pdf
Vignettes: Equivariant Estimation
Likelihood Inference
Package source: tensr_1.0.0.tar.gz
Windows binaries: r-devel: tensr_1.0.0.zip, r-release: tensr_1.0.0.zip, r-oldrel: tensr_1.0.0.zip
OS X El Capitan binaries: r-release: tensr_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: tensr_1.0.0.tgz

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