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>.

Version: 1.0.1
Imports: assertthat
Suggests: knitr, rmarkdown, covr, testthat
Published: 2018-08-15
DOI: 10.32614/CRAN.package.tensr
Author: David Gerard ORCID iD [aut, cre], Peter Hoff [aut]
Maintainer: David Gerard <gerard.1787 at gmail.com>
BugReports: http://github.com/dcgerard/tensr/issues
License: GPL-3
NeedsCompilation: no
Citation: tensr citation info
Materials: README
CRAN checks: tensr results


Reference manual: tensr.pdf
Vignettes: Equivariant Estimation
Likelihood Inference


Package source: tensr_1.0.1.tar.gz
Windows binaries: r-devel: tensr_1.0.1.zip, r-release: tensr_1.0.1.zip, r-oldrel: tensr_1.0.1.zip
macOS binaries: r-release (arm64): tensr_1.0.1.tgz, r-oldrel (arm64): tensr_1.0.1.tgz, r-release (x86_64): tensr_1.0.1.tgz, r-oldrel (x86_64): tensr_1.0.1.tgz
Old sources: tensr archive

Reverse dependencies:

Reverse imports: catch, hwep, TensorClustering, TULIP


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